Journal of Coastal Research
SI 56
1464 - 1468
ICS2009 (Proceedings)
Portugal
ISSN 0749-0258
Semiautomatic Detection and Validation of Geomorphic Seafloor
Features Using Laser Airborne Depth Sounding (LADS)
V. Achatz†, C. W. Finkl‡ and G. Paulus∞
†School of Geoinformation
‡ Coastal Planning & Engineering Inc.
University of Applied Sciences, Villach, Boca Raton, FL 33431, U.S.A.
9500, Austria
[email protected]
[email protected]
∞School of Geoinformation
University of Applied Sciences, Villach,
9500, Austria
[email protected]
ABSTRACT
ACHATZ, V., FINKL, C. W. and PAULUS, G., 2009. Semiautomatic Detection and Validation of Geomorphic
Seafloor Features Using Laser Airborne Depth Sounding (LADS). Journal of Coastal Research, SI 56
(Proceedings of the 10th International Coastal Symposium), 1464 – 1468. Lisbon, Portugal, ISSN 0749-0258.
The paper is based on the diploma thesis of Achatz (2008) and deals with the development of a method that
provides semiautomatic detection and validation of geomorphic seafloor features using Laser Airborne Depth
Sounding (LADS) Bathymetry. A Digital Elevation Model (DEM) is derived from the LADS digital data files.
Geomorphic features are detected using standard terrain analysis attributes such as slope, aspect and curvature
from the Open Source Software SAGA GIS, a product of the Göttingen University and Scilands GmbH
Göttingen. Equations that combine the different topographic attributes are set up to define the individual
geomorphic seafloor features based on their topographic character. A geomorphic map of the seafloor is created
by incorporating the resulting individual geomorphic features. The map so produced is compared with expert
interpretations of Finkl et al. (2008) to validate these findings. This cartographic interpretation is performed in
the same study area and provides necessary information on the spatial location of each geomorphic feature.
Based on this comparison, the hypothesis of the thesis, which states that it is possible to define a classification
system to semiautomatically detect geomorphic features of the seafloor, is approved. Geomorphic features can be
uniquely detected in the study area by using the topographic equations and restrictions represented in the
developed classification scheme. For this analysis, areas of the continental shelf offshore Palm Beach and
Miami-Dade counties along the southeast coast of Florida are chosen to serve as its study areas. In sum, the
semiautomatic approach described in this paper is an alternative solution that complements manual expert
interpretation. It is recommended to incorporate the classification process as part of expert interpretation
procedures. The interpretation and visualization process is facilitated and enhanced by using the equations. Time
and thus costs can be saved in this way.
ADITIONAL INDEX WORDS: Geoinformation, Geomorphology, Classification, Seafloor, Mapping
INTRODUCTION
The goal of science is to discover universal truths in the
universe and to use this knowledge to protect on the one hand the
environment and on the other hand to improve the quality of
human life. Our environment hides a rich variety of forms that has
to be discovered, not only on land, but also under water. To study
these features accurate maps are essential, regardless of the
purpose. Nowadays there are several possibilities to produce such
maps by using geographic information systems (GIS). The
opportunity to use satellite or aerial photographs with high
accuracy facilitates the interpretation of landforms and the
creation of base maps.
When it comes to the marine environment, scientists have to
consider water depth and therefore, conventional remote sensing
methods mentioned before are not useful due to their depth
limitations (attenuation of energy pulses in the water column).
Laser Airborne Depth Sounding Bathymetry is a new survey
method that is applicable in water depths ranging to about 50-55m
in clear coastal waters. Using this method, it is possible to collect
depth information on a small pixel size (2m by 4m grid) and to
represent it in the form of a continuous bottom image. Taking a
closer look on these shaded topographic maps, geomorphic
features like sand flats, coral reefs or channels can be detected
(Finkl et al., 2005). Several approaches focusing on the
interpretation of the seafloor exist including visual interpretation,
interpretation based on seismic data, etc. Detecting features based
on their topographic character was already performed on land and
hides great potential.
Elaborating on the time involved in such interpretations
performed by marine experts, the idea of developing a
classification system that uniquely defines and locates geomorphic
features is desirable. Creating such a system to supplement the
human interpretation process, would reduce time, efforts and
costs. Furthermore, the creation of geomorphic maps can be
relieved as well as the observation of changes taking place,
focusing on the seafloor, over a specific time period.
Elaborating on the fact that 70% of the earth’s surface consists
of water and that the coast lines of ocean margins extend to
855.038km, the importance of such a detection systems, to
automatically define and map the seafloor arises (Finkl et al.,
2004).
METHODS
To detect marine geomorphic features, a DEM serves as the
basis for the research (see Figure 1). Using the Open Source
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Semiautomatic Detection and Validation of Geomorphic Seafloor Features Using Laser Airborne Depth Sounding (LADS)
Software SAGA GIS, a product of the Göttingen University and
Scilands GmbH Göttingen, the survey area is analyzed based on
morphologic criteria. Here the surface derivatives, describing the
primary topographic attributes and residual analysis, representing
attributes dealing with the relative position, are differentiated.
Figure 1: Conceptual Model displaying topographic attributes
derived from the DEM to detect natural geomorphic features of
the seafloor and to provide a basis for the final classification.
The selection which topographic attributes are used to detect the
geomorphic features during the analysis process is defined in the
characterization phase. Here parameters are set up for each single
feature based on the research of Finkl et al. (2008) along the
southeast coast of Florida, a special purpose classification derived
from this study and cross-sections of the seafloor features. The
research of Finkl et al. (2008) is a visual cartographic approach
that divides the seafloor into geomorphic units based on their
geomorphic type and represents the areas of interest as a map in
analogue and digital form. These areas of interest or rather their
spatial location form the basis for the topographic analysis and
establish the basis for the final classification and comparison.
Furthermore, a special purpose classification derived from the
research of Finkl et al. (2008) is incorporated to set up the
classification scheme. In combination with sketchy cross-sections
of the geomorphic seafloor features, general statements on the
features to be interpreted can be made. Resultant, a preselection of
the topographic attributes for each individual geomorphic feature
is established.
If the areas that are subjects of the analysis process are defined
the geomorphic features are parameterized. In this phase the
redefined topographic attributes, like slope, aspect, elevation
percentile, curvature and value range, are preformed on the
elevation model. To differentiate the geomorphic features of the
seafloor, the topographic attributes are combined to uniquely
define the individual features. To receive the value range of each
topographic attribute, the topographic analysis are performed on
the whole area and are then limited according to each single
feature.
In the ensuing classification phase, equations containing
destrictions of the topographic attributes are set up to define each
single geomorphic feature of the seafloor based on its determined
topographic character. These equations are then brought into a
uniform classification scheme.
Finally, a geomorphic map is created by vectorizing the
resulting areas. The maps as well as the single polygonal and
linear features are then compared to the ones gained through the
interpretation of an expert (Finkl et al., 2008). By performing the
final comparison the hypotheses of this research is discussed.
Cartographic Seafloor Interpretation
An interpretation of the seafloor along the southeastern of
Florida was done by Finkl et al. (2004; 2005; 2008). In the most
recent study from 2008, mapping units for the areas reaching from
Palm Beach County down to Miami-Dade County were defined,
classified and mapped as well as a common symbolization for
these features was developed. The bases for the interpretation
were images derived from the same LADS survey used for the
research in this research. The only difference is that the
interpretation by Finkl et. al (2008), was done based on 2D
images, whether the detection of the marine geomorphic features
in this research is based on an elevation model or rather 3D
representation of the terrain.
By analyzing the areas derived from this research, topographic
analyses are assigned to the single geomorphic features. Briefly,
this research is used as evidence that the features to be detected
really occur in these areas. This study is also part of the final
comparison. The resulting map of this research is opposed to the
maps created by Finkl et al. (2008). By performing this
comparison differences in the arrangement of the detected features
can be recognized.
Special Purpose Classification
Derived from the interpretation of Finkl et. al (2008) a special
purpose classification was created. This scheme provides
descriptive information on the marine geomorphic features
including their form and characteristics. The classification scheme
that results of the research performed in this research is based on
this generated special purpose classification. It uses parts of the
definitions as additional information to describe the geomorphic
features and to support the semiautomatic approach. Elaborating
for example on a “borrow area”, the classification provides
information that it is a “dredged anthropogenic depression” in the
seafloor and due to this fact it suggests that the borrow area has to
have a clear break in slope. The descriptions listed in this
classification scheme are derived from Huggett (2003), Finkl
(2004) and Bird (2000).
Calculation of geomorphic features based on
their morphology
Potential geomorphic features of the seafloor are calculated
using morphological criteria. These topographic analyses are
performed based on a DEM with a resolution of 5m.
Hereby combinations of the topographic attributes are used to
define each single seafloor feature. To receive the value range of
each topographic attribute, the topographic analysis are performed
on the whole study area and are then limited according to each
single seafloor feature. Due to visual detection, the results of the
individual analysis are combined and reclassified until a
preferably accurate detection of each feature is achieved.
Hierarchical approaches for interpretation
To define the marine geomorphic features that are detected and
extracted from the DEM, a closer look on the survey areas is
taken. For this research only a selection of the geomorphic
features that occur in the study are chosen and are therefore the
main subjects of the classification and interpretation. The
descriptions of the features are provided by the classification
scheme, the research of Finkl et al. (2008), information derived
from sketchy cross-sections of the seafloor and on personal
expertise of the survey area. These features are structured and
detected based on their form by using the top down approach,
which is performed by detecting rough structures first and
continues by going into more detail and the bottom up approach
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Achatz et al.
which starts by analyzing edges and building up the seafloor and
its features based on these structures.
Characterization
The characterization deals with the assignment of parameters to
each single geomorphic feature. The parameters are of
topographic origin and are set up to provide the analysis
performed on the DEM of the southeastern coast of Florida.
Geomorphic Seafloor Feature Detection and
Validation
The deductive interpretation of the seafloor is performed based
on a DEM derived from a LADS survey in the survey areas of
Palm Beach and Miami-Dade. Hereby a resolution of 5, 10, 15 and
20m is used. The characterization of the geomorphic features is
described above forms the basis for the interpretation process.
Topographic attributes are used for the calculation and
morphological analysis of the individual seafloor features.
To identify the geomorphic features the method of Zevenberg
and Throne (1987) is used considering finite differences. The
output of slope and aspect is given in radians. Due to the fact that
radians are not very useful for the interpretation process and no
classification is possible, degrees should be used to express slope
and aspect. To change the values from radians to degrees, the
depth values are multiplied with 57.2958 (180/PI).
Curvature is also calculated using the method of Zevenberg and
Throne (1987). Resulting positive values describes convex
curvature, whether negative values describe concave curvature.
Flats or rather terrain with light curvature are defined by zero.
Hereby we speak of quantitative descriptions. To receive
qualitative information on the curvature in SAGA, the curvature
classification can be used. This method eases the detection of plan
curved areas whether the quantitative analysis provide useful
information on concave and convex forms. To determine the
elevation range and the elevation percentile they are computed in
SAGA GIS using a defined radius of 5 cells for the calculation
window. The area covered by this radius equates 25m on a DEM
with 5m resolution.
The detection of the geomorphic features is performed in SAGA
GIS using the “Local Morphometry” and “Geostatistic” Module
for Grids. Therefore, all default adjustments of the program were
maintained.
The results of the individual topographic analysis are combined
to achieve the final equation to uniquely define the geomorphic
features in a topographic way. These combinations are performed
using the GRID Calculator provided in SAGA GIS. Within this
function mathematical operations can be performed on one or
several grids. To achieve the desired result, the results of the
individual topographic attributes are loaded within the calculator,
limited using Boolean Functions like greater than (gt), less than
(lt) or equal (eq) and are combined using simple Addition (+) or
Subtraction (). To set up the equations resulting of the analysis
process and to classify the geomorphic features of the seafloor
local (<,>) as well as Boolean (AND, OR) operators are used.
As mentioned above, the detection process is performed on a
DEM using different resolutions. The original DEM has a
resolution of 5m and was resampled using a BSpline interpolation
to reduce the grid on a resolution of 10, 15 and 20m. The detection
process remains the same for all resolutions and is performed for
comparison reasons. The results or rather the differences in the
detection process are discussed in the discussion below.
Vectorization of seafloor features
To provide a basis for the final comparison a geomorphic map
of the seafloor had to be created within ESRI’s ArcGIS. To create
this geomorphic map of the seafloor the individual seafloor
features have to be transferred into a shape file format or rather
vector format, so that they can be represented and edited in
ArcGIS. To create these maps the individual shape files are loaded
within ArcGIS and colors are applied to them for visual purposes.
The validation and comparison of the final maps are illustrated
and discussed in the following sections.
RESULTS
Incorporating the methodology described in the previous
sections geomorphic features of the seafloor were detected based
on a DEM using predefined topographic attributes, vectorized and
displayed in form of geomorphic maps for the study areas of
Miami-Dade and Palm Beach along the southeast coast of Florida.
The outcomes of the individual analysis lead to the generated
classification scheme displayed in Table 1. This classification
scheme uniquely defines the main geomorphic features occurring
in the study areas.
Table1: Classification Scheme
Figure 2: Geomorphic Map representing the automatically
detected geomorphic features that occur in the study area of the
Port of Miami.
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Semiautomatic Detection and Validation of Geomorphic Seafloor Features Using Laser Airborne Depth Sounding (LADS)
Furthermore the resulting geomorphic maps at a scale of 1:30000,
displayed in Figure 2, were produced to visualize the detected
geomorphic features.
As described in the legend of Figure 3 nine detected
geomorphic features are represented within the map of the study
area of Miami-Dade. In between the individual features white
areas are shown. These areas represent features that were not
subject of the topographic analysis performed.
Selectivity of detected features
To identify overlaps of the individual shapes or rather polygons,
the vectorized features are analyzed (see Figure 3). To achieve this
result, the individual grids are added in the grid calculator. Taking
a closer look on each grid, cells with a grid value of 1 represent
the geomorphic feature. The grid value 0 describes the areas
where no feature occurs. By adding the individual grids, the cells
with a value greater than 1 are characterized as overlapping areas.
Briefly, cells that hold more than one feature are distinguished as
overlaps.
Within the study area 20 % of the detected features overlap each
other. These overlaps occur due to interpretational reasons. All in
all, these overlaps have to be considered by creating the final
geomorphic map. Taking an overall look at the maps, most of the
overlapping areas are quite small and are therefore, not of great
significance for the final map. 70% of the overlaying areas are
arbitrarily and independently located of each other within the
survey area and represent less than 10% of the actual geomorphic
feature they belong to. Another 24% cover less than 30% of the
mapping unit they are assigned to.
Figure 4: Geomorphic maps of the seafloor derived from (1)
topographic analysis and (2) visual expert classification of Finkl et
al. (2008) in the study area of Miami-Dade
Figure 5: Overlap of detected seafloor features with the
interpretation of Finkl et al. (2008) in the study area of MiamiDade
DISCUSSION
Figure 3: Overlay of all detected seafloor features within the study
areas of (1) Miami-Dade and (2) Palm Beach
Comparison with expert interpretation
As mentioned above, an interpretation of the seafloor along the
southeast coast of Florida was already performed by Finkl et al.
(2004;2005;2008) which provides an ideal basis for a comparison.
The comparison is done in two ways. First, the resulting
geomorphic map is compared with the one created by Finkl et al.
(2008) and is analyzed visually (see Figure 4). Afterwards, the
single features are compared overlaying the interpretation of the
expert with the one achieved in this research (see Figure 5).
This comparison highlights that the results gained in the
topographic analysis are close to the expert interpretation and the
real geomorphic situation of the seafloor. Facing the reality, the
interpretation is maybe closer to the real situation than the visual
interpretation due to the fact that flat areas in between the
geomorphic features are hardly visually recognizable. Therefore,
the approach focused in this research is suggestive and can be
used to support experts within their interpretations.
Finally, the results presented above as well as problems that are
detected during the topographic analysis will be discussed in this
part of the paper. Additionally, a closer look is taken on the need
and use of the approach to set up a semiautomatic detection
scheme. Concluding, the sensitivity of the approach is discussed.
During the detection process several problems occurred.
Especially in the detection of coral reefs and dredged spoil no
absolutely unique result is achieved. The reason for this detection
problematic lies on the one hand in the local conditions of the
survey area and on the other hand on the variety in form of the
individual features. Taking a closer look on the seafloor in this
area, the elevation range of the different coral forms does not
differ much. Furthermore looking at the curvature of the
individual coral forms, a change of convex and concave forms is
located in such a short distance that it is not detectably as one unit
performing the topographic attributes provided in SAGA GIS. To
solve this detection problematic different resolutions (5, 10, 15,
20m) were chosen, whether no diverging result could be achieved.
Therefore, no unique solution is found in this research for the
detection of coral reefs. Dealing with the dredged spoil a similar
issue arises, whether hereby the spoil itself is the problem. This
seafloor feature is made by humans and differs therefore in shape
and height. Analyzing the DEM no equation was found to define
the spoil in a unique way. Combinations of convex and concave
forms all over the survey area where detected owning the
topographic attributes defined. Within this research one way is
found to differentiate the spoil from the other forms its roundish
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Achatz et al.
shape. This method may not apply for all forms of dredged spoil.
Hereby it must be said that this problem has to be faced dealing
with almost all of the anthropogenic features of the seafloor, for
example the pipeline. The pipeline has a unique form. It is a
roundish shaped pipe running straight through the sand flat.
Laying the pipeline it is partly dredged into the sand flat. Due to
natural processes like tides or currents it occurs that sand is
deposited on the pipeline and that the pipeline thereby vanishes
under the sand shield. If this happens the unique form can not be
detected any more performing a LADS study. Also the detection
of the pipeline is solved in this research giving additional
information on the shape of the feature to ease its visual detection
looking at the results. Discussing the detection process it has to be
mentioned that the bigger part of the features that are detected in
this research can be defined uniquely. According to this result the
hypotheses of this research can be approved. Although some
features can not be uniquely detected using the defined
topographic equations, they can be partly extracted from the
results regarding the restrictions added in the classification
scheme. In addition to that, the need of setting up a classification
scheme has to be critically discussed. Geomorphic features are in
general approximations of the moment and therefore they are
rapidly changing. In addition forms that occur at the coast of
Florida do not have to be part of the seafloor structure of Australia
or Spain. Therefore, it is not possible to set up a unique
classification system. The system has to be individually adapted to
each single region (Huggett et al., 2003; Bird, 2000). The need of
such classification schemes is of great importance, especially for
research purposes. Changes of the arrangement of geomorphic
features at the seafloor as well as special forms influenced by
geological processes, like fracture zones or channel systems, can
be detected using such interpretations over longand shortterm
periods, to mention some of its needs.
To focus on the need of the topographic equations defined in
this research, the attention has to be turned on the time that is used
to interpret and digitize the individual geomorphic forms. In
general, digitizing involves a lot of time and has to be often
reworked due to the fact that experts do interpret the boarders of
the individual geomorphic features in the same way. It is clear that
such interpretation frictions can not be prevent with any
classification system or approach due to the fact that this is
personal reasoning, but performing the equations set up in this
research can facilitate the overall interpretation process. A general
overview of the study area can be given using these topographic
combinations. The editing and bordering done afterwards, is left to
one’s own device.
Another point that has to be discussed is the degree of sensitivity
of the approach developed. In general the resolution sustainable
influences the results and efficiency of the derivation of the
topographic attributes (Wilson et al., 2000). Therefore, the
selection of the right resolution is of great importance. If the
resolution is too low, small features could vanish, whereas too
much information can be achieved using too high resolution. The
characterization in this research is emerged from a DEM with a
resolution of 5m and therefore, the achieved results are dependent
on it. The defined topographic analyses were also tested on DEMs
with 10, 15 and 20m resolution and close detections were reached.
Performing the analysis on a lower resolution than 20 resulted in a
generalized view of the seafloor. Neither the channel nor the bars
were uniquely detected. As a conclusion, it must be said that the
classification scheme defined in this research is a semiautomatic
approach to detect geomorphic features of the seafloor and is
intended to support the expert during the interpretation process.
CONCLUSION
The research described in this paper deals with the development
of a method to semi-automatically detect marine geomorphic
features using the Open Source Software SAGA GIS.
In this approach the study areas of Miami-Dade and Palm Beach
were analyzed based on morphological criteria using a DEM with
a resolution of 5m that served as the research’s basis. The
detection of the features was carried out by the classification of
the surface according to slope, aspect and curvature and further
combinations of the topographic attributes. According to the
results of the topographic analysis, equations including
topographic attributes defining the single features were set up,
combined and structured within a classification system.
The semiautomatic approach developed in this research is an
alternative solution that complements manual expert
interpretation. It is recommended to incorporate the classification
process as part of expert interpretation procedures. The
interpretation and visualization process is facilitated and enhanced
by using the equations. Time and thus costs can be saved in this
way.
LITERATURE CITED
ACHATZ, V.(2008):"Semiautomatic Detection and Validation of
Geomorphic Seafloor Features Using Laser Airborne Depth
Sounding (LADS)", 98 p., Villach (Austria)
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John Wiley & Sons Ltd., West Sussex
FINKL, C.W . & ACHATZ, V. & ESTEBANELL, BECERRA J. &
ANDREWS, J.L. (2008): “Geomorphological Mapping along
the Upper Southeast Florida Atlantic Continental Platform:
Mapping Units, Symbolization and GIS Presentation of
Interpreted Seafloor Topography”, Proc. of AAG Meeting
Boston, pp. 291
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system”, Journal of Coastal Research, 20(1), pp. 166–213,
West Palm Beach (Florida)
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