EXTENDING GEOMATICS CONCEPTS AND
CAPABILITIES FOR SCIENTIFIC VISUALIZATION
AND COMMUNICATION: INTEGRATING
PHOTOREALISM WITH GEOVISUALIZATION
by
Zoran Reljic
A thesis submitted to the Faculty of Graduate and Postdoctoral Studies
in partial fulfillment of the requirements for the degree of
Master of Science of Geography
Department of Geography
UNIVERSITY OF OTTAWA
© Zoran Reljic 2006
Preface
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Abstract
This thesis evaluates the importance and impact of photorealistic visualization in
contemporary geomatics and identifies and operationalizes state of the art technology for
integrating photorealistic visualization within geomatics. Currently, the creation of
photorealistic visualization is a challenge due to present deficiencies in geomatics
technology. The visualization capabilities of state of the art geomatics platforms are at
least a decade behind contemporary 3D visualization packages and workflows. Therefore,
this thesis identifies a need for an integrated approach where the advantages of both
technologies can be combined to improve communication in geomatics. To illustrate the
integration, a case study underlines the benefits of photorealistic visualization as a
geomatics communication tool and clarifies the benefits of this approach over the
capabilities of current geomatics visualization approaches and technologies. The case
study applies a novel integrated approach combining geomatics, earth observation data
and 3D visualization technologies to create photorealistic visualizations in select
Canadian National Parks. The potential of these visualizations as communication tools
for public outreach on various levels (e.g. local, national and specific populations, such as
students) is made explicit by example. Finally, this research provides a demonstrated
workflow that integrates earth observation data, geomatics and 3D visualization
technologies as a vehicle for extending contemporary geomatics into the realm of
contemporary photorealistic geovisualization.
Preface
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Résumé
Cette thèse évalue l'importance et l'impact de la visualisation photoréaliste dans la
géomatique contemporaine et identifie et opérationnalise une technologie dernier cri pour
intégrer la visualisation photoréaliste dans la géomatique. Actuellement, la réalisation de
la visualisation photoréaliste est un défi dû aux insuffisances technologiques actuelles de
la géomatique. Les possibilités de visualisation des plateformes de technologie récente en
géomatique sont au moins une décennie derrière les applications contemporaines de la
visualisation 3D et de leurs déroulements des opérations. Par conséquent, nous avons
identifié un besoin pour une approche intégrée où les avantages des deux technologies
peuvent être combinés pour améliorer la communication en géomatique. Pour illustrer
l'intégration, une étude de cas souligne les avantages de la visualisation photoréaliste
comme outil de communication en géomatique et clarifie les avantages de cette approche
au-delà des possibilités des approches et des technologies courantes de visualisation en
géomatique. L'étude de cas applique une nouvelle approche intégrée combinant la
géomatique, les données d'observation de la Terre et les technologies de la visualisation
3D pour créer des visualisations photoréalistes pour certains parcs nationaux canadiens
sélectionnés. Le potentiel de ces visualisations comme outils de communication pour la
diffusion publique vers des populations de niveaux divers (par exemple locaux, nationaux
et plus spécifiques, tels qu’auprès des étudiants) est rendu explicite par ces exemples. En
conclusion, cette recherche démontre un déroulement des opérations qui intègre des
données d'observation de la Terre ainsi que des technologies géomatiques et de
visualisation 3D comme véhicule pour prolonger la géomatique contemporaine dans le
domaine de la géovisualisation photoréaliste contemporaine.
Preface
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Acknowledgements
During the course of this work, I was blessed with the support of many people.
Here is my acknowledgement of the most important of them – those who shaped my
work and me as a person.
Dr. Sawada, I am thankful to you for your teaching, mentoring, encouragement,
and above all friendship during this work. You are the teacher who made the difference in
my career and life. The decision to join your research group was one of the best I have
ever made. You encouraged me to grow as a researcher and as a person, to discover great
happiness in my work and to achieve success that I have never dreamt possible.
I am also grateful to Dr. Konrad Gajewski and Dr. Luke Copland, who
contributed to shaping this work into its final form.
Very, very special thanks go to my wife, Renata: The winner of the best wife
award for unconditional love “since clocks kept time”. Without you, I would have never
dared to explore the world beyond my cocoon and have never grown into the person I am
today.
Thanks are also due to the members of the LAGGISS team who convinced me
that there are no strangers in the world just friends that we haven’t met yet. Thanks, our
discussions and friendship are precious to me.
For the financial support during this project, I am grateful to Parks Canada
Agency and Dr. Michael Sawada.
Preface
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Table of Contents
ABSTRACT ..................................................................................................................................................II
RÉSUMÉ.......................................................................................................................................................II
ACKNOWLEDGEMENTS ....................................................................................................................... III
TABLE OF CONTENTS ........................................................................................................................... IV
LIST OF FIGURES.................................................................................................................................... VI
LIST OF TABLES...................................................................................................................................... IX
GLOSSARY ..................................................................................................................................................X
CHAPTER 1 INTRODUCTION .................................................................................................................. 2
1.1.
RESEARCH OBJECTIVES................................................................................................................ 4
1.2.
THESIS STRUCTURE ...................................................................................................................... 6
1.3.
REFERENCES............................................................................................................................ 7
CHAPTER 2 PHOTOREALISTIC GEOVISUALIZATION: A REVIEW .................................................. 8
2.
ABSTRACT.......................................................................................................................................... 9
2.1.
INTRODUCTION............................................................................................................................. 9
2.2.
VISUALIZATION OF GEOSPATIAL DATA: GEOVISUALIZATION ..................................................... 11
2.2.1. Definition.............................................................................................................................. 11
2.2.2. History .................................................................................................................................. 11
2.3.
IMPORTANCE OF GEOVISUALIZATION ......................................................................................... 13
2.4.
IMPACT OF GEOVISUALIZATION ON GEOMATICS SCIENCE.......................................................... 15
2.4.1. Landscape visualizations ...................................................................................................... 15
2.4.2. Geovisualization in urban planning and development ......................................................... 18
2.4.3. Other applications of geovisualization ................................................................................. 19
2.5.
PRESENT CHALLENGES IN PHOTOREALISTIC GEOVISUALIZATION .............................................. 20
2.6.
CONCLUSIONS ............................................................................................................................ 22
2.7.
REFERENCES .............................................................................................................................. 24
CHAPTER 3 INTEGRATION OF 3D VISUALIZATION AND GIS FOR MONITORING AND
COMMUNICATION OF ECOLOGICAL INTEGRITY IN CANADA’S NATIONAL PARKS............... 28
3.
ABSTRACT........................................................................................................................................ 29
3.1.
INTRODUCTION........................................................................................................................... 29
3.1.1. Objectives of the study .......................................................................................................... 30
3.2.
PHOTOREALISTIC GEOVISUALIZATIONS CHALLENGE .................................................................. 33
Why use photorealistic visualizations as a public outreach tool? ...................................................... 34
3.3.
VISUALIZATION CASE STUDIES: NATIONAL PARKS IN CANADA................................................... 36
3.3.1. Auyuittuq National Park, Nunavut........................................................................................ 37
3.3.2. Nahanni National Park Reserve, Northwest Territories....................................................... 39
3.3.3. La Mauricie National Park, Québec..................................................................................... 42
3.3.4. Key features to be visualized ................................................................................................ 44
3.4.
AN INTEGRATED APPROACH TO PHOTOREALISTIC LANDSCAPE VISUALISATION ....................... 45
3.4.1. Data Collection and Evaluation ........................................................................................... 46
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3.4.2. Modeling............................................................................................................................... 50
3.4.3. Terrain modeling techniques ................................................................................................ 55
3.4.4. Animation (Photorealistic dynamic visualization)................................................................ 65
3.4.5. Light and camera positioning............................................................................................... 68
3.4.6. Rendering ............................................................................................................................. 68
3.4.7. Compression of output data.................................................................................................. 70
3.4.8. Post-processing .................................................................................................................... 70
3.5.
PUBLIC OUTREACH: TOOLS AND RESULTS .................................................................... 73
3.5.1. Photorealistic fly-through presentations .............................................................................. 74
3.5.2. Public outreach: Various Levels........................................................................................... 81
3.6.
CONCLUSIONS AND RECOMMENDATIONS .................................................................... 84
3.7.
REFERENCES .............................................................................................................................. 86
CHAPTER 4 CONCLUSIONS AND RECOMMENDATIONS................................................................ 89
APPENDICES ............................................................................................................................................ 93
APPENDIX 1 COMPUTER GRAPHICS FOR PHOTOREALISTIC LANDSCAPE VISUALIZATION 94
APPENDIX 2 A SIMPLE STUDY EVALUATING THE POTENTIAL OF VISUALIZATIONS AS A
COMMUNICATION TOOL IN GEOMATICS ........................................................................................ 127
APPENDIX 3 INTERNATIONAL ENVI CHALLENGE 2005 AWARD ............................................... 146
APPENDIX 4 CANADIAN INSTITUTE FOR GEOMATICS 2005 CONFERENCE PAPER............... 154
Preface
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List of Figures
CHAPTER 2 PHOTOREALISTIC GEOVISUALIZATION: A REVIEW............................................ 8
Figure 1 Moore’s Law .................................................................................................................................. 12
CHAPTER 3 INTEGRATION OF 3D VISUALIZATION AND GIS FOR MONITORING AND
COMMUNICATION OF ECOLOGICAL INTEGRITY IN CANADA’S NATIONAL PARKS....... 28
Figure 2 Proposed National Parks of Canada for the Government Related Initiatives Program (GRIP)
Project ................................................................................................................................................. 32
Figure 3 Auyuittuq National Park, Buffin Island, Nunavut, as shown in a Landsat 7 ETM+ scene from
Geobase (www.geobase.ca) ................................................................................................................ 38
Figure 4 Nahanni National Park, NWT ........................................................................................................ 41
Figure 5 La Mauricie NP, Québec as shown in a Landsat ETM+ mosaic based on data from Geobase
(www.geobase.ca) ............................................................................................................................... 43
Figure 6 Workflow for the integration of GIS and scientific visualization .................................................. 45
Figure 7 An example of a DEM structure: a) raw data Auyuittuq NP, b) 400% zoomed, c) 800% zoomed 47
Figure 8 Cartographic model of DEM data pre-processing.......................................................................... 50
Figure 9 DEM Pre-processing. a) 4 adjacent raster datasets b) merged raster dataset. Data source:
www.geobase.ca, Scale 1: 50 000....................................................................................................... 51
Figure 10 Different channel combinations lead to various composite images. Modified from Aronoff
(2005). Numbers below composite images represent Landsat band combinations, e.g. 321 is a color
composite of band 3 - red, band 2 – green and band 1 – blue. Band numbers are explained on a left
panel.................................................................................................................................................... 53
Figure 11 Image fusion. Example from Auyuittuq NP. a) panchromatic image (15 m resolution); b) true
color composite LANDSAT 7 ETM + image (30 m resolution); c) the results of image fusion (15 m
resolution) ........................................................................................................................................... 55
Figure 12 a) NTDB contour lines (scale 1:50 000) and b) LANDSAT image of the same area of the
Auyuittuq NP ...................................................................................................................................... 56
Figure 13 Cartographic model of contour lines pre-processing.................................................................... 57
Figure 14 a) Imported CAD drawing into 3ds Max as editable splines; b) Generated triangulated mesh in
3ds Max based on contour data of the Auyuittuq NP.......................................................................... 57
Figure 15 A 3ds Max terrain model with applied color for elevation zones of the Auyuittuq NP. Left: top
view; Right: oblique view. .................................................................................................................. 58
Figure 16 An example of a gray scale image used as a displace map obtained from ................................... 59
Figure 17 A screen capture of imported DEM data of Auyuittuq NP in 3Dem............................................ 60
Figure 18 Dreamscape Terra Editor Workspace........................................................................................... 61
Figure 19 Different tools that can increase detail and realism of terrain. a) Terrain erosion; b) Elevation; c)
Slope; d) Texture map paint................................................................................................................ 62
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Figure 20 Vegetation distribution in La Mauricie NP. a) SPOT 5 panchromatic image(resolution 5m); b)
resulting forest distribution determined according to the SPOT 5 image ........................................... 64
Figure 21 Vue 5 Infinite: a) Fir tree instance in the tree toolbox; b) A rendered example of photorealistic fir
tree ...................................................................................................................................................... 65
Figure 22 Populating sparse vegetation areas covered with grass and shrubs with Vue 5 Infinite............... 65
Figure 23 Story board for Thor peak in Auyuittuq NP................................................................................. 67
Figure 24 A screen capture of Adobe Encore DVD workspace ................................................................... 71
Figure 25 DVD Main Menu ......................................................................................................................... 72
Figure 26 Visualizations on mobile dissemination devices; a) Apple® iPod video mp3 player; Hewlett
Packard iPaq® PDA (personal digital assistant)................................................................................. 73
Figure 27 Major glacial geological features of Auyuittuq NP...................................................................... 75
Figure 28 Four fly-through routes in Auyuittuq NP, green dots represent start and red dots are the end of
the routes............................................................................................................................................. 76
Figure 29 A winter scene from Auyuittuq NP with procedural texture (computer generated snow) ........... 78
Figure 30 Photorealistic fly-through routes through Nahhani NP ................................................................ 79
Figure 31 La Mauricie NP: Different views of a clear-cut area.................................................................... 81
Figure 32 DVD for educational outreach: Main menu-NPs ......................................................................... 83
APPENDIX 1 COMPUTER GRAPHICS FOR PHOTOREALISTIC LANDSCAPE
VISUALIZATION...................................................................................................................................... 94
Figure 33 Elements of computer graphics used for photorealistic landscape visualization.......................... 94
Figure 34 A 3D Cartesian coordinate system ............................................................................................... 95
Figure 35 A scene with global (a) and local (b,c) coordinate systems ......................................................... 96
Figure 36 An example of a triangle structure ............................................................................................... 97
Figure 37 Elementary modelling primitives: A) Points; B) Triangles; C) Wireframe; D) Polygons............ 98
Figure 38 Increasing the number of polygons improves the object smoothness .......................................... 99
Figure 39 B-spline curve with its vertices (3D Studio Max 2003) ............................................................. 100
Figure 40 NURBS Surface; A) control vertices, curves and surface mesh; B) rendered surface. .............. 101
Figure 41 a) Contour lines; b) Zoomed-in segment with splines; c) Triangluated surface; d) Rendered
terrain model ..................................................................................................................................... 101
Figure 42 Examples of 3D procedural fractal terrains................................................................................ 103
Figure 43 A tree rendered with: A) ray-tracing algorithm in 3ds Max; B) basic OpenGL......................... 104
Figure 44 Landscape without and with clouds ........................................................................................... 106
Figure 45 Geospecific and computer generated textures. a) IKONOS (Resolution: 1m); b) QuickBird
(Resolution: 0.6m); c) LANDSAT (Resolution 15m); d) Procedural texture ................................... 108
Figure 46 Two different camera types in a 3d scene: a) Target camera; b) Free camera with predefined
motion path ....................................................................................................................................... 111
Figure 47 Example of bump mapping. a) 3D model without bump mapping, b) Gray scale image as a bump
map; c) 3D model with applied bump mapping................................................................................ 115
Figure 48 Factors influencing the effectiveness of visualization. Modified from Clark and Lyons (2004) 117
APPENDIX 2 A SIMPLE STUDY EVALUATING THE POTENTIAL OF VISUALIZATIONS AS
A COMMUNICATION TOOL IN GEOMATICS................................................................................ 127
Figure 49 Key frames for animations: a) Latitude/Longitude, b) Graticule, c) Geoid, d) Map projection e)
GPS ................................................................................................................................................... 134
Figure 50 Average response of all students on different questions in the questionnaire ............................ 136
Fgure 51 Typical working space in 3D Studio Max................................................................................... 138
Figure 52 Reponses by gender.................................................................................................................... 141
Figure 53 Responses of the 2nd year geography students ........................................................................... 142
Figure 54 Evaluation of dynamic visualizations by different year of study ............................................... 143
Figure 55 Evaluation of textbook presentations by different year of study................................................ 143
APPENDIX 3 INTERNATIONAL ENVI CHALLENGE 2005 AWARD........................................... 146
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Figure 1 Converting contours to DEM…………………………………………………………………….147
Figure 2 a- panchromatic image (15m spatial resolution), b-color image (30m spatial resolution), c-fused
image (15m spatial resolution)…………………………………………………………………………….147
Figure 3 a-color, b-wireframe, c-Landsat image draped, d- IKONOS image draped……………………..148
Figure 4 The Band Math tool………………………………………………………………………………149
Figure 5 NSDI images in Auyuittuq National Park obtained by utilizing Band Math………………….....149
Figure 6 A map of Penny Ice Cap in Auyuittuq NP made with ENVI’s QuickMap………………………150
Figure 7 3D view of the part of the Auyuittuq NP showing the Fork Beard Glacier in the summer of 1991
(a) and the summer of 2000(b)…..…………………………………………...……………………..151
Figure 8 a–Nerutusoq Glacier (Ikonos), b–Summit lake(Ikonos), c–NerutusoqGlacier with vector snow line
and rivers, d-Summit lake with vector snow line and Rivers, e–Thor peakand Fork Beard Glacier
(Landsat 321 Composite), f–Crater lake (Landsat 321 composite)…………………...………...….152
APPENDIX 4 CANADIAN INSTITUTE FOR GEOMATICS 2005 CONFERENCE PAPER ........ 154
Figure 1 Auyuittuq National Park…………………………………………………………………………155
Figure 2 La Mauricie National Park……………………………………………………………………….155
Figure 3 Methodology and process flow for scientific visualization……………………………...………157
Figure 4 Example key frames for different visualizations, a) ice-berg near Pangnirtung Fjord; b) Snow
accumulation in Auyuittuq; c) Crater Lake in Auyuittuq with glacier……………………………..158
Figure 5: Textured vegetation, a) Visualization of a single tree using 3ds Max; b) textured ground
simulating grass and a forest canopy using Vue 5 Infinite* ecosystem generator…………………159
Figure 6 An example of a terrain generated and showing a view of Auyuittuq National Park..…………..160
Figure 7 A terrain visualization example of the Summit lake region of Auyuittuq National Park in Nunavut
using IKONOS and pansharpened and fused Landsat datasets as a texture base………………….160
Figure 8 a) 2004 SPOT 5 false colour composite of campground near entry of parkway in La Mauricie
National Park. The red area indicates smaller spectral response to vegetation structure/canopy; b)
NDVI derived from SPOT 5 image where dark colours indicate less green vegetation; c) 1999
Landsat TM NDVI for same region illustrating darker colours around campground. Note that the
SPOT 5 and Landsat derived NDVI are not radiometrically equivalent so the intensities are not
directly comparable; This area of the park has had problems with spruce budworm infestations over
the past years. d) Same as a) but illustrating an area razed in 2003; e) same as d) but for 1999
Landsat true colour…………………………………………………………………………………162
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List of Tables
CHAPTER 3 INTEGRATION OF 3D VISUALIZATION AND GIS FOR MONITORING AND
COMMUNICATION OF ECOLOGICAL INTEGRITY IN CANADA’S NATIONAL PARKS....... 28
Table 1 A comparison of basic building elements of animation................................................................... 33
Table 2 Comparison: Geometric, photorealistic and original scene (Angsuesser and Kumke 2001)........... 35
Table 3 Key features to be visualized in order to develop a workflow integrating photorealistic
geovisualization with contemporary geomatics. ................................................................................. 44
Table 4 Comparison between rendering algorithms (3D Studio Max 2003) ................................................ 69
APPENDIX 1 COMPUTER GRAPHICS FOR PHOTOREALISTIC LANDSCAPE
VISUALIZATION...................................................................................................................................... 94
Table 5 Comparison: Ray tracing and radiosity. Reproduced from (3D Studio Max 2003) ...................... 114
Table 6 Combined various classifications of preattentive features............................................................. 118
Table 7 Comparison between different presentations reproduced from (Angsuesser and Kumke 2001)... 121
Table 8 Advantages and disadvantages of visualizations. Adapted from (Libarkin 2002)......................... 129
Table 9 Statistical Summary: All responses ............................................................................................... 141
Table 10 Summary statistics for male and female students: Visualizations vs. Textbook ......................... 142
Table 11 Summary statistics for differences in judging the usefulness textbook and the dynamic
visualization by different peer groups............................................................................................... 142
Table 12 ANOVA: Evaluation of dynamic visualizations, differences among different years of study .... 143
Table 13 ANOVA: Evaluation of textbook, differences among different year of study ............................ 144
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Glossary
2D: two-dimensional geometry characterized by Cartesian (x,y) coordinates.
3D: three-dimensional. Descriptive of a region of space that has width, height and depth.
Characterized by Cartesian (x,y,z) coordinates.
3DS: a file format in 3ds Max which contains only details of the geometry and surface
properties of an object.
Aerial oblique: a view taken from above looking down at an angle.
Albedo: a measure of the brightness of a reflective object or surface.
Ambient light: surrounding or environmental light that is everywhere equally intense
and has no directionality.
Animation: a medium that creates the illusion of movement trough the projection of a
series of still images or ‘frames’.
Anti-aliasing: an algorithm to prevent the jagged appearance of edges in an image,
which works during rendering by averaging adjacent pixels with sharp variations in color
or brightness.
Aspect ratio: the ratio of the width (x-axis) of an image to its height (y-axis).
Atmospheric effect: components of a 3D software solution that produce effects like fog,
fire and volumetric lighting effects.
AVI: Audio Video Interleave; a file format for animations and multimedia developed by
Microsoft Corporation.
Bitmap: a digital raster image. Strictly speaking it is a 1bit black and white
(monochrome) image. However the term is often applied to any two-dimensional image,
regardless of bit depth.
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Boolean: an object created by combining two objects using mathematical operators.
Bump map: a black and white image used in computer rendering to simulate the threedimensional detail on the surface of an object.
CAD: Computer Aided Design software, design for creating digital representation of 2D
and 3D objects and space.
Camera: a virtual viewpoint in 3D space that possesses both position and direction. In a
3D scene, the camera represents the viewer’s eye.
Camera move: a movement of the virtual camera within a 3D software analogous to one
in real world cinematography.
Camera path: the path in virtual space along which the camera moves during the course
of animation.
Compression: a technique for reducing the quantity of data required to make up a digital
image.
CPU: central processing unit (processor) a computer’s component that processes data
contained in computer programs.
DEM: Digital Elevation Model. The representation of continuous elevation values over a
topographic surface by a regular array of z-values, referenced to a common datum.
Typically used to represent terrain relief.
Displacement map: a black and white image that modifies the actual underlying
geometry.
Depth of field: a way to enhance the realism of a rendering by simulating the way a realworld camera works. With a broad depth of field, all or nearly all of a scene is in focus.
With a narrow depth of field, only objects within a certain distance from the camera are
in focus.
DPI: dots per inch, used to measure the resolution of images either on screen or on paper.
Drape: projecting an image onto a 3D surface such as DEM
to create a realistic
representation.
DVD: Digital Versatile Disc, a format and optical medium for storing large amounts of
digital data (e.g. 4.7 GB).
EO: Earth observation. EO data are data collected by satellites, aircrafts or land based
environmental stations.
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Extrusion: a modeling technique in which a two dimensional profile is duplicated
outwards along a linear path, and the set of duplicated profiles joined to create a
continuous three-dimensional surface.
Face: the smallest possible mesh object: a triangle formed by three vertices. Faces
provide the renderable surface of an object. While a vertex can exist as an isolated point
in space, a face cannot exist without vertices.
Fall-off: the way in which the intensity of a light diminishes with the distance from its
source.
Flythrough: a type of animation in which the camera moves around a scene, rather than
object moving in front of a stationary camera.
Frame: a still two-dimensional image.
Frames per second (fps): a measurement of the number of still frames displayed in one
second of real time to give the impression of a moving image. The standard rates are as
follows: NTSC video—30 frames per second, PAL video—25 frames per second, Film—
24 frames per second.
Geo-reference: to assign accurate real-world coordinates from
a known reference
system, such as latitude/longitude, UTM to the page coordinates of an image or a planar
map.
Geo-TIFF: a file format for images, which contains geo-referencing information.
Global illumination: enhances the realism of a scene by simulating radiosity, or the
interreflection of light in a scene.
GUI: Graphical User Interface. An icon based interface that controls a 3D software
package.
Hardware rendering (display rendering): previews a 3D scene within the viewports on
a 3D software package providing real-time on-screen feedback about the effects of
change made to the scene.
Immersion: a method for projecting images such that the viewer’s peripheral vision is
engaged.
Keyframe: an image or set of attributes for a 3D scene, used as a reference point in
animation.
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Level of detail: lets you specify objects with varying face counts that are appropriate for
different viewing distances. Browsers display the less detailed objects when the viewer is
far away from them and substitute the more detailed objects at closer ranges
Light: a point or volume that emits light onto a 3D object. Types of light supported by
within 3D packages include point, spot, directional and area lights.
Maps: the images that are assign to materials are called maps. Examples are standard
bitmaps (such as .bmp, .jpg, or .tga files), procedural maps, such as Checker or Marble,
and image-processing systems such as compositors and masking systems.
Material: a set of mathematical attributes that determine the ways in which the surface of
a model to which they are applied reacts to
Mb: megabit, a unit of information storage ( 1Mb = 1000000 bits).
Mesh: a digital representation of a surface consisting of multiple, possibly curved, line
segments whose intersections form a regular grid.
Model: Used as a verb, to model means to build a 3d object. Used as a noun, it means the
3D object created as the end product of the modeling process.
Morph: to transform a shape image or object smoothly from initial state to a different
final state.
MOV: a file format for QuickTime movies and animations, developed by Apple
Computers Co.
Network rendering: is the rendering of animations using more than one computer
connected by a network. Large and complex animations take many hours to render, even
on the fastest PCs. Network rendering allows us to use the power of other computers to
speed up the process.
NTSC: (National Television Standards Committee) is the name of the video standard
used in North America, most of Central and South America, and Japan. The frame rate is
30 frames per second (fps).
NURBS: (Non-Uniform Rational B-Splines) are a technique for interactively modeling
3D curves and surfaces.
Object: an object in the scene, such as primitive geometry like boxes and spheres, more
complex geometry such as Booleans, and so on.
Orthorectification: correcting distortion in satellite images caused by uneven terrain.
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Particle systems: are objects that generate non-editable sub-objects, called particles, for
the purpose of simulating snow, rain, dust, and so on.
Plane: a two-dimensional surface in Cartesian coordinate space.
Photo-realism: the effort to create synthetic images such as computer renderings,
indistinguishable from photographs or real objects or scenes.
Pixel (picture element): the smallest unit of information in an image or raster map.
Polygon: a geometry element formed by connecting three or more points.
Polyline: a line created by a series of shorter line segments.
Post production: the manipulation of a rendered image, either to improve the quality of
that image, or to create effects that cannot easily be achieved directly within 3D software.
Primitive: a simple three-dimensional form used as the basis for constructive solid
geometry modeling techniques.
Quick Time: a digital technology and file format for animations, developed by Apple
Computer Co.
Radiosity: a technique for rendering 3D scenes. Radiosity simulates the way in which
light bounces from surface to surface within a scene.
Raytracing: a technique for rendering 3D scenes. Raytracing traces the path of every ray
of light from its source until it either leaves the scene or becomes too weak to have an
effect.
Rendering: the process of converting the 3D data into the two- dimensional image ‘seen’
by the camera within the scene. Rendering brings together the scene geometry, Z-depth,
surface properties. Lighting set-up and rendering method to create a finished frame.
Resolution: the size of the final image in pixels when rendering out a scene.
RGB: Red, Green, Blue; a method for representing colors as mixtures of the tree primary
colors of light.
Scene: a set of 3D objects , including the models themselves and the lights and camera
that will e used when rendering them out.
Shading: the mathematical process of calculating how a model’s surfaces react to light.
Specularity: a surface property of an object that determines the way in which
Texture: a 2D raster image used in computer rendering to give color and other apparent
surface characteristics to 3D objects.
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TIFF: a file format for color image data, which enables ‘loss-less compression”.
Timeline: a fundamental element of the graphical user interface of most modern 3D
software packages which shows the timing of the keyframes in a sequence of animation.
Tweaking: the process of moving individual vertices of a 3D geometric object.
UVW Coordinates: Most material maps are a 2D plane assigned to a 3D surface.
Consequently, the coordinate system used to describe the placement and transformation
of maps is different from the X, Y, and Z axis coordinates used in 3D space.
Viewport: the region of the interface of a 3D software package in which the scenes
displayed to the user.
Virtual reality: simulated environments and the methods used to create them.
Visualization: the process of creating images using computers
Wireframe: a shading method in which a simple grid of lines is used to represent he
basic contours of the underlying model.
Word space: world space is the universal coordinate system used to track objects in the
scene. When you look at the home grid in the viewports, you see the world-space
coordinate system.
Chapter 1
Introduction
Chapter 1 Introduction
3
1. INTRODUCTION
We live in the most visually oriented society in human history. Today, visuals are the
predominant way of communication because our perception and understanding of visual
information is more efficient when compared to the perception and cognition of
numerical or textual data. Visuals are multifaceted tools that mould and define us and
influence every aspect of our lives. Contemporary science is not excluded from this trend.
Visualization of scientific information is a tool that enables us to “see the unseen” (NRC
2003) and is changing the way we interact, analyse and present information.
Visuals in geomatics science are used to enhance the performance, usability and
accessibility of geospatial information (NRC 2003). However, depending on the purpose
and intended audience, some visuals are more effective than others (e.g. a high level of
scientific abstraction will not be effective for communication with the general public).
Determination and utilization of the right level of abstraction and the appropriate medium
for presentation are the major challenges faced by geomaticians when using visuals to
communicate their results.
The main motivation for this thesis work is the growing indication that photorealistic
visualizations could play a major role in the future of geomatics science not only as a data
exploratory tool but also as a communication tool. Studies have shown that photorealistic
visualizations are the most effective means of communicating with the general public. A
high degree of realism enables the audience to bond easily with the objectives of
visualization and to understand almost instantaneously and universally the intended
message with little or no ‘design-side’ interpretation (Tress and Tress 2003). The
identification of major challenges and the improvement in the efficiency of photorealistic
visualizations as communication tools by integration of GIS and 3D visualization
technology are the major motivations for this research.
Chapter 1 Introduction
4
1.1. RESEARCH OBJECTIVES
Current geomatics software lack the concepts and capabilities of today’s 3D visualization
software and standards. By way of illustration, it was not until 1997 that ESRI released
their 3D Analyst extension for ArcView, an extension with landscape visualization
capabilities. Within the field of geomatics there are very few individuals actively trying
to extend or upgrade the geomatics toolbox to contemporary visualization standards. This
is surprising given the benefits of modern 3 or 4D (3D + time) photorealistic animation to
both the communication and representation of earth-based data. Thus there exists a
technological, theoretical and practical gap that begs the questions of why and how to
integrate geomatics and contemporary photorealistic visualization. In response, the
objective of this thesis is to determine how current geomatics visualization software
and procedures can be extended and integrated with state of the art 3D photorealistic
visualization. As such, this research will identify weaknesses of current geovisualization
approaches in geomatics, show the benefits of contemporary 3D photorealism to
geomatics, and provide by example a framework to enhance the workflows necessary to
bring state of the art geovisualization into geomatics practice.
To achieve this main objective, a literature review and a case study are presented.
Developing a framework to integrate geomatics and modern 3D visualization concepts
and capabilities first requires reviewing and justifying the potential utility of
geovisualization in geomatics science, and the value added by photorealism. Therefore,
this thesis begins with a comprehensive literature review outlining the importance and
impact of contemporary photorealistic visualization on geomatics science. The working
hypothesis is that visualization plays an important role in the process of scientific
discovery in geomatics (e.g. as an exploratory and confirmatory tool), but more
importantly, photorealistic geovisualization can increase the communication of complex
geomatics concepts, capabilities and datasets, especially to audiences with various
backgrounds. In other words, photorealism can add a significant new dimension to the
Chapter 1 Introduction
5
geomatician’s toolbox and provide tangible benefits beyond what currently exists in
geomatics packages.
With the potential of photorealistic visualization identified, the current state of 3D
visualization concepts and capabilities in geomatics software are enumerated and
compared to state of the art 3-4D visualization capabilities. Through this enumeration, the
concepts of photorealism are explored and the specific question addressed is: How are
photorealistic visualizations created at present, where are the challenges and the
possibilities for integration with geomatics, and how will this improve the discipline? As
such, the need for an integrated approach brings geomatics up to date with the state of the
art visualization technologies through identification of fundamental concepts and
capabilities that should be included in effective geovisualizations. This review,
enumeration and comparison provides a substantive framework for integration of the two
fields.
Subsequently a case study is undertaken to illustrate the workflow necessary to integrate
geomatics and photorealistic geovisualization. Here, photorealistic geovisualizations of
three Canadian National Parks are used for public outreach. This case study utilizes an
integrated approach where advances in GIS, remote sensing and visualization technology
are combined in order to produce photorealistic animations of the major attractions in the
Auyuittuq, Nahanni and La Mauricie National Parks. The hypothesis was that the
integrated approach will lead toward value added visual tools that can be used in
exploratory research and for public outreach and communication, for example,
communication of the need for the preservation of ecological integrity of these National
Parks. The first step in this communication process was to engage the interest of the
public by presenting them with photorealistic visualizations of the areas that are in some
cases rarely visited by humans. This integrated case study provides a concrete example of
the photorealistic geovisualization framework and extends this framework operationally.
Chapter 1 Introduction
6
1.2. THESIS STRUCTURE
This thesis consists of four chapters that can be read separately. Each chapter, with the
exception of the first and last, is an independent scientific publication to be submitted to a
refereed scientific journal. Chapter I (Introduction) and Chapter IV (Conclusions and
Recommendations) relate to Chapter II and III.
Chapter II - Photorealistic Geovisualization: A Review. This chapter is a review of
contemporary research in geovisualization. The emphasis is on the importance and
impact of photorealistic landscape visualization in contemporary geomatic science due to
its relevance to the research objectives of the thesis. The use of photorealistic landscape
visualization as an analysis and communication tool in various application areas is
summarized. The areas relevant for our research such as preservation of ecological
integrity, and communication of climate change etc., are especially of interest.
Chapter III - Integration of 3D Visualization and GIS for Monitoring and
Communication of Ecological Integrity of Canada’s National Parks. The objective of
this chapter is to present a case study on the utilization of photorealistic visualization for
three of Canada’s National Parks. The emphasis is on two research questions: 1. How to
bridge the gap in contemporary geovisualization technology by the integration of GIS,
remote sensing and visualization for creation of photorealistic visualizations of Canada’s
National Parks. 2. The use of photorealistic animations created in the first part of this
work for public outreach. The aim of this chapter is to address the central research
question of the thesis by providing a specific workflow and solution to the integration of
geomatics concepts and capabilities with contemporary state of the art photorealistic
visualization.
A DVD with sample visualizations and detailed tutorials is part of this thesis. The
detailed step by step guide for creation of the visualizations that is given in the tutorials is
omitted in the written work due to space limitations.
Chapter 1 Introduction
7
The work outlined above was partially presented at the following conferences:
1. Z. Reljic, M. Sawada. (2006) Value added mapping: 3D modeling and photorealistic
representation of Arctic landscape. GeoTech, Ottawa, June 18-21.
2. Z. Reljic, M. Sawada, J. Poitevin, and G. Saunders. (2005) Integrating GIS and 3D
Visualization for Dynamic Landscape Representation in Canada’s National Parks.
Canadian Institute for Geomatics, Ottawa Chapter, Ottawa, May 2005
A part of Chapter III was presented at the International ENVI Challenge 2005 where it
won 2nd place for an innovative integration of scientific visualization and geomatics for
improved monitoring and communication of ecological integrity. This work was used as
the basis for a one-day workshop on the integration of GIS and photorealistic
visualization as a part of the program for GeoTech 2006.
1.3. REFERENCES
NRC (2003). IT Roadmap to a Geospatial Future. Washington D.C., The National
Academies Press: 1-16.
Tress, B. and G. Tress (2003). "Scenario visualisation for participatory landscape
planning - A study from Denmark." Landscape and Urban Planning 64(3): 161178.
Chapter 2
Photorealistic Geovisualization: A Review
Chapter 2 Photorealistic Visualization: A Review
9
2. ABSTRACT
Geovisualization is one of the emerging areas of geomatics science. It is a multidisciplinary
field integrating exploratory data analysis, GIS, remote sensing and computer graphics in
order to help geomaticians efficiently analyse, understand and communicate geospatial data.
The ever increasing volume and complexity of geospatial data along with advancements in
computer technology have powered the ascent of geovisualization. An additional factor is the
fact that human perception and cognition of visualised information are more effective than the
perception and cognition of numbers alone. Furthermore, the absorption of complex
contextual information is better facilitated when computer generated models closely resemble
reality. Therefore, photorealistic landscape modeling has promise as an effective visual
method that can potentially address several major geomatics challenges simultaneously:
georeferenced data proliferation, data exploration and data communication. This review
summarizes ongoing research efforts in photorealistic landscape modeling and evaluates their
impact in various application fields. Moreover, evidence suggests that there is an existing gap
between 3D visualization and geomatics technologies. The existence and consequences of this
technological gap for the integration of photorealistic visualizations with contemporary
geomatics workflows provide significant impetus for further study.
2.1. INTRODUCTION
It is estimated that about 2 exabytes (= 2 Million terabytes) of data are generated every year
around the world (Keim et al. 2005). In terms of data generation, the 20th century was the
information age, while the 21st is the hyper-information age (Bishop 2000). It was recognized
as early as the 1960s that modern science would need an alternative to communication via
numbers since most of the large datasets generated from then on could no longer be described
by numbers and traditional scientific methods (NSF 2006). “A technical reality today and a
cognitive imperative of tomorrow is the use of images” (NSF 2006). Combining science and
art, and relying on the premise that human perception and cognition of visual data is more
effective than perception of textual or numerical data (Tufte 1990), scientific visualization has
become a corner stone of modern science. Whether medicine e.g. brain mapping
(Panchaphongsaphak et al. 2005), virtual surgeries (Lamadé et al. 1999), engineering e.g.
Chapter 2 Photorealistic Visualization: A Review
10
fluid flow visualizations (Sims et al. 2000), virtual oil drills, emergency scenarios, biology
(e.g. decoding DNA structure (Vernikos et al. 2003), chemistry e.g. molecular modeling
(Morrissey 2005) or business e.g. visualization of various business concepts (NSF 2006),
progress in human knowledge today depends on emerging methods of visual data
presentation, analysis and communication (NSF 2006).
The implementation of scientific visualization in geomatics science has been so intensive that
a new term was devised for this visualization type, namely, geovisualization (MacEachren
1994). This term was a direct consequence of the nature (e.g. different types, dimensions,
scales and sources) and large volume associated with georeferenced data. While there are
different geovisualization methods, for communication among various interest groups,
photorealistic geovisualization could be the most efficient one (Appleton and Lovett 2005). It
includes a high degree of realism that appeals to the audience and provokes natural perception
of the modeled landscapes (Hirtz et al. 1999). Photorealistic and other geovisualizations help
policy makers and the general public in decision making (Al-Kodmany 2001; Kubota and
Kubota 1994), in the preservation of ecological integrity (Hardin et al. 2005) and the
management of real landscapes (e.g. forests) using virtual models (Qi et al. 2004). Taking
tours through virtual cities (Shiode 2000), improving infrastructure, developing better traffic
networks and visualizing preparedness in the case of natural (Brodlie et al. 2005) and other
disasters (Naphtali and Naphtali 2003) are also supported by geovisualization.
The major objective of the work presented here is the evaluation of the current state of
photorealistic landscape visualization with a specific application to public outreach. This
objective involves close examination of various application areas such as landscape and urban
planning, with implications for environmental monitoring and management. Of interest are
those studies that indicate the use of photorealistic landscape visualization as a
communication tool for public outreach and decision making since such intentions provide a
reference frame for the intended applications of high-quality geovisualizations. In addition,
visualization technology itself is examined. The advantages and disadvantages of particular
approaches are collected and evaluated in order to identify the most efficient way of
generating a photorealistic visualization as a precursor to developing a workflow for the
integration with current geomatics technologies.
Chapter 2 Photorealistic Visualization: A Review
11
2.2. VISUALIZATION OF GEOSPATIAL DATA: GEOVISUALIZATION
Geomatics science was among the first scientific disciplines to embrace and benefit from the
utilization of scientific visualization. Geospatial data (e.g. raster or vectors with known
location) are a combination of scientific data with a spatio-temporal component requiring
large volumes of storage and the consequent need for numerous levels of presentation (e.g.
different dimensions, views and level of details). These requirements combined with the
human preference for the visual medium and limitations in our innate spatial thinking (NRC
2003) make scientific visualization an ideal tool for data exploration, analysis and
presentation in geography and GIS.
2.2.1. Definition
The definition of the term geovisualization reflects the multidisciplinary and dynamic nature
of this field. Geovisualization is a new field that combines human visual potential and
technology in order to make spatial contexts and/or problems visible (MacEachren et al.
1999). MacEachren and Kraak emphasised the multi-disciplinary nature of geovisualization
by defining it as a science that combines GIS, cartography, scientific visualization, and
exploratory data analysis that have considerable potential to “provide theory, methods, and
tools for the visual exploration, analysis, synthesis and presentation of data that contains
geographic information” (MacEachren and Kraak 1997). Going beyond exploratory
visualization, Kraak (1999) extended the role of geovisualization in scientific discovery to
include its potential as a confirmatory visual method contributing to the formulation of a
scientific question and finally contributing to general knowledge. Contemporary definitions
usually emphasize various advantages and/or attributes of geovisualization. For instance,
Jiang defined geovisualization as a method that “serves two purposes: communication and
analysis” (Jiang and Li 2005).
2.2.2. History
Since the dawn of civilization, humans have been using pictures to convey information
(Bishop and Lange 2005). Conveying spatial data through pictures is as old as the first
drawing a plan of attack in the sand before hunting or a battle. Starting with the first
Chapter 2 Photorealistic Visualization: A Review
12
visualization of a landscape recorded on a clay tablet 4000 years ago (Lange 2002), the
elements of landscape modeling can be found throughout history. For instance, perspective
was known to the Greeks as early as 500 A.D. and was re-invented a thousand years later
(1500) by the Renaissance painter Durer (Bishop and Lange 2005). Similarly, presentation of
scale was pioneered by Rampton in the 1800s. Ever since, sketches, drawings, maps, and later
photomontages and physical models dominated the communication of geographic concepts.
Although they contained a limited amount of information, these traditional visualization
methods were surprisingly effective and well appreciated (Wood 1994). The beginning of
more advanced and realistic geovisualizations started in the late 1960s. The development of
geographic visualization was influenced by the development of the computer capabilities
which in turn is known to follow Moore’s law (Moore 1965) of integrated circuits. Moore, the
co-cofounder of Intel (the world leader in processor technology), predicted that the
complexity (i.e. power) of integrated circuits will double itself every 18 months while
decreasing in cost two-fold (Figure 1). Computers on which geovizualization is based have
doubled in power every 18 months.
Figure 1 Moore’s Law
In the late 1970s, the first dynamic, 3D computer-generated surfaces were presented
(Moellering, 1978). In the 1970s, intensive work on the fundamentals of computer graphics
Chapter 2 Photorealistic Visualization: A Review
13
e.g. color, texture, half tones) was paramount. In the 1980s, the diversification of rendering
techniques occurred (Nakamae and Tadamura 1995). Enabled by the implementation of the
newly created ray tracing algorithm (Whitted 1980), the fractal (Smith 1984) and particle
models (Reeves and Blau 1983), in the 1980s natural objects such as trees obtained more
realistic attributes and combined with more natural lighting resulted in more realistic
landscape elements and scenes. Today, with numerous applications such as landscape and
urban modeling and planning, environmental monitoring and assessment, disaster scenario
visualization and emergency preparedness visualization, geovisualization has become an
irreplaceable tool for modern geoscientists (NSF 2006).
2.3. IMPORTANCE OF GEOVISUALIZATION
Judging by the proliferation of the topic within the scientific literature, geovisualization has
established itself as an integral part of contemporary geomatics science. However, the
question remains as to how important geovisualization is in the context of the present and
future developments in geomatics. In general, the answer lies in the following three areas:
1. Geovisualization can handle current and incoming georeferenced data proliferation.
2. Geovisualization is a method to accelerate scientific discovery.
3. Geovisualization is a method to improve communication.
Geovisualization can handle current and incoming georeferenced data proliferation. The
rate of georeferenced data proliferation is the strongest justification for the research and
development of new visualization tools. By way of illustration, one satellite alone (e.g. Terra)
in the NASA’s Earth Observing System daily collects approximately 3 TB (terabytes) of data.
This data must be effectively stored, transferred, re-referenced, analysed, and presented in
order to be useful. At present, traditional data exploration and presentation methods are
becoming more and more ineffective or in some cases obsolete. Thus far, geovisualization has
shown considerable potential in dealing with present and expected data volume and
complexity. Present research indicates that geovisualization is a multi-faceted tool that can be
used as an exploratory visual method, confirmatory visual method (e.g. to assist in rejecting
or accepting working hypotheses) and as a synthetic and presentational visual method
(DiBiase 1990).
Chapter 2 Photorealistic Visualization: A Review
14
Geovisualization is a method to accelerate scientific discovery. In today’s world there is a
constant need for acceleration of scientific discovery. It has already been reported that the
utilization of visualization can accelerate new discoveries (Sims et al. 2000). The authors
claim that the acceleration requires synergism of expertise between computation and
visualization scientists in order to accelerate modeling of natural phenomena. Although it is
hard to quantify the importance of a scientific field, two of the measurable variables can be
the number of institutions that have an interest in developments in the field and the financial
support given to these developments. In that regard, geovisualization is judged as a fast
growing and well-funded area of research (Ma 2004). In the United States of America, the
National Research Council established its geovisualization development program in 1987
(NRC 2003). Subsequently, more and more agencies are recognizing the importance of
geovisualization. For example the establishment of the Commission on Visualization of the
International Cartographic Association, the Environmental Simulation Center and various
other institutions such as the Scientific Visualization Studio at NASA are clear evidence of
the importance of this field in accelerating discovery within modern geomatics science.
Geovisualization is a method to improve communication. The science of geomatics is
unique in its needs for effective communication methods. Firstly, to model a geomatic
concept, usually a large amount of complex data is necessary and secondly, the modeling
results need to be presented to an audience with a broad range of backgrounds and interests.
Using 3D photorealistic modeling for communication enables “non-spatially aware” users in
the audience to closely examine various scenarios using static (Appleton and Lovett 2005),
animated or immersive photorealistic applications (Orland et al. 2001). For instance,
photorealistic landscape scenarios are the state of the art support for participatory planning
and development (Tress and Tress 2003), environmental monitoring, urban and landscape
planning as well as other disciplines. Highly realistic presentation of various landscape
scenarios improves the bonding of the audience with the concepts and milieu presented and
thus plays a key role in finding a balance between the various and sometimes mutually
exclusive interests of planners, clients, and the public (Muhar 2001).
Chapter 2 Photorealistic Visualization: A Review
15
2.4. IMPACT OF GEOVISUALIZATION ON GEOMATICS SCIENCE
Geovisualization has transformed the way in which geoscientists model landscapes.
Drawings, paintings, wood, gypsum and other models of our landscapes have been replaced
with three dimensional, interactive and often immersive, user-centric representations1. The
strongest impact of photorealistic geovisualization in geomatics science has occurred in:
1. landscape visualization, and
2. urban geovisualization.
Impacts are also being felt in other applications such as industrial applications and emergency
preparedness support scenarios.
Although landscape and urban geovisualization each have various objectives such as analysis
of environmental risks (Kraak 1994) or generation of photorealistic virtual cities (Pietsch
2000), both areas of geovisualization have the common need to visualize multi-dimensional,
large volume, dynamic data with the ultimate objective of communicating results to other
scientists, law and policy makers or the general public.
2.4.1. Landscape visualizations
The landscape is constantly changing due to natural forces (e.g. water, fire, earthquakes) and
human influence (e.g. agriculture, mining, urban developments, infrastructure). Visualization
as a technique used to evaluate changes in the landscape has been employed in various forms
(e.g. sketches, photographs, photomontage, physical models…) throughout history (Lange
2001). In 1803, Rampton in a work that can be considered pioneering in landscape
visualization, compared “before” and “after” scenarios for the evaluation of his proposed
changes in the landscape (Lange 2001). Today, due to the shift in natural resources
management toward more detailed landscape models on a larger scale, more demanding
environmental regulations, and the emphasis on data exploration and communication during
the planning stage, interest in highly realistic landscape visualizations has increased (Orland
et al. 2001). In order to enable data exploration, support discussion and facilitate decision
making (Orland et al. 2001), contemporary landscape visualization cannot rely on 2D
1
One can liken the development of geovisualization in geomatics to the development of ergonomics (a.k.a.
human design) within industrial design and engineering. By way of illustration, ergonomics is user-centric,
fitting the workplace to the worker rather than making the worker fit the workplace. Likewise, geovisualization
is designed to bring geospatial data into conformity with a user or viewer's world view, reducing mental fatigue,
visual fatigue and maximizing the absorption of knowledge.
Chapter 2 Photorealistic Visualization: A Review
16
visualizations where the terrain is usually represented as a set of contour lines with circles as
trees and rectangles as buildings (Lange 2001). With increasing capabilities of computer
graphics and the availability of powerful personal computers, highly realistic 3D landscape
visualizations are fast becoming the norm in landscape assessment and planning. Although
estimations of the current number of users are not reported, it can be said that more and more
professionals in corporate and government agencies as well as the public sector are relying on
3D photorealistic landscape visualizations in their work (Sheppard 2001). Approximately
91% of the participants in a large study on the utilization of 3D geovisualizations in landscape
planning in Germany confirmed that they integrate 3D landscape visualization in their daily
work and expect to reap even more benefits from its utilization in the future (Paar 2005).
In the last decade there has been an increase in the number of works applying landscape
visualizations for environmental monitoring and assessment (Bishop and Lange 2005).
Landscape models of various levels of realism assisted in the preservation of biodiversity
(Hardin et al. 2005; Hehl-Lange 2001), the monitoring of climate change (Dockerty et al.
2005; Nicholson-Cole 2005; Sheppard 2005), supported sustainable forest management (Bell
2001) or assisted in the management of natural disasters (Brodlie et al. 2005; Salter et al.
2005).
One of the largest ongoing projects utilizing photorealistic geovisualization for environmental
monitoring is the project SERVIR (Hardin et al. 2005). The project involves geovisualization
of Mesoamerica (Central American countries and part of Mexico) and is undertaken with
NASA support. This small percentage of the world contains approximately 8% of the planet’s
biodiversity. NASA and the SERVIR partners such as Oak Ridge National Laboratory,
USAID, various USA-based Universities, the World Bank, and others are developing
interactive geovisualizations of the region in order to monitor and understand various factors
affecting its ecological integrity. The occurrence of natural disasters such as volcanic
eruptions, hurricanes, earthquakes, and landslides, as well as human-induced changes to this
rain forest region, severely influence ecological integrity and endanger this unique pool of
biodiversity on Earth. Thus, there is a global and regional interest in the utilization of state of
the art scientific methods for the data analysis and geovisualization of the region at various
scales. Combining 15 m resolution LANDSAT 7 and 1 m IKONOS imagery with a DEM and
adding vector data and linking the results to the World Wide Web is a highlight of usercentered geovisualization (MacEachren 1994). Such communication media allow users
Chapter 2 Photorealistic Visualization: A Review
17
(researchers, decision makers, educators, students, non-government organizations) to display
any data of interest for a particular area and control the way that they view it (static,
interactive, animated).
Visualization of the landscape has assisted in the preservation of endangered species. For
example, in order to investigate feeding habits and paths toward the feeding areas of bats,
amphibians and the green woodpecker in the watershed of Lake-Laurez (Switzerland) a DEM
of the area was combined with LANDSAT TM, ortophotographs and digital topographic
maps to create non- and photorealistic landscapes (Hehl-Lange 2001). After the
geovisualizations were released, the visual form of data presentation positively encouraged
public participation in the issues of the biodiversity preservation.
Common in the works of Nicholson-Cole (2005), Dockerty et al. (2005) and Sheppard (2005)
is the use of geovisualization for increasing public awareness of climate change. Dockerty et
al. (2005) demonstrated in detail how to generate a GIS-based visualization of a rural
landscape to present changes to the landscape caused by climate change. It is not only the
direct influence of climate change on the landscape, but also indirect factors such as changes
in hydrology or fluvial geomorphology that can be visualized and communicated to the public
(Brandt and Jiang 2004). Nicholson-Cole (2005) suggested that the visualization of climate
change could establish a link between the abstraction of the topic and everyday experience
making the people aware of its importance. Sheppard (2005) agreed with this statement and
further explored the benefits as well as the potential risks of using visualizations to induce
behavioural changes and prompt people to not only be aware but to take action regarding
climate change. The author concluded that photorealism, the existence of relevant local and
recognisable details, and the demonstration of future consequences among others will reach
the emotional side of the viewers and have a greater potential to trigger an action when
compared to facts alone. Among the potential risks, the author singled out the risk of biased
responses, disbelief, confusion and overkill or even increase of the acceptance of the climate
change. Hence, the need to adhere to a set of ethical standards when creating
geovisualizations for public outreach and communication.2
2
The ability to produce untrue visuals in geography has been recognized for some time, and in particular, the
common knowledge of how cultural and world views affect individual societies’ map production is known and
other political motivations are clear in the maps of various propaganda efforts throughout the centuries. Ethics in
geovisualization generally follow those exhorted by Monominer in his book, "How to Lie with Maps" which is a
tenet of cartography education (Monominer 1991).
Chapter 2 Photorealistic Visualization: A Review
18
Sustainable forest management utilizes landscape visualizations for forest planning and
design as well as the communication of changes to the general public (Bell 2001). The
visualization of forest landscapes is multi-faceted (Salter et al. 2005). The challenge is not
only visualization of large data sets with specific attributes at various levels of detail and
extent of realism, but also to balance economical and environmental requirements in
conveying complex short and long term changes in the forest landscape (Salter et al. 2005).
The evaluation of existing resources and the impacts of proposed forest operations are among
the main objectives of forest landscape visualization (Honjo and Lim 2001). In most cases,
the focus is not on individual trees but on the growth and management of the forest stands
(e.g. groups of similar trees of similar structure and age) (Honjo and Lim 2001). It is not only
man-made changes such as planting, thinning and harvesting (Honjo and Lim 2001) but also
changes induced by natural factors such as forest fires (Ahrens et al. 1997) or forest blowdowns (e.g. uprooting of a large number of trees due to strong winds) (Orland 2005) are
visualized. Forest landscape visualization simultaneously provides justification of various
management scenarios for forest managers as well as helps the general public to concentrate
not only on short-term changes (e.g. highly controversial “clear-cut” practices) but also to
examine how long-term impact of the current policies can affect the visual landscape (Orland
2005).
2.4.2. Geovisualization in urban planning and development
The utilization of photorealistic geovisualization in urban planning and development can be
coarsely divided into four main categories: planning and design, infrastructure and facility
services, commercial sector and marketing, and finally promotion and learning (Shiode 2000).
Anything from road construction, emergency planning, traffic control, determination of the
optimal placement of pipes, cables or wireless stations is covered by 3D visualizations of
urban environments (Lee et al. 2003). Based on terrestrial, panoramic, aerial, ranging or
satellite images, urban models (visualizations) of various degrees of reality can be developed:
from low-detailed 2D ortho-photographic, panoramic image-based models, prismatic block
models that combine 2D building footprint with airborne survey data, and block models with
image-based texture mapping to the highest full volumetric models (Shiode 2000).
Photorealistic virtual city models such as Virtual Los Angeles, San Francisco, Atlanta, and
Tokyo just to name a few of the 60 large scale urban models being currently developed worldwide (Pietsch et al. 2005) are based on GIS data such as LIDAR, 3D Doppler data and aerial
photography. The aforementioned are just examples of things to come in the future of urban
Chapter 2 Photorealistic Visualization: A Review
19
development and planning (Ribarsky 2005). 3D models with the addition of animated views
such as walk- and/or fly-through usually do not require special training of the participant
viewers / users prior to their use (Al-Kodmany 2000). However, in some cases it is possible
for community participants to receive basic training in the software that enables them to
rearrange the proposed model according to their wishes (e.g. arrangement of schools,
apartment buildings, townhouses et) (Al-Kodmany 2000). Virtual and augmented reality tools
in urban planning enable highly engaging and interactive environments when appropriate
(Levy 1995). Although more expensive, the immersive models have an advantage over
animated, interactive 3D models: they enable group participation and interaction. However,
one of the challenges to be solved is recording the feedback of the participants (e.g. their
thoughts, emotions and preferences) in such an environment (Al-Kodmany 2001).
Hypermedia and Internet technologies as in other areas are shaping urban development and
planning (Al-Kodmany 1999). The release of the Google Earth project gives a new tool for
the integration of GIS, remote sensing, and visualization in urban design and planning (Butler
2006; Pietsch et al. 2005).
2.4.3. Other applications of geovisualization
Geovisualization also helps in various industries, for example Akzo Nobel Saltz B.V., a
mining company, used 3D visualization to improve various aspects of its mining operations
(Jagt et al. 2003). Using 3D geovisualizations, they increased the knowledge of the mining
site and improved the safety of the cavity by visualizing potentially dangerous scenarios. In
addition, the company utilized 3D visualizations as an effective communication tool
internally, in multidisciplinary teams during the planning activities and externally, to
communicate the effects of their mining activities on the environment to the stakeholders.
Shell, one of the world leaders in oil supply, has established a series of scientific models that
are able to predict and visualize various emergency scenarios following intentional (e.g.
terrorist attack) or unintentional release of hydrocarbons in the air (Shell 2006).
The Department of Homeland Security of the United States of America is using 3D
geovisualization data for examination of various “what if” scenarios, emergency management,
contingency and event planning (EON 2006). Visualization of planned events such as a visit
of the President enables detailed examination of terrain and buildings in the 3D environment
in order to establish, for example, line of sight, sniper positions and escape routes.
Chapter 2 Photorealistic Visualization: A Review
20
Visualisations of unplanned events such as terrorist attacks are also supported by the
utilization of 3D geovisualization (EON 2006). A larger GIS-based study involving
geovisualization was performed after September 11, 2001 in New York (Naphtali and
Naphtali 2003). The New York GIS community and public officials agreed that GIS and
geovisualization are valuable tools for timely response as well as data transfer and easy
communication among different teams involved in the emergency response (Naphtali and
Naphtali 2003).
Geovisualization is also used as a platform for data analysis related to various natural
disasters (Stern et al. 2006). Since, in these cases, data analysis and ‘what if’ scenario
development include various combinations of geological, hydrological, terrain, human and
other factors, the utilization of geovisualization to model and visualize these complex systems
is on the rise. For example, emergency efforts during hurricane Katrina were supported by
geovisualization (Nourbakhsh and Sargent 2006). Geovisualization was used to visualize the
potential impact of the hurricane as well as to improve communication among various
emergency response teams (Nourbakhsh and Sargent 2006). The Pacific Disaster Center
(PDC) is developing models that will enable it to predict and visualize various natural
disasters relevant to that region (PDC 2006). For example, dynamic visualizations of tsunami
travel time helps to communicate the potential danger to community officials and to increase
their preparedness level (PDC 2006). Real-time visualizations of costal flooding or lava flows
are also undertaken by PCD with similar objectives. On the other side of the world, in
Switzerland natural disasters in Alpine areas are common e.g., land slides, snow avalanches,
etc. The impact of these is also analysed and communicated to the public using
geovisualization (Stern et al. 2006). Consequently, it is clear that the utilization of
geovisualization for evaluation of natural disasters and emergency preparedness is becoming
a general trend across the world.
2.5. PRESENT CHALLENGES IN PHOTOREALISTIC
GEOVISUALIZATION
Despite its successful application across various geomatics and Earth Science fields,
photorealistic visualization technology is still facing a major challenge: There is a large gap
between contemporary 3D visualization technology and the current geomatics technology
Chapter 2 Photorealistic Visualization: A Review
21
used in geovisualization despite their common reliance on computer graphics, ability to
handle large data sets and approximately same number of decades of development.
Although considerable improvements occurred in the last decade, even state of the art
geomatics software are still producing 3D visualizations of low visual quality compared to
professional 3D visualization software (e.g. 3ds Max, Maya and Vue 5 Infinite). Why? There
are a number of factors including the perceived needs of the geomatics user community and
functionality demands. In addition there is the extended learning curve required for
development of high-quality geovisualizations. Moreover, geomatics software companies do
not have the resources to build effective photorealistic geovisualization modules for their
software from scratch (short of a corporate merger or takeover) – that would take decades and
even then geomatics companies would still lag decades behind state of the art visualization
companies. The only company currently in existence that has both technologies is Autodesk
Inc. who owns 3ds Max and Maya in addition to AutoDesk Map and AutoCad among others.
Within that milieu, potential cross-fertilization of geovisualization functionality into their GIS
software is possible but has not yet been realized. This is most likely due to the market trends
in geomatics as previously mentioned. By analogy, the market driven decisions of profit
maximization companies did keep spatial statistics out of mainstream GIS packages like
ArcGIS for decades. Rowlingson and Diggle (1993) commented clearly on the lack of
integration of spatial statistics in GIS and this was echoed prior to and following that time by
many others. However, it was not until the release of ArcGIS 9.0 in 2005 that a few point
pattern analytical routines were incorporated into a GIS toolbox. Even then, the functionality
was basic compared to the advances in spatial statistical analyses in the preceding decades.
Likewise, and now returning to photorealistic geovisualization’s connections with
contemporary geomatics, the inherent complexity of the technology necessary for modeling
and rendering photorealistic scenes is beyond a bottom-up solution for a company focusing on
GIS development. For the production of high quality photorealistic visualizations, it is
necessary to apply multi-resolution terrain models and dynamic terrain texturing and these are
current challenges facing geomatics technology developers (Doellner 2004). While the
strength of geomatics technology is in integrating and analyzing data from multiple sources,
at present this technology utilizes the low-level graphic systems such as OpenGL. These
graphic systems do not support the computational algorithms required for rendering complex
photorealistic scenes where usually multi-pass, advanced rendering technology is required.
Further details on rendering are given in Appendix 1.
Chapter 2 Photorealistic Visualization: A Review
22
The major advantage of 3D visualization technology is in its sophisticated rendering
capabilities that enable modeling and rendering of highly photorealistic scenes. On the other
hand, when working with 3D technology one has to be aware of the special nature of the
georeferenced data. This data has an exact location in the real world based on various
geographic / geodetic / projected coordinates system. In addition, there is a lack of data
connectivity between current geomatics technologies and 3D technology because most state
of the art photorealistic visualization technologies do not employ the data formats or
coordinate systems utilized in geomatics. For example, a land parcel in geomatics software is
a polygon, while in 3D software the same parcel is represented by four independent lines (e.g.
their movement is not necessarily mutually coordinated, but has to be specified as so if
desired).
It is evident that both technology platforms could benefit from an integrated solution that will
utilize the advantages of geomatics and 3D visualization approaches. Since the
implementation of advanced level rendering in the geomatics platform at present is
technically difficult and in addition, not supported by the software developers, this present
research on integration is mostly concentrated on improving the shortcomings of 3D
visualization software when handling georeferenced data. Data exchange between platforms is
a major challenge. Unavoidably, this step can result in the loss of data precision and accuracy.
Accuracy is the degree to which a visualization matches the actual values it represents, while
precision refers to the level of measurement and exactness of a visualization (Wallace and van
den Heuvel 2005). At present, there is no general integration strategy within geomatics
software that is applicable for all required photorealistic landscape visualizations.
2.6. CONCLUSIONS
Rapid advancements in information technology have changed the way we collect, analyse,
display, interact with and communicate georeferenced data. While spatial statistics and other
traditional approaches (e.g. analytical modeling) are certainly helpful, their effectiveness and
efficiency are limited by the rate of data gathering (Jiang and Li 2005). Termed
geovisualization, a multidisciplinary approach merging computer graphics, exploratory data
analysis, image analysis, and GIS is increasingly replacing and/or complementing traditional
Chapter 2 Photorealistic Visualization: A Review
23
methods. With its roots dating back to the 1960s (Nakamae and Tadamura 1995) and
intensive development since the 1980s (Bishop and Lange 2005) geovisualization is
becoming a corner-stone of contemporary knowledge discovery and the decision making
process in geomatic science (Jiang and Li 2005). This work has briefly assessed contemporary
photorealistic landscape visualization and its impact on contemporary geomatics and defined
the gap between contemporary geomatics and 3D photorealistic visualization technology.
Advanced modeling, lighting, and texturing tools as well as sophisticated rendering methods
(e.g. ray tracing, radiosity) have enabled the creation of models with an increased extent of
realism (Nakamae and Tadamura 1995). More details are given in Appendix 1. However, one
challenge remains, the integration of these state of the art advancements in 3D visualization
technology with geovisualization technology. This has proven to be the most important
obstacle to the implementation of photorealistic visualization methods in geomatic practice
for those practitioners who need a solution now.
Despite the above challenges, photorealistic geovisualization has shown an impact in various
fields such as the preservation of ecological integrity, communication of climate change and
urban planning. It is expected that in the future, the “traditional” applications of
geovisualization such as those in landscape and urban development and planning and
environmental planning will continue to grow while new ones will continue to emerge (e.g.
data analysis and subsequent development of emergency preparedness scenarios in case of
natural disasters, terrorist attacks, etc.).
Chapter 2 Photorealistic Visualization: A Review
24
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Chapter 3
Integration of 3D Visualization and GIS
for Monitoring and Communication
of Ecological Integrity in Canada’s National Parks
Chapter 3 Integration of 3D Visualization and GIS
29
3. ABSTRACT
In an effort to evaluate and protect the ecological integrity of Canada's National Parks and
increase public awareness of the subject, several government agencies have joined forces
through the Government Related Initiatives Programme (GRIP). One of their objectives is to
create 3D photorealistic visualizations of Canada’s National Parks and use them as data
exploration and communication tools. Towards that end, the first step in this research is to
establish a workflow necessary to achieve the objectives and to assess how far photorealism
can be taken when utilizing geomatics datasets.
In order to generate highly photorealistic 3D geovisualizations, an integrated approach
consisting of georeferenced data, geomatics and state of the art 3D visualization technology is
developed in this research. As a basis, digital elevation models (DEM) with various levels of
detail are used. Different 3D terrain modeling and texturing techniques are evaluated. Multispectral and panchromatic satellite imagery with different geometric and radiometric
properties are utilized to increase the level of realism.
In the second part of the study, these photorealistic visualizations are used for public outreach.
The objective was to increase awareness, understanding and knowledge about Canada’s
National Parks and the need for the preservation of their ecological integrity.
3.1. INTRODUCTION
As the second largest country in the world, Canada has large uninhabited or rarely visited
areas. However, these areas are not as “untouched” as they seem. Climate change may
dramatically alter the 42 Canadian National Parks and National Park Reserves (NPC 2003).
Among these, Canada’s Arctic National Parks are the most vulnerable (Suzuki 2000).
Northern climate change is marked by glacier retreat, the movement of permafrost boundaries
northwards, and rapid changes in arctic ecosystems (Suzuki 2000). Reducing Canada’s overall
vulnerability to climate change is one of the major objectives of the newly introduced
Government Related Initiatives Program (GRIP) (Wong and Chilar 2004).
Chapter 3 Integration of 3D Visualization and GIS
30
The work reported here is a part of the project undertaken within the scope of GRIP. Parks
Canada, the Canada Centre for Remote Sensing and the University of Ottawa joined forces
with two main objectives:
1. Application of new tools for the assessment and monitoring of the ecological integrity
of Canada’s National Parks using Earth Observation data (EO).
2. Application of new tools for effective communication of the results to the general
pubic.
Ecological Integrity as defined by the Canada National Parks Act is “…a condition that is
determined to be characteristic of its natural region and likely to persist, including abiotic
components and the composition and abundance of native species and biological
communities, rates of change and supporting processes (NPC 2003).” For various parks the
stressor and ecosystem indicators of interest for assessment and monitoring are different (e.g.
Arctic and prairies ecosystems differ greatly).
While contemporary data acquisition technology provides access to a large amount of
information that could be potentially used for the preservation of ecological integrity, the
complexity of the data is such that making any inferences using traditional data exploration
methods is highly limited. There is an indication, however, that novel visual data exploration
and presentation methods could be beneficial for the assessment and monitoring of ecological
integrity.
Visualization of endangered areas can assist us in data exploration by providing cognitive
support (e.g. enhanced recognition, allowing monitoring of a large number of potential events
or recognition of patterns) (Tory and Moeller 2004). This cognitive support is a basis for the
engagement of the audience in the decision making process.
3.1.1. Objectives of the study
Canada’s 42 National Parks and National Park Reserves cover 224,470 km2 in total (NPC
2005). This 2% of Canada’s land coverage does not include numerous provincial parks. Ten
out of 42 National Parks were selected for the GRIP project (Figure 2). The first to be
assessed using the 3D photorealistic modeling approach were Auyuittuq, La Mauricie and
Nahanni National Parks. Due to the projected influence of climate change, assessment of the
ecological integrity of these three parks was of the highest priority. Effective communication
Chapter 3 Integration of 3D Visualization and GIS
31
of the park environment and potential changes within the park boundary are fundamental to
understanding the mechanisms of change and in soliciting the engagement of the public.
In the first part of this work the objective was to establish an integrated approach for using
geospatial data, geomatics technology and scientific visualization to create 3D photorealistic
visualizations of Canada’s National Park (NP) environment. The sub-objective was the
exploration of various methods of 3D, terrain modeling and texturing to achieve highly
photorealistic visualization of mountain terrains with snow and glacier structures as well as
vegetation coverage. To achieve this objective we had to develop a new, integrated approach
that combines Earth observation data, geomatics science and contemporary 3D visualization
technology.
The second objective was to use the resulting photorealistic visualizations as a
communication tool for public outreach. That is to say, we needed to assess whether the
visualizations were seen as important and useful by the non-project and project individuals.
Our intended audience was a broad range of geomatics professionals, local high-school
students, and the general public. The hypothesis was that presenting highly photorealistic
visualizations of the areas to the general public will have two effects. First, photorealistic
visualizations of NPs will engage the general public and induce an interest in preserving
ecological integrity in the parks. Second, with an increased awareness of these remote areas,
the general public will have more understanding and support for the actions taken on the
preservation of ecological integrity.
Chapter 3 Integration of 3D Visualization and GIS
Figure 2 Proposed National Parks of Canada for the Government Related Initiatives Program (GRIP) Project
32
Chapter 3 Integration of 3D Visualization and GIS
33
3.2. PHOTOREALISTIC GEOVISUALIZATIONS CHALLENGE
Despite considerable improvements and increased research efforts, geomaticians are still
facing a challenge in the area of visualization since there is a big gap in the visualization
quality and capabilities between current geomatics software and professional 3D visualization
technology (Appleton et al. 2002). The work presented here intends to overcome this gap by
development and implementation of an integrated solution leveraging on the advantages of
both technologies. Even state of the art geomatics software (e.g. ArcGIS, Mapinfo) offers
limited capabilities when 3D photorealistic visualization tools are in question. Not only is the
number of available tools limited but also the quality of the generated visualization is inferior
to those provided by professional 3D modeling/ animation software (Table 1).
Table 1 A comparison of basic building elements of animation
Professional 3D
Geomatics
Geometry
complex
simple
Lighting
complex
simple
Camera
complex
simple
Shading
advanced
basic (flat)
Rendering
advanced
basic
In terms of technology and complexity, one can estimate that current geovisualization
applications are at least 10 years behind professional 3D applications3, leaving geomaticians
to evaluate different state of the art visualization software and find a way to integrate them
with powerful GIS and remote sensing technologies. Since there is no “universal landscape
visualization solution”, it is inevitable that geomaticians will need to be familiar with at least
several visualization packages to compensate for various trade-offs in level of detail or
interactivity (Appleton et al. 2002).
3
This observation comes from a review of feature films, considering only high-budget “A” films from the last
thirty years, industry design, architecture and comparing special effects with those produced by geomatics
software over the same time period with regard to the geographic representations of land and cityscapes.
Chapter 3 Integration of 3D Visualization and GIS
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Walsch et al. (2003) visualized the influence of geomorphic processes on the alpine terrain
ecotone in Glacier National Park, Montana, USA. The authors claimed that the combination
of GIS, remote sensing and scientific visualization for assessment of the effects of
geomorphic processes and patterns at alpine treeline improved alpine slope models and
enabled the determination of alpine landscape attributes within a spatio-temporal context.
They concluded that the use of visualization (e.g. image flyovers, rotations, and animations)
of cellular automata can lead to new hypotheses and insights. The authors suggested that the
use of visualization in the future could potentially result in multiscale models of vegetation
patterns in alpine terrain that include topography, geology and climate changes.
High resolution photorealistic visualization of Mount Everest was another attempt to visualize
alpine terrain (Gruen and Murai 2002) by combining remote sensing imagery and scientific
visualization. The heavy pressure that tourism and lodging place on the UNESCO protected
area of Sagarmatha (i.e. an area with dramatic mountains, glaciers and steep valleys,
dominated by Mount Everest) has initiated action for the preservation of ecological integrity
in this unique area. As such, the first task was recording the current situation by producing
high-resolution visualizations of the area. The authors emphasized the challenge in the
texturing of steep faces of 3D mountain models since the pixel information from aerial
imagery is only coarsely available. In addition, they acknowledged the importance of shadows
for realistic model appearance and suggested careful lighting of shadow regions.
These studies provide helpful insights for the design of an integrated approach to
photorealistic visualizations. First, the similarity of the objectives (e.g. protection of
ecological integrity of a protected area) and the tools utilized in this work provided a good
basis for the development of an integrated workflow. We have carefully examined the
problems and challenges the above authors experienced, such as coarse aerial imagery or
shadows mode lining, and considered new strategies such as pansharpening to address them.
Why use photorealistic visualizations as a public outreach tool?
It has been shown that a high level of abstraction and complex scientific descriptions are not
efficient tools when there are non-experts in the audience (Tress and Tress 2003). On the
other hand, there are suggestions that photorealistic visualization during the decision making
process can improve communication among the interest groups, professionals and nonexperts, and thus increase the efficiency of the process (Appleton and Lovett 2005; Tress and
Chapter 3 Integration of 3D Visualization and GIS
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Tress 2003) since the audience appears to readily understand high quality visualizations
(Hehl-Lange 2001). The more closely the visualization resembles the real world and our
perception of it, the more likely the audience will establish a connection with the project and
claim ownership (Appleton and Lovett 2005). A comparison (Table 2) between the original
scene (nature) and low and high realism scenes reveals that photorealistic presentation scores
closer to the original scene as compared to a geometric presentation.
Table 2 Comparison: Geometric, photorealistic and original scene (Angsuesser and
Kumke 2001)
Criterion
Origin
Level of details
Generalisation degree
e.g. abstraction degree
Individualisation degree
Time dependence
Information perception
Geometric
Presentation
artificial
low
Photorealistic
Presentation
artificial
high
Nature
(Original scene)
natural
infinite
high
low
none
low
low
little (selected)
information in a
short time
high
high
only individuals
complete
infinity of
information in
infinity of time
much information
over a long time
Photorealistic visualizations at a small scale with a high level of detail and realistic
appearance enable easy identification of an area by non-expert stakeholders and require little
additional interpretation to convey the message (Tress and Tress 2003). Despite differences in
individual responses to visuals, to understand how visualizations improve our communication
we have to understand some general mechanisms by which visuals support human cognition
(Tory and Moeller 2004):
1. Visualisations increase the resources of our perception system. Visualized data are
processed faster with less demand on the human memory and perceptual system.
2. Visualizations enhance recognition. The process of information recognition is easer
and faster compared to the process of information recall.
3. Visualizations support perceptual monitoring. Visualization supports the use of preattentive visual characteristics (e.g. color difference, motion, form) to attract human
attention and allows monitoring of a large number of potential events.
In addition, visualizations can present large amounts of data in a form that is easy to access
Chapter 3 Integration of 3D Visualization and GIS
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and manipulate by the end user. The user can selectively omit, aggregate, rearrange or
emphasize the data in order to recognize higher levels of patterns in the data
A study from Denmark (Tress and Tress 2003) provides a representative example of the
effectiveness of photorealistic visualization in the communication of ecological integrity.
During a debate on the future landscape development of a small rural area in Denmark, the
stakeholders were presented with photorealistic visualizations of four scenarios (i.e. the
preservation of ecological integrity, industrial farming, recreation/tourism, and residential
expansion) (Tress and Tress 2003). Although only one scale and perspective were offered,
photorealistic scenes enabled non-expert stakeholders to easily identify known landmarks and
key features in the projected scenarios. Thus, only a small amount of clarification was
necessary from the experts in the field. The stakeholders were able to take an active role in the
decision making process with less misinterpretation and confusion. These results are in
accordance with the finding of Appleton and Lovett (2005) who stated that non-experts in the
audience will likely rely on visualisations during their decision making. Furthermore, this
paper indicates that photorealistic visualization can be an effective tool for the communication
of ecological integrity.
3.3. VISUALIZATION CASE STUDIES: NATIONAL PARKS IN
CANADA
Before effective communication of ecological integrity within regions can take place the
communication tools must be furnished. As such, an integrated approach and workflow for
using geospatial data, geomatics technology and scientific visualization to create 3D
photorealistic visualizations of Canada’s National Parks (NP) environments is a prerequisite
to the development of visualizations for communicating ecological integrity. The
development of this workflow concentrates on using geospatial datasets in three National
Parks, namely Auyuittuq, Nahanni and La Mauricie. Therein, the exploration of various
methods of 3D, terrain modeling and texturing are provided in order to achieve highly
photorealistic visualization of mountain terrains with snow and glacier structures as well as
vegetation coverage. The result of this exercise is a new, integrated approach that combines
EO data, geomatics science and contemporary 3D visualization technology.
Chapter 3 Integration of 3D Visualization and GIS
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3.3.1. Auyuittuq National Park, Nunavut
The area of “the land that never melts” (the meaning of the word Auyuittuq in Inuktitut
language) covers 19,098 km2 and was established in 1976 as a National Park (Figure 3).
Located in the eastern Arctic on southern Baffin Island, the park is described by National
Parks Canada as land “where sweeping glaciers and polar ice and sea meet jagged granite
mountains” (NPC 2005).
Chapter 3 Integration of 3D Visualization and GIS
Figure 3 Auyuittuq National Park, Buffin Island, Nunavut, as shown in a Landsat 7 ETM+ scene from Geobase (www.geobase.ca)
38
Chapter 3 Integration of 3D Visualization and GIS
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Auyuittuq is an area containing (NPC 2005):
a) Moraines - ridges formed by rock debris that were transported by moving ice and
deposited at the margins of glaciers.
b) Cirques - bowl-like hollows carved out by glaciers in the tops of mountains. Some
cirques are ice-free, while others are still occupied by glaciers.
c) Sand Deposits - created by the erosive actions of ice, wind, and water
d) Perched Boulders - large boulders sitting atop small rocks, deposited by retreating
glaciers
e) Talus or Scree Slopes - cone-shaped accumulations of rocks pried loose from steep
glacier-scoured valley walls by frost action.
The plethora of glacial features makes this Arctic park of particular interest for research on
the preservation of its ecological integrity in light of continuing climate changes. The effects
of climate change can be observed from satellite imagery taken in 1991 and 2000. Since this
is a remote area, it is essential to effectively communicate these changes to the general public.
Towards this end, the first step is to address how photorealistic geovisualizations of such
phenomena and environments can be achieved through integration with geomatics.
3.3.2. Nahanni National Park Reserve, Northwest Territories
Located in the Northwest Territories, Nahanni (Figure 4) was established as a National Park
Reserve in 1972 and declared a UNESCO heritage site in 1978. Four great canyons of the
South Nahanni River are surrounded by alpine tundra, mountain ranges and forests in this area
of the Mackenzie Mountains.
The biggest challenge to the ecological integrity of the Nahanni National Park Reserve is the
fact that it covers only 1/7 of the total area of the South Nahanni River watershed. Because
the park covers the lower portion of the watershed, upstream activities such as mining, oil and
gas exploration in addition to climate change can influence the ecological integrity of the
park. In the light of the pending discussion on the expansion of this national park, public
outreach is especially important.
Chapter 3 Integration of 3D Visualization and GIS
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The area covered by Nahanni National Park Reserve in its present form is the result of three
major geological events in the past (NPC 2005). First, 200-500 million years ago the area was
covered by a sea. Up to 6,000 m of sedimentary rocks consisting of sandstone, mudstone and
shale were deposited in the area during this period. Approximately 100 million years ago
mountain-building started in the area. The areas in the eastern part of the national park are still
rising (NPC 2005). Between 2 and 8 million years ago began a third geological era for the
Nahanni region: the glacial era. Short periods of glacial erosion (the last one ending 10,000
years ago) were combined with long periods of river erosion.
Extensive glaciers, hot springs, tufa mounds (i.e. geological features created by the
precipitation of dissolved minerals, primarily calcium carbonate, from thermal spring water)
and the four canyons of the South Nahanni River are not the only areas of research interest in
the area (NPC 2005). In comparison to Auyuittuq, the National Park Reserve is rich in flora
and fauna. Among 700 plants, boreal forest and alpine tundra species are dominant. Grizzly
bears, caribou and moose share the area with wolves, small mammals and over 170 bird
species. As such, there are numerous aspects of the environment within Nahanni NP that can
be potentially communicated to stakeholders using photorealistic geovisualizations, if such
visualizations can be formed by integrating geospatial data and state of the art visualization
technologies.
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Figure 4 Nahanni National Park, NWT
Chapter 3 Integration of 3D Visualization and GIS
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3.3.3. La Mauricie National Park, Québec
Created in 1970, this national park (Figure 5) protects the Laurentian area and the
southernmost part of the Canadian Shield. Almost 955 million years ago the Canadian Shield
was as high as today’s Himalayas (NPC 2005). However, a number of erosion events, the last
one being the Wisconsinan glaciation of 25,000-12,000 years ago, reduced the height of the
Shield considerably and left numerous lakes. After the retreat of the Wisconsinan glacier the
lowest part of La Maurice NP was covered by a marine incursion for approximately 2,000
years (NPC 2005).
The preservation of ecological integrity of this National Park is a priority due to the
abundance of flora and fauna in this “land of lakes and brooks”. Among 400 plant species
inhabiting the NP, 70 are considered rare (NPC 2005). Approximately 40 species of birds of
the total of 180 depend on the park’s aquatic environment for survival (NPC 2005). Numerous
geological events shaped today’s topography, soil types and altitudes that host more than 30
tree species, covering approximately 93% of the total 592 km2 of the park’s area. Black and
white spruce, balsam fir and white birch are the dominant tree species. However, the
surrounding area of the Park is being defragmented by intensive, clear cut forest management,
endangering the species in the surrounding areas and the park itself. In addition, intensive
tourism also has a negative impact. For instance, wolves are now only present during the
winter when the number of visitors is low.
Chapter 3 Integration of 3D Visualization and GIS
Figure 5 La Mauricie NP, Québec as shown in a Landsat ETM+ mosaic based on data from Geobase (www.geobase.ca)
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Chapter 3 Integration of 3D Visualization and GIS
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3.3.4. Key features to be visualized
Since the output of the research objective is to increase public awareness and interest in the
National Parks as well as to communicate the need for the preservation of their ecological
integrity, representative areas were selected for visualization, such as the glacier features of
Auyuittuq NP affected by climate change, the clear-cut forest areas of La Mauricie NP and
the topography and overall landscape of Nahanni NP. In Table 3, the key features to be
visualized, their relevance and the EO data used for the visualizations are outlined.
Table 3 Key features to be visualized in order to develop a workflow integrating
photorealistic geovisualization with contemporary geomatics.
FEATURE
RELEVANCE
DATA
Auyuittuq National Park
97 km long canyon created by the movement of
Akshayuk
continental glaciers during the last ice age.
Pass
Crater lake
Marks the limit of the last advance of the glaciers.
Summit lake
Located at the highest point of Akshavuk Pass.
Mount Tohr
The longest uninterrupted cliff face in the world.
Nahanni National Park Reserve
Topography and landscape of another
1,2
representative region containing highlands and
low lands.
La Mauricie National Park
1
Clear-cut forest areas.
LANDSAT (30 and 15m),
IKONOS (1m)
LANDSAT (30 and 15m),
QuickBird(60 cm)
LANDSAT (30 and 15m),
SPOT (5m)
Chapter 3 Integration of 3D Visualization and GIS
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3.4. AN INTEGRATED APPROACH TO PHOTOREALISTIC
LANDSCAPE VISUALISATION
The methodology used to integrate photorealism with contemporary geomatics that is
developed in this work can be broken down into five major steps (Figure 23): identification of
the scientific concept, model building, animation, rendering and postproduction.
Figure 6 Workflow for the integration of GIS and scientific visualization
Similar to other scientific approaches, the photorealistic modeling process starts with the
problem definition (Figure 6A). At this stage an outline of the modeling procedure is
envisioned. Data collection and evaluation is the next logical step. The first task in the
modeling step (Figure 6B) is data pre-processing. In most cases, the available data requires
pre-processing within GIS and RS software to achieve the desired coverage, resolution or
quality of presentation. At this stage the scientific concept is transformed into key-frames.
Texturing, shading and terrain modeling are then performed. There is no general modeling
procedure. Each concept is unique in the way the scientist will approach the required
visualization. In addition, there is not a single unified software applicable to all models. At
present, the situation is similar to the animation step (Figure 6C). Various commercial
software packages are available to produce dynamic visualizations (eg. Vue 5 Infinite,
Terragen, 3ds Max) with various compatibility levels to GIS software. Thus, it is up to the
geomatician to select the most efficient software combination for the given objective. Camera,
Chapter 3 Integration of 3D Visualization and GIS
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light positioning and incorporation of lighting models (direct, indirect, diffuse, caustic effects
etc.) in the visualization software are the key elements to achieve a high extent of realism in
the final product. Subsequently, the selection of a rendering algorithm (Figure 6D) is based on
a good balance between the rendering speed and the level of detail required for the
photorealistic visualization. A suitable file compression utility is necessary to reduce the file
size of the final video. In the final post-production step (Figure 6E), various other effects such
as text and/or music are added to the photorealistic visualization to increase the information
level of the presentation.
3.4.1. Data Collection and Evaluation
For this work a wide variety of primary geographic data in digital format were obtained from
many diverse sources. For Auyuittuq NP, Nahanni and La Maurice NPs, DEM and
LANDSAT imagery were obtained from GeoBase (www.geobase.ca). GeoBase is a
geomatics database established and maintained by the Canadian Council on Geomatics and
Natural Resources Canada that provides data at no cost and with no restrictions for the users.
For Auyuittuq NP, IKONOS imagery, LANDSAT TM 1991-07-12, Path/Row 17/13 and
LANDSAT ETM 2000-08-13 Path/Row 17/13 were provided by GeoBase. For La Mauricie
NP, Spot imagery was acquired from EADS MATRA Systems and Information and vector
data from Parks Canada. For Nahanni NP, LANDSAT and Quickbird imagery were obtained
from GeoBase and Digital Globe, respectively. The Nahanni DEM was produced prior to
visualization using the National Topographic Database (NTDB) and the ANUDEM 5.1
algorithm. La Mauricie visualizations were supported by the use of LANDSAT and SPOT
(Spot Image).
Imagery obtained from GeoBase was free of charge. Quickbird panchromatic and
multispectral imagery was purchased for CAN $ 6 per square kilometre (educational price for
archived imagery).
DEM data
Digital Elevation Models (DEM) are data files that contain the elevation of the Earth’s surface
(z-coordinate) over a specified area (x,y coordinates), usually at a fixed grid interval. The
intervals between each of the grid point are always referenced to a geographical coordinates
system (e.g. NAD83). It is called a model because a DEM is a generalized representation of
Chapter 3 Integration of 3D Visualization and GIS
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reality where reality is true elevation at a point. A DEM contains a resolution or cell size over
which elevation is an average of the elevation values observed within a region defined by the
raster cell. Since a DEM is a raster representation, its structure (an array of pixels) represents
a defined area of Earth (Figure 7).
Figure 7 An example of a DEM structure: a) raw data Auyuittuq NP, b) 400% zoomed, c) 800% zoomed
A DEM of the Canadian landmass is stored in GeoBase (www.geobase.ca) as the Canadian
Digital Elevation Data model (CDED). The North American Datum 1983 (NAD83) is used as
the reference system. Elevations are orthometric and expressed in reference to Mean Sea
Level (Canadian Vertical Geodetic Datum 1928 (CVGD28)) (CTI 2005). Currently available
data, extracted from the hypsographic and hydrographic elements of the National
Topographic Data Base, is an average scale of 1:10 000 to 1:250 000 (CTI 2005) which is
denoted as GeoBase Level 1. At present only the 1:250 000 CDED provides complete
seamless coverage of the entire Canadian landmass (CTI 2005). This scale is sufficient for
visualization purposes when only qualitative visual data analysis is performed. For the 1:250
000 scale, the grid spacing is based on geographic coordinates at a maximum 3, 6 and 12 arc
seconds depending on latitude.
The concept of geographically referenced grids deserves closer attention because when
imported into the ArcGIS working environment (ArcGIS, ESRI) as raw data, grids registered
using arc seconds must be reregistered and reprojected for further use in ArcGIS workspace
since ArcGIS does not consistently recognize arc second units. Details of this process will be
explained in the data pre-processing section.
Chapter 3 Integration of 3D Visualization and GIS
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Four remote sensing datasets (e.g. LANDSAT, IKONOS, QuickBird, SPOT) were utilized in
the project to create geospecific photorealistic texture maps in accordance with the workflow
outlined in Figure 6b. These datasets represent two radiometric resolutions: 8 bit or 11 bit, and
five different spatial resolutions: 0.6 meters, 1 meter, 4 meters, 15 meters and 30 meters. The
combination of these data ensured the balance between large areas of the parks that were to be
visualized and also allow for a high level of detail required for fly through animations.
The radiometric resolution refers to the number of and width of spectral bands that is stored in
an image. In order to work efficiently with images (e.g. compressing and stretching) it is
important to understand bit depth. Computers work with binary data, which means that every
number has a value of 0 or 1. More complex numbers are represented by a sequence of binary
digits. For example, 2 bit data would result in 4 possible values: 00, 01, 10, and 11. Thus, 8
bit data can store 256 (28) possible values in each pixel or band, and 11 bit (211) allows 2048
possible values for each spectral band. More information can be extracted from 11 bit data but
some software does not support that format and it requires more disk storage space.
LANDSAT-ETM+ (LANDSAT 7 satellite), satellite images were obtained from the GeoBase
portal. LANDSAT ETM + data are collected from a nominal altitude of 705 kilometres in a
near-polar, near-circular, sun synchronous orbit at an inclination of 98.2º, imaging the same
183-km swath of the Earth’s surface every 16 days (CTI 2003). Radiometric resolution is 8
bit. That is to say, each band captures a given range of light such as the blue part of the
radiometric spectrum and the variation in this spectrum is reduced to a range of values from 0
to 255. Landsat ETM orthoimages are stored as raster data and consist of 9 spectral bands: a
panchromatic band with a pixel size of 15 m, 6 multispectral bands with a pixel size of 30 m.,
and 2 thermal infrared bands with 60 m ground resolution. These have been produced in
accordance with NAD83 (North American Datum of 1983) using the Universal Transverse
Mercator (UTM) projection (CTI 2003). Bands 1, 2 and 3 are used for producing true color
composite image. The panchromatic band 8 is used for a pansharpening process.
IKONOS data
IKONOS data are collected from a nominal altitude of 681 km in a sun synchronous orbit at
an inclination of 98.2º. The orbit time around the Earth is 98 min. Image swaths are 11 km at
nadir (the point in the sky with an inclination of -90°) and 13 km off-nadir. Image bands are
panchromatic, blue, red, green and NIR (near infra-red). Spatial resolution at the nadir is 0.82
Chapter 3 Integration of 3D Visualization and GIS
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meters panchromatic and 3.2 meters multispectral, and off- nadir resolutions are 1 and 4 m for
panchromatic and multispectral mode, respectively. The radiometric resolution for the
multispectral bands is 8 bit and for the panchromatic band is 11 bit. The 11 bit allows for
more detailed extraction in areas of low contrast and shadows. The temporal resolution is 3
days, which means an image of the same geographical space can be collected every 3 days.
QuickBird data
QuickBird imagery (Digital Globe) is the highest spatial resolution imagery commercially
available. This high spatial resolution was used to increase the resolution of the fly-through
3D photorealistic visualizations. The QuickBird satellite collects data at a nominal altitude of
450 km in a sun synchronous orbit at an inclination of 97.2o with an orbit time of 93.5
minutes. Swath width at and off-nadir is 16.5 km. The spatial resolution of the panchromatic
imagery is 61cm at nadir and 72 cm at 250 off-nadir, while the resolution of multispectral
imagery is 2.44 m at nadir and 2.88 m off-nadir. The temporal resolution is 1-3.5 days
depending on latitude. The radiometric resolution is 11 bit. Image bands are: panchromatic,
blue, red, green and NIR (near infra-red). A selected image included as many characteristics
of the Nahanni region as possible, such as rivers, mountains and topography. An important
factor for image selection was atmospheric quality. Images with less than 10% cloud coverage
or other atmospheric effects were selected since they can be used for visualization without
pre-processing. The cloud coverage and atmospheric effects such as haze, rain or snow affect
the accuracy of data retrieved from the satellite imagery (Sjoberg and Horn 1983). While the
presence of haze, rain and snow reduce contrast and thus affect the level of detail in a
photorealistic visualization, clouds appear to lay flat on the ground looking like snow
coverage (Hirtz et al. 1999).
SPOT data
Spot SYSTEM SCENE level 1A (SpotImage) was used for the determination of vegetation
distribution for visualization of La Mauricie NP. The Spot orbit is polar with an inclination of
98 degrees, circular, sun-synchronous and phased. The satellite has 832 km altitude and 14.19
revolutions per day with a period of 101 minutes. Cycle duration is 26 days with westward
drift between successive ground tracks of 2823 km. SPOT imagery had 4 channels and ground
location accuracy better than 350 m. Imaging swath is 60 km x 60 km. Geocoding tables
identification was EPSG 5.2 (European Petroleum Survey Group) and the geographic
horizontal coordinate system was WGS 84.
Chapter 3 Integration of 3D Visualization and GIS
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3.4.2. Modeling
Data pre-processing
Three major pre-processing steps were undertaken to obtain the data at the necessary level of
detail for the subsequent animations. The pre-processing steps were:
a) DEM data preparation
b) Production of LANDSAT composites
c) Data fusion (pan-sharpening)
Pre-processing of DEM within a GIS environment. The DEM pre-processing consists of
several steps as shown in Figure 8.
Figure 8 Cartographic model of DEM data pre-processing
The area covered for every downloaded raster file corresponds to half an NTS (National
Topographic System) map, which means that there are western and eastern parts to the
CDED1 for each NTS map. Therefore, there are multiple raster datasets. Because all of them
have the same spatial reference, pixel size and no differences in overlapping regions of
adjacent tiles, they can be merged into a single raster dataset that will be further used in the
visualizations. The process is presented in Figure 9 for the Auyuittuq NP.
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Figure 9 DEM Pre-processing. a) 4 adjacent raster datasets b) merged raster dataset. Data source:
www.geobase.ca, Scale 1: 50 000
Because the ArcGIS platform does not consistently support arc seconds, such units are
converted into decimal degrees in the second step of DEM data pre-processing prior to DEMdata projection into UTM Zone 19. The polar geographic reference system considers the
globe to be an ideal sphere divided into 360 equal parts called degrees where each degree is
further subdivided into 60 minutes and these are subsequently composed of 60 seconds.
According to this representation, one arc second is the distance of latitude or longitude
traversed on the earth’s surface while traveling one second (1/3600th of a degree) (ESRI
2006). An arc second of longitude is equal to an arc second of latitude at the equator.
However, the closer to the poles one goes, the shorter the ‘real world’ distance that one arc
second longitude becomes in a cosine-based fashion due to convergence of longitude lines at
the poles. In comparison, the distance traveled in one arc second latitude remains almost
constant. Therefore, the easiest transformation to polar coordinates is around the equator.
There, one arc second of longitude corresponds to 1/60th of a nautical mile (1825 m) which
equals 30.87 m. This is regardless of whether one travels along latitude or longitude lines.
Toward the poles the transformation must consider the latitude of the point (e.g. 1 arc second
of longitude at 49o north latitude is equal to 30.87 × cos 49o = 20.25 m). As such, the
consequence of the different distances one unit of longitude represents at different latitudes is
that the resolution of an image varies in the x orientation for large regions like Nahanni NP
Chapter 3 Integration of 3D Visualization and GIS
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and this has to be taken into account when creating the DEM or when merging the Landsat
imagery.
Pre-processing LANDSAT Imagery
To obtain the desired information various composite images usually need to be created. The
results of various band combinations from Auyuittuq NP are presented in Figure 10.
Depending on the objective of the work, various composites were found to be useful. True
color composite represents the most realistic view of the earth’s surface, closely resembling
what is seen by the human eye. To create an image that is close to a photograph a true color
composite combines band 3 (visible red), band 2 (visible green), and band 1 (visible blue).
For generation of 3D photorealistic representation, the true color composite was the most
useful composite. On the other hand, if one’s purpose is the monitoring of glacier retreat rate,
the normalized difference snow index (NDSI) was the most useful composite image. The
NDSI is useful to distinguish snow and ice from similarly bright features like clouds or rocks.
It is calculated using Landsat TM2 (green band) and Landsat TM5 (mid-infrared band) as:
NDSI =
TM 2 − TM 5
TM 2 + TM 5
The pre-processed images can then be used as textures to illustrate and highlight different
aspects of the Arctic landscape.
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Figure 10 Different channel combinations lead to various composite images. Modified from Aronoff
(2005). Numbers below composite images represent Landsat band combinations, e.g. 321 is a color
composite of band 3 - red, band 2 – green and band 1 – blue. Band numbers are explained on a left panel.
Chapter 3 Integration of 3D Visualization and GIS
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Pansharpening (Image fusion)
Image fusion integrates color information from a low-resolution multispectral image with the
geometric detail of a high-resolution panchromatic image to increase the spatial resolution of
the multispectral imagery. This technique, also called pan-sharpening, is important because
most satellites such as Landsat, IKONOS, SPOT and QuickBird provide both panchromatic
(higher spatial resolution) and multispectral (lower spatial resolution) images. Therefore, data
fusion can increase the application potential of remotely sensed images. Two technical
limitations are the major reason why most satellites do not collect high-resolution
multispectral images directly:
1. The incoming radiation energy to the sensor (pan image covers a broader wavelength
range), and
2. The data volume collected by the sensor is larger for multispectral images.
Thus, an effective image fusion technique is an optimal solution for providing high spatial
resolution and high spectral resolution remotely sensed images for use in visualization. Since
pansharpening increases both the spatial and spectral resolution (Walsh et al. 2003) of the
original panchromatic image, the fusion is an integral part of the data pre-processing. The
high level of detail at variable scales required by photorealistic animations can be achieved
only when the original image is of a high resolution.
At the present, the quality of the fused imagery depends on the method used for the fusion as
well as on the experience of the user. The main problem for this technique is color distortion
of the fused imagery (Zhang 2004). Although there are various image fusion techniques (e.g.
intensity-hue-saturation (IHS), hue saturation value (HSV), texturization, principal component
analysis (Walsh et al. 2003)), in this work a hue saturation value technique was used. Here a
traditional, red-green-blue (RGB) model is changed to the HSV model. The value band is
replaced with the high-resolution image. Hue and saturation bands are automatically resampled to the high-resolution pixel size using a nearest neighbour, bilinear, or cubic
convolution technique. Finally, the image is transformed back to an RGB image that has the
pixel size of the input high-resolution data.
A Landsat ETM+ panchromatic image (spatial resolution 15m) was used to enhance the
spatial resolution of a natural color composite image [blue (band 1), green (band 2), and red
(band 3)] (spatial resolution 30 m) covering Auyuittuq National Park (Figure 11). The images
are georeferenced, which is a requirement for successful pansharpening. The image fusion
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resulted in a high resolution color composite image with a spatial resolution of 15 m. In this
case, the image sharpening technique used a hue-saturation value (HSV) to automatically
merge the lower-resolution color and higher resolution panchromatic images.
Figure 11 Image fusion. Example from Auyuittuq NP. a) panchromatic image (15 m resolution); b) true
color composite LANDSAT 7 ETM + image (30 m resolution); c) the results of image fusion (15 m
resolution)
3.4.3. Terrain modeling techniques
Three different texturing methods (with reference to the workflow given in Figure 6b) were
employed in this work with the aim of testing the effectiveness and to offer recommendations
for future work. Simultaneously, during this step we were able to evaluate various terrain
modeling software.
Modeling of Nahanni and Auyuittuq NPs
The approaches used for modeling of Nahanni and Auyuittuq NPs were the following:
a) Modeling with contour lines where photorealistic visualization (3ds Max 7) and GIS
(ArcGis 9.1) software were combined.
b) Modeling with displacement mapping.
c) Modeling with a commercial plug-in called Dreamscape® for 3ds Max.
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Modeling with contour lines
For this method free contour data were obtained from the Geomatics Canada database, NTDB
(National Topographic Data Base) (Figure 12a). Data are based on the North American
Datum of 1983 (NAD83), scale 1:50 000. The measuring units are expressed in meters and
contour interval is 40 m. A high resolution digital elevation model was used to create contours
with lower intervals (i.e. 1m or 0.5m) in order to model a terrain with a higher level of detail.
In this case, the contour interval is appropriate because of the size of area being modeled.
Larger area size and smaller contour intervals will result in a 3D terrain model that contains a
large number of faces with significantly slower performance.
Figure 12 a) NTDB contour lines (scale 1:50 000) and b) LANDSAT image of the same area of the
Auyuittuq NP
Contour lines were prepared for terrain modeling in four steps (Figure 13) using ArcGis 9.1
technology (ModelBuilder 9.1). Four different tiles covering the area of interest were merged
and projected to UTM Zone 19. Subsequently, topological errors such as coincident lines and
line crossing were removed by line smoothing. As a final step, the contour lines (Figure 29a)
were exported as a CAD drawing. Also, a georeferenced and pansharpened Landsat image
(Figure 29b) was exported with the same extent as contour lines.
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Figure 13 Cartographic model of contour lines pre-processing
Within 3ds Max it is necessary to change the units to the metric system. The CAD drawing
file is then imported to 3ds Max which displays elevation contours as editable splines (Figure
14a). The imported editable splines behave independently and it was necessary to check for
and fix any existing gaps for each spline. Using Terrain tool in 3ds Max, a new triangulated
mesh surface (Figure 14b) was created based on the imported data. Furthermore, to check and
analyze the elevation change of the model compared to the contour lines, colors were assigned
to the model according to elevation zone values as presented in Figure 15.
Figure 14 a) Imported CAD drawing into 3ds Max as editable splines; b) Generated triangulated mesh in
3ds Max based on contour data of the Auyuittuq NP
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Figure 15 A 3ds Max terrain model with applied color for elevation zones of the Auyuittuq NP. Left: top
view; Right: oblique view.
Modeling terrain with displacement mapping
Displacement mapping is an effective technique to increase the level of detail on a polygon
based surface while allowing a fewer number of polygons to model a surface when compared
to using contour data. A base geometry is displaced and modified using a displacement
function usually sampled and stored in an array, a so-called displacement map. Displacement
mapping creates new geometry by first dividing (tessellating) existing polygons into smaller
ones, and then perturbs the new geometry by displacing the created vertices according to a
displacement map. Figure 16 demonstrates these processes performed with 3ds Max software.
The simplest land surface, which is a flat plane, here presented with an editable mesh, was
modeled with a displacement map using planar mapping coordinates. A grayscale image
representing DEM data for the area being modeled is used as the displacement map. Lighter
colors in the grayscale image displace the base geometry more strongly than darker colors,
resulting in a 3D displacement of the geometry.
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Figure 16 An example of a gray scale image used as a displace map obtained from
DEM data (source: www.geobase.ca ,scale 1: 50000)
Modeling terrain with DreamScape Terra®
A commercial plug-in for modeling terrain, DreamScape Terra for 3ds Max, was used for
visualization of Auyuittuq NP. A procedural texture was generated to texture snow coverage.
The term “procedural terrain” refers to a terrain generated by advanced procedural models
such as fractals or particular models with unlimited amounts of detail, meaning that when the
camera moves closer to the terrain, the terrain does not become blurry or pixelated because
more detail is added the closer the camera gets. Similarly to some previous works (Gruen and
Murai 2002), the elevation and slope were used as the main factors for the snow coverage
generation. A maximum slope of 45 degrees was chosen, which limits the snow retention to
mesh faces that are not steeper than 45 degrees. The advantage of the DreamScape Terra is in
a novel rendering technology that enables rendering of very large detailed terrains with
minimal memory consumption. In addition, since the terrain is procedural, a large amount of
detail can be added without slowing rendering speed.
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Figure 17 A screen capture of imported DEM data of Auyuittuq NP in 3Dem
Dreamscape modelling was done using DEM data downloaded from Geobase. DEM data
were imported into 3dem® software (Figure 17). To obtain more information about the terrain
surface an examination of the elevation profile was conducted. The 3dem’s elevation profile
tool shows terrain elevation as a function of distance between two points and as a
familiarization tool it is helpful during subsequent modeling steps.
In a subsequent step, data were converted from 3Dem® format into Terragen format and then
imported into DreamScape Terra Editor (Figure 18). The resulting terrain is procedural,
created according a grayscale image that has the information about elevation. White areas are
displaced to an overall height setting, black areas are not displaced at all, and all other values
in between are displaced proportionally. Different filters and effects can be applied in the
terrain editor to further increase level of detail and the extent of the realism of the modeled
terrain (Figure 19).
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Figure 18 Dreamscape Terra Editor Workspace
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Figure 19 Different tools that can increase detail and realism of terrain. a) Terrain erosion; b) Elevation;
c) Slope; d) Texture map paint
Because it does not require many pre-processing steps – only a gray scale image as an input –
the displacement mapping is the fastest to apply among the three methods. It is easy to
manipulate, and to decrease and increase the number of polygons. On the other hand, high
level of detail can be achieved only with an increased number of polygons, which in turn
require more processing power. This technique is very useful for pre-visualization of a terrain
when rendering speed is more important than quality of a model.
Modeling with contour lines is of interest because they are the most common data form for
terrain representation. Once converted into the triangulated mesh (also known as a Triangular
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Irregular Network or TIN in GIS), it is possible to adjust the level of detail by interpolating
the number of points horizontally or vertically. A higher number of points require higher
processing power. The major disadvantages of the method are the errors that occur during the
data transfer from the GIS to the visualization software (e.g. broken lines). The weld tool in
3ds Max can be used to correct the errors. This tool connects two adjacent broken segments
within a user defined distance threshold. Since DreamScape uses procedural terrains and a
new rendering technology it is possible to render more detailed terrain with minimum
memory consumption. In Dreamscape there are also a set of sophisticated tools (e.g. terrain
erosion, texture map paint) for terrain modeling and manipulation that can be used to increase
the extent of realism. DreamScape is a commercial plug-in and thus represents an additional
investment for a visualization project.
Modeling La Mauricie NP
Since La Mauricie NP has different land coverage compared to the other two NPs, modeling
of this landscape required a different approach. Here, vegetation cover rather than snow cover
was of interest. For the texturing of La Mauricie NP, Vue 5 Infinite® software was used in
combination with 3ds Max.
After importing to the Vue 5 Infinite ® workspace, the DEM was populated with vegetation.
While the selection and population are just a click away in the Vue 5 toolbox, the problem is
the original vegetation distribution. Pansharpened SPOT 5 imagery covering the area of
approximately 30 ha of La Mauricie NP (resolution 5m), was analyzed to obtain the
information about the real vegetation distribution.
As the image suggests (Figure 20), vegetation in La Mauricie NP is not evenly or randomly
distributed. For an easier distinction between dense and sparse vegetation, a panchromatic
SPOT 5 image was transformed into a black and white image and used in Vue 5 Infinite
software as a vegetation distribution map (Figure 20).
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Figure 20 Vegetation distribution in La Mauricie NP. a) SPOT 5 panchromatic image(resolution 5m); b)
resulting forest distribution determined according to the SPOT 5 image
Using the Advanced Material Editor in Vue 5 Infinite, approximately 25000 instances of the
fir tree (Figure 21) were used to populate densely populated areas. Although in reality tree
density is not constant, in this work an arbitrary and constant population was used to simplify
visualization process. There are 30 tree species in La Mauricie NP. However, in the initial
visualization attempt only fir tree was used because of its readily available 3D photorealistic
model and low number of polygons which make it ideal for testing of vegetation distribution.
To improve the realism more dominant tree species should be modelled and incorporated into
visualizations. The Ecosystem functionality of the Vue 5 Infinite (now in release 6) allows
one to populate multiple species according to various parameters of distribution, for example
elevation, proximity to streams etc. The rest of the sparse vegetation areas which represented
clear-cut areas were populated with grasses and shrubs, as well for purposes of simplicity
(Figure 22). Next, atmospheric effects (e.g. clouds and haze) were added for more
photorealistic representation. Although considered non-essential for decision making,
atmospheric effects such as clouds and shadows contribute to the more photorealistic
presentation and natural perception of the scene. Elements like the atmosphere are important
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in photorealistic visualization because one does not want the viewer to concentrate or notice
what is missing at the expense of the focus of the visuals being presented.
Figure 21 Vue 5 Infinite: a) Fir tree instance in the tree toolbox; b) A rendered example of photorealistic
fir tree
Figure 22 Populating sparse vegetation areas covered with grass and shrubs with Vue 5 Infinite
3.4.4. Animation (Photorealistic dynamic visualization)
Pre-visualization story-board
Relating the scientific concept of interest to a previsualization storyboard is one of the
fundamental steps in scientific visualization. For example, the relation between a glacier and
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proglacial lake such as Crater Lake in Auyuittuq NP may be one such ecological/geomorphic
process that can be monitored using EO data. A storyboard is constructed that determines the
motion and timing at key points of interest that illustrate the physical or ecological
relationship. This is a visual process by which key-frames are established and rendered like
the examples in Figure 23. With these key frames and timing established, an OpenGL-based
previsualization or “previs” is created for direction purposes. It can be seen that even for short
movies the number of steps in previs can be considerable. The more steps that are defined, the
greater the control is over the content timing that is presented to the viewer, although at the
expense of rendering time and disc space.
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Figure 23 Story board for Thor peak in Auyuittuq NP
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3.4.5. Light and camera positioning
The transition between the key frames is determined by defining the movement of the camera
alone, movement of the whole 3D object or its parts, or both camera and the object/parts
(Fabio 2003) along the motion curves. Two types of cameras are used to generate computer
animation: a target which is a restricted camera, and a free camera (Boardman 2005). The
target camera has two components: a camera and a target. The camera is restricted since it is
always pointed toward the target. The free camera resembles the real camera: it can be freely
positioned in working space. Various types of splines utilized for the transition between the
two adjacent key-frames are left for the computer to interpolate. Control over velocity and
special constrains along the movement paths must also be predefined by the user.
While different media (e.g. movie, TV) in different countries (e.g. UK, USA) have fixed
speed of key-frames, a speed between 18-24 fps is usually used (Mealing 1998). Similarly to
the camera, the lights on the photorealistic scene are positioned and oriented in a particular
direction. Since in this work satellite imagery was used as texture, the scene lighting was
positioned in such a way as to match the shadows on the satellite imagery. The sun angle can
be quickly determined from the date of the image acquisition and so terrain effects in the EO
should be accounted for, otherwise shadow’s can confuse viewers. As a guideline for a more
natural appearance of the modeled scene, an iterative image enhancement is recommended.
Contrast, color saturation and hue should be adjusted until both very bright (e.g. snow,
glaciers) and very dark (e.g. shadows) areas on the image show clear structure (Hirtz et al.
1999). In most cases, the light was linked and animated together with the camera and its
intensity and dynamics was adjusted to closely resemble natural light on the scene.
3.4.6. Rendering
Two rendering algorithms were combined in the 3ds Max environment to obtain maximum
benefits from both approaches. In Table 4 advantages and disadvantages of both algorithms
are given.
The level of detail in dynamic visualizations is one of the present challenges for rendering.
Here, the traditional solution is applied. The object database is constructed at different levels
of detail, where more detailed presentation is used when the object projection becomes larger
and vice versa (Watt 1997).
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Table 4 Comparison between rendering algorithms (3D Studio Max 2003)
ALGORITHM
Ray-Tracing
ADVANTAGES
Accurately renders direct
illumination, shadows, specular
reflections, and transparency
effects.
DISADVANTAGES
Computationally expensive. The
time required to produce an
image is greatly affected by the
number of light sources.
Process must be repeated for
each view (view dependent).
Memory efficient
Calculates diffuse interreflections
between surfaces.
Radiosity
Provides view independent
solutions for fast display of
arbitrary views.
Offers immediate visual results.
Does not account for diffuse
interreflections.
3D mesh requires more memory
than the original surfaces.
Surface sampling algorithm is
more susceptible to imaging
artifacts than ray-tracing.
Doesn’t account for specular
reflections or transparency
effects.
The speed of rendering is one of the issues to be examined here. For example, with
approximately 10 minutes of rendering time per frame, a 1 minute animation playing at an
average of 30 frames per second (fps) (a standard speed for USA and Canada) would require
12.5 days (30fps×10min×60s/min=18000min) for rendering on one local station. Therefore,
the use of a network rendering is recommended. Network rendering uses a number of
computers connected together over a network to perform a rendering task. This network is
also called a rendering farm. Usually, computers are used as standard workstations during the
day and for network rendering overnight. Each computer renders one frame at a time. One
computer is set up as the network manager which sends the work to render to other computers
(servers). The communication between them is ensured via a management interface for
rendering that splits the rendering project at the beginning and collects the rendered images at
the end in a common, shared directory. Our rendering farm consisted of 80 workstations. Ten
of those workstations are state of the art with system configuration: Dual Core Intel®
Xenon™ CPU 3.20 GHz, 4GB RAM, NVIDIA® Quadro FX4000 video cards of 256 Mb.
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3.4.7. Compression of output data
Once rendered, even short movies of as little as 20 seconds created by the above mentioned
methodology can be very large in size (over 1GB) due to high data volume. This file size is
not suitable for web based applications and storage on CD or even DVD media could be a
problem. The usual methods for file size reduction such as lowering resolution, the number of
frames per second, or color format cannot be applied here since they will undermine the final
objective of the work: photorealistic representation. Therefore, one of the final steps in the
creation of 3D photorealistic dynamic visualization is the compression of output data. With
this method all movies retain their original photorealistic representation while reducing the
file size. Windows Media Encoder is used for data compression. It creates windows standard
AVI (Audio Video Interlace) format files in Windows Media 9 format. In our case, some of
the generated photorealistic visualization with over 2 GB in the original version were
compressed down to 42 MB. Thus, the compression with AVI was over 98% with little
influence on the quality of the presentation. There are other options for compression that rely
on compression formats similar to Windows Media 9. These are called MPEG4 compliant
formats. MPEG2 for example is the format used for compression on most commercial DVDs.
MPEG is an acronym for the Motion Picture Experts Group audio/video compression format.
QuickTime, DivX and Flash Video are compliant with those levels of compression and high
quality output. However, many of these compliant formats, including MPEG4 itself are costly
to purchase whereas the Windows Media Encoder and format are freely available from
Microsoft.
3.4.8. Post-processing
The final step in the process of making animations is post-processing. Usually, photovisualizations could be used as stand alone products. In most cases, however, value added
post-processing is the final step in the methodology (Figure 6e). Here, various aspects are
added to a photorealistic visualization to increase its multi-media potential. While
visualization accompanied with textual messages offers an increased level of information to
the audience, the visualizations with added music offer an audio-visual rich multi-media
experience for the audience while simultaneously conveying a serious underlying message.
In our case, both text and music ware added to the final photorealistic visualization. For music
creation Adobe Audition was used. In addition, original music was composed and performed
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for the Auyuittuq scenes by a classical singer (Ms. Nathalie Paquette). Customized music that
is copacetic with the animation is very effective in conveying emotion with the scene. Various
toolboxes are available for creation of the desired audio effects which are then embedded with
the running video presentation. The final output product was a multi-media DVD (Digital
Versatile Disc) presentation since DVD technology offers the high quality and speed
necessary for video presentations with appropriate storage capacity. A DVD was produced in
the Adobe Encore DVD environment. A typical screenshot of this environment is shown in
Figure 24.
Figure 24 A screen capture of Adobe Encore DVD workspace
During this step it was necessary to consider various aspects that enable high interactivity and
a user friendly interface. Even during the creation stage it should be considered that the
visualization might be played on a standard definition or high definition television. The DVD
technology enables anamorphic video storage, e.g. each pixel stores as much video
information as possible. This format can later be transformed to the aspects required by
standard or high definition TV. Audio formats supported by DVD-Video technology are
MPEG-2, Dolby Digital, and linear PCM (LPCM). However, Dolby Digital is currently the
format most widely used for audio on DVD-Video.
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For DVD authoring it is necessary to consider the following:
a) Identifying the media necessary to be incorporated in a DVD
b) Assembling the video and audio assets, identifying chapter points, titles, and title sets
c) Organizing the content to fit a flowchart
d) Defining the links between various presentation groups and defining interactivity and
accessibility (e.g. action following a particular selection on a menu)
e) Testing the menus and navigation.
An example menu from the DVD created for the three National Parks used in creating the
workflow for integration is presented in Figure 25.
Figure 25 DVD Main Menu
The menu offers three parks as the main selection and in the background the geographic
location of each park is presented. Various types of information about the parks are also
offered. With a click, the user will get to the selection of various photorealistic presentations
for each park.
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Other dissemination mediums were used in the project. Visualizations can be played using
web-based players such as Windows Media Player or QuickTime. We have also used mobile
solutions such as video enabled cell phones, video mp3 players (e.g. iPod video) or PDA
(personal digital assistant) devices (Figure 26) for dissemination of visualizations. Because of
its screen resolution (320x240 pixels) to play visualizations on an iPod video device a file
conversion was necessary (from avi to m4v extension). The high quality of visualizations
remains the same after the conversion process because the m4v compression is MPEG4
compliant.
Figure 26 Visualizations on mobile dissemination devices; a) Apple® iPod video mp3 player; Hewlett
Packard iPaq® PDA (personal digital assistant)
3.5. PUBLIC OUTREACH: TOOLS AND RESULTS
Public outreach is a first step in the preservation of the ecological integrity of various
Canadian National Parks. Therefore, the visualizations of these environments were used to
engage the general public’s interest and imagination. The hypothesis was that the
visualizations would serve as a main tool for communicating the environment of the parks,
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produce interest in the viewer, and therefore induce an appreciation of the need to preserve
the ecological integrity of these environments. As a result, the well informed public would
have an attachment to the issue after being visually introduced to the beauty of these
environments, thus prompting more concrete action for their preservation.
3.5.1. Photorealistic fly-through presentations
The selection of the National Parks for the photorealistic visualizations in this project was
based on the need for their preservation. Auyuittuq NP is a remote Arctic environment full of
glacial features that are currently in danger due to climate change. Visualization of this
environment will first record its current state and will be used in the future for the monitoring
of glacier retreat. The introduction of this NP to the general public should increase awareness
of the impacts of climate change in remote Arctic environments and the need for their
preservation. As a final product, various fly-through presentations were generated. For
Auyuittuq NP these involved several routes along major glacial features (Figure 27). In Figure
28, the four pre-design fly-through routes are presented.
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Figure 27 Major glacial geological features of Auyuittuq NP
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Figure 28 Four fly-through routes in Auyuittuq NP, green dots represent start and red dots are the end of the routes.
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Four different fly-through photorealistic presentations were created for Auyuittuq NP. The
fly-through path Number 1 in Figure 28 is the visualization of Akshayuk Pass. This 97 km
long canyon was created by the movement of the continental glaciers during the last ice age.
Today, however, it is completely ice-free. The fly-through path follows these 97 km with
emphasis on various geomorphological features along the way. During the flight, the camera
is focused in on these futures to provide greater detail.
The second path (Figure 28) is the fly-through of Crater Lake and the surrounding ridge
which marks the limit of the last advance of the glaciers that occurred about 100 years ago.
Today, when the glacier melts, the ridge acts as a natural dam. The photorealistic visualization
presents the ridge along its length and finishes with a view of this unique circular lake.
Another lake presented here was Summit Lake, located at the highest point of the Akshayuk
Pass. It was also formed by the pooling of glacial melt waters. The glacial waters from this
lake flow into the rivers and drop about 500m before reaching the Arctic Ocean.
The visualization of Mount Thor, named after the Norse god of thunder, was of special
interest since this 1675 m high mountain, has a cliff face of 1 km, the longest uninterrupted
cliff face in the world. In the fly-through photorealistic presentation (Figure 28), fly path
Number 4) offers an in-depth, high resolution perspective of this unique geological feature of
high interest for public outreach. The major challenge was modeling of snow coverage
(Figure 29). Snow covered landscape is recognized as an issue in photorealistic image
synthesis due to the difficulty in utilizing common primitives (e.g. polygons, curves) to model
snow-like shapes (Yanyun et al. 2003). Some authors suggested unique techniques for dealing
with snow coverage (Yanyun et al. 2003) such as a hybrid multi-mapping technique where
displacement mapping is used to model snow on the object in the vicinity of the observer,
while a volumetric texture is used for modeling snow coverage of distant objects. This method
provided the most realistic snow cover for the visualizations created for this research.
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Figure 29 A winter scene from Auyuittuq NP with procedural texture (computer generated snow)
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The photorealistic fly-through presentation of Highway Glacier (Auyuittuq) demonstrates
how this great river of ice creeps down from the high plateau of the park's interior towards the
valley floor. Among the three visualized National Parks, the visual appreciation of the
audience was highest for these areas.
Another NP selected for visualization was Nahanni NP. The fly-through animations of
Nahanni NP (Figure 30) included two different routes. Here, photorealistic visualization was
used as a communication tool to engage the interest of the public and increase support for
park expansion. Pollutants from activities such as mining and logging performed in the
upstream areas outside the park boundaries are travelling downstream and affecting the
environment within the park. Thus, the 3D photorealistic visualizations represent two
different areas inside and outside the park, emphasizing the similarities and richness of both
areas.
Figure 30 Photorealistic fly-through routes through Nahhani NP
La Mauricie NP is an area rich with rare flora and fauna nested in a combination of lakes and
forest within the Laurentian area. Modeling the park offered a challenge of another type.
Here, the dominant feature is vegetation. While various options were attempted in modeling
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the variety and distribution of vegetation, a combination of Vue 5 and 3ds Max was found to
be the most valuable approach. While the Vue 5 environment was excellent for modeling the
population of large areas with various vegetation types, 3ds Max was invaluable for terrain
modeling and rendering using the Mental Ray renderer. However, among the various models,
this one was found to be the most challenging in terms of texturing large landscape areas with
a sufficient level of detail for fly-through presentations. In particular, vegetation realism is
still not at the level of scientific scrutiny, but if camera motion is maintained during
visualization, the trees can be very realistic and details of leaf structure, branch order and
angle are not noticed by the observers. Some of the created visualizations are presented in
Figure 31.
While there are various models for creating vegetation, it should be pointed out that the tree
growth models, e.g. forest-gap models, and seasonal changes were not incorporated into the
current presentation. These are pure photorealistic landscapes based on remote-sensed
imagery aquired at a specific point in time. Thus, they capture vegetation distribution in a
particular space at a particular moment in time. Photorealistic presentation re-created this
distribution with the help of computer generated vegetation.
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Figure 31 La Mauricie NP: Different views of a clear-cut area
3.5.2. Public outreach: Various Levels
The second objective was the utilization of the photorealistic fly-through presentations for
public outreach on the issue of the preservation of the ecological integrity of the modelled
National Parks. While we expected a mild curiosity given the novelty of the methods
responses were more than overwhelming.
Public outreach. Experts in the field. Immediately after the initial presentation to Parks
Canada, there were indications that the integrated approach was a success. Despite the lack of
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measurable parameters, e.g. the number of people that have seen the presentation and their
recorded response, a general conclusion in the Parks Canada team was that the methods
employed here would be further utilized for the visualization of other national parks. This
initial presentation and several highly specialized conference presentations lead to the
conclusion that the method is indeed valuable for geomaticians since colleagues from United
States, Germany and Australia requested help with their visualizations or showed an interest
in collaboration on the subject. The best confirmation of the success of the approach applied
in this work came from the International ENVI Challenge 2005. The innovative methodology
on photorealistic visualizations as a tool for the preservation of ecological integrity won
second place in this international contest. The work also received an award from PCI
Geomatica, a leading geomatics software producer in Canada.
The interest in the integrated methodology applied here was confirmed by the high attendance
at a one-day workshop (the first of its type ever offered in North America) on the integration
of GIS and visualization technology that explained in detail the approach used this project. In
addition, the Canadian edition of National Geographic expressed interest in a collaborative
work on photorealistic visualizations for public outreach and education.
Public outreach. Local level. To promote National Parks in the Ottawa area, a local TV
station (CJOH) was approached. Several minutes on prime time local news covered the flythrough presentation of Auyuittuq National Park, our approach, and various modes of
dissemination such as an iPod, PDA or cell phone. Approximately 150,000 viewers (CJOH
2006) normally view this newscast.
Public outreach. Secondary schools. A special component of the public outreach on the
local level was educational outreach. The target audience were students attending 149
secondary schools in the Ottawa metropolitan area. A DVD with the photorealistic
visualizations of the National Parks among other geomatics-related educational material was
created in the context of a research-based learning project. The main menu for the
visualizations of National Parks is shown in Figure 32.
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Figure 32 DVD for educational outreach: Main menu-NPs
Public outreach. National level. The largest breakthrough in public outreach occurred when
the Canadian Broadcasting Corporation (CBC, Canada) became interested in the project.
Several minutes of prime time national news coverage ensured a Canada-wide, in-depth
introduction of our objectives in communicating ecological integrity and the geovisualization
approach. Broadcasting of the fly-through photorealistic presentations of Auyuittuq NP on
The National with Peter Mansbridge was the largest part of the coverage (>1.5 minutes of air
time). This broad public exposure confirmed that geovisualization is not only a technique of
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the future. It is already here, and geoscientists should utilize its possibilities despite the
present challenges. It is estimated that The National attracts on average 1,000,000 viewers
(Burman 2006). This number indicates an enormous potential. We expect that visualizations
will promote the National Parks, communicate the need for their preservation, and ultimately
win audience support and action. At this point we concluded that our objective of assessing
whether such photorealistic geovisualizations would be of wide public interest for public
outreach was achieved.
3.6. CONCLUSIONS AND RECOMMENDATIONS
In this work, we have presented a systematic approach on the integration of GIS and scientific
visualization as a communication tool for the preservation of the ecological integrity of three
of Canada’s National Parks (Auyuittuq, Nahanni and La Mauricie).
While there is no unified method for landscape visualization, our experience has shown that it
is possible to generate photorealistic presentations with a high level of detail using the
advantages of contemporary geomatics and 3D visualization technology. The feedbacks we
have received from geomaticians around the world as well as an international award are
encouraging us to work further to improve this systematic, integrated approach. The following
are general conclusions and recommendations for the development of 3D photorealistic
visualizations:
1. When possible, the highest DEM resolution should be used to achieve the highest
level of detail necessary for providing high realism to the scene. In addition, we have
observed better results when the DEM resolution matches the resolution of the satellite
imagery used for texturing.
2. Pansharpening was found to be a valuable tool. Depending
on the visualization objective, mostly free of charge LANDSAT imagery (30 m) of a
coarse resolution was improved to 15 m resolution.
3. The 3ds Max visualization software platform was capable of addressing most of the
requirements for high end visualizations. However, its performance was considerably
improved by a commercial plug-in for terrain. Among the three tested terrain
Chapter 3 Integration of 3D Visualization and GIS
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visualization possibilities, the DreamScape Terra® has shown to have the best
performance. When budget is a constraint, a more affordable 3D visualization
platform, Vue 5 Infinite, developed in particular for landscape modeling can be used
instead 3ds Max. Moreover, Vue 5 Infinite can be used with Vue 5 xTreme as a plugin to 3ds Max allowing for full leverage of both applications.
4. We have found a professionally designed DVD to be an acceptable dissemination
media. We have also tested alternative, mobile options such as PDAs or video mp3
players that could be part of the Bill Gates vision of “anytime, anyplace” information
access (Gates 2001). When working with dissemination media, sound is the only other
sense that can be simultaneously stimulated for the generation of interest in the visual
content.
The second objective was the use of 3D photorealistic visualizations of three Canada’s
National Parks for public outreach. Due to climate change or human activity (e.g. clearcutting, mining) the ecological integrity of the Auyuittuq, Nahanni and La Mauricie National
Parks is endangered. To increase awareness of the problems, engage the interest and induce
action, 3D photorealistic visualizations were used. Photorealistic visualizations are an
effective means of communicating ecological integrity since the audience can almost
instantaneously recognize the effects of human activity and climate change with little or no
additional interpretation of the visuals required.
For Auyuittuq NP, the visualizations helped to inform the audience of the current status of the
glaciated areas of the park, emphasizing the most impressive geomorphological features such
as Mount Thor in order to engage their interest. For Nahanni NP, visualizations presented the
low lands and upland areas that need to be preserved from human activity occurring upstream
from the park boundaries. The La Mauricie visualizations communicated the impact of the
extensive clear cuts as an ecological integrity problem to be addressed within the greater park
area (GPA). By visualizing these clear cut areas and the rich neighbouring species, the
influence of clear cutting is visually outlined.
Our target audience was the general public locally and nationally, as well as local high-school
students. Locally we have accessed approximately 150,000 people through news coverage of
a local TV station, CJOH. This leading local news provider showed the 3D photorealistic
Chapter 3 Integration of 3D Visualization and GIS
86
animations during its prime time news broadcast. Thus, our objective of reaching the local
audience was achieved.
Nationally, the national news provider, the Canada Broadcasting Company (CBC), showed an
interest in our visualizations. Over a minute of prime time news was devoted to these
visualizations. With an estimated 1,000,000 viewers, The National is the most watched news
coverage in Canada. This enabled us to convey the message on ecological integrity on a
national level.
Future work will involve the development of new tools that will increase compatibility
between geomatics and 3D visualization platforms and enable easer and faster visualization of
spatial data. The development of the new, improved tools will be useful in visualization of the
remaining National Parks. Furthermore, quantitative measurement of the effectiveness of
photorealistic visualizations on the public perception of the need for preservation of
ecological integrity should be conducted.
3.7. REFERENCES
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Appleton, K., A. Lovett, G. Sunnenberg and T. Dockerty (2002). "Rural landscape
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Aronoff, S. (2005). Remote Sensing for GIS Managers. New York, ESRI Press.
Boardman, T. (2005). 3ds Max 7 Fundamentals. Berkeley, CA, Peachpit Press.
Burman, T. (2006). "CBC Newsworld."
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Gruen, A. and S. Murai (2002). "High-resolution 3D modelling and visualization of Mount
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Hehl-Lange, S. (2001). "Structural elements of the visual landscape and their ecological
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Sjoberg, R. W. and B. K. P. Horn (1983). "Atmospheric effects in satellite imaging of
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Suzuki, D. (2000). "Climate change and Canada's National Park system." David Suzuki
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Walsh, S. J., D. R. Butler, G. P. Malanson, K. A. Crews-Meyer, J. P. Messina and N. Xiao
(2003). "Mapping, modeling and visualization of the influences of geomorphic
processes on the alpine treeline ecotone, Glacier National Park, MT, USA."
Geomorphology 53: 129-145.
Watt, A. (1997). Mainstream rendering techniques. The computer sicence and engineering
handbook. Tucker Jr., A. B. Boca Raton, FL, CRC Press: 1254-1269.
Wong, M. and J. Chilar (2004). Using satellite remote sensing technology to monitor and
assess ecosistem integrity and climate change in Canada's National Parks, Canada
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Yanyun, C., H. Sun, L. Hui and E. Wu (2003). "Modelling and rendering of snowy natural
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Chapter 4
Conclusions and Recommendations
Chapter 4 Conclusions and Recommendations
89
4. CONCLUSIONS AND RECOMMENDATIONS
This work is a part of ongoing research in geomatics regarding the evaluation, integration and
benefits of photorealistic visualization technology for more effective communication of
geomatics concepts. This thesis has elaborated on several aspects of contemporary
geovisualization. To reiterate, the specific objective of this thesis was to extend current
geomatics visualization software and procedures with state-of-the-art 3-4D photorealistic
visualization applications. Therein, the weaknesses of current approaches to geovisualization
were made explicit and at the same time case studies clearly illustrated the benefits of
contemporary 3D photorealism. Finally, this work has provided guidelines, tools, procedures
and examples that enhance the workflows necessary to bring contemporary geomatics in line
with the state of the art of geovisualization.
The second chapter of this thesis reviewed the importance and impacts of photorealistic
visualization on contemporary geomatics science. The review indicated a number of
significant impacts of photorealistic geovisualization for geomatics, such as:
1. While traditional visual methods are challenged by the continuous proliferation
of georeferenced data, photorealistic visualization and animation tools enable
not only data analysis but also effective communication.
2. Photorealistic visualization has a potential to accelerate knowledge uptake. The
use of photorealism is becoming an integral part of discovery where it can act
as an exploratory, confirmatory, synthetic or presentation method.
3. Relying on human perception and cognition of visual data, photorealistic
visualization is gaining recognition as an effective communication tool for
spatio-temporal data.
A number of traditional applications of photorealistic visualizations were identified, such as
landscape and urban planning and development, and earth sciences. Also a growing trend in
the use of visuals in emergency preparedness situations will likely be a future avenue for
research. In this work we have also identified one of the major challenges in contemporary
photorealistic visualization: a gap between state of the art geomatics visualization capabilities
and those of state of the art 3D visualization technology. Despite advancements in the
Chapter 4 Conclusions and Recommendations
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development of visualization tools, geomatics software is still generating visualizations of a
low degree of realism and complexity. Advanced modeling, lighting, and texturing tools as
well as sophisticated rendering methods (e.g. ray tracing, radiosity) that enable the creation of
models with an increased realism that are employed in 3D visualization platforms are not an
integral part of geomatics technology today. Rather the development of geomatics has been
largely focused on analytical tools for spatio-temporal data analysis while data presentation
was left at the lower level of realism. In a related vein, even the cartographic capabilities of
current GIS software are rather poor and one requires expensive software for high-quality
automated cartographic design output.
It is therefore evident that a need exists for an integrated approach where the advantages of
3D visualization technology such as advanced texturing, lighting and rendering methods are
combined with advances that are offered by geomatics technology in handling earth
observation data in order to design 3D photorealistic landscapes. To develop an integrated
solution and workflow, two case studies presented in Chapter 3 applied knowledge gained
from the review in Chapter 2 for the creation of photorealistic geovisualizations.
The first case study in Chapter 3 devised an integrated approach that used earth observation
data to bridge the two technology platforms: geomatics and 3D visualization. The objective
was to create 3D photorealistic visualizations of three of Canada’s National Parks in order to
communicate the need for their preservation to the general public. A valid and operational
workflow was presented. We found that:
1. 3ds Max was the most suitable 3D visualization platform for generation of the
photorealistic landscapes.
2. The level of realism achieved is highly dependent on the quality of the DEM data as
well as Earth observation data.
3. To save the expense of high resolution imagery, it is recommended to use a
pansharpening method. Pansharpering is an invaluable tool for inexpensively
increasing image resolution.
4. Among the three methods tested in this work for terrain texturing (e.g. map
displacement, contour lines, DreamScape Terra®), DreamScape Terra® has shown the
best performance. Dreamscape Terra® enables creation of a procedural terrain and has
a set of tools for advanced terrain manipulation.
Chapter 4 Conclusions and Recommendations
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5. Among different models, it is recommended to use fractal for modeling of complex
terrains where a dynamic level of detail is required while Grammar-based models are
recommended for modeling of vegetation, like those within the software system Vue
d'Espirit® by E-On Software Inc.
6. Particle based systems are the most suitable to generate atmospheric effects, but too
processor intensive for average rendering. As such, volumetric effects and alpha
planes can be used as effectively to simulate atmospheric phenomena such as
inversions with their low-lying dense fog/haze effects.
7. Advanced rendering platforms (e.g. radiosity, ray tracing) are necessary to obtain high
levels of realism.
8. When creating dynamic photorealistic visualizations it is recommended to validate
speed of animated movements since too fast or too slow presentation can loose on its
efficiency.
The end-user is an integral part of photorealistic visualization. Therefore, in the second part of
the study we have investigated the role of human perception and cognition in photorealistic
visualization. In order to be effective, photorealistic visualizations should be free of clutter,
distracting colors and unnecessary details.
Based on the experience with 3D visualization platforms, in order to improve capabilities of
geomatics software in photorealistic modeling of landscapes, the following recommendations
are made:
1. An increased number of view ports (e.g. top, front, perspective) would be beneficial
for the creation, manipulation and preview of models and objects inside the 3D
environment.
2. More terrain modeling options should be included in geomatics software that can
increase level of details and realism of terrain such as terrain erosion.
3. Incorporation of advanced lighting methods with different light options such as global
illumination will enable the simulation of natural light and thus, improve the level of
realism.
4. Improvement of camera handling and increasing the number of camera options (e.g.,
free and target cameras) will enable fine tuning and better quality of animation.
Chapter 4 Conclusions and Recommendations
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5. Improve tools for the generation and animation of atmospheric effects (e.g. clouds,
mist…) that contribute to photorealism of the scene. These effects are available for
both OpenGL based systems and ray tracing systems.
6. Include advanced rendering platforms that will improve the quality of the final
visualization.
7. Increase number of available file type formats for saving static or animated images.
The visiualizations produced by the integrated workflow in this thesis sufficiently proved the
public outreach objectives on various levels. In our experience, a variety of dissemination
media e.g., DVD, video mp3 players, iPods, PDAs or video cell phones helped to achieve this
objective since the today’s audience has higher expectations and demands the convenience
that such media offer. The quality of the photorealistic visualizations generated by the
integrated approach was such that one local and one national broadcasting company became
interested in presenting them to a wide audience. With over 150,000 and 1,000,000 viewers
during prime time news, the local CJOH and the national CBC broadcasting companies have
contributed to the success of the outreach. New visualizations using the same methodology
are already planned for future parks.
The feedback and interest in the work presented herein, that has been received from
colleagues, other geomaticians, broadcasting corporations, government agencies and the
general public has demonstrated the effectiveness of a photorealistic approach in generating
interest in the subjects visualized. This is encouraging for future work on the improvement of
the integrated approach and serves as a reminder that geovisualization is a new area for
geomatics science. Geovisualization is here to stay, and geomaticians should explore the
boundaries of this new technology further while simultaneously utilizing its capabilities for an
improved exploration and communication of complex geomatics processes.
Appendices
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APPENDIX 1
COMPUTER GRAPHICS FOR PHOTOREALISTIC LANDSCAPE
VISUALIZATION
The objective of this work is to provide a short summary of the state of the art computer
graphics for novices in photorealistic landscape visualization. Firstly, the three major areas of
computer graphics: modeling, animation and rendering were reviewed and a set of
recommendations for their use in 3D photorealistic landscape visualization were provided.
Secondly, the principles of human perception and cognition, which are influencing the
effectiveness of photorealistic visualization as a communication tool were summarized. A set
of guidelines for the creation of photorealistic visuals that have the capacity to engage the
interest of the audience and aid in the decision making process while never losing their
accuracy, visual clarity and legitimacy is also provided.
Figure 33 Elements of computer graphics used for photorealistic landscape visualization
In Figure 33, the elements of computer graphics used for photrealistic landscape visualization
are presented. Generation of photorealistic landscapes begins with the creation of a 3D
computer-based model. A model is a description of 3D objects with a certain viewpoint,
texture and lighting. Basic geometric primitives such as lines and polygons as well as
advanced models such as fractals, grammar or particle models suitable for modeling of
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various aspects of the landscape such as terrain, vegetation and water, have been utilized with
various levels of realism for landscape modeling (Bishop and Lange 2005). Once a 3D
computer model of landscape is generated it is necessary to transform it into an image in a
process called rendering. The rendering process generates a 2D image from a 3D scene by
bringing together the scene geometry, surface properties and lighting. A general rule is that
the more complex the model and object attributes such as lighting and texturing, the more
computationally intense is the rendering. In addition, any walk-thorough or fly-over dynamic
features with a high level of detail, increases the demand on the rendering platform. Thus, for
photorealistic landscape rendering, it is necessary to select a rendering platform that provides
a balance between the desired level of realism and available computer power.
The effectiveness of visuals depends on the how audience perceives them (Tory and Moeller
2004). Therefore, in the last section of this review, I have emphasized which aspects of human
perception and cognition should be considered while creating photorealistic visualizations for
communication with the general public. These concepts are subject to research as much as the
technology behind the creation of photorealistic visualizations (Tory and Moeller 2004).
COORDINATE SYSTEM
Since a computer screen is 2D and modeling takes place in 3D virtual space, it is important to
understand 3D coordinate systems. Coordinate systems enable positioning and easier
manipulation of the elements of the 3D scene. The most common coordinate system in 3D
graphics technology is the 3D Cartesian coordinate system (Figure 34).
Figure 34 A 3D Cartesian coordinate system
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The point in space where the three axes intersect is called world origin and represents the
main point of reference in 3D space. A location of each object in 3D space is defined by a xyz
triplet of coordinates of this global coordinate system of the working scene. In addition, all
objects in the scene can have their own local coordinate system (Figure 35 b and c), usually
placed in the center of the object. A local coordinate system in a 3D graphic system is
described by a set of operations called the affine transformations: translation (e.g. a change in
the position of the origin of the local system), scaling (e.g. a change in the scale of the
measurement in the local system), rotation (e.g. a change in the orientation) and shear (e.g.
transformation from an orthogonal to a non-orthogonal system and vice versa) (House 1997).
Such transformations are common in GIS and Remote sensing as a component of the
coordinate rectification process (House 1997).
Figure 35 A scene with global (a) and local (b,c) coordinate systems
GEOMETRIC 3D MODELING
The first step in producing a 3D photorealistic geovisualization is creation of a model.
Depending on the objective, various models of different levels of complexity can be created.
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Photorealistic landscape visualizations require simultaneous use of various models (e.g.
terrain, vegetation, water…). Hence, it is useful to understand basic model characteristics in
order to select an appropriate model for a particular objective.
Geometric primitives
3D photorealistic visualizations can be created using a set of elementary “building blocks”
called primitives (Giambruno 2002; Rockwood 1997). Initially, these were points, line, faces
or triangles, however with the development of the computer hardware, the sophistication of
the primitives increased. Today almost all commercially available visualization software has
its own set of geometric primitives such as spheres, cubes, cones and cylinders. The simplest
primitives with low level of detail but easy for manipulation are points. A point, a
dimensionless entity, is defined by its xyz values.
A line dimension entity is defined by the xyz values of its two endpoints. Closing a polyline
by matching start and end points creates the most commonly used geometric primitive - a
polygon (Rockwood 1997). The most popular polygon is the simple triangle (Rockwood
1997). The points of a triangle are called vertices, and the sides are called edges (Figure 36).
An object is created by combining either filled or empty polygons.
Figure 36 An example of a triangle structure
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Wireframe models are a system of complex curves generated by combining lines and arcs end
to end. A 3D landscape model in Figure 37 is generated with various primitives. The model
made with point primitives (Figure 37a) is easy to manipulate (e.g., rotate or zoom) but does
not support realism. The wireframe model (Figure 37c) on the one hand has good visual
precision. However, it does not support realism and light is not reflected from the 3D model.
The triangular facet (Figure 37b) supports more complex polygons and is fast to draw (House
1997). The 3D surface looks smoother as the number of faces increase. The more faces a
model has, the more complex it becomes. Because such a model takes more descriptive
information, it takes up more storage space and memory.
A
C
B
D
Figure 37 Elementary modelling primitives: A) Points; B) Triangles; C) Wireframe; D) Polygons
Most of the confusion when using polygons comes from the fact that they do not distinguish
between the inside and outside of the object (Rockwood 1997). Thus, for modeling of solid
objects where it is necessary to distinguish between the inside and outside of an object,
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implicit primitives are used. An implicit primitive has both an inside and outside surface. On
the other hand, there are two main advantages of polygon modeling:
1. The polygon mesh can be of varying density, i.e. adjustable level of detail (Figure 38)
2. These are low-resolution models that are faster to render (Fleming 1999; House 1997).
This makes them suitable for the use in the multi-resolution (different level of detail)
landscape modeling of larger areas with different features (e.g., rivers, lakes and mountains).
The major disadvantage of polygon modeling is generation of truly smooth curves. To model
a smooth curve it is necessary to increase the number of polygons (Figure 38) which results in
longer rendering time and more storage space.
882 Polygons
421362 Polygons
7442 Polygons
611342 Polygons
Figure 38 Increasing the number of polygons improves the object smoothness
Parametric curves
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Another major set of primitives for geometric modeling is parametric curves and surfaces
such as Bezier curves and surfaces and B-spline curves (Figure 39) and surfaces (Figure 40).
Developed by P. Bezier for computer modeling in automobile design, Bezier curves can be
controlled by “control points” called vertices that control the degree of curvature along the
line (Mealing 1998). Each vertex is controlled. The vertices control by two other points that
adjust the endpoint tangent vectors which enables a smooth appearance of curves at any scale
as opposed to polygonal curves which do not scale up properly (Rockwood 1997).The Bspline (basis spline) curve is a generalization of the Bezier curve. The advantage of B-spline
curves over Bezier curves is that the control vertices of a B-spline curve affect only the local
region of the curve or surface (Figure 39). They provide more flexibility and compute faster
than Bezier curves (3D Studio Max 2003). A special form of B-spline curve, non-uniform
rational B-spline, (NURBS) is referred to as a standard tool due to its flexibility in designing a
large variety of shapes (e.g. standard analytical shapes such as cubes, cones, etc. as well as
free form shapes) (Rogers and Earnshaw 1991). The only disadvantage of the NURBS
algorithm is the need for additional storage to represent traditional shapes such as a circle
(Rogers and Earnshaw 1991).
A collection of B-splines can be used to define surfaces, produce a curved edge of an object or
generate the pathway of a moving object (e.g. pathway of a camera). The NURBS surface
(Figure 40) contains both a mesh and its control vertices. The control vertices can be
manipulated individually or as a group in order to model different shapes. Current 3D
modeling software has a very powerful set of tools for manipulating the NURBS (Figure 40)
surfaces. Thus, digital terrain modeling can closely resemble real-world sculpting.
Figure 39 B-spline curve with its vertices (3D Studio Max 2003)
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Figure 40 NURBS Surface; A) control vertices, curves and surface mesh; B) rendered surface.
Geometric primitives from point elevations, contour lines, 3D meshes, triangulated surfaces,
and curved surfaces (Figure 41) such as those obtained by the use of various B-spline models
e.g. NURBs (Ervin and Hasbrouck 2001) are the basis for landscape modeling. They can be
used for modeling of simple landscape forms such as tilted plains and artificial shapes
designed by landscape architects as well as for modeling of the complex natural surfaces of
variable slopes, convexities and rough edges (Ervin and Hasbrouck 2001).
Figure 41 a) Contour lines; b) Zoomed-in segment with splines; c) Triangluated surface;
d) Rendered terrain model
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Geometric primitives (e.g. point, lines, and polygons) on the one hand are effective in the
reduction of the points necessary for modeling of the terrain and thus, in reducing rendering
time and storage space. On the other hand the major disadvantage is that the generated terrains
are treated only as a surface rather than as a solid (e.g. when seen from a side the terrain is
thin and has no mass) (Ervin and Hasbrouck 2001). These so-called 2.5D representations can
present a problem if the terrain model needs to interact with other models such as
hydrological and geological models (Ervin and Hasbrouck 2001). In addition, geometric
primitives generate terrains of low level of realism.
ADVANCED 3D GEOMETRIC MODELING
Significant developments in computer hardware and CPU power have supported the
expansion of geometric modeling techniques that are now closer to representing the visual
complexities of nature (Ebert 1997). These advanced so-called procedural modeling
techniques store details about the model rather than explicitly storing numerous low-level
primitives (Ebert 1997). Used mostly for visualization of natural objects and phenomena, all
advanced geometric modeling techniques can be divided into two major groups: surface-based
modeling (e.g. fractals, grammar-based models, and implicit surfaces) and volumetric
modeling techniques (e.g. volumetric procedural models and particle systems).
Fractal models
Although fractals have a precise mathematical definition, in computer graphics fractals denote
models with a large degree of self-similarity: parts of the objects appear to be scaled down,
translated or rotated versions of the original object (Ebert 1997). A fractal is also defined as
“a geometrically complex object, the complexity of which arises through the repetition of form
over some range of scale” (Ebert et al. 2003). Many natural objects exhibit this characteristic,
for example plants, trees, coastlines, mountains etc.
Therefore, the utilization of fractal models in geovisualization for landscape modeling in
particular is extensive. Among the first models applied for modeling plant growth were fractal
models (Oppenheimer 1986; Voss 1988). When applied in 3D, fractals could be used for
modeling of complex moving objects such as leaves (Mealing 1998). Random fractals are the
most common technique used for modeling mountains (Figure 42) (Ebert 1997). The
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principles of recursive subdivision and pseudorandom perturbation are used for modeling the
surface (Ebert 1997). In other words, the first iteration gives the large peaks on the surface
and later subdivisions add smaller-scale detail that reflect better the results of natural surface
processes such as mechanical and chemical weathering and long/short term erosion. Such
models are, therefore, particularly suitable for the geovisualization of complex terrain with a
higher level of photorealism.
Figure 42 Examples of 3D procedural fractal terrains
Grammar-based models
Similar to the fractals, grammar-based models reduce natural complexity to a simple number
of parameters (Ebert 1997). The most common grammar model is the L-system that was
originally developed for modeling of plant growth (Prusinkiewicz and Lindenmayer 1990).
An L-system is a formal language in which all the rules are applied in parallel to provide a
final word describing the object (Ebert 1997). These models are usually behind most
commercially available software for plant generation such as Vue 5 Infinite’s EcosystemTM.
Both fractal and grammar-based models are used for modeling vegetation. There are two
issues in modeling landscape: visualization of vegetation at a large scale (e.g. modeling of
individual trees) and at a small scale (e.g. modeling continuous fields or forests) (Ervin and
Hasbrouck 2001). Even modeling of a single tree is a daunting task involving millions of
elements (polygons), not to mention lightning, shadows or more complex plant attributes such
as growth, seasonal changes or the addition of movement of or through vegetation (Ervin
2001). The fact that the branching pattern for a genera is constant somewhat simplifies the
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problem. Therefore, the above mentioned numerous elements can be modeled using
automatic, algorithmic plant generation (Ervin and Hasbrouck 2001).
The utilization of VRML (Virtual Reality Modeling Language) for vegetation modeling was
investigated by Lim and Honjo (Honjo and Lim 2001; Lim and Honjo 2003). In a three-step
procedure, forest cover can be visualized and presented using a local or network PC (Lim and
Honjo 2003). The steps involve the combination of vegetation and terrain data and their
conversion into VRML format. The authors successfully rendered up to ten thousand trees in
real-time. However, the visualizations were not photorealistic as shown in Figure 43B. While
even state of the art geomatics software is still using VRML or open GL type tools,
commercially available visualization software such as 3D Studio Max, Maya, and Vue 5
Infinite are equipped with advanced tools for highly realistic visualization of vegetation, as
seen in Figure 43A.
A)
B)
Figure 43 A tree rendered with: A) ray-tracing algorithm in 3ds Max; B) basic OpenGL
When visualizing vegetation on a smaller scale, plants/trees are not considered as discrete
objects but rather as fields and can be modeled as a terrain texture (Muhar 2001). Here, the
major challenge is obtaining a good balance between the level of detail and the rendering time
(Lim and Honjo 2003). Most of the approaches in that regard are oriented toward
improvement of rendering time while retaining the desired level of detail (Kumsap et al.
2005). A combination of land cover data from GIS, DEM and remote sensing imagery can
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lead to better vegetation texture models where the canopy height is taken into consideration
(Muhar 2001).
Implicit surfaces
Implicit surfaces are surfaces of constant value, isosurfaces, created by moving the control
points of the curves such as Bezier and B-spline curves in three dimensions (Rockwood
1997). Implicit surfaces also called Bezier and B-spline surfaces, blobbly molecules,
metaballs or soft objects are used in modeling organic shapes, complex man-made shapes and
soft objects that are difficult to animate. These types of surfaces are highly useful for
modeling of movements induced by wind, e.g. movement of grass, shrubs or individual leaves
as well as movement of water in photorealistic representations.
Volumetric procedural models
In volumetric procedural modeling, also called hypertexturing, volume density functions and
fuzzy blobbies are used for modeling and animation of 3D objects and natural phenomena
such as fire, smoke, fog (Ebert 1997). Here, complex volumetric phenomena are described
with a few parameters such as a point location in space and time, and parameters that describe
the object being modeled. The output is the density and color of the object for that location in
space. These can increase the level of photorealism when applied in landscape modeling (e.g.
fog generation). Modeling forest fires, volcanic eruptions and atmospheric conditions could
be achieved by volumetric procedural modeling.
Particle systems
Particle-system objects are a large collection called cloud of very simple geometric particles
that change stochastically over time (Ebert 1997). Utilized to visualize volumetric natural
phenomena such as fire, water, clouds, snow and rain, this modeling technique has also found
extensive application in landscape modeling (Ervin and Hasbrouck 2001). Each particle has
attributes such as initial position, velocity, size, color, transparency, shape and lifetime (Ervin
and Hasbrouck 2001). A particle system is defined by a collection of geometric particles and
the algorithms that govern their creation, movement and death (Ebert 1997).
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Particle models in landscape visualization are mostly used for modeling atmospheric effects,
avalanches and landslides. Although considered auxiliaries, since they are not the main focus
of decision making, visualization of atmospheric elements (e.g. clouds (Figure 44), fog, haze,
etc.), contributes directly to the overall reality of the scene (Appleton and Lovett 2003). While
sky is usually a background color or bitmap image, cloud-generation is based on the creation
of some random “noise” to induce variable densities and “clumping” (Ervin and Hasbrouck
2001). A similar technique is used for the creation of stars on the night sky or rain droplets.
Figure 44 Landscape without and with clouds
Particle system models are also used for modeling water in landscape visualizations. Water as
a flat plane has three important characteristics that determine its appearance in the landscape:
transparency, refractivity and reflectivity. The proportion of the three factors will determine
the properties of the water (Ervin 2001; Ervin and Hasbrouck 2001). State of the art software
offers a wide variety of tools for modeling water. For example, using water tools inside 3D
Studio Max it is possible to adjust the density and viscosity of water that will determine if the
objects on the water will float or sink, and adjust buoyancy and depth of the water surface.
Irregularities such as waves and ripples are of simple underlying mathematical structure
overlaid with random “noise” (Ervin and Hasbrouck 2001). Tools such as ripples and wave
creator (3D Studio Max) allow the user to control the amplitude of waves and ripples as well
as parameters such as phase and decay of the wave length among the others.
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Attributes of a geometric object: texture and color
The first computer generated images were filled with uniform painted surfaces giving the
image a synthetic look (Peachery 2003). Since then it has been clear that to obtain more
realism it would be necessary to improve texturing of the surfaces. Thus, texturing became a
constant research objective in computer graphics (Peachery 2003). The biggest steps forward
occurred in the late 1970s with the introduction of bumped and 2D textures and color halftones while texturing of water, fog, human skin and animal furs are considered among the
more recent advancements (Ervin and Hasbrouck 2001). However, Schilling emphasized that
more research in texturing is needed (Schilling 1997).
Texture mapping denotes a technique where a 2D image is used to give color, texture and
other apparent surface characteristics to the 3D objects (Figure 45). Satellite imagery, aerial
photos or procedural textures (e.g. a texture map generated by mathematical function) could
be used as a 2D image and applied to the 3D object using a digital elevation model (DEM).
Texture mapping in geovisualization can involve various challenges, from texturing large
landscape surfaces (e.g. fields, forests, mountains) to texturing individual tree barks and
leaves, still or running water, buildings. etc. and all with an as high as possible extent of
realism. By combining DEM and panchromatic aerial imaging, Premoze et al. ((1999))
generated realistic alpine terrain (Premoze and Ashikhmin 2001). IKONOS (Hardin et al.
2005), LIDAR (Forlani et al. 2001) and other satellite data have also been utilized. Merging
various satellite data such as SPOT and LANDSAT to obtain various levels of detail prior to
draping them over a DEM is also common (Boehler et al. 2001). This latter idea of merging
data is known as data fusion in remote sensing.
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Figure 45 Geospecific and computer generated textures. a) IKONOS (Resolution: 1m); b) QuickBird
(Resolution: 0.6m); c) LANDSAT (Resolution 15m); d) Procedural texture
In geospecifc texturing, a digital elevation model (DEM) of the terrain is used as a basis and
aerial photos, satellite images or procedural textures are applied as a texture. The DEM is an
array of elevation points created form contour lines and spot heights. While this method
ensures a highly realistic terrain model, there are several issues to resolve in order to obtain
such a high level of reality. Often it is necessary to remove shadows from the imagery, or
combine several satellite images of various resolutions in order to obtain solid terrain that
could be used for fly-through and/or zoom-in animation or in interactive virtual reality
applications (Scheepers 2001). Further improvements in obtaining photorealistic terrains are
obtained by modeling different lighting conditions, reflections and shadows. These, as well as
the addition of other main components such as vegetation or man-made objects are essential
for highly realistic landscapes.
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COMPUTER ANIMATION
The only constant in nature is change. Visualizing changes around us has been a challenge for
centuries. Motion is one of the most common dynamic changes (Foley et al. 1997). There is
evidence that our ancestors 15,000 years ago used the texture of cave walls and the
illumination of open fire to create the illusion of motion in carved horses, bison, and deer in
the caves in Cap Blanc, France (Chalmers and Cater 2005). An intensive search for tools
depicting motion was evident in 19th century inventions such as photography (1827) and a
camera for capturing motion (1888) finally, culminated in the invention of motion picture by
Lumiere brothers (1895) (Kuperberg et al. 2002). The next revolutionary step in the search for
tools capable of capturing motion and change in general was powered by the development of
computer graphics.
Today, 3D computer animation is not only a tool for depicting change but also a technique
that offers a plethora of possibilities for scientific data presentation. The ultimate objective of
animation is the simulation of reality by artificial creation of objects that match their
appearance and attributes in real life (e.g. photorealistic animation of dynamic processes or
systems) (Kuperberg et al. 2002).4 Terrain fly-through applications for military, space
simulations, walk-through applications in architecture, archaeology, medicine and engineering
are among the fields reaping benefits from photorealistic animation (Mealing 1998). Given
the dynamic nature of geospatial data it is not surprising that animation has become one of the
most widely used tools in geomatics sciences. The ability to track changes, the fundamental
characteristics of complex geographic processes, is essential for an understanding of these
processes (Yattaw 1999). Regardless whether applied in cartography (Harrower 2002), for
landscape planning (Lange and Bishop 2001) or environmental assessment and protection
(Daniel 1992), the main objective of 3D animation in geosciences is a better understanding of
the complexity of geographic changes. A computer generated 3D animation can represent
both the “state of a geographic system at a given time (i.e. space-time structure) and the
behaviour of that system over time (i.e. trends)” (Harrower 2002). As an exploratory
geovisualization tool, animation allows end users to qualitatively assess large data volumes
and potentially discover space-time patterns that remain hidden in static representations
(Harrower 2002).
4
Here we are concerned with photorealism whereas there are clearly more generalized forms of animation, e.g.,
cartoons being one of these, and these generalized forms of animation are a focus of study in computer science.
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Although animation has much to offer, its acceptance among geoscientists in the previous
decade was slow (Johnson 2002). The slow adoption was mainly due to two factors.
Foremost, the avoidance of animation is related to centuries of experience with other static
representation forms (e.g. maps) (Peterson 1994) and secondly, to the challenge of learning
the new skills necessary for the creation of animation (e.g. learning new programming
languages or software) (Harrower 2002). However, today it is accepted that animation is one
of the techniques that will be increasingly utilized alone or as a part of a virtual and
augmented reality presentations for the exploration of large, spatio-temporal data sets
(Kirschenbauer 2005).
A central research question in visualization in general revolves around the effectiveness of
animation as a communication tool (Tversky et al. 2002). In general, an animation must be
carefully designed in order to be effective. The animation has to be clear and slow enough for
the observer to perceive change, movement and timing as well as relationships between the
parts and the sequence of events (Tversky et al. 2002). Thus, in order to be effective
animation must be planned (Mealing 1998). The planning occurs in a process called
keyframing where the important animation events are identified and placed into frames called
key-frames inside animation software. For instance, in a straight movement of a 3D model
from a state A to a state B, the key events to be placed into key-frames are the state of the 3D
model at the points A and B. Careful selection of the key-frames is one of the most important
factors determining the animation's effectiveness. The more complex the 3D models and their
dynamics, the more key-frames are required for the storyboard (Mealing 1998). The transition
between the key-frames is defined by defining the movement of the camera alone, movement
of the whole 3D object or its parts, or both the camera and the object/parts (Fabio 2003) along
the motion curves in a process called betweeing or (tweeing).
The camera is the observer's view and its changing viewpoint is the main difference between
static and dynamic presentation. Two types of cameras are used to generate computer
animation: target and free cameras (Boardman 2005). The target camera has two components:
a camera and a target. The target camera is limited in its movement since it is always at the
same distance from the target and it is always pointed toward the target which is most often an
object (Figure 46a). The free camera reassembles the real camera: it can be freely positioned
in the working space (Figure 46b). The transition between two adjacent key-frames is
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accomplished via interpolation within the software. Various types of splines described in the
previous sections are utilized for camera path interpolation; depending on the level of
smoothness in the movements desired by the programmer (Mealing 1998). The user also
predefines control over velocity and special constraints along the movement path. While
different media (e.g. movies, TV,..) in different countries (e.g. UK, USA.) have a fixed speed
for key-frames, it is usually a matter of experience and experimentation to establish an
appropriate speed for the key-frames for a particular computer animation (Mealing 1998).
One has to consider that 12 frames per second (fps) are considered sufficient but more than
often 18-24 fps are used. For example, film cameras operate using 24fps while video and
television use 30fps.
Although animation significantly improved the description of the complex geographic
systems and processes, its predefined nature limits interactivity with the end user (Tversky et
al. 2002). This limitation is not necessarily a negative connotation because the degree of
interactivity depends on the purpose of the geovisualization. The ability of the end user to
interact with the content is one of the ultimate objectives of the user-centered concept of
geovisualization (MacEachren and Edsall 1999). A completely new world of highly
interactive environments is emerging for geomatic specialists to use virtual and augmented
reality.
Figure 46 Two different camera types in a 3d scene: a) Target camera; b) Free camera with predefined
motion path
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RENDERING
Rendering is the process of transforming a 3D scene into a 2D image. Here the physical
process that occurs in a camera when a picture is recorded on film is simulated by a computer
(House 1997). The renderer is an engine that drives the picture-making process. The main
steps in the rendering process are (House 1997):
1. Point of view – orienting the 3D scene to be seen from a particular point in space.
2. Projection – associating points in a 3D scene with a 2D image plane.
3. Visible surface determination – which surfaces in the 2D plane will be visible.
4. Sampling – fixing a set of sample points across a 2D plane and associating these with
visible points on the 3D scene.
5. Shading calculation – determining what color will be reflected/transmitted from these
sample points with respect to scene’s geometry, lighting, materials.
6. Image construction – from the shaded samples, determining and storing colors for
each pixel in the output image.
There are three main rendering methods: rendering polygon mesh objects, ray tracing and
radiosity.
Rendering polygon mesh objects
This algorithm performs two functions (Watt 1997):
1. It identifies the set of pixels that make the polygon that is subsequently changed
from a vertex list to a set of pixels in screen space by the process called rasterization. If not
performed accurately, this process can result in holes in the picture – the most common defect
seen in rendering software.
2. It identifies the light intensity associated with each pixel. Shading makes objects
more “volumetric” (Shirley 2005). Three common options for shading are: Flat, Gouraud and
Phong shading in order of increasing computational expense and increasing image quality.
Flat shading shades each polygon with the same intensity and it is usually utilized as a fast
preview tool. Gouraud and Phong shading are more advanced, efficient and they eliminate the
visibility of the polygon boundaries. The Gouraud shading method averages the light
intensities at the edge of each polygon and subsequently interpolates along each scan line
across the plane lying between these averages. The result is a smooth, eggshell-like gradation
(Mealing 1998). While an excellent tool for shading of diffuse components, Gouraud’s
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shading has a problem with the specular component, i.e. if there is a highlight within a matte
polygon that does not extend to the vertices, Gauraud’s scheme will completely miss the
highlight. The highlights are actually reflections of the light and they move as the viewpoint
moves (Shirley 2005). To overcome the specular problem of Gouraud shading, Phong
developed a shading method. Details about the light geometry and accompanying
mathematical expressions behind Phong shadings are given by Shirley (Shirley 2005), but in
general, the Phong method calculates intensity of the light at a given point along the scan line
from its approximated normal.
Ray tracing
Despite being computationally intensive due to the fact that it operates pixel-by-pixel, ray
tracing is still the preferred method of rendering given its straightforward computation of
shadows and reflections (Shirley 2005) that results in high quality imagery. Mathematically a
ray is described by a point of origin, a propagation direction and an algorithm for ray-object
intersection (e.g. ray-sphere, ray-triangle or ray-polygon intersection) (Shirley 2005). As a
result of perfectly sharp shadows, the absence of fuzziness and perfect focus sometimes
makes the final images too “crisp” (Shirley 2005). To minimize this effect a distribution ray
tracing technique that allows soft shadows and fuzzy reflections is applied. Accounting for
light as an area rather than a point is the key for generation of soft shadows (Shirley 2005).
Minimal changes to shadowing code in the original algorithm of ray tracing, such as
representing the area light as an infinite number of point sources and choosing one at random
for each viewing ray instead of a discrete number of point sources, will result in soft shadows
(Shirley 2005). For so-called “soft focus”, the depth of field should be adjusted. Instead in a
point, the light should be “collected” into a non-zero size “lens” (Shirley 2005). The depth of
focus simulates the natural blurring of the foreground and background. An elaborate palette of
tools is available in commercial software (e.g. 3D Studio Max or Maya) for definition of ray
tracing parameters (3D Studio Max 2003). Another useful feature of ray tracing for the
generation of photorealistic scenes is anti-aliasing. Aliasing is a consequence of the fact that
the computer screen has a finite resolution while mathematical functions used to describe a
phenomenon are infinite – thus, the appearance of “jagged” effects especially along the edges
of objects. This effect can be minimized by using anti-aliasing where more than one ray per
pixel (usual for low level rendering techniques) is sent into the scene. Therefore, by averaging
the results of several rays, the edges appear to be smoother (Lintermann and Deussen 2004).
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Radiosity
Defined as the amount of energy leaving a particular point on a surface, this rendering
technique is unique compared to all other rendering techniques due to the fact that it takes into
account the relationships among all objects present in a scene (Mealing 1998). Although more
advanced compared to other techniques, radiosity has a disadvantage in long rendering times.
Even before the rendering, the computer must calculate the “radiosity model” based on a
thermal engineering model for emission and reflection of radiation (Watt and Watt 1992).
These time-consuming calculations are the result of the nature of the radiosity model where
the scene surface is divided into a grid. Each grid segment is considered as a secondary light
source and its interaction with all other surrounding segments is calculated in an iterative
process (Mealing 1998). Despite the computational burden, radiosity provides for a significant
boost in realism. The advantages and disadvantages of ray tracing and radiosity algorithms are
summarized in Table 5 (3D Studio Max 2003).
Table 5 Comparison: Ray tracing and radiosity. Reproduced from (3D Studio Max
2003)
Algorithm
Ray-Tracing
Advantages
Accurately renders direct
illumination, shadows, specular
reflections, and transparency
effects.
Disadvantages
Computationally expensive. The
time required to produce an
image is greatly affected by the
number of light sources.
Process must be repeated for
each view (view dependent).
Memory Efficient
Calculates diffuse interreflections
between surfaces.
Radiosity
Provides view-independent
solutions for fast display of
arbitrary views.
Offers immediate visual results.
Does not account for diffuse
interreflections.
3D mesh requires more memory
than the original surfaces.
Surface sampling algorithm is
more susceptible to imaging
artifacts than ray-tracing.
Doesn’t account for specular
reflections or transparency
effects.
Light, shadows and surfaces of a natural scene are complex. Thus to achieve the high level of
realism required in a photorealistic visualization usually both methods (radiosity and ray
tracing) are combined (3D Studio Max 2003). Furthermore, it is often necessary to add
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another advanced rendering technique such as multi-pass rendering (e.g. processing of a scene
several times using different rendering techniques and settings) to achieve the natural
appearance of the objects in a photorealistic scene.
One of the additional rendering techniques is bump mapping. Bump mapping as a rendering
effect increases the level of detail on a surface of an object (Figure 47). Effective bump maps
are greyscale images (3D Studio Max 2003). When an object is rendered with a bumpmapped material, lighter (whiter) areas of the map appear to be raised, and darker areas
appear to be low. The result is a richer, more detailed surface representation obtained without
true geometric deformation (e.g. richer surface without loss in rendering speed). In
photorealistic landscape visualization, bump mapping of the color images onto a DEM as 3D
texturing techniques can be used to visualize vegetation surfaces (Muhar 2001). Management
of the level of detail (LOD) in highly realistic visualizations is based on the viewer distance
criterion: the closer the viewer, the higher the required LOD (Lluch et al. 2004; Remolar et al.
2003). This enables a high level of realism in walk-through applications where the user
always has the highest LOD at the closest distance (Kumsap et al. 2005).
Figure 47 Example of bump mapping. a) 3D model without bump mapping, b) Gray scale image as a
bump map; c) 3D model with applied bump mapping
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Since rendering of a natural scene due to its complexity is a challenge, the combination of the
above mentioned rendering methods is necessary to achieve high level of realism and detail.
In comparison, state of the art geomatics software (e.g. ArcGIS) currently uses low level
rendering systems such as OpenGL or DirectX (Microsoft) that provide only elementary
functions for shading and lighting. Thus, it is impossible to achieve a high level of realism
using current off-the-shelf geomatics software packages. Therefore, one of the most important
tasks at present in geovisualization is an effective integration of state of the art rendering
capabilities of commercial visualization software with state of the art geomatics software that
currently lacks this capability.
GUIDELINES FOR IMPROVED EFFECTIVENESS OF
GEOVISUALIZATION
Although complex, the human visual system is not unlimited in its capacity (Waddington
2001). The designers of geovisualization should be aware that visual displays can be too
complex or too confusing to comprehend, especially when burdened with clutter, distracting
colors, over-animated, etc. (Owen 1993). Therefore, there is considerable interest in how to
design a visualization model with an end-user in mind, to ease his/her perception, to overcome
confusion and the inherent biases of perceptions in order to make geovisualization a simple
and effective means of communication (Gershon 1994). A scheme from Clark and Lyons
(2004), originally developed for the evaluation of effectiveness of visuals in the learning
process, can be used here with slight modifications to summarize the factors influencing the
effectiveness of geovisualizations as a communication tool (Figure 48).
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Figure 48 Factors influencing the effectiveness of visualization. Modified from Clark and Lyons (2004)
When using geovisualizations as communication tools one has to be aware of these three
interconnected groups of factors. Cultural, gender and age differences as well as prior
knowledge, experience and spatial ability of the information receiver determine the perception
and interpretation, and thus, the usefulness and effectiveness of geovisualizations (Tory and
Moeller 2004). Perception influences understanding and understanding influences decision
making.
Guidelines for an effective perception
Human perception is simultaneously a challenge and an opportunity for a visualization
developer (Mackinlay 2000). It is a challenge because an erroneous perception can lead
toward misinterpretation of visualization, which in turn can ultimately lead toward erroneous
decision making. It is an opportunity because a developer can use the existing and ever
expanding knowledge about human perception to develop more effective geovisualizations
(Mackinlay 2000). Processing some visual information is accomplished automatically with a
low level of effort and without conscious thought (Ebert 2005). This processing, referred to as
preattentive processing, is essential for processing large sets of information, and thus is the
essence of effective geovisualization (Ebert 2005). Table 6 combines various classifications
of preattentive features and their influence on perception (Gershon 1994; Healey and Enns
2002; Ware 2000).
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According to Ware (2000) all preattentive features can be classified in four major categories.
A classification proposed by Healey and Enns (2002) based on the order of effectiveness of
the visual cues, is easily connected to the original classification by Ware (2000). Finally, one
can establish a link between the two classifications and the usage of geovisualization in design
and communication proposed by Gershon (Gershon 1994).
Color. Color is a first order preattentive feature that attracts human attention. Color scales
based on brightness are better perceived than those based on hue scales (Gershon 1994).
Therefore, a recommendation for non-photorealistic representations of data is: the more
important the data, the brighter the color (Gershon 1994). However, if the accuracy of the size
of the object is of importance, one has to bear in mind that the bright objects on a dark
background appear larger. When “realism” is important, the selection of colors is essential
and usually photographic sources are consulted. In terrain visualization, using only a plain
color from the software color palette will result in an artificial look. More realism is obtained
when color is combined with textures and ray tracing effects (e.g. transparency, reflection)
(Ervin and Hasbrouck 2001).
Table 6 Combined various classifications of preattentive features
Classification of
preattentive features
(Ware 2000)
Color
o Hue
o Intensity
o Brightness contrast
Motion
o Flicker
o Direction of motion
Spatial position
o 2D position
o Depth
o Shading
o Lightning
Form
o Line orientation
o Length, width
o Size
o Curvature
o Spatial grouping
o Number of items
Order of effectiveness
(Healey and Enns 2002)
Usage in
design/communication
(Gershon 1994)
First order features to guide
attention
To improve visibility of the
displayed data
Second order features to
guide attention
To make the process of
observing visual display
faster and effortless
Second order features to
guide attention
To make the process of
observing visual display
faster and effortless
Third order features to guide
attention
To increase the faithfulness of
visual representation
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Motion. The usefulness of dynamic geovisualization is higher compared to static visualization
because of our effortless perception of motion. Healey and Enns (2002) summarized the effect
of motion in a sentence “human vision is made to capitalize on the fact that the world is in
general a quiet place.” Therefore, objects that move or change their position attract our
attention effortlessly. On the other hand, our perception of motion of even a single object on a
simplified path may not be accurate (Tversky et al. 2002). Fast motion of multiple objects on
complex trajectories therefore is even harder to perceive. In addition, regardless of how
smooth and continuous the motion is, some people may conceive it as composed of discrete
steps rather than being continuous. In some cases, this should be an indication that the motion
should be presented in discrete steps instead of forcing continuous animation. The perception
of movement is better with peripheral vision (Hearnshaw 1994). Therefore, this type of
movement should be favoured when generating fly-though or walk-through landscape
presentations. Tversky (2002) pointed out two general principles for effective dynamic
visualization:
1. Congruence Principle: “The structure and content of the external representation
should correspond to the desired structure and content of the internal representation.
For example, since routes are conceived as a series of turns, an effective external
visual representation of routes will be based on turns.”
2. Apprehension Principle: “The structure and content of the external representation
should be readily and accurately perceived and comprehended. For example, since
people represent angles and lengths in gross categories, finer distinctions in diagrams
will not be accurately comprehended. In the case of routes, exact angles of turns and
lengths or roads are not important.”
Movement could be used to make “hard-to-see objects” in geovisualization more visible
(Gershon 1994). The most useful utilization of the movement (animation) in geovisualization
is the representation of time related changes in the data (Hearnshaw 1994). For landscape
visualization, dynamics (i.e. movement through, and movement of) of terrain, water, and
vegetation are of interest (Ervin and Hasbrouck 2001). When designing movements through a
landscape, the positioning of the free and target cameras, their imaginary paths and type of
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interpolation between the start and final position are the major factors to be considered since
the terrain model remains fixed. For modeling the movement of the landscape (e.g. modeling
of an earthquake) it is necessary to apply dynamic terrain modelling. Here, the appropriate
selection of the procedural or algorithm approach that includes time parameter is of the
essence (Ervin and Hasbrouck 2001). In photorealistic landscape visualization, dynamic
terrain modeling is important for zoom-in effects where the level of detail has to remain high
regardless of the scale the user selects. For example, a forest seen from a bird’s eye view has
to have a sufficient level of detail so that individual trees can be recognized. As the viewer
gets closer, more details about the tree structure should be visible, such as branches or leaves.
Spatial position. There are multiple cues for our spatial 3D orientation. Although constantly
and unconsciously used, our understanding of them is limited. Ambiguities associated with
the representation of a 3D scene in a 2D display can easily lead to misinterpretation of visuals
(Hearnshaw 1994). Thus, the research in this area is intensive. The most important
preattentive factors for our perception of spatial positions are the combination of pictorial
(e.g. shading, lighting, texture) and moving cues (e.g. motion parallax). Combined, all affect
our perception of spatial position and depth. Motion parallax denotes differences in relative
speed between parts of an object when either the object is moving or when the observer
moves his head. Parts of the object that are further away will appear to move slower while
closer parts will appear to move faster (Boyd 2000). Stereopsis (e.g. stereoscopic vision)
arises from the fact that human eyes are horizontally separated and each eye provides a unique
viewpoint of the world (Ijsselsteijn et al. 2005). Stereoscopic vision enhances our ability to
perceive differences in depth, specially close differences (Ijsselsteijn et al. 2005). A
combination of pictorial cues such as shading, lighting and textures can further improve our
ability to perceive depth and spatial positions of the objects in a landscape (Gershon 1994;
Interrante 2005; Lee et al. 2004).
Guidelines for improved realism
The degree of reality is defined as the amount of detail captured and reproduced in the model
(Shiode 2000). Realism is superseding symbolism in cartography and GIS sciences
(Sidiropoulos and Vasilakos 2006). A comparison between different levels of realism is given
in Table 7.
Appendix 1 Computer Graphics for Photorealistic Landscape Visualization
121
Table 7 Comparison between different presentations reproduced from (Angsuesser and
Kumke 2001)
Criterion
Origin
Level of detail
Generalisation degree
e.g. abstraction degree
Individualisation degree
Time dependence
Information perception
Geometric
Presentation
artificial
low
Photorealistic
Presentation
artificial
high
Nature
(Original scene)
natural
infinite
high
low
none
low
low
little (selected)
information in a
short time
high
high
only individuals
complete
infinity of
information in
infinity of time
much information
over a long time
Although there is not reliable visual predictive-evaluation model (Steinitz 2001), there have
been numerous attempts and studies designed to evaluate the extent of realism especially in
photorealistic landscape visualizations (Lange 2001; Lyu and Farid 2005; Nakamae and
Tadamura 1995; Pietsch 2000). Dominant are human perception-based empirical studies
(Daniel and Meitner 2001) designed to evaluate “beauty”, “quality”, “preference”,
“memorability”, “imageability” etc. of photorealistic visualizations as the evaluation criteria
(Steinitz 2001). For example, Daniel and Meitner (2001) investigated the influence of four
different presentations (photorealistic, black and white sketch, greyscale and 4 bit color of the
same forest vista) on the perception of scenic beauty in forest vistas. The authors have
concluded that only photorealistic representation induced the correct perception of the beauty
of the scene. Participants of a study conducted by Appleton and Lovett (Appleton and Lovett
2003) indicated that the increased level of realism of the existing elements in a landscape will
positively influence evaluation of a photorealistic landscape planned for the future.
Despite the efforts of these studies, it is still not possible to strictly define the minimum level
of realism required to achieve the objectives of a visualization (Appleton and Lovett 2003;
Lange 2001). In general however, an increased level of detail, especially in the foreground,
positively affects the rating and degree of perceived realism in a landscape visualization
(Appleton and Lovett 2003; Lange 2001). However, if the resources are limited “the best
place to spend is on the addition of details to the ground surface and foreground vegetation”
(Appleton and Lovett 2003). Other recommendations include improving surface texture,
object material depth, radiosity and specularity as factors affecting the photo-realism of the
3D scene (Fleming 1999).
Appendix 1 Computer Graphics for Photorealistic Landscape Visualization
122
Guidelines on ethics and accountability
The power of geovisualization to influence perception and behaviour as well as decision
making is evident (Sheppard 2001; Sheppard 2005). With an increasing interest in the
utilization of visualization for enhancing public participation in planning, design, decision and
policy making (Al-Kodmany 1999; Sheppard 2005), the responsibility and accountability of
geovisualization professionals comes into focus (Sheppard 2005; Wallace and van den Heuvel
2005). The problems here are similar to those associated with ethics and accountability in
traditional cartography outlined by Monmonier in his book, “How to lie with maps”
(Monmonier 1991). The author emphasized that “maps are subjected to distortion due to
ignorance, greed, ideology or malice” (Monmonier 1991). There are two essential errors that
a geovisualization specialist can induce in viewers: seeing incorrectly or not seeing at all
(MacEachren 1995). There are a number of initiatives to introduce a code of ethics for
geovisualization professionals that will include not only the design of geovisualizations but
also presentation to viewers and documentation of viewer responses (Sheppard 2001). General
principles should include the following (Sheppard 2001):
1. Accuracy: visualization should simulate the actual or expected appearance of the
landscape.
2. Representativeness:
visualization
should
represent
typical
or
important
views/conditions of the landscape.
3. Visual clarity: the details, components, and overall content of the visualization should
be clearly communicated.
4. Interest: the visualization should engage and hold the interest of the audience.
5. Legitimacy: the visualization should be defensible and its level of accuracy
demonstrable.”
When designed correctly, landscape visualizations are powerful and persuasive tools that
should be used with full understanding of their ethical implications (e.g. influencing decision
making). Clutter-free geovisualizations, designed with human perception in mind (e.g. color,
motion and space) should be objective and accurate presentations of reality that do not
confuse but rather inspire the audience.
Appendix 1 Computer Graphics for Photorealistic Landscape Visualization
123
Due to the complexity of natural processes when creating a photorealistic landscape model
one has to take into consideration not only the utilization of various state of the art technology
but also principles of human perception and cognition. This end user-oriented approach is
necessary because the final visualization is usually employed as an information carrier and a
tool for discourse between different groups as a support in the decision making processes.
In conclusion, the selection of modeling, animation and rendering tools should support
accuracy and visual clarity of the presentation. Photorealistic landscape visualizations should
engage and hold the interest of the public, and aid in decision making while never losing
accuracy, visual clarity and legitimacy.
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APPENDIX 2
A Simple Study Evaluating the Potential of Visualizations
as a Communication Tool in Geomatics
OBJECTIVES OF THE STUDY
The primary objective of this study was to investigate if dynamic visualization is an
alternative to 2D static textbook representations for conveying difficult geomatics concepts of
geomatics. However, 3D animation as a means of explaining the complex theory and
principles upon which the ruling paradigm of geomatics and derivative mapping technologies
are based, requires systematic evaluation. We have two sub-objectives in that regard:
1) To evaluate the effects of dynamic visualization as a communication tool in the
classroom, by asking the question: Do students in an introductory geomatics course
prefer animation over standard textbook illustrations?
2) To evaluate current software for 3D photorealistic visualization and their integration
with geomatics for educational purposes.
Experience shows that the most difficult concepts for students to understand involve those
involving several dimensions and changes in dimensions e.g., 3D to 2D or 4D. Such
fundamentals are also the most challenging concepts to communicate using traditional 2D
textbook static visualizations, numerics or textual descriptions. Difficulties in comprehension
of the aforementioned principles come from the individual differences in students’ spatial
level of thinking that can only be improved through adequate training (Ishikawa 2005). With
this in mind, we devised a series of dynamic visualizations to illustrate basic concepts in the
field of geomatics.
Appendix 2 Visualizations as a Communication Tool in Geomatics
128
BACKGROUND
In geomatics education, it is estimated that, due to the complexity of the topics, 99% of the
effort is allocated to description of the phenomena/problems with only 1% for explanations
and prediction (Wellar 1989). As such, it was recognized early on that visualization would
become one of the most important tools in geomatics education (Wellar 1989; Wellar 1995).
New trends such as web-based, distance learning and self-dedicated learning further
emphasize the need for visualizations as an important teaching tool in constructivist learning
environments (Dickey 2003).
While 2D static visualization (e.g. 2D pictures, graphs and photographs) is traditionally well
established, the utilization of dynamic visualization for educational purposes expanded after
the use of this technology on NASA’s website in the late 1990s. NASA’s dynamic
visualization of satellite images of the Earth was judged as revolutionary for earth science
education (Barstow 1997). NASA’s project leaders emphasised that dynamic visualization in
the classroom improved the communication of complex 3D concepts and eliminated the
cognitive and perceptual confusion usually associated with static 2D representations of those
same concepts (Barstow 1997). The stimulation of cognitive processes comes from the fact
that when presented as 3D dynamic visualizations, dynamic processes are presented in the
dimension of human experience (Sarjakoski 1998). Johnson (2002) thus correctly suggests
that dynamic visualization not only improves students’ understanding but also motivates
students for further learning. On the other hand, Harrower et al. (2000) reported the benefits
of animated and/or interactive visualizations for learning about spatiotemporal processes
depends on the knowledge level of the student; novices and those with an intermediate level
of understanding of the underlying subject benefited more than those with advanced
knowledge. However, the latter provided more valuable tips and suggestions for improvement
of the visualizations used. Libarkin (2002) summarized the benefits and limitations of various
visualization techniques in the classroom (Table 8).
Dynamic visualization or animation, defined as simulating motion pictures depicting the
movements of drawn or simulated objects with its three main parts (picture, motion, and
simulation) in combination with photorealistic visualization has a great potential to enhance
learning (Mayer and Moreno 2002).
Appendix 2 Visualizations as a Communication Tool in Geomatics
129
Table 8 Advantages and disadvantages of visualizations. Adapted from (Libarkin 2002).
Teaching
Learning
Advantage
Disadvantage
Advantage
Static
Easy to design; low
cost.
Limited instruction
as to what is
immediately visible.
Low cognitive load.
Easer to evaluate
important points.
Dynamic
Difficult verbal
descriptions can be
translated into
visual images.
For novices, time
consuming to
develop.
Otherwise
unobservable
phenomena become
visible.
Interactive
Teacher’s role is
observing learning
rather than leading.
Time consuming to
develop.
Investment
necessary.
Active engagement
in real world
phenomena. Student
controls directions
and ideally
discovers.
Disadvantage
Passive learning.
Incorporation of
active learning
depends on
students’ motivation
Passive learning.
Incorporation of
active learning
depends on
students’
motivation.
May be difficult to
extract important
points from
complex
background.
Hunter and Lewandowski (2004) suggest that dynamic visualization should be used as an
additional tool in the geomatics classroom for better communication of complex dynamic
concepts. Barstow (1997) argues that this tool should take the central stage. Regardless of
where the tool fits within the nomothetic component, it is likely to continue to grow as
predicted by Wellar (1989) and Pailliotet and Mosenthal (2000). However, simply applying
this new technology does not guarantee educational benefits (Hegarty 2004). It is evident that
those educators who intend to prepare for the future cannot afford to oversee or underestimate
the enormous potential of dynamic visualization. To maximize the benefits of scientific
visualization, educators have to understand under what conditions to use the technology, as
well as to consider changing their perceptions and traditional educational practices.
When evaluating a new technology or tool in an educational environment, (Agnew 2001)
advocates the consideration of whether or not the tool will: a) promote greater understanding;
b) lead to skill development; c) foster active (deep) learning; d) motivate students; e) save
time (for students and teachers); f) save resources; and g) save finances. We are particularly
interested in the development of dynamic visualizations for the teaching of geomatics
concepts and capabilities. Because dynamic visualizations are directed, the educator
determines the content, focus and flow of the visual content presented. The major advantage
of this approach is that such visualizations can supplement verbal and static descriptions and
indeed illustrate processes that are otherwise unobservable. For example, the process of
Appendix 2 Visualizations as a Communication Tool in Geomatics
130
coordinate transformation, the definition of longitude and latitude, and GPS positioning
among others can be effectively conveyed using animation. Through such visuals the student
is engaged in a passive learning mode and thus expends minimal cognitive effort for
understanding. We argue that 3-4D dynamic animations visualizations of geomatics concepts
do indeed meet all of the criteria of Agnew (2001) and therefore are an indispensable tool in
the geomatics educator's arsenal.
METHODS
The visualizations chosen for this test were all based on horizontal positioning concepts. In
the authors’ experience, these tend to be the most difficult concepts for undergraduate
students to comprehend. Therefore, these concepts were seen as one of the best ways to test
the effectiveness of 3D animation. The following animations were created and utilized:
1. Geographic latitude and longitude. Geographic longitude is the east or west angle on the
equatorial plane made between any point on a spherical representation of the earth and the
prime meridian. These are inherent 3D concepts. Our 3D, dynamic presentation started by
presenting a position on Earth model's surface. Subsequently, the animation showed the
opening of the earth from the surface to the core with the selected point being left on the
surface. The imaginary line connecting the point on the surface with the core was drawn to
indicate the angle for easier understanding.
2. Geodetic latitude: For a given location on an ellipsoidal model of the earth, the geodetic
latitude is the angle formed between a line normal (perpendicular) to the surface of the
ellipsoid model at the given location and the plane of the equator (Snyder 1987).
3. Graticule. As a network of latitude and longitude lines on a map or chart that relates points
on a map to their true location on the earth, the graticule was a logical step after the animation
of latitude and longitude. The animation presented the realistic Earth as seen from space being
covered with the network of latitude and longitude lines. At the beginning, the latitude and
longitude lines form a 2D network that winds around the Earth. At the end, it is obvious that
the network at the poles must be modified. This modification is a dynamic transformation
Appendix 2 Visualizations as a Communication Tool in Geomatics
131
from cylindrical shape to the shape of the earth. At the end of the animation, the points at the
North and South Pole were zoomed in to illustrate convergence of longitude lines.
4. Geoid. The geoid is an equipotential surface of the Earth’s gravity field and can be
approximated by mean sea level. Because the gravity distribution of the Earth is not uniform
(due to irregularities in the density of Earth materials) the shape of the geoid is rather irregular
and therefore not used for mapping. Given that its undulating surface varies more than about a
hundred meters above or below a well-fitting ellipsoidal model of the earth, it was interesting
to present this model in a 3D mode and zoom out selected features. For dynamic visualization,
first the undulating surface was created as a 3D surface. To emphasise the scientific
visualization approach, the gravitational field used in the animation was downloaded from
NASA’s GRACE (Gravity Recovery and Climate Experiments) database (NASA 2003). The
GRACE project is a set of two satellites recording the complete gravitational field of Earth
every 30 days with a precision that is over 100 times higher than any existing measurement
(NASA 2003). The generated image is an equirectangular projection based on the WGS84
datum and it was color coded with colors ranging from deep red (maximal gravitational force)
to deep blue indicating minimal gravitational force. Given that the image was an
equirectangular projection (e.g. the pixels laid out in a regular longitude-latitude grid), texture
mapping and surface displacement were performed using 3D Studio Max. Zooming-in and
rotation features were also used to further emphasize the irregularity of the surface of the
geoid. Rotation of such a geoid was the first segment while comparison of its color coded,
undulating surface to the uniform, featureless earth model representation as an ellipsoid was
the last animation segment, while zooming in was used in between these two segments to
“dive in” to specific structures of interest and indicate those that indeed have different
altitudes in real life.
5. Ellipsoidal Earth is a model defined with a semi-major (long) and semi-minor (short) axis
that approximates the complex shape of the Earth. The ellipsoid is used in mapping because it
has a smooth surface upon which horizontal coordinates of longitude and latitude can be
determined. Many different ellipsoids are used because the earth’s surface is not perfectly
symmetrical and one semi-major and one semi-minor axis that fit some particular
geographical region do not necessarily fit another. A smooth surfaced, ellipsoidal structure
was used in this visualization to represent the earth. In the visualization, latitude and longitude
Appendix 2 Visualizations as a Communication Tool in Geomatics
132
lines begin to stream from the North toward South Pole, filling in the surface of the ellipsoidal
earth. The angle of the ellipsoid in the space was the real angle of the Earth axis.
6. Map projections. Map projection is a mathematical transfer of the Earth’s graticule of
longitude and latitude lines onto a two dimensional surface (paper or computer). Such a
transformation can be challenging to present using a 2D representation only. At the beginning
of the dynamic representation, the earth with the network of longitude and latitude lines was
presented. The scientific component behind this animation is emphasized in the following
sequences. After the initial rotation of the 3D globe that truly represents the surface of the
entire earth without any distortion, the longitude and latitude lines on the globe (e.g. graticule)
were followed from the South to the North Pole during the animation to demonstrate
convergence of longitude lines toward the poles and their divergence towards the Equator.
This initial network of latitude and longitude lines encircling the globe is in the next sequence
transformed into a 3D cylinder wrapped around the globe tangent to the Equator. In the final
stage of the animation this 3D cylinder transforms into the 2D, planar network. The resulting
planar is the Plate Carreé projection, the oldest (according to some records first generated in
100 AD) and the most commonly used map projection due to its simplicity (Tufte 1990). The
transformation from 3D graticule to 2D planar grid was used to illustrate the distortion of the
shape and the area as an inevitable consequence of mathematical transformation. By
following the animation sequences, students will be able to visually examine a complex
mathematical transformation behind the concept of map projection and at the same time
understand the relative importance of distortion in area and shape.
7. Global Positioning Systems (GPS). To comprehend the movements of 24 satellites along
6 orbits and the determination of individual location on earth using a 2D presentation is a
challenge. Using a 3D dynamic visualization, the clarification is simple. Students have an
opportunity to observe individual orbits and satellites as they are added subsequently to the
screen. It is also possible to isolate a single orbit or a single satellite to illustrate how GPS
ensures correct navigation. This is an advantage over 2D static representation where such
realistic representations are only partially possible (e.g. relative distances from the Earth).
However, representation of the constellation in motion and movement of the satellites in 4D
(space + time) relevant to the Earth with 2D representation is not possible. To distinguish
scientific animation from other non-scientific, descriptive approaches depicting satellite
orbits, the distances from the Earth and the orbital velocities of the satellites were scaled
Appendix 2 Visualizations as a Communication Tool in Geomatics
133
down. First, the Earth was scaled down, and keeping the proportions, the orbital distances
were calculated. To model orbital movements, the rotation periods of the satellites were kept
proportional to the rotation periods of the Earth. For example, it is known that highly elliptical
satellite systems such as GPS have a rotation period of 12 h. Thus, in the scaled down model,
one full rotation of the satellite around the Earth corresponds to two full rotations of the Earth
around its imaginary axis. In addition, the angles of the orbital planes were kept between 50
and 70o to simulate reality. In all animations depicting satellite orbits (e.g. geostationary
satellite orbits were also animated in this project), these rules were followed.
All animations (Figure 49) were made using a combination of 3D Studio MAX (v.7) for
modeling, animation and rendering and Adobe Premier Pro for postproduction (e.g. text and
title addition, special effects, file compression…). 3dsMax 7 is the state of the art software
that contains the essential high-productivity tools required for modeling, animation, rendering,
and design visualization.
All animations are created at NTSC video resolution with 720 pixels wide by 480 pixels high
at 29.97 frames per second. A windows media encoder has a high level of compression and
was used to reduce the file size without any perceptible reduction in video/image quality.
The effectiveness of 3d visualizations of basic geomatics principles was assessed by
undergraduate students enrolled in an introductory geomatics course. Here it must be
emphasized that no particular sampling strategy from the population (all undergraduate
students at the Department of Geography, University of Ottawa) was applied. The class was a
mixture of 20 second, 15 third, and 5 fourth year students within the Department of
Geography at the University of Ottawa, Canada. Out of 40 participants, 28 were female and
12 male. There was no division of the students into a treatment and a comparison group. All
students were exposed to both novel, dynamic 3D visualizations and the 2D textbook
representation of the same concepts. Although this can be considered true statistically based
experimental design for the study where a treatment and control group were compared, for the
first, simple evaluation of the effectiveness of visualizations it was considered applicable.
Appendix 2 Visualizations as a Communication Tool in Geomatics
a)
b)
c)
134
d)
e)
Figure 49 Key frames for animations: a) Latitude/Longitude, b) Graticule, c) Geoid, d) Map projection e)
GPS
The classes were held in a multimedia classroom where each student had access to a state of
the art desktop computer. All visualizations were displayed using a high definition screen. For
comparative purposes, both the visualizations and the textbook were equally accessible.
Therefore, the students were able to re-run visualizations after the class as well as read the
textbook.
Appendix 2 Visualizations as a Communication Tool in Geomatics
135
The questionnaire. After viewing the animations and textbook representations, an
anonymous short questionnaire presented at the end of this work was given to the students.
The questionnaire contained two major parts. In Part A, background information about
students such as gender, age, program of study, etc. was collected. Part B contained the
evaluation of the visualizations and complementary textbook presentations. Students were
asked to rate the presentations from the textbook and the visualizations according to their
personal:
1. Understanding of geographic and geodetic Latitude/Longitude
2. Understanding of Graticule
3. Understanding of Geoid
4. Understanding of Map projection
5. Motivation for learning
6. Presentation clarity
All visualizations and textbook explanations were rated on a scale from 1 to 5 with 1
indicating that a student finds a particular visualization/textbook explanation not useful at all
for understanding of the concept, to 5, indicating a visualization/textbook explanation as
being very useful for understanding of the concept. The students were also asked about their
motivation to study using visualization compared to the traditional textbook diagrams.
Finally, they were asked to judge the contribution of the presentation clarity on their
understanding of the concept. A section was included for open comments/suggestions.
RESULTS
In response to our central research question, students found visualizations more useful for
understanding a geomatics concept as compared to the 2D representation offered in the
textbook. Figure 50 shows the average response to all questions. For statistical comparisons,
all p-values were reported as two-tailed from paired t-tests assuming equal variance.
Regardless of gender and program enrolled, or previous experience with geographical
information science concepts, all students have found that the visualizations improved their
understanding compared to the textbook. The average mark on all questions for scientific
Appendix 2 Visualizations as a Communication Tool in Geomatics
136
visualization was 4.39 with (95% CI = ± 0.243), while the average mark for textbook
diagrams was 2.86 (with 95% CI = ± 0.373) (p <1.37×10-7 (α=0.05))..
6
Text book
Dynamic Visualizat ion
Mean with 95% CI
5
4
3
2
1
0
1
2
3
4
Question
5
6
7
Figure 50 Average response of all students on different questions in the questionnaire
Comparing individual visualizations, the biggest difference in understanding of the concept
was observed for the concept of the geoid. The average mark for the dynamic visualization of
the geoid concept was 4.25(with 95% CI = ± 0.259), while 2D presentation in the textbook
scored only 2.61 (with 95% CI = ± 0.363) (p < 7.91× 10-11 (α=0.05)), clearly indicating that
the students found dynamic visualization more useful than static diagrams. Considering the
mode of presentation on motivation for learning the concepts, the average mark for all
visualizations was 4.25 (with 95% CI = ± 0.269) while text book presentation scored only
2.94 (with 95% CI = ± 0..465) (p < 1.16 × 10-11 (α=0.05)).
It is well documented that male and female student have different approaches to learning
(Hartley 1998). Specifically, evidence suggests differences in spatial reasoning between males
and females (Bonanno and Kommers 2005; Colom et al. 2004; Spelke 2005). Spatial thinking
includes knowing about a) space (e.g. information perception, understanding distance,
orientation, direction, Lat/Long coordinates) (DeVarco 2005) b) representation (e.g.
dimensions and translation from one dimension to another, the effect of projections) and c)
reasoning (e.g. the ability to extrapolate and interpolate, calculate shortest distance, decision
making) (NRC 2006). Recently, spatial thinking was recognized as a new framework in the
geosciences and education (DeVarco 2005; NRC 2006) Therefore, it was of interest to
evaluate if there were any gender differences in perception and learning when scientific
concepts are communicated using dynamic visualization as compared to classical textbook
Appendix 2 Visualizations as a Communication Tool in Geomatics
137
representation. However, we found no statistically significant difference between the
evaluations from male and female students. Further statistical details are given at the end of
the text. Both genders found the dynamic visualization more helpful for understanding of the
various GIS concepts compared to the textbook explanations. This is encouraging, since it
illustrates that scientific visualization is an effective media for the communication of the
complex concepts regardless of the gender of the information receiver. As the internalization
of these visual concepts may differ, however, that is left for future research.
Students’ Perceptions. From a qualitative viewpoint, student responses underline the
statistical inferences found we found. General comments included:
- “They (animations) keep me and probably everyone else extremely motivated.”
- “I think the animations keep my attention to the subject at hand.”
- “The animations are much more useful because it is a high concentration on an
important topic, versus the text placing equal importance on useless stuff.”
- “I found the graphical animations highly useful because they truly do represent what it is
we are to understand and therefore eliminates confusion. I think animations are in this
class particularly important because the concepts study regime full-view plus 3D
representation.”
- “The animations help a lot because it clarifies the theory and concepts.”
- “I find the animations make it easier to understand concepts and they’re better for
visually learning (compared to textbook).”
- “I think the visuals help keep people understand concepts as an easy method compared to
reading a page of writing.
SOFTWARE EVALUATION
There is an abundance of software that can be used for the development of scientific
visualizations. In addition, the use of multiple software packages is not uncommon.
Therefore, one of our objectives was to evaluate software used to create 3d dynamic
visualizations. Although there is a plethora of visualization software available, a combination
of 3D Studio Max and Adobe Premier5 were found to be state of the art for the development
5
3D Studio Max is a trademark of Discreet Media and Entertainment, Adobe Premiere is developed by Adobe
Ssytems Inc. and both are available in North America for educational purchase and pricing from Torcomp
Systems Inc – www.torcomp.com
Appendix 2 Visualizations as a Communication Tool in Geomatics
138
of modern and well designed visualizations for teaching purposes. A typical working space
with different view points (e.g. top, left, right) and tools is presented in Fgure 51.
3D Studio Max offered a range of easy, accessible and identifiable tools for visualization of
various GIS concepts. For novice users, it is important to emphasize that the software has a
learning curve. However, a significant extent of realism can be achieved with a wide range of
pre-programmed textures, materials and lighting tools. Lighting in 3D Studio Max uses
photometric lights with a radiosity solution and ray tracing that results in improved image
quality. Photometric lights use light energy to simulate more natural lighting. While radiosity
realistically simulates interaction of the light with the environment by calculating
interreflection between the surfaces, ray-tracing provides better direct illuminations, shadows
and refractions. The combination of these techniques inside 3D Studio Max software results
in an exceptional quality and realism. In the visualizations presented in this study (e.g. the
basics of GPS) the use of lighting was even more pronounced since the central feature in the
visualization was the Earth illuminated in space.
Fgure 51 Typical working space in 3D Studio Max
Appendix 2 Visualizations as a Communication Tool in Geomatics
139
For the generation of the geoid visualization, for example, the use of the texture and material
tools in 3D Studio Max was invaluable. Selection, change or compounding of materials in 3D
Studio Max is easily accomplished with the number of tools provided. Apparent level of detail
is also increased compared to other software.
The pre-programmed animation management system was extensively utilized in the study. It
was found to be similar to other tools: intuitive and well defined to help the user to achieve
the extent of the animation necessary to convey the message. 3D Studio Max animates not
only transformations (e.g. position, rotation, and scale) but also any accessible parameter (e.g.
angle, material parameters, such as the colour or transparency of an object, etc.). These give
the user a wide range of animation possibilities not available in other visualization packages.
A variety of rendering systems, e.g. native, mental ray or third party renders included in the
software, is an additional advantage of 3D Studio Max over other visualization software
packages and one of the reasons for its utilization in the presented study. 3D Studio Max
comes with its own network rendering software called Backburner. Network rendering uses
multiple computers connected over a network in order to decrease the amount of rendering
time, which is the most computationally intensive task.
While we utilized 3D Studio Max, software in the same class and with similar functionality
include Maya, LightWave (www.newteck.com), and Cinema 4D (www.maxon.net). The use
of any of these would accomplish the same tasks for animation as described above.
Additionally, for geovisualization and landscape visualization in particular, Vue Infinite
(www.e-onsoftware.com) is the least expensive solution with the shallowest learning curve
and best price. The Vue product line is particularly suited to landscape visualization.
In conclusion, it can be assumed that the future development of geomatics will include a
considerable amount of scientific visualization. This small study confirms that both students
and educators could benefit from such an approach to learning. However, a larger statistically
based study should be conducted prior to making any firm conclusions about the influence of
visualization on the communication of the complex geomatics concepts in the classroom.
Appendix 2 Visualizations as a Communication Tool in Geomatics
140
QUESTIONNAIRE
GEG 2320 B Please fill in the following questions. All responses are anonymous.
Gender
Female
Male
Age
17-20
21-23
24-26
27-29
> 30
Year of Study
1
2
3
4
5 (Graduate)
Other
Program
B.A. Environmental Studies
BSc Geography
BA Geography
BSc Biology
BSc Environmental Science
BSc Earth Sciences
Other_________________________
Have you previously taken a geomatics course
at university or college or high-school?
YES
NO
Textbook Animations
Understanding
Latitude/Longitude
Understanding
Graticule
Understanding
Geoid
Understanding
Ellipsoidal Earth
Understanding
Map projection
Motivation for
learning
Presentation
clarity
Please grade on scale from 1-5, with 1
being not useful and 5 being very useful.
Please put your comments in the empty
box below.
Appendix 2 Visualizations as a Communication Tool in Geomatics
141
ADDITIONAL INFORMATION
Table 9 Statistical Summary: All responses
Question
Question
Question
Question
Question
Question
Question
1
2
3
4
5
6
7
4.45/3.25
4.58/2.97
4.25/2.61
4.33/2.97
4.25/3.06
4.25/2.24
4.16/2.94
0.72/0.90
0.46/1.25
0.65/0.98
0.64/0.74
0.71/1.31
0.71/1.31
0.24/1.15
t-stat
-5.67
-7.52
-7.67
-6.92
-5.16
-8.62
-8.69
p-value (two tailed)
1.94E-7
1.38E-10
7.91E-11
1.72E-9
2.18E-6
1.16E-12
9.52E-13
Mean Response
Visualization/Textbook
Variance
Visualization/Textbook
For all questions: t-crit=1.99437, α=0.05
10
Mean (with 95% confidence interval)
Female.Textbook
Female.Visualization
8
Male.Textbook
Male.Visualization
6
4
2
0
1
2
3
4
5
Question
Figure 52 Reponses by gender
6
7
Appendix 2 Visualizations as a Communication Tool in Geomatics
142
Table 10 Summary statistics for male and female students: Visualizations vs. Textbook
Mean
Visualization/Textbook
Variance
Visualization/Textbook
p-stat
p-value
4.33/2.74
0.05/0.12
-9.99
3.64E-7
4.54/3.09
0.02/0.18
-8.72
1.54E-6
Female
Students
Male
Students
p-crit=2.17
10
Textbook
Dynamic.Visualization
Mean with 95% CI
8
6
4
2
0
1
2
3
4
Question
5
6
7
Figure 53 Responses of the 2nd year geography students
Table 11 Summary statistics for differences in judging the usefulness textbook and the
dynamic visualization by different peer groups
2nd year
Students
3rd year
students
4th year
students
p-crit=2.17
Mean
Visualization/Textbook
Variance
Visualization/Textbook
p-stat
p-value
4.37/2.86
0.02/0.11
-10.63
1.84E-7
4.37/2.80
0.03/0.11
-10.75
1.63E-7
4.34/2.83
0.03/0.10
-11.09
1.16E-7
Appendix 2 Visualizations as a Communication Tool in Geomatics
143
Evaluation for Dynamic Visualizations
7.0
2nd year responses
3rd year responses
4th year responses
Mean with 95% CI
6.0
5.0
4.0
3.0
2.0
1.0
0.0
1
2
3
4
Question
5
6
7
Figure 54 Evaluation of dynamic visualizations by different year of study
Table 12 ANOVA: Evaluation of dynamic visualizations, differences among different
years of study
ANOVA
Source of
Variation
Between
Groups
Within Groups
0.005426
0.496136
2 0.002713 0.098425 0.906748 3.554557
18 0.027563
Total
0.501562
20
SS
df
MS
F
P-value
F crit
Evaluation for Textbook
7.0
Responses
6.0
2nd year responses
3rd year responses
4th year responses
5.0
4.0
3.0
2.0
1.0
0.0
1
2
3
4
Questions
5
6
7
Figure 55 Evaluation of textbook presentations by different year of study
Appendix 2 Visualizations as a Communication Tool in Geomatics
144
Table 13 ANOVA: Evaluation of textbook, differences among different year of study
ANOVA
Source of
Variation
Between
Groups
Within Groups
0.012663
2.031672
2 0.006331 0.056095 0.945614 3.554557
18 0.112871
Total
2.044335
20
SS
df
MS
F
P-value
F crit
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Appendix 3 International ENVI Challenge 2005 Award
146
APPENDIX 3
International ENVI Challenge 2005 Award
PHOTOREALISTIC VISUALIZATION USING ENVI
Zoran Reljic, MSc Student, Laboratory for Geomatics and GIS Science (LAGGISS),
Department of Geography, University of Ottawa, CANADA
BACKGROUND
Today’s applied research milieu requires fast information exchange between
multidisciplinary team members, the public and policy makers. Traditional ways of
communicating scientific results via static maps or graphs can be greatly enhanced
through alternative representations that better approximate the human experience.
3D photorealistic visualization is one of the growing fields within the geographic
sciences that address the current challenge to science to rapidly disseminate
information while at the same time facilitating the mental absorption of complex
earth-based datasets. 3D geovisualization offers new dimensions in the earth
observation data interpretation that assist in the easy communication to the various
interest groups.
THE CHALLENGE
The advantages of geovisualization are recognized by the Canadian government. In an
effort to preserve and monitor the ecological integrity of protected areas such as Canada's
National Parks, and increase public awareness of the subject, several government agencies
have joined forces through the Government Related Initiatives Programme (GRIP) and
with the Laboratory for Applied Geomatics and GIS Science (LAGGISS) at the University
of Ottawa, Department of Geography. For community outreach and education, one goal
of GRIP is to produce 3D photorealistic dynamic animations to communicate the natural
beauty of our national parks and to highlight ecological integrity using remotely sensed data
from a variety of imaging sensors. GRIP is funded by the Canadian Space Agency and led
by Parks Canada in partnership with Natural Resourced Canada and the Canadian Center
for Remote Sensing.
STATE-OF-THE-ART ENVI SOLUTIONS
The 3D visualization methodology is broken down in a number of stages, including
topographic data manipulation, earth observation data sourcing, data pre-processing,
identification of key environmental features and finally previsualization and photorealistic
visualization. In all steps, the ENVI tools are indispensable. Here, examples of four key
tools utilized in the 3D photorealistic visualization of the landscape and ecological changes
of Auyuittuq National Park are presented.
Vector Elevation Contours to Raster DEM. Data preprocessing is a necessary step
for photorealistic animation. Elevation data in gridded formats are not always available in
remote regions at sufficiently detailed resolutions. A starting point in terrain visualization is
the production of a digital elevation model (DEM). The DEM is further used for draping
satellite
Appendix 3 International ENVI Challenge 2005 Award
147
Figure 1 Converting contours to DEM
imagery and deriving shaded relief, in addition to its importance in the orthorectification
process. Using the Convert Contours to DEM, a DEM for the Auyuittuq National Park
was easily produced from the Canadian National Topographic Data Base (NTDB) at the
scale of 1:50,000. Here, the new enhanced method in ENVI 4.1 for opening vectors makes
the converting process even faster.
Data Fusion. Data fusion combines lower resolution multispectral datasets with higher
resolution panchromatic data to increase the spatial resolution of the multispectral imagery
(this is also called pan-sharpening). Fusion is necessary for the development of the
majority of the photorealistic animations that use satellite imagery as base textures used as
surface matials for DEMs. A high-resolution Landsat ETM panchromatic image (spatial
resolution 15m) was used to enhance the spatial resolution of a natural color image [blue
(band 1), green (band 2), and red (band 3)] (spatial resolution 30 m) covering Auyuittuq
National park in Nunavut, Canada. ENVI data fusion tools resulted in a pansharpened,
high resolution color composite with a spatial resolution 15 m. In this case, the image
sharpening techniques used a hue-saturation value (HSV) transform to automatically merge
the lower-resolution color and higher resolution panchromatic images in ENVI.
Figure 2 a- panchromatic image (15m spatial resolution), b-color image (30m spatial resolution), c-fused
image (15m spatial resolution).
Appendix 3 International ENVI Challenge 2005 Award
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3D SurfaceView. To visualize terrain elevation prior to adding the fused imagery (Figure
2) in the texturing step, the DEM was displayed in the ENVI 3D SurfaceView TM. Easy to
use and navigate, the 3D Surface ViewTM allows display of a DEM as a color, a wireframe
or draped satellite image (Figure 3) which also helps to familiarize the user and the terrain
features. Furthermore, to explore and determine the points of interest for a flight path over
the Auyuittuq terrain, a set of comprehensive tools were utilized. The vertical exaggeration,
real-time rotation and zooming, the ENVI’s annotation tool for interactively drawing of
flight path, are some of the basic and easy to use tools - just to name a few.
Figure 3 a-color, b-wireframe, c-Landsat image draped, d- IKONOS image draped
Band Math. Additional image processing related to Auyuittuq national park involved
image ratios and the calculation of the normalized difference snow index (NDSI). The
NDSI is useful to distinguish snow and ice from similarly bright features like clouds or
rocks. It is calculated using Landsat TM2 (green band) and Landsat TM5 (mid-infrared
band) as:
Appendix 3 International ENVI Challenge 2005 Award
NDSI =
149
TM 2 − TM 5
TM 2 + TM 5
The Band MathTM tool Figure 4 was used to quickly perform image arithmetic and apply
it to the particular bands opened in ENVI.
Figure 4 The Band Math tool
a
a) Landsat TM 1991-07-12
(Path/Row: 17/13)
b) Landsat ETM 2000-08-13
(Path/Row: 17/13)
Figure 5 NSDI images in Auyuittuq National Park obtained by utilizing Band Math. The white
areas represent ice and permanent snow cover. The largest white area is Penny Ice Cap, one of the largest
ice-caps outside of Greenland and Antarctica.
The Landsat image that was processed with ENVI is used in the project to describe the
glacierized and permanently snow covered area of the park. In the far north, monitoring
the parks integrity in response to natural climate change requires monitoring of the
snowline and glacier cover and changes therein. Areas of concern are those such as Penny
Ice Cap (Figure 5 & 6). The massive Penny Ice Cap Figure 6, an area of solid ice over 300m
thick that covers around 5100 km2 is at the heart of Auyuittuq NP. This area is of special
Appendix 3 International ENVI Challenge 2005 Award
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interest because it is a remnant of the Laurentide Ice Sheet that once covered North
America 21,000 years ago.
Figure 6 A map of Penny Ice Cap in Auyuittuq NP made with ENVI’s QuickMap
The pattern of glacier cover and the topography in Auyuittuq are of interest for
communicating environmental integrity in the park and thus lend themselves to 3D
visualization whereby viewers can be brought through the valley and shown the various
glaciers that are being monitored for changes. There are two major glacier types: ice caps
that occupy inland mountain areas and valley outlet glaciers. Penny ice cap is composed of
interlocking outlet and cirque glaciers forming highland ice fields Figure 6.
Monitoring glacial retreat is very important to both the local ecosystem and to the global
environment. Analyzing historical photographs and data, scientists from the University of
Ottawa and Geological Survey of Canada have suggested that some of the glaciers in
Auyuittuq are retreating up to 8 meters per year. In order to make a 3D visualization
depicting the state of the glaciers trough time Landsat TM from 1991 and Landsat ETM
from 2000 were utilized with the ENVI software. A comprehensive set of the tools that are
Appendix 3 International ENVI Challenge 2005 Award
151
easily accessible made the job much easier and faster compared to the other similar
software.
a)
b)
Figure 7 3D view of the part of the Auyuittuq NP showing the Fork Beard Glacier in the summer of
1991 (a) and the summer of 2000(b)
Landsat TM image source: http://glcfapp.umiacs.umd.edu:8080/esdi/index.jsp
Landsat ETM image source: http://geobase.ca/
Appendix 3 International ENVI Challenge 2005 Award
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Figure 8 a–Nerutusoq Glacier (Ikonos), b–Summit lake(Ikonos), c–NerutusoqGlacier with vector snow
line and rivers, d-Summit lake with vector snow line and Rivers, e–Thor peakand Fork Beard Glacier
(Landsat 321 Composite), f–Crater lake (Landsat 321 Composite)
Appendix 3 International ENVI Challenge 2005 Award
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Limitations
Since all geographic information have limitations due to scale, resolution, and time of
acquisition they have various precision to which they depict shape, distance or other
geographic characteristics. While every precaution has been taken in the processing of the
data to ensure consistent horizontal references (e.g., datum conversions) it is important to
note that because of the scale of the data, the horizontal errors incurred could be greater than
the level of detectability of the changes we are hoping to monitor. For example, the
maximum accuracy of the NTDB digital topographic data at the 1:50 000 scale is
approximately +/-10 m vertically and +/-10-12 meters horizontally due only to the map
scale. As such, the vertical and horizontal errors within NTDB data largely preclude the use
of topographic map derived snowlines and permanent ice-cover from serving as a basis of
comparison (e.g., Figure 8c). Landsat images can be easily processed (geocoded,
orthorectified and combined with DEM data), but on the other hand they have limitations of
monitoring small glaciers (spatial resolution) and seasonal coverage (time resolution).
However, if processes are not controlled over the different imagery available horizontal
errors due to changes in nadir and other parameters accumulate and propagate thus making
inferences on small changes in glaciers, for example, difficult to assess when such changes are
in the range of a few meters a year.
Conclusion
Throughout this project, the utilization of ENVI’s comprehensive package tools have been
exceptional. The number of tools and applications, easy navigation through the tool pallets as
well as high quality graphical user interface are making this state of the art software an
indispensable tool for enhancing photorealistic visualization and contributing directly to the
preservation and monitoring of natural environments and ecological integrity within the
Canadian National Parks system.
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APPENDIX 4
Canadian Institute for Geomatics 2005 Conference Paper
Integrating GIS and 3D Visualization for Dynamic Landscape
Representation in Canada's National Parks
Zoran Reljic*, M. Sawada*, Jean Poitevin**, Greg Saunders***
* Laboratory for Applied Geomatics and GIS Science (LAGGISS), Department of Geography, University of
Ottawa, Ottawa, ON K1N 6N5. www.geomatics.uottawa.ca
**Applied Research Coordinator, Ecological Integrity Branch, National Parks Directorate, Parks Canada
Agency, 25 Eddy Street, 4th floor (25-4-S), Gatineau, Quebec K1A OM5.
***Ecosystem Data Technician, Resource Conservation, St. Lawrence Islands National Park, Parks Canada
Agency, 2 County Rd. 5, RR #3, Mallorytown, ON K0E 1R0
ABSTRACT
In today’s world of multidisciplinary approaches and intensive information exchange,
communication of geographic predictions and observations are not strictly limited to the
community of Earth scientists. More often it is necessary to communicate these results to the
scientist from other, non-related fields, policy makers, share and stake holders and the general
public.
Given that, humans perceive and absorb visualised information more effectively than numbers
alone, the integration of visualization with geographic information science is an emerging tool
for geographers to communicate the results of complex geographic models or observations in
a novel and visually attractive way. In particular, the 3D approach to visualization, when
appropriate, approximates the dimension of human environmental experience and therefore
facilitates the absorption of complex contextual information. Therefore, the presentation of
different landscape development scenarios using photorealistic, 3D dynamic landscape
visualization can facilitate easier and effective yet scientifically-based decision making.
The results of ongoing study integrating geographic information systems, remote sensing and
photorealistic 3D visualization are reported. This integration is done for the benefit of
communicating the utility of earth observation data for the monitoring of ecological integrity
within Canada’s National Parks. We illustrate the complexity and efficiency of integration of
GIS and 3D visualization for dynamic landscape representation.
Key words: 3D visualization, dynamic visualisation, landscape visualization, La Mauricie,
Auyuittuq, animation
INTRODUCTION
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There is a growing interest in the implementation of geovisualization in contemporary
geography. Utilization of visualization techniques can enhance the understanding of systems
and processes, and even reveal patterns in data that would otherwise be hidden – even to the
most experienced researchers (Monmonier, 1990). In addition, geovizualisation is an effective
tool for scientists to communicate their research results to different interest groups with
different levels of knowledge (e.g. policy makers, public, farmers).
Contemporary efforts in the preservation of the environment and species on our planet are
demanding a better understanding of the factors affecting the ecological integrity of protected
regions like Canada's National Parks. Currently, however, the interpretation of data from
parks science is usually in the form static maps, leaving the observer of such data to use
his/her imagination with regards to the landscape scale and environmental context as well as
observed changes over time. The rise of geovisualization is offering new tools for
investigation of the patterns of ecological changes that are not available in static
representations. Ecological Integrity is defined for parks by the Canada National Parks Act as:
…a condition that is determined to be characteristic of its natural region and likely to
persist, including abiotic components and the composition and abundance of native
species and biological communities, rates of change and supporting processes. (Parks
Canada 2004)
As such, monitoring environmental changes within national parks through time can illustrate
the degree to which ecological integrity is maintained and also indicate areas where
ecological integrity may be affected by human use or other environmental changes. In
particular, both archived and currently available earth observation datasets are fundamental
for monitoring changes within and surrounding Canada's National Parks. This is the main goal
of the Government Related Initiatives Project (GRIP) led by the Canadian Space Agency in
conjunction with Parks Canada, the Canadian Centre for Remote Sensing and the University
of Ottawa, Department of Geography. A major objective of GRIP is education and outreach,
that is, finding effective ways to communicate the science component to the public and policy
makers. Doing so will illustrate precisely how earth observation data can be useful for
monitoring ecological integrity. Here we present such communications using Auyuittuq
National Park (Figure 1) in Nunavut and La Mauricie NP in Quebec (Figure 2).
Feu
Brulage dirigé
Surface de camping aménagé
4
0
0
15
30
60 Kilometers
Figure 1 Auyuittuq National Park
2
4
8 Kilometers
Campground
FIRE 30 May 2003
Park Boundary
Figure 2 La Mauricie National Park
Park Boundary
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VISUALIZATION
Since the dawn of civilization humans have been trying to create a graphic representation of
the world around them. From the hunting scenes on the walls of Altamira cave (14000 B.C.)
to the modern 3D models of earthquakes, jet engine combustion, and DNA replication, one
thing is clear: An image is worth a 1000 words and literally a dynamic animation is thousands
of images. The human brain processes the visual information much more efficiently than
textual or audio (MacEachren, 1992). Today, the main objective of visualization is to enhance
human cognition of complex multi-dimensional data and large datasets.
Scientific visualization was defined by its early developers as “first and foremost an act of
cognition, a human ability to develop mental representations that allow us to identify patterns
and create or impose order“ (MacEachren, 1992) but also as a method of computing, a “tool
for interpreting image data fed into the computer, and for generating images from complex
multi-dimensional data“ (McCormic et al., 1987). Currently several approaches are used to
visualize objects in geographic research. The most common are static, dynamic, interactive,
immersive and animation.
Static visualization. A typical static visualization in geography includes maps, plans, photos,
perspective drawings, photomontage, or physical models where an object is seen by a static
observer. Static visualization refers to the process of visualizing the state information of
objects. This involves defining the objects under study and a finite set of states of the objects,
classifying objects by their states, extracting state information from the original data set,
finding the appropriate way to present and explain the results (Lange, 2001).
Dynamic visualization. Change is a fundamental characteristic of processes in nature and
interactions among them (Goud, 2004). Thus, static representation cannot depict the true
characteristics of such a dynamic system. Vegetation in nature grows, change with seasons
and eventually die. Realistic representation of trees for example, even in static visualization is
a challenge due to various levels of details that are necessary. Another issue is the response of
plant communities to simple terrain variables, such as elevation, slope, and aspect. In some
commercially available software it is possible to specify which plants are to be found in
which ranges of elevation, slope, and aspect, and then corresponding images textures are used
to generate a rendering (Ervin, 2001).
Interactive visualization. In this approach, not only there is a dynamic linking between the
graphical user interface with the underlying geospatial data but also with the end-user. The
result is the change in virtual scene as a response to changes in data or end-user actions. Such
environment Goodchild has named as “user-centric geographic cosmology” (Johannson,
2000). These interactive environments could be used as a tool to visualize and communicate
the results of various scenarios to the interest groups who in turn can interact with these
scenarios, relate to it and move around it in order to facilitate their decisions (Batty et al.,
2000).
Animated visualization. Animation is the creating a timed sequence or a series of graphic
images or frames together to give the appearance of continuous movement. Surprisingly or
not, the driving force for the development of animated landscapes was/is the video game
industry. Here, the advancement in animated landscapes are already so advanced that game
engines have become a basis for scientific landscape visualizations (Herwig and Paar, 2002).
However, there is more to animation in geovisualization than landscape. Animations are
“scale models in both space and time” and as such are potentially powerful tools for depicting
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change in information (Monmonier, 1990). Moreover, an animation integrates the two major
senses of sight and sound together to full effect.
Immersive visualization. Immersion implies feeling of “being inside” the virtual
environment on the side of the end-user (MacEahren et al., 1999). Here, the user manipulates
virtual objects as in the real world as opposed to pointing, clicking or typing (Bajwa and Tim,
2002). Most of this feeling of “being in the virtual world” comes from stimulation of different
senses in the real world (i.e. sound, visual, touch via feedback and smell). The degree of
stimulation will influence the degree of immersion in the virtual environment. The major
research interest is to find which display characteristics of the virtual world are inducing this
sense of “being in” (MacEahren et al., 1999).
Figure 3 Methodology and process flow for scientific visualization
METHODOLOGY OF PHOTOREALISTIC VISUALIZATION
In this project, the objective was to produce 3D photorealistic dynamic animations using earth
observation datasets for Auyuittuq National Park and La Mauricie National Park that illustrate
the utility of using remotely sensed data for monitoring ecological integrity. Several steps
were used to create 3D dynamic animations. The general stages included searching for
remotely sensed and topographic data availability, followed by data pre-processing with GIS
and remote sensing software, development of textures and texture maps, OpenGL based storyboarding and pre-visualization and then final rendering (Figure 3).
We utilize animation for the visualization of scientific and landscape principles. The process
of animation follows from the processes used in script-writing and film-making, however, the
script is equivalent to the scientific principle, the actors are the datasets/objects and the
process of filming is done within the computer. The elements of scientific visualization are
presented in Figure 3. The four main steps are development of scientific concept, modelling
of the concept, animation of the concept and rendering. Scientific visualization is an iterative
Appendix 4 Canadian Institute for Geomatics 2005 Conference Paper
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process where the final photorealistic visualization is assessed for visual artefacts and other
problems and the process is re-iterated until the scientist/director is satisfied.
a)
b)
c)
Figure 4 Example key frames for different visualizations, a) ice-berg near Pangnirtung Fjord; b) Snow
accumulation in Auyuittuq; c) Crater Lake in Auyuittuq with glacier.
One fundamentally necessary step is to relate the scientific concept to a previsualization
storyboard (Figure 3). For example, the relation between a glacier and proglacial lake such as
Crater Lake in Auyuittuq NP may be one such ecological/geomorphologic process that can be
monitored using EO data (Figure 4). A storyboard is constructed that determines the motion
and timing at key points of interest that illustrate the ecological relationship. This is a visual
process by which key-frames are established and rendered like the examples in Figure 4. With
these key frames and timing established, an OpenGL based previsualization or “previs” is
created for direction purposes. Figure 4a and 4b are other examples of the establishment of
key-frames.
However, before the data is visualized (Figure 3), considerable pre-processing in both GIS
and image analysis software is required. LANDSAT and IKONOS datasets for Auyuittuq
National Park as well as SPOT 5 for La Mauricie NP formed the basis of texture maps within
our 3D photorealistic animation software. For both parks texture maps from the earth
observation datasets were used for data fusion in order to combine lower resolution multi-
Appendix 4 Canadian Institute for Geomatics 2005 Conference Paper
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spectral datasets with higher resolution panchromatic data to increase the colour-based spatial
sharpness. For example, our remotely sensed imagery was processed within PCI Geomatica 9
and ENVI 4.1 where image enhancements were made via various stretch algorithms.
Effective texture development for 3D environments requires the use the latest image
processing tools. Textures are either materials created within a 3D package or image maps or
a combination of both that are applied to the surface of 3D objects. The materials composing
a given texture can have different ambient, diffuse and specular properties and thus respond in
different ways to light. For example, sedimentary rock like shale would have lower
specularity than say a metamorphic rock like slate. Likewise leaves of different trees or
species will have different reactions to ambient, diffuse and specular lighting (Figure 5).
a)
b)
Figure 5: Textured vegetation, a) Visualization of a single tree using 3ds Max; b) textured ground
simulating grass and a forest canopy using Vue 5 Infinite* ecosystem generator.
For base textures we developed our image maps first within image processing software which
allows for such processes as automatic detection/removal of hot spots, automatic radiometric
color balancing between overlapping images and global optimization over the entire mosaic,
and the automatic cut-line determination to minimize visibility of seams was crucial. Cutlines and balancing are stored for review or adjustment. Automatic mosaicking in PCI
reduced interaction and wait-time and thus streamlined the production workflow of texture
pre-processing. The textural development process requires describing to the computer the
visual and optical properties which differ among vegetation, water, soil, rock etc. These and
other considerations were taken into account in the development of textures.
In Auyuittuq, the pan-sharpened Landsat image was fused with an IKONOS image of the
main valley to provide more detail in that area. Likewise in La Mauricie, the SPOT 5 10 m
data were fused with the 5 m panchromatic channel and false colour images were created in
addition to fusion of Landsat data for the same region. ArcGIS was used to clip the CDED
Level 1 DEM to areas corresponding to our processed imagery before importation into the
visualization software.
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RESULTS AND DISCUSSION
The first step was the utilization of a DEM and other models for definition of the terrain
inside visualization software like 3D Studio Max*. To illustrate the potential of our approach
to communicating ecological integrity, different regions of Auyuittuq National Park in
Nunavut (Figures 6 and 7) and La Mauricie National Park (Figure 8) were visualized.
Figure 6 An example of a terrain generated and showing a view of Auyuittuq National Park. This terrain
utilizes an IKONOS image as a base texture with snow distribution added procedurally based on slope
and elevation.
Figure 7 A terrain visualization example of the Summit lake region of Auyuittuq National Park in
Nunavut using IKONOS and pansharpened and fused Landsat datasets as a texture base.
*
Vue 5 Infinite is landscape visualization software for photorealism from e-on Software, www.e-onsoftware.com. 3D Studio Max or 3ds
Max is created and distributed by Discreet www.discreet.com and educational licensing is managed by Torcomp Systems www.torcomp.ca .
Appendix 4 Canadian Institute for Geomatics 2005 Conference Paper
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It can be seen that the extent or realism achieved with the software is acceptable for the
intended objectives of the project. 3D Studio Max is capable of handling large elevation
models such as mountains or portions of the continent.
For La Mauricie, the SPOT 5 and Landsat datasets were used to assess the health of
vegetation within the park. Vegetation and canopy density can be affected by a number of
factors ranging from natural processes, disease and insect infestation to human use.
Visualizing earth observation data in 3D can help communicate the utility of such data for
monitoring and identification areas undergoing variations in ecological integrity. For
example, using colour composites and the normalized differenced vegetation index (NDVI) in
La Mauricie, we were able to clearly delineate areas with abnormal vegetation characteristics
(Figure 8).
CONCLUSIONS
In general for both parks, the key environmental values and processes were visualized. The
fact that the scientific visualization is gaining popularity among various governmental
agencies such as Parks Canada has offered a unique opportunity to test the relevance of
animation as a means of effective communication of earth observation science. Given the
advances in the geovisualization and CPU power it is necessary to utilize these tools for better
understanding of the systems and process in the nature. Visualization of the scientific data
offers a new, dynamic dimension in the search for the patterns and relationships among the
data. In addition, communication of the results to the scientist from other, non-related fields,
policy makers, share and stake holder or simple, general public was simplified and presented
in a visually appealing display that enables fast identification of the processes of interest in
these unique ecological surroundings.
Appendix 4 Canadian Institute for Geomatics 2005 Conference Paper
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a)
b)
c)
d)
e)
Figure 8 a) 2004 SPOT 5 false colour composite of campground near entry of parkway in La Mauricie
National Park. The red area indicates smaller spectral response to vegetation structure/canopy; b) NDVI
derived from SPOT 5 image where dark colours indicate less green vegetation; c) 1999 Landsat TM NDVI
for same region illustrating darker colours around campground. Note that the SPOT 5 and Landsat
derived NDVI are not radiometrically equivalent so the intensities are not directly comparable; This area
of the park has had problems with spruce budworm infestations over the past years. d) Same as a) but
illustrating an area razed in 2003; e) same as d) but for 1999 Landsat true colour.
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