Alaska Statewide Digital Mapping Initiative
Imagery Workshop Whitepaper
www.alaskamapped.org
Report produced for:
Alaska Department of Natural
Resources
ASP 10-07-075
June 2, 2009
Final Version V.2.3.1
Prepared by:
i-cubed - information, integration, and imaging LLC
Russ Cowart, Jill Mamini, Selima Siddiqui
Contributions by:
Michael Baker Jr., Inc. - Charlie Barnwell
Dewberry - Dave Maune
SDMI Imagery Workshop Whitepaper
Contents
Project Sponsors ........................................................................................................................................... 6
Executive Summary....................................................................................................................................... 6
Recommendations ...................................................................................................................................... 14
Introduction ................................................................................................................................................ 16
Alaska Imagery Status ................................................................................................................................. 19
Historical Orthoimagery .......................................................................................................................... 19
National Imagery Programs .................................................................................................................... 20
USGS High Resolution Orthoimagery .................................................................................................. 21
National Agriculture Imagery Program - NAIP .................................................................................... 22
Imagery For The Nation (IFTN) – Alaska ............................................................................................. 23
SDMI Imagery Requirements ...................................................................................................................... 24
Breadth of the SDMI User Survey ........................................................................................................... 25
Application Requirements ...................................................................................................................... 27
Land Management .............................................................................................................................. 28
Land Cover .......................................................................................................................................... 33
Environmental Analysis & Mapping .................................................................................................... 37
Natural Resource Inventory ................................................................................................................ 39
Transportation Planning & Engineering .............................................................................................. 42
Utilities and Infrastructure.................................................................................................................. 48
Public Safety and Military Mapping .................................................................................................... 49
User Requirements ................................................................................................................................. 51
Geographic Coverage .......................................................................................................................... 53
Spatial Resolution ............................................................................................................................... 55
Spectral Properties.............................................................................................................................. 57
Geometric Accuracy ............................................................................................................................ 59
Terrain Source ..................................................................................................................................... 59
Cloud Cover Restrictions ..................................................................................................................... 60
Temporal, Seasonal, and Update Requirements ................................................................................ 60
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SDMI Imagery Workshop Whitepaper
Sensor Platform................................................................................................................................... 61
Dynamic Range.................................................................................................................................... 61
Datum, Geoid Model and Projection Requirements .......................................................................... 62
Requirements for Data Format, Delivery and File Storage ................................................................. 62
Metadata............................................................................................................................................. 63
Licensing.............................................................................................................................................. 63
Summary of SDMI Imagery Requirements ............................................................................................. 63
Display Scale vs. Accuracy Scale.................................................................................................................. 67
Orthorectification Considerations .............................................................................................................. 69
Overall ortho-positional error................................................................................................................. 72
Horizontal Control Considerations.............................................................................................................. 75
Block Bundle Adjustments ...................................................................................................................... 75
Control Sources Statewide ...................................................................................................................... 75
Alaska DEM Analysis ................................................................................................................................... 77
DTM vs. DSM for Terrain Modeling ........................................................................................................ 77
Image Artifacts Caused by Terrain Artifacts ........................................................................................... 78
Slope Analysis.......................................................................................................................................... 79
Terrain Options & Availability ................................................................................................................. 80
Advantages & Disadvantages of Competing Technologies ......................................................................... 82
Panchromatic vs. Mutli-spectral Imagery ............................................................................................... 82
Aerial vs. Satellite Imagery ...................................................................................................................... 82
Optical vs. Radar Imagery ....................................................................................................................... 83
Imagery Workshop – Technology Options Presented ................................................................................ 84
Questions Posed ..................................................................................................................................... 84
Vendor Responses................................................................................................................................... 86
Digital Globe........................................................................................................................................ 86
GeoEye ................................................................................................................................................ 90
SPOT .................................................................................................................................................... 92
RapidEye.............................................................................................................................................. 94
ALOS .................................................................................................................................................... 95
Vendor Comparison ................................................................................................................................ 95
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SDMI Imagery Workshop Whitepaper
Spatial Resolution ............................................................................................................................... 95
Spectral ............................................................................................................................................... 97
Accuracy .............................................................................................................................................. 98
Distribution of Ground Control ......................................................................................................... 104
Acquisition ........................................................................................................................................ 104
Sensor Lifespan ................................................................................................................................. 108
Conclusions ............................................................................................................................................... 109
Discussion of Specific Options .............................................................................................................. 114
Digital Elevation Model ..................................................................................................................... 114
Imagery ............................................................................................................................................. 116
References ................................................................................................................................................ 119
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SDMI Imagery Workshop Whitepaper
LIST OF ACRONYMS AND ABBREVIATIONS
AGDC
Alaska Geographic Data Committee
AHAP
Alaska High-Altitude Aerial Photography Program
ANCSA
Alaska Native Claims Settlement Act
ADNR
Alaska Department of Natural Resources
AGPS
Airborne Global Positioning Systems
ASPRS
American Society for Photogrammetry and Remote Sensing
BLM
Bureau of Land Management
CBJ
City & Borough of Juneau
CIR
Color Infra-Red (imagery)
COE
Corps of Engineers (US Army)
CORS
Continuously Operating Reference Stations
COTS
Commercial Off The Shelf (in reference to software)
DCCED
Department of Commerce, Community and Economic Development
DEC
(Alaska) Department of Environmental Conservation
DEM
Digital Elevation Model
DF&G
(Alaska) Department of Fish and Game
DGGS
(Alaska) ADNR-Division of Geological and Geophysical Surveys
DGPS
Differential Global Positioning System
DOC
DMVA
DOD
DML&W
DOG
DOT&PF
U.S. Department of Commerce
Alaska Division of Military and Veterans Affairs
U.S. Department of Defense
(Alaska) ADNR-Division of Mining Land and Water
(Alaska) ADNR-Division of Oil and Gas
(Alaska) Department of Transportation and Public Facilities.
DRG
Digital Raster Graph is a scanned image of a USGS topographic map
DSM
Digital Surface Model (not bare earth)
DTED
Digital Terrain Elevation Data
DTM
Digital Terrain Model (of the bare earth)
FAA
Federal Aviation Administration
FHWA
Federal Highway Administration
FNSB
Fairbanks North Star Borough
GCP
GINA
Ground Control Point
Geographic Information Network of Alaska, part of UAF
GIS
Geographic Information System
GPS
Global Positioning System
GRS
ground receiving station, used to collect satellite data
GSA
General Services Administration
IAP
Instrument Approach Procedure
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SDMI Imagery Workshop Whitepaper
ICAO
International Civil Aviation Organization
IFTN
Imagery for the Nation, a pending federal initiative for ortho-imagery
KPB
Kenai Peninsula Borough
KGB
Ketchikan Gateway Borough
MOA
Municipality of Anchorage
MS
NAIP
NDGPS
Multi-Spectral imagery
National Agriculture Imagery Program
Nationwide Differential Global Positioning System
NED
National Elevation Dataset
NGA
National Geospatial-Intelligence Agency
NMAS
National Mapping Accuracy Standards
NOAA
National Oceanic and Atmospheric Administration
NPS
National Park Service
NRI
Natural Resource Inventory
NRCS
NSB
Natural Resources Conservation Service
North Slope Borough
NSGIC
National States Geographic Information Council
NSSDA
National Standard for Spatial Data Accuracy
NTSB
National Transportation Safety Board
NWI
National Wetlands Inventory of USFWS
OHMP
(Alaska) Office of Habitat Management and Permitting
PAN
Panchromatic (black & white) imagery
PSM
Pan-Sharpened Multi-spectral imagery
RFP
Request For Proposal
RMSE
Root Mean Squared Error
ROM
Rough Order of Magnitude
RSS
Root Sum Squared
SDMI
Statewide Digital Mapping Initiative
TIN
Triangular irregular network: elevation points networked for elevation surface creation
UAF
University of Alaska, Fairbanks
URISA
Urban Regional Information & Systems Association
USCG
U.S. Coast Guard
USDA
U.S. Department of Agriculture
USFS
U.S. Forest Service
USFWS
USGS
VFR
U.S. Fish and Wildlife Service
U.S. Geological Survey
Visual Flight Rules
WAAS
Wide-Area Augmentation System
WRST
Wrangell-St. Elias National Park
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SDMI Imagery Workshop Whitepaper
Project Sponsors
This whitepaper is sponsored by the Alaska Statewide Digital Mapping Initiative (SDMI). Digital copies
of this whitepaper, and presentations from the SDMI sponsored Imagery Workshop, can be found online
at http://www.alaskamapped.org. The focus of the Alaska SDMI is on obtaining imagery and a DEM to
support 1:24,000 scale ortho-image production for a new statewide base map. The SDMI is looking for
near-term solutions, to meet the immediate needs of the Alaskan Mapping Community. While a nearterm solution is being sought, the SDMI is aware that new technologies/products will enable
improvements to the base map in the long term. With that in mind, the Alaska SDMI aims to create a
sustainable program that takes advantage of, and delivers, those improvements to the user community.
The Alaska SDMI is a cooperative state program endorsed by the Governor and implemented by the
Department of Natural Resources, Department of Military and Veteran’s Affairs and the University of
Alaska. This work was supported by the above-listed sponsors and performed under ADNR Contract #
10-07-075 issued to HDR Alaska on July 17, 2007.
Executive Summary
The goal of this part of the SDMI study is to provide decision makers with the information and tools to
help balance the multiple user requirements against available content sources and processing
approaches available in the vendor community, and thus refine the Request for Proposal (RFP)
specifications to most cost-effectively meet the priorities as determined by the SDMI Executive
Committee.
The opening presentation at the workshop communicated the SDMI priorities and specifications. The
top goal for the workshop was to seek consensus on a statewide orthoimagery specification. This aligns
with the strategic goals published at the beginning of the program in 2007:
1) CREATE A BASE MAP OF ALASKA: Acquire imagery and digital elevation data necessary to meet
specifications, generate ortho-imagery and DEMs, and assess accuracy of final products.
2) CREATE THE ARCHIVE: Develop the project infrastructure to warehouse, archive and make
products available to the public.
3) LIFECYCLE MANAGEMENT OF THE BASE MAP DATA: Provide ongoing management of the base
map data.
The first goal – creating the base map – is the current focus of the SDMI management team and this
whitepaper. Previously, the SDMI co-sponsored a Digital Elevation Model (DEM) workshop and
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SDMI Imagery Workshop Whitepaper
whitepaper that outlines sources and solutions for the DEM part of the base map. The imagery
workshop was convened to confirm or modify the imagery specifications as determined from the user
survey. As interpreted from the user surveys and the imagery conference, the following are the updated
Statewide Specifications:
•
•
Requirements:
–
Statewide coverage (95% coverage, allowing for some areas that may experience
persistent cloud cover that cannot be captured by an optical sensor)
–
5.0 meter ortho-image pixel resolution or better for statewide coverage
–
Higher resolution (1-5m) for large parts of the state to cover the needs of the statewide
user community.
–
Ability to collect the state in 3-5 years or less (leaf-on, snow free, <10% cloud cover)
–
Multi-spectral optical, including an infrared band for many applications
–
Create ortho-image map products of 1:24,000 or larger, meeting National Map Accuracy
Standards (NMAS)
–
At a minimum, initial public agency use of full dataset by all State, Local, Academic,
Native, and non-DoD Federal Agencies; add license uplift option for: DoD use; Internet
access to full resolution, georeferenced files and streams (including Web Mapping
Services (WMS) of ortho-image lossy-compressed (e.g. jpeg, MrSID) mosaics for
unrestricted public use; eventual (if not immediate) uplift for unrestricted public use of
full dataset (would consider non-resale restriction).
–
Data managed, maintained, and distributed via open standards protocols by central
SDMI data clearinghouse
Additional considerations for evaluating potential solutions:
–
Ability to monitor and provide near-real-time support to time sensitive public safety
events (fire, flood, earthquake etc.)
–
Ability to refresh on a 3-5 year cycle
–
Availability of leaf-off data for some applications (e.g. fire fuels modeling)
–
Availability of stereo data for some applications (e.g. fire, climate change)
–
Leverage co-funding opportunities with other Federal and State programs
–
Alaska job creation
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SDMI Imagery Workshop Whitepaper
Imagery Needs
Three tiers of need are identified from our analysis, and corresponding options that could satisfy
these. The following pyramid diagrams illustrate the range of applications and corresponding
imagery sources. SDMI’s focus is to deliver a broad scale statewide base map, but many users will
require moderate scale features for part of the state that result in an additional requirement.
Projects requiring detailed scale features are being and should continue to be captured through
project based funding efforts that are beyond the goals of the SDMI; but which can (and are
encouraged to) share data with the public through the SDMI infrastructure.
Figure 1 Application use cases by detail of features mapped
Figure 2 Imagery sources for feature tiers PAN = panchromatic MS=multi-spectral PSM –Pan Sharpened Multi-spectral
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SDMI Imagery Workshop Whitepaper
The spatial distribution of requirements is also a function of the application or use case. Six
primary use cases of imagery were identified in the SDMI User Survey. The following table and
diagrams illustrate the requirements if all uses are given the same weight. Sources for the maps
appear in the body of the whitepaper. For example, as shown in the table below, the Transportation
use case divides into two levels of need: a statewide and project level. Tiers best meeting the level
of need are Tier 1 for statewide, Tier 2 for major regions and many users and Tier 3 for projectbased needs.
Use Case
User Groups
Transportation
DOT&PF, Aviation
(management), FHWA, utilities
Land Management
BLM, NPS, ADNR, DCCED, Native
corporations/organizations
Tier
Statewide
2
Project based
3
Example Features
1,2
3
Land Cover
USFWS, NPS, BLM, USFS, Private
Industry
Environmental
mapping/analysis
Academia, Conservation groups,
USFWS, USFS, NPS
Public Safety
FAA, DMVA
1,2
3
1,2
3
1,2
3
Natural Resource Inventories
USGS, ADNR, USFS, Native
Corporations & Organizations,
Private Industry
1,2
2 or 3
Roads (general)
Centerlines, Airports
Parcels
Land ownership boundaries
Mining claims
Oil and Gas Leases
Land cover, e.g. LANDFIRE, NWI
Wetlands, discrete wetlands,
e.g. COE
Land cover
Hydrography, e.g. coastlines,
stream banks, water bodies
Roads, airports, ice cover,
hydrography/water-bodies,
manmade features, general
land cover
Forest/timber, geologic units,
mining exploration features,
renewable energy sites,
hydrographic (stream
networks), water resources,
Table 1 Six primary application use-cases - requirements for statewide & project based work
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Figure 3 Acquisition areas for Tier 1 - broad scale features
Figure 4 Acquisition areas for Tier 2- moderate scaled features
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SDMI Imagery Workshop Whitepaper
Figure 5 Acquisition areas for Tier 3 - detailed scaled features (many of these areas have been recently mapped by projects)
Tier 2 imagery vendor responders include SPOT and ALOS as well as higher-resolution options
such as Digital Globe’s QuickBird and the upcoming Worldview-2, and GeoEye’s IKONOS and
Geoeye-1 sensors and some aerial platforms. Although the resolutions may be greater than
required, they may still be cost competitive and should be included in any RFP. It is important
to note that for Tier 3, there is a substantial archive of existing recent imagery (2003-2008)
collected by State, Federal, and local government agencies throughout the state addressing the
needs of projects that can reused to support some Tier2 requirements. Of the vendors who
responded to the RFI those that appear to be the best fit for Tier 1 imagery options include
ALOS, RapidEye and SPOT.
Imagery Requirements
1) Tier 1 - A 1:24,000 scale image map, 2.5m – 5m resolution, multi-spectral, updated no less
often than every 3 years is a consensus requirement. Most user groups and use cases require
statewide coverage at a broad resolution. This could be met with RapidEye (5m, annual refresh,
recently operational system) and/or SPOT (2.5m, ~3yr. refresh) or ALOS (2.5m, unknown
refresh, new operational mode required). Costs have not been submitted by vendors, but a
rough order of magnitude (ROM) is $1-2M per year.
All responding satellite imagery solutions will require a DEM of DTED-2 accuracy for
orthorectification. The forthcoming (scheduled for summer 2009) ASTER G-DEM will likely be
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sufficient (with clean-up, ROM $500k), but it is unproven at this time. The low-accuracy and
mid-accuracy DEMs recommended by the author of the DEM whitepaper could both meet the
requirements for image orthorectification to 1:24,000 NMAS accuracy, provided the image and
DEM both have the horizontal accuracy required for that scale, i.e. 12.2 m CE90, and provided
the incidence angle of the imagery and the slope of the terrain do not cause excessive
displacement. The mid-accuracy product is considerably more expensive than the low-accuracy
DEM product. However, when completed (estimated 3-5 years) it will offer improved geometric
accuracy for ortho-products derived from it. In addition the mid-accuracy terrain product will
allow for greater imaging flexibility which will improve the collection capacity of source image
data.
The requirements for ground control vary by system. For example, RapidEye estimates that
approximately 1500 well distributed ground control points (GCPs) will be required statewide
whereas SPOT estimates approximately 100 GCPs if the entire state was processed at once
using Pixel Factory or other Commercial Off The Shelf (COTS) software which employs block
bundle adjustment; more points may be required if processing was done regionally. ALOS has
not yet provided an estimate but it would likely be 1000 points or more. Regardless of imagery
source, SDMI and partners will need to invest in the acquisition of additional ground control and
high-resolution and accuracy image chips (ROM $500k). Note that all responding satellite
vendors can meet NMAS 1:24,000 with reasonable look angle restrictions, a DTED-2 DEM, and
sufficient ground control. The satellite accuracy spreadsheet developed for SDMI can be used to
examine alternative collection scenarios.
2) Tier 2 - Moderate scale image maps will be required for regional 1m – 2.5m work. Some of
the needs in this tier could be met by the 2.5m solutions offered by SPOT or the ALOS
PRISM/AVNIR-2 sensors, serving both the Tier 2 and Tier 3 needs. However, some moderate
scaled image maps of a higher resolution may be needed for some applications. The new
GeoEye-1 and forthcoming Worldview-2 sensors will provide much improved collection
timeframes, better accuracy and more spectral information than earlier sensors from GeoEye
and DigitalGlobe. Statewide coverage at 0.5-m to 1m resolution would take an estimated 5+
years at a ROM cost of $10-20M (license multipliers will apply based on breadth of distribution
desired). Individual, smaller projects would obviously be less expensive.
3) Tier 3 - Large scale image maps will be required for project 0.5m – 1m work. The GeoEye and
Digital Globe sensors are capable of meeting the needs of project based work requiring higher
resolutions. Aerial collection platforms are improving both in efficiency of collection, and the
minimization of ground control required and speed of delivery of a final product. Aerial costs
vary significantly based on project parameters, but a ROM for large area National Agriculture
Imagery Program (NAIP) equivalent 1m color imagery at 1:24k NMAS would be $12-13 / sq. mi.
in the lower 48 and $55-74 / sq. mi. in Alaska (Natural Resources Conservation Service (NRCS)
actual costs from workshop presentation).
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4) Swath / footprint size matters to automated classification applications (wetland delineation,
fire fuel modeling etc.) because of spectral continuity. The larger the footprint of a nearsimultaneous acquisition, the easier it is to do consistent classification over large areas without
adjusting for spectral variation between swaths/footprints. Larger footprints typically correlate
with coarser resolution found in Tier 1 and Tier 2 solutions. While higher resolution imagery is
more accurate for visual interpretation of classes, traditional automated classification
approaches often break down with higher resolution imagery. Thus many of these applications
actually do better with 2.5m – 5m resolution image sources.
5) Orthorectification and mosaicking costs are not insignificant. SDMI must be sure to determine
if vendors are providing raw imagery or finished product as the production costs for the state
could be significant. Ortho / mosaic costs can add 20% to 100% over raw data. Key
considerations include the swath / footprint per image and stability and quality of the sensor as
it relates to required number of ground control points to achieve an ortho-image product.
8) Ground receiving stations (virtual or real) provide benefits. Virtually all vendors expressed
clear plans to either offer the establishment of a new ground receiving station (GRS), leverage
existing GRS or provide subscription offerings that provide benefits which may include (1) a
lower total cost per pixel, (2) collection priority or the ability to task the system to respond to
emergency situations, (3) fixed budget so there are no additional budget hurdles during
emergencies and so that budgets can be approved in advance, (4) lower latency or a quicker
delivery to end-users after collection (this can also be critical during an emergency, perhaps
reducing the time from days to hours), (5) the ability to gain utility from scenes with otherwise
unacceptable cloud cover (for example, a typical scene delivery specification of 10% or less
clouds means that a scene with 80% usable information would never be delivered to the enduser; however, that 80% may be perfectly usable and in fact may be the only collect opportunity
during a season where that particular area on the ground doesn’t have clouds) . A GRS offers
the ability to maximize the collect times over the highly limited windows of favorable conditions.
A ground receiving station, virtual or real, offers more flexibility and responsiveness when
meeting mapping needs for divergent user groups.
Discussion: The challenges to mapping Alaska are many - (1) the size of the state, (2) the short collection
season where the sun angle is sufficiently high and there is minimal snow cover, (3) cloud and smoke
cover, (4) the lack of a digital elevation model (DEM) suitable for orthorectification at map accuracy
scales of NMAS 1:24,000 or better, and (5) the lack of consistent, well-spaced image-identifiable ground
control points.
Project Funding: Current funding levels are adequate to issue a substantial RFP against specifications
and target requirements, but are not adequate to address all user needs identified to date. SDMI has
sufficient funds in hand to make substantial progress meeting the in-scope Tier 1 and Tier 2
requirements.
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SDMI Imagery Workshop Whitepaper
This solicitation and the planning effort developed in support of the solicitation are intended to meet
the criteria of a recognized State Ortho-imagery Program under the National Digital Orthoimagery
Program (NDOP). The RFP supports all objectives of the NDOP and remains open to further leveraging
financial support from participating federal agencies or new programs, including an Alaska component
that may develop under the Imagery for the Nation Initiative. One challenge will be to clearly establish
the requirements and evaluation criteria for vendor responses. Another will be to solicit participation
from public agencies that will directly benefit from the program, either in the opening round of
acquisition, or in future rounds as new funding and partnerships become available.
Recommendations
SDMI is a statewide effort. Funding for the near-term RFP will be from State funds. The existing data
served by SDMI was largely funded and contributed by Federal agencies. It is hoped that State and
Federal funding can continue to work together to create and maintain the new base maps for Alaska.
The goal is statewide ortho-imagery product that will be available within three years. This program
must be realistic related to funding available and an RFP should be issued in the near future to take
advantage of the 2009 collection season.
We have formulated a set of recommendations for SDMI imagery. These recommendations are based
on the following data:
Input received from the SDMI user community survey and follow-up interviews
Analysis of technical factors
A rough understanding of funding levels that might be available
Experience of the users with certain image types
The expected availability of consistent follow-on sensors of the same class
The promised, but as yet unproven nature of some of the sensors
The final accuracy specification and product quality for the Aster G-DEM
In formulating recommendations, an extensive amount of real-world experience with all kinds of
sensors, DEMs, orthorectification, visualization and classification projects has been leveraged.
Obviously, it is the role of the SDMI executive committee to prioritize applications and allocate funding,
but given the understanding at this point, the following are the recommendations for a path forward.
1)
Create a RFP for statewide coverage with a focus on Tier 1 and Tier 2 needs that meets approved
SDMI product specifications.
2)
Allow projects and agency business requirements to continue to lead Tier 3 (detailed scale) needs.
SDMI should continue to provide distribution services and license uplifts to these project data.
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3)
Consider proposals for leveraging of existing or new Ground Receiving Stations (GRS) or
subscription agreements with the satellite vendors, to gain cost and responsiveness advantages.
4)
Leverage existing contracts, funded programs, or facilities if those vehicles are ready and able to be
put to work in the very near-term.
5)
Evaluate proposals based upon cost of producing final ortho-imagery that meets SDMI accuracy
requirements. Consider vendor track records for production of final ortho-imagery as an
evaluation criterion.
6)
In the short term, use the upcoming Aster G-DEM for 1:24,000 broad scale ortho work. The Aster GDEM will offer improvements over the current National Elevation Dataset (NED). Fund statewide
cleanup and validation of this dataset in partnership with, G-DEM project sponsors, the U.S.
Geological Survey (USGS).
7)
Acquire SPOT Ref3D or Worldview or GeoEye-1 DEMs for moderate scale work where the Aster GDEM will not suffice due to accuracy, artifacts or resolution.
8)
If a mid-accuracy DEM is completed, this product should be used for all scales of work, except for
detailed level projects where some high-accuracy LIDAR DEMs may be required.
9)
Construct a control point network for statewide ortho-imagery production, using both image chips
and survey grade ground control points. Use existing detailed imagery over populated places;
collect some new high-resolution imagery; and leverage additional existing control sources. This
will benefit all potential data providers and will be a long-lived foundation for future work.
The recommendations outlined above will provide the SDMI with a pathway that is designed to be both
a short term and long term strategy for meeting critical Alaskan needs.
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Introduction
This whitepaper is the outcome of the analysis conducted by HDR Alaska and its subcontractors (i-cubed
LLC, Michael Baker Jr. Corporation) during 2008-2009, and incorporating ancillary work done by SDMI
project team members. The goal of this whitepaper is to outline imagery options available to the Alaska
SDMI that can meet statewide mapping requirements.
This whitepaper identifies the imagery requirements as identified in SDMI user needs analysis, including
stakeholder use cases that influenced the requirement decisions. It outlines imagery options, and
related issues, as presented to the Alaska SDMI during the Imagery Workshop held in Anchorage on
March 2nd and 3rd, 2009.
The conclusions and recommendations in this report are based on user needs identified through the
user needs analysis, and on extensive analysis of technical factors including resolution, spectral
characteristics, accuracy requirements, acquisition constraints, and costs.
The objective of this report is to provide the required knowledge to make an informed decision with
regards to meeting statewide ortho-imagery requirements for the broad Alaskan mapping community.
The approach is to provide the reader with details regarding the following key items:
Executive Summary: The Executive Summary aims to provide the SDMI decision makers a
starting point and context for drafting Request for Proposal (RFP) specifications.
Recommendations: This section outlines nine recommendations for achieving the SDMI goal of
statewide ortho-imagery both in the short and long term.
Alaska Imagery Status: Reviews the current state of imagery in Alaska in terms of available
imagery, and federal programs aimed at imagery acquisition, and their role within Alaska. In
general, current statewide imagery sources are either of an unsuitable resolution and accuracy
or are severely outdated. Landsat 30 meter spatial resolution data is the only current statewide
dataset; the Alaska High Altitude Aerial Photography Program produced near-statewide, nonorthorectified imagery between 1978 and 1986. Unlike the contiguous 48 states, all of which
have their imagery refreshed every three years through the NAIP program, there are currently
no active federal imagery programs acquiring statewide imagery in Alaska. However, it is hoped
that some of the goals of the Alaska Statewide Digital Mapping Initiative (SDMI) can be met by
partnering with Imagery for the Nation (IFTN).
SDMI Imagery Requirements: Covers statewide imagery requirements from a user based
approach. This information is compiled from the SDMI User Survey and extensive follow up
interviews with the Alaskan mapping community. This approach is aimed at allowing user
requirements to drive the proposed solutions. From this analysis the SDMI has been able to
draw major conclusions about the mapping communities need for specific:
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SDMI Imagery Workshop Whitepaper
o
o
o
o
Spatial resolution
Spectral characteristics
Geometric accuracy
Refresh requirements
In general the SDMI has confirmed the need for statewide imagery that is:
o
o
o
o
capable of meeting resolution requirements for broad and moderate scale features
multi-spectral, with good spectral continuity over large areas for analysis purposes
can achieve a geometric accuracy of 1:24,000 NMAS scale
can be acquired in a time-frame to satisfy 3-5 year refresh requirements
The user based approach identified the need for higher resolution imagery that can achieve
higher geometric accuracies, and may require annual refresh rates. However, these
requirements are necessary for specific applications, and limited areas within Alaska, and do not
constitute statewide needs.
Technical Overview: This report aims to document the technical considerations involved in
analyzing proposed solutions. The following discussions are intended to add technical insight
into how solutions are evaluated, but they do not provide any direct analysis of proposed
solutions. For direct analysis of solutions, please refer to the Imagery Workshop section of this
document. The technical overview breaks down into 5 main discussions:
o
Display Scale vs. Accuracy Scale – Scale is often used to refer to two different concepts.
This section aims to add clarity between scale as a function of display and when it is
used to refer to geometric accuracy.
o
Orthorectification Considerations - This section will help inform decision makers of the
finer details involved in determining if a proposed solution will be able to meet the
accuracy requirements of the Alaska mapping community. It introduces the concept
that ortho-positional accuracy is determined by three major components:



Satellite Accuracy
Terrain Accuracy
Horizontal Control Accuracy
It is not necessary for decision makers to understand every aspect of this section. It has
been provided to add technical insight for those who would like it. The achievable
geometric accuracy of vendor proposed solution is documented in the vendor
comparison portion of the Imagery Workshop section of this document.
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o
Horizontal Control Considerations – While horizontal control requirements were covered
in the previously published SDMI Control Requirements Report, this section looks at
some new information that was discovered during the Imagery Workshop including:


o
Vertical Control Considerations – Alaska DEM Analysis. This discussion aims to address
terrain issues as they relate specifically to use as a control element in producing orthoimagery. It includes:




o
The concept of block bundle adjustment for reduced control requirements
Identification of some potential new sources of control
Terrain requirements to meet 1:24,000 NMAS geometric accuracies
DTM vs. DSM for use in creating ortho-products
Image artifacts caused by terrain artifacts
Terrain options and availability
Advantages & Disadvantages of Competing Technologies – This section focuses on some
of the main user requirements, and how different technologies are able to meet those
needs. It gives some general technical insight into why the SDMI is focused on an
optical, multi-spectral, satellite based solution for meeting statewide mapping needs.
Imagery Workshop: This section focuses on the vendor based responses to the SDMI Imagery
questionnaire. All vendors that responded were satellite based vendors with an optical, multispectral solution to meet statewide mapping requirements within a 3 to 5 year timeframe. The
vendor responses are summarized in tabular format, and are followed by a more detailed
comparison of solutions by specifics such as:
o
o
o
o
o
Spatial Resolution
Spectral Characteristics
Achievable Accuracy
Horizontal Control Requirements
Collection Capacity
Conclusions: This section re-addresses the variable application requirements of the Alaska
mapping community, and provides maps that illustrate the spatial distribution of those
requirements. This section discusses rough order of magnitude (ROM) funding requirements for
potential solutions, including the cost of seamless ortho-imagery production, and associated
vertical (DEM) and horizontal (GCPs) control requirements.
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Alaska Imagery Status
The SDMI believes reliable, current, statewide base geographic information is essential for continued
economic development, livability, and public safety. Orthoimagery is considered a foundation element
for the framework of base geographic data. At this time, Alaska does not have a current vintage of
statewide digital orthoimagery. The most recent near-statewide moderate resolution imagery available
is at least 25 years old and does not reflect the current Alaska landscape, especially in light of climate
change driven modifications such as coastal shoreline erosion, melting permafrost, retreating glaciers,
and shrub-line migration . The most recent near-statewide moderate resolution imagery coverage was
acquired between 1978 and 1986 through the Alaska High Altitude Aerial Photography Program (AHAP).i
The AHAP imagery is not orthorectified statewide; it is estimated that less than 10% of the AHAP
imagery statewide has ever been orthorectified. USGS topographic mapping is even more outdated.
Most of the USGS mapping is based on 1950’s, 1960’s, and 1970’s aerial photography and
contemporaneous survey control. As identified in the 2008 SDMI User Survey, if users are lacking
appropriate base map imagery, most users now use either USGS topographic mapping as a general
mapping base, or increasingly Google Earth© which will typically give the user access to the best public
domain data available, which for Alaska is often not of sufficient resolution or vintage. A new statewide
orthoimagery base would provide a common layer that would show current conditions and trends over
the Alaska landscape and allow other types of geographic information to be extracted and registered.
This orthoimagery will allow State and Federal agencies, local government, Native corporations, nonprofit, and commercial organizations to better utilize GIS and mapping technologies to aid in responsible
decision making.
Historical Orthoimagery
The last Alaskan statewide imagery program funded by State and Federal agencies was the Alaska HighAltitude Aerial Photography (AHAP) Program, started in 1978 to develop a statewide image base layer.
From the AHAP Program booklet executive summaryii:
Until 1978, State and Federal land resource management originations had been restricted in
their oversight responsibilities by the lack of a uniform mapping database. Few maps had been
made and those maps dated back to the Second World War. By the early 1970’s existing
geographic information and aerial photographs were so outdated and inconsistent that they
were unusable for current mapping.
In 1978, State and Federal agencies formed the Alaska High-Altitude Aerial Photography (AHAP)
Program to develop a uniform aerial mapping photographic database. Funding was shared
between the State of Alaska and the Federal government. Since the initiation of the program,
approximately 90 percent of Alaska has been photographed.
The finished product of the AHAP Program is a set of unified and coordinated aerial
photographs. Some of the uses of the AHAP photographs are the identification of diseased tree
stands, monitoring shoreline changes, charting vegetation regrowth after a fire, delineating
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transportation corridors, making land conveyance determinations for bodies of water, and
accelerating conveyance of land to the State and Native corporations.
The AHAP was a statewide imagery program that photographed the State at a scale of approximately
1:60,000 and 1:120,000 using two camera sensors. The program utilized a 12-inch focal length camera
capturing color-infrared (CIR) imagery at 1:60,000 scale and a 6-inch camera capturing black and white
imagery at 1:120,000 scale for alternate frames.iii During the time of the program, the USGS used the
black-and-white imagery, coupled with the existing 1:250,000 scale USGS DEMs, to produce orthoquadrangles for the State at 1:63,360-scale. No attempt was made to comply with NMAS, and the
completed ortho-quadrangles covered less than 25% of the state, and are not in digital format. The
AHAP program was wound down in 1986 and accomplished creation of a set of black and white, and CIR
imagery that cover 90% of Alaska (outside of the Aleutian chain).
Figure 6 AHAP 1:60,000 color infrared (CIR) aerial photogrpaphy
National Imagery Programs
Aside from AHAP, there are other imagery programs that have been active in Alaska over the last
decade: NRCS, US Forest Service, US Census, US Park Service, State Forestry USGS/BLM in NPR-A are
some of the programs that have focused on ortho-imagery collection in Alaska. Since 2000, high
resolution satellite imagery datasets have been acquired for State, local government, and other
organizations in areas from Southeast Alaska to the North Slope. However, none of these programs have
concentrated on consistent statewide imagery for the state of Alaska. The following sections discuss
three national imagery programs (USGS High Resolution Ortho-imagery, National Agriculture Imagery
Program (NAIP), and Imagery for the Nation (IFTN)), and their role within meeting the ortho-imagery
needs of the Alaskan mapping community.
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USGS High Resolution Orthoimagery
Another source of public orthoimagery is produced by the United States Geological Survey (USGS) and
the National Geospatial-Intelligence Agency (NGA). The High Resolution Orthoimagery provided by these
agencies covers major metropolitan areas and state capitals in the Lower 48. Since 2002, imagery in the
conterminous U.S. has been collected at resolutions of 6 inches to 1 foot (Figure 7). Due to its wide
variety of uses, this digital imagery is the foundation of many private and public Geographic Information
Systems and provides the base for the National Map.
Figure 7 USGS High Resolution Orthoimagery by year for the United States
The current USGS High Resolution urban Orthoimagery coverage for Alaska includes the cities of
Anchorage and Juneau. The Anchorage imagery was acquired in 2006, and Juneau in 2007, at resolutions
range from 0.3 to 1.5 meters. The acquisition of imagery for Fairbanks is in the works for 2009. The
imagery was funded jointly by local governments and the USGS.
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Figure 8 USGS High Resolution Orthoimagery by year for Alaska
National Agriculture Imagery Program - NAIP
The National Agriculture Imagery Program (NAIP) is the digital orthoimagery product produced by the
United States Department of Agriculture (USDA). NAIP began pilot projects in 2001, and by 2003 was
being funded by federal, state and regional government agencies. However, the Aerial Photography
Field Office (APFO) has acted as a contractual administrator for the Natural Resources Conservation
Service (NRCS)–funded aerial imagery. The main purpose of the NAIP program is to produce 1-2 meter
resolution orthoimagery for the lower-48 states to maintain common land unit (CLU) boundaries and
assist with farm programs.iv Independent contractors are hired to collect the imagery every year and
follow rigid specifications put forth by the USDA. The aircrafts used have to collect the imagery at
specific elevations and specific intervals to meet a resolution standard of 1 meter ground sample
distance (except in NAIP’s earlier years where a 2 meter ground sample distance was acceptable) and a
horizontal accuracy standard of 5-6 meters. The result is natural color, or RGB, imagery with a nearinfrared option beginning in 2007. Although primarily produced to aid in agriculture oversight, a wide
variety of disciplines use NAIP imagery for analysis purposes. Alaska has been excluded from the NAIP
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Program and currently has no statewide imagery program funded by the Federal government.
Figure 9 NAIP coverage by year for the conterminous United States
Imagery For The Nation (IFTN) – Alaska
Imagery for the Nation is an initiative to establish and sustain a flexible digital imagery program
nationwide that meets the needs of local, state, regional, tribal and federal agencies. Based on the
outcome of the SDMI Imagery Workshop (March, 2009) there is agreement that some of the stated
goals of the Alaska SDMI align with those of IFTN.
State and Federal organizations in Alaska acknowledge and support the IFTN proposal to acquire orthoimagery over Alaska with a refresh cycle of three to five years with resolutions and accuracies suitable to
the needs of the user community. Since a comprehensive imagery collection over Alaska has not been
completed since the 1978 – 1986 timeframe, this proposal will help provide Alaska with an updated
imagery base. The specifications for 1-meter digital orthoimagery at 25-foot/7.6 meter CE90 accuracy
are being used as a placeholder for Alaska statewide requirements. The 1-meter specification was
submitted by the NDOP/IFTN technical group for FGDC approval and will soon be the specification.
Feedback to IFTN from the Alaska SDMI Imagery Workshop included the following:
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The need to open up IFTN Survey to state and local government agencies as well as native
corporations.
The need to include spatial resolutions between 1 and 5 meters—shown by several of the
workshop’s Alaska user presentations to be a very useful resolution—to increase satellite
options for consideration in the IFTN Survey.
The current IFTN draft includes the intention to federally fund statewide coverage which is currently
being defined as a placeholder at 1 m., and will be changed to reflect the resolution specific to Alaska
that is determined by the IFTN Technical Plan Work Group in partnership with the community.
Acquisition of higher-resolution imagery for populated places will follow the national IFTN model of IFTN
covering 1ft. resolution. IFTN will cover 50% buy-up from 1 ft. to 6 inch of the defined area every three
years, with the option for state and local partners to “buy up” to complete coverage of populated places
(note that the program will include additional buy up options for partners to improve the specifications
from the base program). The IFTN has not yet secured budget for any of its data collection plans.
The IFTN has the following specific actions in mind to support Alaska’s imagery needs:
The IFTN Technical Plan Work Group will work with the community through the Alaska
Geographic Data Committee (AGDC) and/or other bodies as appropriate to refine the
specifications and cycle times for Alaska that will be included in the IFTN plan.
The IFTN Technical Plan Work Group will work with the community through the AGDC and/or
other bodies as appropriate to reopen the survey to collect state and local imagery needs for
Alaska. The survey will be revised to include options between 1 and 5 meters.
Because the IFTN plan has not been completed or funded, it is not possible to specify a starting date for
IFTN to acquire new imagery for Alaska. However, IFTN’s 5 year cycle placeholder dollar figure is
currently ~$20+ million, and that there are significant partnership opportunities to align IFTN with state
plans to leverage funding.v In the meantime, Federal agencies are interested in leveraging imagery
partnerships with the state through existing channels such as SDMI and the AGDC, and would like to
work in cooperation with NDOP until such time that IFTN is an operational program.
SDMI Imagery Requirements
The primary goal of the Alaska Statewide Digital Mapping Initiative is to acquire new, updated orthoimagery and elevation base map layers for Alaska. The target base map layers are a statewide orthoimage controlled by an appropriately scaled elevation model and ground control as required. The first
step in meeting this goal was to establish user requirements for ortho-imagery to further define the
goals for a statewide base map. A User Survey was conducted in 2008 to determine user needs and
requirements.
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Breadth of the SDMI User Survey
The SDMI user survey was well received by stakeholder community, with a total of 152 responses. The
majority of SDMI survey respondents were from State and Federal Agencies, with 48, and 40
respondents respectively. In addition to the relatively high turnout of respondents, the SDMI user
survey succeeded in capturing a broad range of users of geospatial content. The following chart
illustrates the breakdown of responses by major user group affiliations:
Figure 10 Source SDMI User Survey
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The chart below illustrates the response to the survey relative to Land Ownership percentages in the
state of Alaska. Percentages for the State are most in line, in terms of their representation in the user
survey and their land management responsibilities. Both Federal and Native Corporation responses
were under-represented relative to their land ownership percentages. Despite this, Federal response
was still relatively high, composing 25% of survey respondents, and equivalent to the number of State
respondents.
Figure 11 Source SDMI User Sruvey
While Native corporation respondents represent a relatively small response percentage relative to their
land ownership, most of the major corporations responded to the survey.
Native Corporations Responding to the Survey
Arctic Slope Regional Corp (ASRC)
Ahtna Inc.
Calista Corporation
Cook Inlet Regional Corporation (CIRI)
Doyon Corporation
Sealaska Corporation
Region of Alaska Represented
North Slope
Southcentral, Southeast
Southwest
Southcentral
Interior
Southeast
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The survey respondents also covered a diverse set of application use-cases. The following table
illustrates the seven major application categories, and their associated subcategories, along with the
number of respondents in each:
Application Use-Cases
No. of Respondents
Land Management
54
Land Cover
7
o
5
Terrestrial
o Wetlands
Environmental Analysis & Mapping
o
3
35
Environment & Habitat
32
o Floodplain & Hydrology
Natural Resource Inventory
3
50
o
Geologic, Oil & Gas
26
o
Forestry & Soils
20
o Recreation & Wildlife
Transportation Planning & Engineering
4
Utilities & Infrastructure
8
Public Safety & Military Base Mapping
18
7
Table 2 Source SDMI User Survey
Please Note: Some respondents fit more than one use-case profile. In the following sections, statistical analyses are extracted
from survey responses submitted by the cut-off date. Private companies whose primary application is to provide remote
sensing data are not included in statistical analyses.
The following section explores user requirements as they pertain to application use-cases defined in the
above table.
Application Requirements
For each application use-case, the following analysis provides:
A breakdown of respondents by agency affiliation
Current base map usage
Requirements for base map data including:
o Coverage
o Spectral Characteristics
o Refresh
Required features to be mapped
Specific application-based mapping needs
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Land Management
Land management includes the mapping of ownership boundaries, administrative units and parcels and
map applications that support land use planning and development. This is a major application category
in Alaska. Of 152 SDMI User Survey respondents, 44 respondents’ profiles fit this use-case, with the
following agency affiliation breakdown:
Seven Federal agencies and sub-divisions:
BLM-Glennallen Field Office and FDO
Census and Geographic Information Network
NPS-Katmai National Park & Preserve
NPS-Kenai Fjords National Park
NPS-Southwest AK Network, Alaska Regional Office, Lands
USDA National Forest Service
USDA National Forest Service-Chugatch National Forest
Four State agencies and sub-divisions:
ADCCED
ADNR
ADNR, Div. of Land Records Information Systems
ADNR, Div. of Mining, Land and Water
One Academic institution and sub-division: UAF Center for Distance Education (DCE)
Eight Municipalities/Boroughs:
City and Borough of Juneau
City and Borough of Sitka
Fairbanks North Star Borough
Kenai Peninsula Borough
Ketchikan Gateway Borough
Matanuska Susitna Borough
Municipality of Anchorage
North Slope Borough-Planning Dept.
One non-profit Native organization: Tanana Chiefs Conference
Twelve Private Industries including four ANCSA Native corporations:
Ahtna, Inc.
Alaska Map Science
Allied GIS
Arctic Slope Regional Corporation (ASRC)
Boutet Company
Calista Corporation
Cook Inlet Regional Inc. (CIRI)
eTerra LLC
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HDR Alaska, Inc.
John Oswald and Associates LLC
Resource Data, Inc. (RDI)
Sealaska Corporation
One non-profit Native organization: Tanana Chiefs Conference
The greatest percentage of users in this category conduct operations primarily in Southcentral Alaska
(reflecting the large number of respondents from the Municipality of Anchorage (MOA), Mat-Su and
Kenai Peninsula Boroughs), with the second largest proportion operating stateside. Nearly half the
respondents operate within a full range of area types, including economic, highway and river corridors,
villages, urban areas, conservation and environmentally sensitive areas, etc. Three-quarters of this
group use natural color imagery, whereas only half that number use multispectral and fewer than 20%
use color-infrared or panchromatic. Over one-third of users feel that a refresh rate of five years would
be adequate, whereas 20-25% desire updates every three years or annually. Notably, MOA has
collected new high-resolution imagery every two years since 2000 and have found this to be an
adequate refresh rate. This may establish a precedent for other urban areas.
Two-thirds of respondents in the Land Management use-case category also employ imagery and/or
elevation data for cadastral applications. Forty to fifty percent also use imagery for water resource
management, property appraisal/real estate, transportation and infrastructure, surveying, urban and
regional planning, as well as land cover mapping and environmental analysis. One-third of respondents,
primarily the local government agencies and BLM, use base map data for emergency/disaster planning
and response. Between 20% and 28% of users, including BLM, ADNR Division of Mining, Land and
Water, North Slope Borough Planning Dept., CIRI and Tanana Chiefs Conference, use base map data for
energy and mining applications, and for fisheries and forestry management.
Two-thirds of respondents involved in Land Management map parcels; over half map building
footprints; and almost half map roads, centerlines, and utilities. Two-thirds map general and discrete
hydrographic features; half map wetlands and vegetation; and one-third map geologic features. Twentyfive of twenty-nine ADNR survey respondents (82%) are involved in land management activities, in
addition to their other primary applications.
Certain organizations report the following details regarding their land management applications:
In Alaska, the Bureau of Land Management (BLM) administers approximately 80 million acres of
federal public land. BLM's Fairbanks District Office manages 58 million acres of public lands in
northern Alaska, and is divided into three field offices, all located in Faribanks. The BLM
Anchorage District Office manages approximately 25 million acres of public lands in the
southern half of Alaska. The district is divided into two field offices located in Anchorage and
Glennallen. The focus of the BLM in Alaska includes:
o
Alaska Land Conveyances. Alaska is a young state and land ownership is still being
settled. The BLM is tasked with surveying and conveying federal lands to the State of
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o
o
Alaska, Alaska Native Corporations and individual Alaska Natives. Once final land status
is determined, the BLM will manage about 70 million acres of federal lands and 220
million acres of subsurface mineral estate in Alaska.
Land management - Under the Federal Land Policy Management Act of 1976, BLM is
mandated to manage its lands and resources for multiple-uses. Some important issues
addressed by BLM managers are recreation, wildlife, fisheries, cultural/archaeological
and minerals resource management.
Energy development - The BLM is committed to sound land use planning for the 23million-acre National Petroleum Reserve Alaska (NPRA). Many resource management
issues transcend the boundaries of NPRA and are applicable to the entire North Slope of
Alaska. The BLM partners with other federal and state agencies form the North Slope
Science Initiative, a newly developed organization that encourages sharing knowledge
to make science-based decisions about development activities on the North Slope.
o
Trans-Alaska Pipeline System oversight - The BLM partners with other federal and state
agencies at the Joint Pipeline Office to work proactively with Alaska’s oil and gas
industry to safely operate the Trans-Alaska Pipeline System.
o
Fire management - The Alaska Fire Service provides wildland fire suppression services
for all Department of the Interior and Alaska Native corporation lands in Alaska.vi
(Further details are provided in the Public Safety & Military Mapping section.)
BLM applications involve the use of imagery to map a wide range and scale of features, including
discrete and general wetlands and hydrography; geologic features and mining sites; land cover
including tree canopies; pipelines; planimetric features and utilities; and parcel/management
area boundaries. BLM staff has used all imagery types, but prefer multispectral data. From
2002-2007, BLM acquired imagery and DEM of NPRA. The 2.5-meter CIR orthophotos and
medium accuracy IFSAR DEM are in DOQQ format. BLM also has acquired QuickBird and
IKONOS imagery of specific areas. BLM requires moderate resolution imagery (1:24,0001:60,000 NMAS accuracy) of its land holdings, which would provide the required base needed to
support land management applications, survey support, and hydrographic mapping as part of
USGS NHD, (mapping of watersheds, streams, and water bodies on tracts statewide), and
wildfire assessment and mitigation for the Alaska Fire Service. BLM also would benefit from
broad-scale statewide imagery, and detailed imagery of pipeline corridors, plus populated areas
and infrastructure within its managed lands.
Alaska NPS-managed lands are vast, covering over 54 million acres of public land, nearly twothirds of the total within NPS nationwide. NPS uses imagery to extract features ranging in scale
from utilities and road center lines to general hydrographic features and vegetation. NPS has an
ongoing contract with GeoEye to acquire 1-meter panchromatic and 4-meter multispectral
imagery of Alaska national parks.vii Nevertheless, most NPS respondents (Joni Piercy, Dorothy
Mortinson, Bob Strobe, Sharon Kim, and Fritz Klasner) feel there is a paucity of available data,
and that the existing data is of poor quality. One user cites the requirement for a statewide 10meter DEM.
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The USDA Forest Service Alaska Region manages more than 22 million acres in Southeast and
Southcentral Alaska. Chugach and Tongass National Forests (NF) are the largest in the national
forest system. Land management categories include congressionally-designated Wilderness
areas, national monuments, land use designation (LUD) management areas, and land use
allocations that disallow development.viii Several Forest Service respondents (Joe Calderwood,
John Baldwin, Karin Preston, Paula Smith and Richard Stahl) rely on digital base map data for
cadastral applications, such as mapping parcel/management area boundaries.
From 2005-2008, multiple imagery datasets were acquired for Tongass NF, primarily at 1-meter
or better resolution.ix The most recent commission was for 1-meter natural color, film-based
orthophotos, provided in a 7.5-minute DOQQ format. A DEM with 20-meter posting was
provided as part of the contract, which, however, ended without completion of coverage. The
first seamless base map for the Chugatch NF will be produced from recently acquired SPOT
imagery and DEM, coupled with extensive control. A newer commission for Chugach NF will
include 4-band aerial multispectral digital imagery with 60-cm resolution at 1:24,000 NMAS
accuracy.x Mark Riley, Remote Sensing Specialist, and Ken Winterberger, Forester, describe the
following requirements for future imagery acquisitions of the two National Forests:
o
o
o
o
o
4-band multispectral imagery at 1-meter or better resolution (30-60 cm optimal);
An airborne platform to meet the high resolution requirement and to achieve rapid
acquisition during windows of clear weather;
A digital sensor with high dynamic range (bit depth), to detect low light reflectance from
shadowed valley walls in coastal regions;
August-September timeframe of acquisition, to minimize shadowing.
Refresh rate varies by area: 5-10 years is adequate for large areas, whereas localized
projects may require multiple captures within a season
Mr. Riley and Mr. Winterberger also note that the SPOT 20-meter DEM obtained for Chugach NF
is a vast improvement over the NED. However, Mr. Riley feels that a 5-meter DEM would be
optimal to meet the fine-scale mapping requirement of the National Forests. The respondents,
however, indicated that impediments to their work flow include a lack of available digital
imagery, difficulty in getting/using the data, and poor quality of imagery and DEMs.
ADNR Division of Mining, Land and Water (Doug Sanvik) uses imagery to analyze lease holdings
to determine if construction has taken place, and if development is within the confines of
authorized areas on State lands. Sanvik currently resorts to imagery available through Google
Earth, but this resolution rarely affords enough detail to make any definitive assessment
regarding development activity within State lands. Chief of Operations, Wyn Menefee, notes
that ADNR could save a substantial amount of money by using high resolution imagery and DEM
to answer site-specific questions, thereby avoiding the need for field inspection.
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The Community Mapping (IAID), or “Alaska Profiles” is a collaborative program to fund and
develop current, detailed, community profile maps to serve the needs of small communities in
the unorganized borough that have not been mapped in the last five years. This assistance
program was developed in 2002 by Alaska Department of Commerce, Community & Economic
Development (ADCCED), Denali Commission, USDA Rural Development, and Alaska Department
of Transportation & Public Facilities. The collaboration resulted in development of a set of
mapping standards that support creation of standardized, user-friendly maps for all
communities in the program. Mapping projects are organized by regions of ten to fifteen
communities, to create cost savings and efficiencies compared to mapping one community at a
time. Each project is developed with a partner organization, such as a housing authority or a
tribal non-profit organization, which works directly with the communities and has been
responsible for providing half the funds for the mapping contract. The acquired base map
imagery consists of 1-foot and 2-foot natural color aerial orthophotography at 1:200 and 1:500
scales, respectively. ADCCED uses the acquired imagery primarily to map village planimetry
(houses, roads, utilities), and secondarily, to maps flood zones and determine variety of natural
and urban features that could suffer impact within a flood zone.xi
University of Alaska at Fairbanks Centre for Distance Learning (CDL) uses imagery to determine
business demographics (Shari George, Course Manager, CDL).
As part of their land management functions, the Municipalities/Boroughs all map planimetric
features, including utilities, roads, buildings, parcels, etc. They require high resolution imagery
with high spatial accuracy. Refresh requirements range from annually to every five years.
Anchorage Municipal Surveyor, Tom Knox, notes that the Municipality of Anchorage (MOA) uses
imagery to extract information relevant to a variety of land management applications, including
land use planning, design and development; parcel mapping; historical land use; watershed
analysis; and topographic mapping. The MOA Fire Department relied on a combination of
IKONOS, QuickBird, LandSat, and aerial photography to develop wildfire exposure models and
risk maps in 2001—2008. Five imagery datasets, primarily natural color, were acquired from
2000-2008, at 1-foot to 1-meter resolutions; and included IKONOS and QuickBird imagery in
addition to photogrammetric orthoimagery. The Kenai Peninsula Borough’s land management
applications include parcel management, archeological site mapping and management, and
other applications. KPB also has used satellite imagery extensively for wildfire risk assessment
and for map books in wildfire response. KPB last acquired imagery of inhabited and roaded
areas in 2003, through a multi-agency purchase of QuickBird 0.6-meter color imagery.xii
Native corporations and organizations, whether regional, village, or Native allotment, rely
heavily on imagery and other layers to map ownership boundaries, and for resource
management. For example, the Denali Commission uses imagery to map various Native land
holdings statewide.xiii Cook Inlet Regional Incorporated (CIRI) uses imagery on a daily basis to
map their resources in Southcentral Alaska, including oil and gas holdings, hardrock mineral and
gravel quarries, coal mines, and timber resources, as well as site plans for renewable energy
sites, for example the wind farm located on Fire Island.
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Land Cover
This use-case comprises the mapping and analysis of vegetation and general land cover, including
wetlands, from digital base map data. This is an important use-case in Alaska, and typically occurs at
two levels: regional and project level. An example of regional application is the statewide land cover
mapping and analysis performed in support of the USGS LandFire Program. In contrast, wetlands
mapping is performed at the project level, to satisfy permit requirements on projects ranging in scope
from local engineering jobs to major gas line development ventures.
Over one-third of survey respondents indicated that they are involved in land cover mapping, including:
Three Federal agencies:
NPS
USFWS Alaska National Wetlands Inventory (AK NWI)
USGS Alaska Science Center
One State agency
ADNR, Division of Forestry
Four Private agencies:
ABR, Inc. Environmental Research and Services
Geographic Resource Solutions
HDR Alaska, Inc.
Science Applications International Corporation (SAIC)
The above survey respondents operate all over the state, mapping diverse land cover features including
wetlands and other hydrographic features, vegetation, tree canopies, and mining features. Reference
features such as parcel/property boundaries, and roadways are also mapped. Priority areas for image
capture include economic corridors, villages, conservation management areas, and environmentally
sensitive areas. This group prefers working with color-infrared imagery, followed by multispectral and
natural color imagery. Most users desire an imagery refresh rate of three to five years.
The land cover use case can be broadly divided into terrestrial land cover mapping applications and
wetlands mapping applications, as discussed below.
Terrestrial Land Cover Mapping
The USGS uses imagery to produce land cover maps for a variety of purposes, including most
recently, the in-progress development of the Landscape Fire and Resource Management
Planning Tools Project (or LANDFIRE) for Alaska. LANDFIRE is a shared project between
the wildland fire management programs of the USDA Forest Service and US Department
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of the Interior, operating on a five-year time-frame to produce consistent and
comprehensive maps and data describing vegetation, wildland fuel, and fire regimes
across the United States.xiv Mike Fleming, an SAIC contractor for USGS, is leading the
development of a statewide land cover map extracted primarily from 30-m Landsat 7 ETM+
data. The land cover map is input to a model integrating ecological, habitat, and climatic data
to produce a statewide LANDFIRE model. Fleming believes that consistent imagery data is most
important, and high resolution takes a lesser priority; he has found Landsat 7 ETM+ to be a very
reliable data source.
The NPS Alaska Region contracted Geographic Resource Solutions to perform land-cover
mapping of the 18.5-million acre Wrangell-St. Elias National Park (WRST) during 2003-2007.
WRST is the largest national park and is characterized by rugged terrain, five 14,000-foot
mountain peaks, huge glacier fields, and diverse ecosystems. Using aerial ortho-photography,
with supportive field work to develop image training sets representing the diverse landscape
characteristics, Geographic Resource Solutions developed comprehensive and quantitative landcover/vegetation maps and associated database characteristics. In-season imagery was used for
primary image classification, and supplemented by winter imagery to resolve class confusion.
The resulting dataset will be made available to resource managers, ecologists, biologists, and
planners that will enable the users to develop and evaluate alternative management scenarios
and strategies.xv
Gordon Worum notes that ADNR Division of Forestry produces land cover maps primarily in
support of wildfire risk analysis, but also for general forestry and other land cover applications.
Primary mapping areas are Interior and Southcentral Alaska. Previously acquired satellite
imagery includes SPOT-5 2.5-m (2002-2007) and QuickBird 0.6-m (2002-2004) pan-shapened
multispectral data of Tanana Valley.
Geographic Resource Solutions specializes in image processing and spectral classification for
land cover extraction, and was awarded an Environmental Services contract from the General
Services Administration (GSA) in 2003, which allows federal agencies to purchase land cover
mapping and image processing services from Geographic Resource Solutions under the GSA
schedule. Geographic Resource Solutions prefers annual imagery acquisition to fulfill the needs
of its contracts.xvi
ABR, Inc. is another private firm strongly involved in land cover mapping throughout the state
for a variety of clients including the oil and gas industry, government agencies, and others (Will
Lentz, ABR GIS Specialist). ABR most often requires high resolution imagery, but also relies on
lower resolution multi-spectral data for image classification tasks. Lentz would like to see
consistent, seamless imagery coverage for Alaska. Sample projects include the following: on
behalf of ConocoPhillips Alaska, Inc., ABR mapped patterns of change in vegetative biomass in
relation to snow melt and caribou densities from low resolution multispectral satellite imagery.
In support of The Nature Conservancy’s ecoregional level conservation planning, ABR mapped
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ecological features of the Alaska-Yukon Arctic ecoregions, documenting environmental patterns
at a broad geographic scale.xvii
Wetlands Mapping
Most regions of Alaska have a land surface that includes extensive areas of wetlands. Treeless expanses
of moist and wet tundra underlain by permafrost occur in the northern and western portions. Interior
Alaska contains millions of acres of black spruce muskeg and floodplain. Shrub and herbaceous bogs are
a predominant feature of the landscape in Southcentral and Southeast Alaska. In mountainous areas
such as the Brooks or Alaska Ranges, wetlands have developed in drainages and on vegetated slopes.
Some of the nation’s most extensive complexes of salt marshes and mud flats occur along the coasts of
the Beaufort, Chukchi, and Bering Seas, and the Gulf of Alaska. Wetlands are abundant in the valleys and
basins associated with large river systems including the Yukon, Kuskokwim, Porcupine, Tanana, and
Koyukuk Rivers. Significant wetland areas also occur on the major river deltas in Alaska, including the
Yukon-Kuskokwim Delta, Colville River Delta on the Beaufort Sea coast, the Copper River Delta in
Southcentral Alaska, and the Stikine River Delta in the Southeast region. As previously stated, wetlands
mapping is a requirement to obtain permits for development projects at all scales in the state of Alaska,
and thus has particular importance in determination of statewide imagery requirements.xviii
The USFWS Alaska National Wetlands Inventory (AK NWI) is responsible for statewide
reconnaissance mapping of Alaska’s wetland habitats. Guided by the USFWS overall national
strategy, the AK NWI conducts strategic land cover classification of high priority wetland
habitats, carries out status and trends analyses of wetlands and other aquatic habitats, and
identifies and assesses threats to aquatic habitats at risk. The USFWS provides funding to image
and map 4 million acres per year, their priority areas being refuges. Additional funding for data
acquisition comes from partnerships w/other agencies, e.g. Boroughs. Currently, 43 percent of
Alaska has been mapped, and 21 percent has been digitized.xix Typically NWI maps are extracted
from AHAP (1:60,000 scale; 1978-86) color-infrared stereo-photo pairs, using traditional
stereoscopic photogrammetry to view relief. More current high resolution satellite imagery has
also been used, e.g., IKONOS and SPOT. Other relevant, publicly available ancillary data is used
to enhance classification, e.g., USGS topographic maps, digital soil data, and field work. The NWI
has jurisdiction over the entire state for general wetland mapping, and therefore requires
statewide imagery at a resolution of 2.5-5 meters. In terms of preferred imagery format,
stereo-imagery is useful for detection of wetlands, which are associated with specific terrain
types, e.g., local depressions; however, it is possible that mono-imagery draped over highresolution DEM could provide adequate terrain visualization. Multispectral satellite imagery
provides an advantage for land cover classification; however, challenges have been the time
frame involved to obtain near cloud-free satellite imagery and the lack of stereo coverage. NWI
requires leaf-off (spring) acquisition dates.xx Priority areas for new image collection are shown
in the map below:
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Figure 12 National Wetlands Inventory: priority areas for new image acquistion
In higher development areas, NWI works in partnership with the Army Corps of Engineers in
consultations to produce more detailed-scale wetlands maps. The higher resolution imagery
requirement (1-2.5 m) to produce these maps has been met in the past by partnering
organizations.
ABR, Inc. is involved in classifying and mapping wetlands using remote imagery and field
surveys. ABR conducted wetlands assessments of three airports in 2003 (Holy Cross, Bettles,
Birch Creek) and four airports in 2004 (Allakaket, Chalkyitsik, Koyuk, Prospect Creek) to support
the preparation of NEPA documents (environmental assessments) and permits for airport
improvements at each site.xxi
HDR Alaska, Inc. conducts many wetlands mapping projects in all Alaska regions for a large
variety of government and private industry clients, and at different map scales. Typically, HDR
maps wetlands at one-quarter to one-half acre units, and follows US Army Corps of Engineers
standards and guidelines. Imagery typically is flown on contract for a project, and consists of
natural color, two-foot to one-meter resolution pixels. Examples of HDR wetland mapping
projects include the Alaska Railroad Northern Rail Extension Project, Pebble Copper-Gold Mine
environmental studies, and the Chuitna Coal project.xxii
The federal Joint Pipeline Office estimates that wetlands are one of the required layers needed
for pipeline permitting. Starting in 2009, proposed natural gas lines will need to meet this
requirement, which will entail a huge amount of mapping.xxiii
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Environmental Analysis & Mapping
Environmental analysis and mapping includes applications such as habitat mapping, hydrologic analysis,
floodplain mapping and climate change analysis. Thirty-seven respondents fit this use-case, belonging
to the following organizations:
Five Federal agencies and sub-divisions:
NOAA-National Weather Service (NWS) Alaska Pacific River Forecast Center
NOAA-National Marine Fisheries Service (NMFS) Protected Resources Division
USFWS
USGS-Alaska Science Center
US Army Corps of Engineers (USACE) Cold Regions Engineering & Engineering Laboratory (CRREL)
Six State agencies and sub-divisions:
ADCCED
ADEC
ADEC-Division of Water
ADFG
ADFG-Sport Fish
ADNR-Office of Habitat Management and Permitting (OHMP)
Three Academic institutions:
UAF
Alaska Pacific University
University of Colorado at Boulder, INSTAAR
Six Private industries:
ABR, Inc. Environmental Research and Services
Alaska Map Science
ENTRIX Inc.
HDR Alaska Inc.
Nuna Technologies.
Resource Data Inc. (RDI)
Three Not-for-profit organizations:
Ecotrust
Kenai Watershed Forum
The Nature Conservancy
As part of their environmental analysis and mapping activities, a large percentage of respondents are
involved in water resources, land, or fisheries management; climate change research; coastal mapping;
earth sciences mapping and research; and GIS and related consulting. A minority are involved in energy
and mining exploration or development. Nearly all respondents map discrete and general hydrographic
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features or wetlands. Other commonly mapped natural features include land cover and geologic
features. Parcel/property boundaries, planimetric features and pipelines are less commonly mapped.
Half the users conduct operations statewide; with about one-third having specific projects in the
Southcentral region, and smaller percentages in other portions of the state. Nearly half the respondents
use natural color imagery; one-third use multispectral/color-infrared, and one-third indicate no
preference. Given the dynamic features mapped by this use-case group, most users want a refresh of
imagery every three to five years, depending on the rate of change in that area. Wasilla is particularly
noted as a rapidly growing urban environment requiring more frequent updates. The largest percentage
of respondents would like to see imagery/elevation data acquired for environmentally sensitive areas,
followed by coastal areas, and then urban areas.
Certain organizations report the following specific applications of base map data, and target map
features:
NOAA-NMFS, Protected Resources Division (Dana Seagars) uses base map data to assess the
relationships between topography, hydrography, and wildlife distributions and habitat on a
statewide basis. Current imagery resources to not meet the agency’s needs.
In addition to wetlands mapping for NWI (described in Land Cover section), USFWS (Phil Martin,
Lisa Saperstein) uses imagery for environmental mapping and monitoring of USFWS refuges, and
for local ecological service projects throughout the state. Imagery is the basis for GIS activities
such as vegetation/tree canopy mapping, monitoring endangered species and fisheries,
environmental impact assessments, and monitoring coastal erosion as an indicator of climate
change. USFWS acquired bundled SPOT-5 2.5-m panchromatic, 2.5-m color-infrared, and 10-m
multispectral imagery for Kanuti National Wildlife Refuge (KNWR) from 2004-2007. KNWR is the
only refuge with complete imagery coverage. Some other refuges have imagery of specific
study areas at varying resolutions; for example, IKONOS imagery was acquired to map barrier
islands of the Arctic Refuge. Lisa Saperstein and Julie Michaelson (USFWS NWI program) feel
that broad-scale imagery resolution (e.g., 2.5m-10m) would be appropriate for mapping the
other refugesxxiv.
ADEC-Division of Water (Chris Miller, Drew Grant) maps potential sources of contamination, and
verifies well/Intake location from imagery/elevation data. They depend on accurate parcel base
information to help identify feature locations.
ADFG uses base map data for wildlife biology, management and research applications, habitat
change detection and intertidal zone mappingxxv. Image resolution, rather than spectral type,
drives image selection; for example, any imagery of sufficient resolution to delineate streams is
usable. Adequate documentation/metadata is also of high priority for ADFG respondents. Jeff
Nichols notes that imagery with a thermal-IR band would be useful for extracting larger water
bodies.
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UAF Professor Matt Nolan uses digital base map data to monitor the potential effects of climate
change and other factors on the retreat of McCall Glacier over the past 45 years.xxvi He prefers
color imagery. UAF Professor David Verbyla uses imagery and other mapping to monitor climate
change effects, mostly in interior Alaska.
Alaska Pacific University (Jason Geck) is using imagery and other digital mapping sources (LIDAR,
other) to assist in studying the retreat of glacial ice cover in the Brooks Range, Alaska.
The Kenai Watershed Forum uses imagery and LIDAR extensively for mapping of environmental
features on the Kenai Peninsula. They recently sponsored the acquisition of extensive LIDAR for
much of the Kenai Peninsula. Lacking comprehensive imagery, they feel that 2-5 meter
resolution imagery would suit their needs for mapping of hydrographic features, wetlands, and
physical features.xxvii
HDR Alaska desires access to imagery for analysis of fisheries, hydrology, and wetlands for many
environmental projects. For example, a major project is in the Chackachama Lake region which
is being assessed for its hydroelectric potential. The project requires extraction of natural
features, such as hydrography, wetlands/land cover, and terrain features. HDR has analyzed a
range of options for the base map including SPOT, Quickbird/Worldview, GeoEye, and
photogrammetry. Cost and large project areas are constraints; thus, they have evaluated SPOT
as a viable option, given the large scene footprint archive availability covering the region.xxviii
Natural Resource Inventory
Alaska is a major resource state having large assets of minerals, timber, fisheries, and energy. Inventory
of these resources is conducted by government, private industry, and academia and encompasses
applications such as resource data mapping, geologic analysis and mapping, fish and wildlife inventory,
and recreational use/planning. The Natural Resource Inventory use-case includes 48 respondents from
federal, state agencies and private industry as follows:
Nine Federal agencies and agency sub-divisions:
NPS
USDA Forest Service
USDA Forest Service-Anchorage Forestry Sciences Lab
USDA Forest Service-Chugach National Forest
USDA Forest Service-Geospatial Services
USDA Forest Service-PNW Research Station
USDA NRCS
USFWS
USGS
One State agency with five sub-divisions:
ADNR-Division of Forestry
ADNR-Division of Geological & Geophysical Surveys
ADNR-Division of Mining, Land and Water
ADNR-Division of Oil and Gas
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ADNR-Parks
Nine Private industries:
Ahtna Corporation
Alaska Earth Sciences
Arctic Slope Regional Corporation (ASRC)
Calista Corporation
Cook Inlet Regional Incorporated (CIRI)
Doyon Corporation
Northern Associates
R.A. Kreig and Associates
Sealaska Corporation
Resource Data Inc.
Forty percent of respondents in this use-case operate in the Southwest region, followed by about one
third statewide, and one-third in the Southeast. Over half the users feel that all location types listed in
the survey (river, economic and highway corridors; villages; conservation, forest, urban, environmentally
sensitive, and coastal areas) are of equal importance with regard to new base map data acquisition.
Three-quarters of users in this category are involved in land management; other common applications
include land cover mapping, earth sciences mapping and research, environmental analysis and mapping,
forestry management and GIS and related consulting. Users are also involved to a lesser extent in
emergency/disaster planning and response, transportation and infrastructure development and
planning, and energy and mining applications. Three-quarters of respondents map general and discrete
hydrographic features. One-half to one-third map land cover/vegetation, major roads, general
wetlands, and geologic features.
Natural color imagery is used by the largest proportion of respondents, followed by color-infrared or
multispectral imagery; almost one-third of users in this group expressed no preference for imagery type.
About one-third of users feel that an image refresh rate of 5 years or 5-10 years is adequate; twenty
percent desire a 3-year refresh rate; and 15% require annual refresh for their applications. The latter
group is involved in change monitoring of urban environments, discrete hydrographic features,
pipelines, or volcanoes. A user from ADNR Division of Oil and Gas (DOG) feels that quarterly image
refresh is desirable for energy, oil and gas applications.
Certain organizations report the following details regarding their specific applications, target map
features, and imagery requirements:
In the Alaska Region, NPS managed areas are increasingly impacted by serious environmental
stressors, including climate change, increasing human use, development within and surrounding
parks, global and local contaminants, and exotic species. The NPS Inventory and Monitoring
(I&M) program was established in 1992 to provide consistent databases of information about
natural resources, including their current condition and how they change over time. Under I&M,
there are four area networks: the Arctic Network, Central Alaska Network, Southeast Alaska
Network, and Southwest Alaska Network. The network is currently conducting baseline
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inventories of selected resources, and is developing and prioritizing a list of “vital signs” for longterm monitoring. Inventory layers are developed using spatial data technologies including
remote sensing and data extraction. GIS layers of land formations, vegetation, animal
populations and other spatial data are superimposed with human activities, natural stressors
and other factors that may influence themxxix. As described in the Land Management section,
NPS has an ongoing contract with GeoEye to acquire 1-meter panchromatic and 4-meter
multispectral imagery of Alaska national parks.xxx
The USDA Forest Service indicates the need for Alaska statewide coverage at varying image
resolutions to match their diverse range of natural resource programs, which include Aquatics
and Land Interactions, Ecosystem Processes (Boreal Ecology), Focused Science Delivery
(Recreation and Toursim), Human and Natural Resources Interaction (Alaska Communities And
xxxi
Forest Environments, Alaska Wood Utilization), and Forestry Inventory & Analysis.
As
previously described in the Land Management section of this white paper, the mapping of
Tongass and Chugach NF requires high resolution imagery (30-60 cm preferred) and DEM (5-m
posting preferred). These resolutions support the extraction and analysis of features ranging in
scale from tree canopies to ecological land cover units, as required by the varied end user
xxxii
community of resource specialists, foresters and planners.
The Forest Service’s mission also
includes conducting inventory and analysis of all state forested lands (the Forest Inventory
Program). This mission is gradually changing toward the monitoring of all state vegetated lands,
for the purposes of change detection. The Forest Service monitors sample plots, which are
extrapolated to state areas through spectral analysis of imagery to map forest type and extract
associated area and biomass attributes. Toward this end, new imagery is desired for Interior
Alaska forested areas, and statewide in general. The highest priority is Tanana Valley, followed
by the Yukon.xxxiii Broad-scale multi-spectral imagery would be adequate for this level of
mapping and the Geospatial Unit desires a 10-m statewide DEM.xxxiv
Fifty percent of respondents fitting the Natural Resource Inventory use-case profile work for
ADNR. Specific applications include environmental analysis, forestry management, geologic
mapping, and land cover mapping. ADNR observes that natural resources applications involve
significant amounts of visual comparison between USGS hard-copy quad maps and master title
plats (MTPs). One user notes that it would be extremely useful and time saving for these
statewide datasets to be available in digital format to automate the comparisons, and also to
overlay current orthophotos for analysis.
ADNR Division of Geological & Geophysical Surveys/Alaska Volcano Observatory (Janet Schaefer)
uses imagery and elevation data for geologic mapping and volcano-hazard assessment in the
Southwest and Aleutians. AVO uses a combination of Landsat-7 ETM, SRTM, and NED data on
most projects, but desire access to higher resolution data.
The Alaska Division of Oil Gas uses imagery frequently as a background for mapping of lease
holdings, and inventory of oil and gas leasing throughout Alaska, particularly the North Slope.
They prefer 1-meter resolution, but believe that 2 to 3 meter natural color imagery could be
appropriate, if current and seamless in consistency.xxxv
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Alaska Earth Sciences is a major mineral exploration consulting firm in Alaska conducting
exploration throughout Alaska. They use GIS heavily in the field and in the office, and would like
to be able to have 1 to 3 meter resolution true color imagery throughout the state. They use
USGS topo mapping and AHAP imagery at present extensively as a base.xxxvi
Arctic Slope Regional Corporation (ASRC) uses imagery for inventory of mineral resources in
addition to land management applications.xxxvii
Cook Inlet Regional Corporation (CIRI) uses imagery for mineral resource, forest resource, and
oil and gas management in the CIRI region.xxxviii
Sealaska Corporation uses imagery and other mapping data extensively for inventory of forest
resources and mineral resources. Due to lack of publicly available high resolution imagery they
have recently acquired 1-meter resolution aerial photography for all of their lands, and LiDAR
for selected locations.xxxix
Transportation Planning & Engineering
This use-case represents the mapping and analysis of transportation infrastructure, including airports
and air-traffic control zones, roads, railways, and sea routes. Nine respondents and interviewees
represent:
One Federal agency: Federal Aviation Administration (FAA)
One State agency: ADOT&PF
Four private companies:
CH2M Hill
Alaska Map Science
ERA Aviation
HDR Alaska, Inc.
This section is focused on imagery requirements and not terrain requirements of SDMI users. Please
refer to the SDMI DEM White paper for terrain requirements, in particular with regard to the
International Civil Aviation Organization (ICAO). The ICAO terrain data requirements are described in
the DEM white paper (Maune, 2008); also, for reference see Appendix 8 of the ICAO Annex 15
Aeronautical Information Services Reportxl.
Aviation requirements for imagery are described as follows, and are based primarily on interviews with
the FAA and ADOT&PF-Northern Region. We conducted phone interviews with George Sempeles, Lead
Cartographer and FAS Western Coordinator, ICAO Coordinator, Washington D.C., and Robert van
Haastert, FAA Alaska Region Coordinator, Anchorage in March 2009; and Ryan Anderson of the
ADOT&PF in Fairbanks, AK. Other comments have been received and incorporated in this current
version of the white paper as follows.
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FAA Aviation Mapping Requirements
Mr. van Haastert and Mr. Sempeles have described the ICAO preferences for digital imagery as two-fold:
1) At a broad scale (equivalent to Tier 1), there is a statewide need for current imagery to update
the USGS base map from which statewide FAA Aeronautical Sectional Charts are derived. The
main need is for imagery to update Visual Flight Rules (VFR) charts. According to Sempeles, no
state in the U.S. is more dependent upon general aviation than Alaska. In his view, the Alaskan
VFR chart series is the primary tool used for low altitude, general aviation flight.
2) Detailed-scale imagery (~1-meter resolution or better -equivalent to Tier 3), is desired by FAA
for 40 airports (and surrounding areas) affected by the new ICAO standard (see Table 3 below,
and Figure 14 for details). In addition, super high resolution imagery could be needed to
conduct airport obstruction surveys. For this purpose, imagery must deliver between a 10-30
cm ground same distance (GSD) in order to be considered useable. The following link provided
by Sempeles includes air photography suitable for Instrument Approach Procedure (IAP)
development.
http://www.ngs.noaa.gov/cgi-test/eAOC_state.prl?region=AAL&state=ALASKA
The Sectional Aeronautical Charts are 1:500,000 scale navigational resources used by commercial and
private pilots. Nineteen charts cover the state of Alaska, and are regularly updated. Mr. Sempeles
indicates that FAA depends on USGS data as the source for these charts, but the features shown are
often out of date or inaccurate. Features that FAA would like to see using imagery are cultural details
such as built-up areas, roads, rail-roads, power lines, pipelines, all of which are considered conspicuous
man-made objects, easily identifiable from the air to be charted for their land mark value; and in general
anything of land mark value to assist VFR pilotage.
Key chart features used for aviation include:
Topography (contour intervals are 250, 500, and 1000 feet)
Hydrography (rivers, streams) and water bodies (lakes)
Ice cover (glaciers, other)
Landmarks, displayed by name and elevation.
A sample portion of an aeronautical chart is shown below, with McGrath Airport centered. The radius in
magenta color around McGrath marks the priority area for updated imagery data for this airport.
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Figure 13 Updated imagery requirements for McGrath Airport
The acquisition of current, statewide, broad-scale imagery would provide a tremendous resource for
updating the FAA Sectional Aeronautical Charts. Private firms supporting the aviation industry would
also benefit. For example, Era Aviation reports that remote imagery has become increasingly useful for
their pilots, and that an updated statewide base map would be of great utility, especially if re-worked
into a similar-formatted product to the Sectional Aeronautical Charts. According to Sempeles, the charts
need to be updated now as existing charts are “old, made from old maps. The charts need revision
now.”
Many pilots are utilizing moving map technologies in the cockpit, using either tablet Personal Computers
(PCs) or laptops, according to Andrew Garrigus who formerly worked for eTerra LLC on aviation GIS and
visualization projects. Currently, most commercial moving map applications for aviation use the section
charts, but some connect to Google Earth©, which can be effective, especially when coupled with a live
GPS feed. Please note that Google Earth© will typically give the user access to the best public domain
data available, which for Alaska has previously explained short comings in resolution and vintage.
Garrigus believes that accurate imagery is a much better background layer than the dated shaded/relief
topo's they use for sectionals. Garrigus thinks the Tier 2 (& 3) designation for imagery is appropriate for
aviation interests. In general, pilots are referencing visual clues to perform an action or to verify their
location. Typically, these reference points for aviation can be seen in 0.6-5.0 m res imagery, depending
on altitude. At low attitudes 50-500 feet), the 0.6-1 m is much superior for picking out the rocks, road
intersections, creeks, buildings, etc. used for navigation.
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Although not an ICAO requirement, FAA has provided a priority list of airports for which they would like
to obtain high resolution imagery data (Table 3). The area of acquisition would include a 45-km buffer
zone around each airport, as shown in Figure 14.
FAA Airport
Class
C
Number of
Airports
1
D
13
E2
E4
22
4
Airport Locations
Anchorage
Bethel, Big Delta, Delta Junction, Eielson AFB, Elmendorf AFB, Fairbanks,
Juneau, Kenai, King Salmon, Kodiak, Ladd AAF, Lake Hood, Merrill Field
(not duplicated by Class D): Aniak, Barrow, Bettles, Cordova, Deadhorse,
Dillingham, Fort Yukon, Galena, Gulkana, Homer, Iliamna, Ketchikan,
Kotzebue, McGrath, Nome, Northway, Sitka, St. Mary's, Talkeetna,
Tanana, Unalakleet, Yakutat
Bethel, Big Delta, Delta Junction, King Salmon
Table 3 FAA list of priority airports for which they would like to obtain updated imagery
Figure 14 FAA Priority list of airports shown with a 45 km buffer
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ADOT&PF Aviation Division
The ADOT &PF Aviation Division has the following needs and requirements for imagery resources:
Areas of Interest: State Aviation projects range from airport rehabilitations to airport relocations. Areas
of interest include all State owned airports, as well as rural communities with airports throughout
Alaska. Priority areas of interest include, but are not limited to, Community Relocation and Evacuation
Road projects. Areas identified by the Immediate Action Workgroup include Shishmaref, Kivalina,
Koyukuk, Shaktoolik and Unalakleet. State Aviation is also evaluating a road to Cape Blossom from
Kotzebue, and a road connecting Noatak to the Red Dog road, and evacuations roads at Gambell and
Point hope which cover large scale areas.
Applications: ADOT Aviation finds all imagery types to be useful, and they make the most of what is
available. However, vegetation mapping is an important support application, requiring multispectral
imagery. The 4-band imagery acquired from DigitalGlobe in the past has been useful, because they are
able to utilize remote sensing techniques and evaluate individual bands depending on the need. It is
also important that the imagery has the appropriate metadata, documenting whether the imagery is
rectified or orthorectified, and the methods used to develop the rectification.
Resolution: ADOT Aviation’s experience indicates that imagery with less than 1.5 meter pixel resolution
is suitable for most applications, although a higher 2 foot pixel resolution is very useful for developing
preliminary site plans of a building site in advance of completing a design survey.
Vintage/refresh: For many projects, ADOT Aviation obtains imagery when project funding is acquired.
Comparison with historical imagery is useful for evaluating changing river/coastline conditions, as well
as erosion damage by storm events, flooding, and permafrost degradation. In rural areas along the
coast, ADOT Aviation has found that imagery refresh every 2-3 years is useful for project planning. In
larger communities such as Fairbanks, Kotzebue, Nome, Barrow, and Deadhorse, annual imagery refresh
is desirable, due to the larger number of projects in these areas, and the more complex issues that
ADOT&PF must deal with in these areas. xli
In summary, aviation requirements for imagery cover two main levels: 1) statewide at a resolution to
update the Sectional Aeronautical Charts, approximately 1:500,000 scale; and 2) detailed, high
resolution Tier 3 imagery to cover airport approaches and the facilities themselves. There are no formal
requirements published to date from either FAA or ADOT&PF for imagery at these two levels in Alaska.
Nor, according to Sempeles, is there budget established by FAA to acquire imagery. ADOT&PF is
acquiring imagery on a case by case basis for locations throughout Alaska, and is working with the
DCCED Profiles program to acquire high resolution aerial photography for key communities throughout
the State.
Despite the fact that the ICAO requirements focus on terrain, key experts in aviation cartography stress
the importance of updating imagery at the two levels as soon as possible, as we currently rely on old,
outdated base maps.
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ADOT&PF and Private Transportation Industries
ADOT&PF operates statewide, utilizing imagery base map data on a daily basis. Highway corridors and
villages are most frequently mapped; economic corridors, river corridors and urban areas are also of
interest. Related applications include engineering, environmental analysis and mapping, surveying, and
GIS. Features mapped from imagery include major roads, intersections and road centerlines (100% of
ADOT users); wetland boundaries, geologic units, vegetation, and buildings (half or more of ADOT
users). Most users prefer to work with natural color imagery.
Priority areas of interest include, but are not limited to:
North Slope and gas line infrastructure projects, with a current focus on the area between the
Dalton highway near Pump Station 2 and Umiat. In the recent past, other North Slope evaluated
potential projects have been between Deadhorse and Nuiqsut, and between Deadhorse and
Bullen Point (50 miles east of Deadhorse).
New landfill roads, erosion protection projects, and existing road upgrades.xlii
ADOT&PF note that their data collection efforts typically affect very narrow, project scale areas or
communities. For example, LiDAR data and orthophotos collected for road design cover 2000-3000
foot-wide swaths, which are too narrow for hydrologic or most other applications. However, the
breadth of data collection areas is large. For example, in the Functional Classification Update project,
ADOT&PF mapped roads serving over 300 communities across the state. xliii ADOT&PF requires imagery
updates every three years in order to determine roadway and other man-made structural changes. One
survey respondent observed that ADOT&PF lacks coordination/communication when it comes to
purchasing, processing, storing, and distributing imagery, resulting in duplication of effort and storage.
He advocates ADOT’s use of ESRI's Image Server or a comparable product to centralize the processing,
storage, and distribution for commonly used imagery, and notes that it would be interesting to see if
this type of approach could be scaled to meet the needs of imagery users across the State.
Two private companies involved in the Alaska transportation industry are CH2M Hill and HDR Alaska.
Both serve a large portfolio of clients ranging from state and federal government to private industry.
Both of these firms conduct a large amount of transportation planning and engineering, and rely heavily
on four key sources of mapping data: project specific imagery, typically aerial photography and derived
planimetrics; USGS quadrangle maps; Google Earth imagery; and GINA imagery provided via a Web
service.xliv
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Utilities and Infrastructure
Utilities include electric, gas, and energy in Alaska, and serve most of the inhabited portions of Alaska.
The following three private electric utilities responded:
Chugach Electric Association (CEA)
Golden Valley Electrical Association (GVEA)
Homer Electric Association (HEA)
One natural gas utility responded:
Enstar Natural Gas Company
One respondent represents a private firm:
Jacobs Engineering is involved in the mapping of
infrastructure utilities (e.g., power poles, hydrants, etc.).
The electric utilities operate in the Southcentral region (CEA and HEA) and the Interior (GVEA). They use
base map data mostly on a daily basis for various applications affecting planning, implementation, and
safety; these commonly include engineering, environmental analysis and mapping, design, and land
cover mapping. All respondents map the following features from imagery: major roads and road
centerlines, utilities (e.g., electric poles), parcel/property boundaries, and discrete hydrographic
features. Less commonly mapped features include houses and buildings, pipelines, land cover, and
geologic units. The respondents feel that SDMI should focus acquisition of base map data along highway
corridors, urban areas, environmentally sensitive areas and river corridors. Natural color imagery is the
preferred format (100%) followed by panchromatic (20%). Two of five users indicated that a 3-year
imagery refresh is acceptable, whereas the other three respondents prefer annual updates.
Gas utilities are a major imagery user in Alaska; for example, Enstar provides the bulk of energy supply in
southcentral Alaska. Enstar Natural Gas Company uses imagery and other mapping sources extensively
in their operations, for example in their current BULLIT gas line project, which encompasses route
options from Deadhorse to the Anchorage area. The lack of publicly available base map data has been a
major cost consideration for Enstar, and they have incurred over $1.5 million in the past year to acquire
orthoimagery and elevation data. xlv
Jacobs Engineering provides an example of a private industry involved in utility survey projects. They use
1-meter or better natural color imagery for survey projects statewide. Geologist Jeremy Miner maps
infrastructure and utilities, such as hydrants and electric power poles, in support of engineering,
environmental analysis and mapping, earth sciences mapping and research, and urban planning
projects. Miner notes that any imagery acquired by SDMI for public use should be not only web
accessible but downloadable at full resolution. Imagery refresh is needed as frequently as possible to
document geomorphic processes affecting sites.
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Public Safety and Military Mapping
This use-case includes a variety of user groups from aviation safety to local public safety agencies spread
across the state. The use-case includes 19 survey respondents, the majority within the US DOD or
Municipalities/Boroughs:
Seven Federal (DoD) agencies and sub-divisions:
US Army - CRRL
US Army – National Guard, 103rd CST
US Army – 611th Civil Engineer Squadron
US Army – National Guard, Environmental Section
US Air Force – ARCTEC Alaska
US Air Force – Elmendorf AFB
US Air Force – Eielson AFB
Two Federal (civilian) agencies:
BLM – Alaska Fire Service
Federal Emergency Management Agency (FEMA)
Seven Municipalities/Boroughs:
City and Borough of Juneau
Fairbanks North Star Borough
Kenai Peninsula Borough
Ketchikan Gateway Borough
Matanuska Susitna Borough
Municipality of Anchorage
North Slope Borough-Planning Dept.
One State agency: Alaska Department of Military & Veterans Affairs (ADMVA)
One private agency: Geographic Resource Solutions
Military Facilities Base Mapping and Homeland Security
A primary DOD application is to map military facilities; this includes extraction of urban features
such as roads and centerlines, parking lots, building footprints, parcel/property boundaries,
utilities, and pipelines. In addition, a majority also map vegetation, hydrographic features and
wetlands (discrete and general) to meet DOD environmental-protection regulations. These
agencies may also map civilian populated areas for public safety applications such as emergency
response, disaster planning, and fire hazard planning/wildfire. Three-quarters of the
respondents report using natural color imagery; nearly half use color-infrared aerial
orthophotography; and almost one-third use panchromatic or multispectral imagery. Recent
acquisitions (2002-2007) included high-resolution (0.25-6 meter) natural color orthophotos and
QuickBird imagery. Priority areas for future imagery data acquisitions include highway corridors,
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villages, urban areas, and conservation areas. Most users prefer annual imagery refresh rates,
although 3-5 years is recognized as being more realistic.
The ADMVA (Daniel Anctil) emphasizes public safety (emergency response and disaster
planning) as well as resource management and development. A specific application is Homeland
Security vulnerability assessments and mitigation planning for critical facilities. For this
application, ADMVA extracts building footprints from imagery to identify critical facilities and
damage assessment locations. ADMV operates statewide, with critical mapping areas identified
as urban zones, villages, and fluvial and highway corridors. They use color imagery and desire
annual imagery updates in order to be able to locate current infrastructure and residences for
damage and threat assessments in emergency response.
Public Safety
The BLM Alaska Fire Service (AFS) located at Fort Wainwright, provides wildland fire suppression
services for all departments of the Interior and Native Corporation Lands in Alaska. In addition,
AFS has other statewide responsibilities, including interpretation of fire management policy;
oversight of the BLM Alaska Aviation program; planning, implementing, and monitoring fuels
management projects; disposing of hazardous materials; and operating and maintaining
advanced communication and computer systems such as the Alaska Lightning Detection System.
AFS operates on an interagency basis: cooperators include the BLM, ADNR, USDA Forest Service,
NPS, BIA, USFWS, and the U.S. Military in Alaska.xlvi Survey respondent Sean Triplett reports that
AFS employs digital imagery for area mapping, feature extraction, and analysis related to
wildland fire suppression and response. In addition to mapping land cover and forestry
features, AFS maps building footprints in wildfire hazard areas. Because of the breadth of the
operation, imagery is required for all geographic area types (e.g., transportation corridors, urban
areas, environmentally sensitive areas, etc.), particularly within the Alaska Interior region. AFS
specified a desired imagery refresh rate of every five years, in contrast to the more stringent
requirements of the other surveyed Public Safety agencies.
FEMA uses features derived from imagery and elevation data as input to HAZUS, FEMA’s
powerful risk assessment software program for analyzing potential losses from floods, hurricane
winds and earthquakes. In HAZUS-MH, current scientific and engineering knowledge is analyzed
in a GIS to produce estimates of hazard-related damage before, or after, a disaster occurs. xlvii
The Boroughs/Municipalities rely on digital base map data for their public safety programs
which include emergency response, fire hazard planning and response, and disaster planning.
For example, the Anchorage Fire Department contracted Geographic Resource Solutions to
perform a fire hazard and exposure modeling analysis. Geographic Resource Solutions used
image classification to develop a comprehensive vegetation/fuels map; then cross-walked these
types into fire-fuels classes and incorporated fuels, topography, weather, risk, and hazard to
develop a fire-exposure model within the MOA urban-wildland interface. Geographic Resource
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Solutions also developed a mobile fire mapping package that allowed firefighters to map fireincident features in the field on a PDA using GPS and ESRI’s ArcPad™ mapping software. The
models and data were made available to planners and managers through a decision support
system that allows the users to evaluate incident scenarios and mitigation strategies.xlviii
User Requirements
For any major imagery program, the intended mapping applications and scale of target mapping
features will largely drive the users’ selection of key imagery parameters:
Geographic coverage
Image resolution
Spectral range and bandwidth
Geometric accuracy
Temporal, seasonal, and update requirements
Allowable cloud cover
Sensor platform
Format requirements, e.g., datum, projection, tiling, etc.
Table 4 lists the SDMI User Survey general mapping applications by user response rate. Based on the
summed response percentages, we observe that most respondents employ imagery for two or more
applications. Management, environmental, and science-related applications are more common than
urban, transportation and economic applications.
Use-case
Applications
Most Common Features Mapped
Response
Land Management
Land Management
Includes cadastral mapping and
land records
Environmental analysis and
mapping
Parcel boundaries, administrative
boundaries
64%
Hydrography,
Other environmental features
44%
Land Cover Mapping
Land Cover Mapping (wetlands,
vegetation mapping)
37%
GIS and related
consulting
GIS and related consulting
Natural Resource
Inventories
Transportation &
Infrastructure
development and
planning
Public Safety
Earth sciences mapping, Forestry,
Mining
Transportation & Infrastructure
development and planning
Vegetation:
Wetlands; tree canopies; remote
sensing derived features
Wide variety of applications
including land management,
environmental, land cover,
transportation
Forest stands, geologic units, land
cover units
Roads, parking lots and
impervious surfaces, buildings;
utilities; pipelines
Roads, hydrography, ice cover,
buildings, parcel boundaries
39%
Environmental
analysis and mapping
Aviation safety, local government
public safety, wildfire mitigation
and response
Table 4 General User Applications
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33%
29%
29%
SDMI Imagery Workshop Whitepaper
Table 5 below lists specific target map features by survey response rate. Hydrographic features, both
large and small scale, are the most commonly mapped features by all users (55-75%). Land cover, parcel
boundaries, wetlands and roads are mapped by about half the respondents. Urban footprints and
infrastructure are mapped by about one-third of respondents. Resolution requirements are discussed in
a subsequent section.
Response:
Hydrographic features (discrete, e.g. river banks, ponds,
discrete coastlines, etc.)
General hydrographic features (e.g. broad outlines of
rivers, coastline)
Vegetation, land cover, e.g. forest stands
Parcel/property boundaries
Wetland boundaries (discrete, e.g. ¼ acre per COE, EPA)
Major roads & intersections
General wetlands
Road center lines
Houses and building footprints
Geologic (e.g. unit mapping)
Utilities e.g. hydrants, electric power pole
Pipelines
Tree canopies
Mining
Commercial buildings
Parking lots/impervious surface
Agricultural
Pivot irrigation
Response %
Response Count
75.2
106
55.3
78
53.2
51.8
50.4
48.9
47.5
46.1
34.8
31.9
31.2
30.5
27.0
26.2
24.8
24.1
10.6
2.8
75
73
71
69
67
65
49
45
44
43
38
37
35
34
15
4
Table 5Features most commonly mapped by SDMI survey respondents
Alaska map-features as listed in Table 5 are mostly dynamic in nature, particularly hydrographic features
which dominate many landscapes. Coastlines, stream banks, and other features are in a state of rapid
change, especially in areas such as the North Slope and western Alaska. Hydrographic enforcement (a
feature of some DTMs) is not a requirement according to some of the key users (North Slope Borough,
BLM, NPS, USFWS, and The Nature Conservancy); rather, the most urgent requirement is current
imagery of consistent quality. Land cover, mapped by both private industry and government, is also in a
state of flux. Standardized, consistent imagery sources for mapping of vegetation/land cover are sorely
lacking in Alaska. Transportation features, e.g., roads and airfields are highly dynamic, requiring a good
quality imagery base for mapping. Aviation safety requires not only current infrastructure mapping, but
the updated mapping of natural features such as ice cover, waterbodies, and hydrography, which are
depicted in the FAA sectional charts., The SDMI User Survey indicates that in many cases redundant
imagery is being acquired at similar resolutions and quality to map these features, often with one
organization having no idea that imagery was acquired for the same area previously. The urgent
requirement is thus to obtain current imagery of consistent quality, in a publicly-available format.
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In addition to user applications and features to be mapped, considerations that may restrict selection of
imagery parameters include program budget, image-target accessibility, programmatic standards and
computer system resources. Challenges particular to mapping the entire state of Alaska include the
tremendous land area, of which a significant portion is remote; high latitude geography, which results in
low sun angles, terrain shadowing, and persistent snow cover during much of the year; and frequent
cloud cover. These factors may restrict the sensor platforms available for the monumental task; limit
suitable timeframes of acquisition (thereby prolonging completion of full state coverage); impose
burdens on system requirements for data access, storage and processing; and finally, require careful
planning for budget expenditure. Compromises may be called for in terms of key image parameters,
such as resolution and geometric accuracy.
The following sections discuss user requirements as ascertained from the SDMI User Survey, within the
framework of industry standards and the above limiting factors.
Geographic Coverage
The results of the User Survey strongly support the SDMI goal for statewide mapping: 42% of
respondents indicated that they operate on a statewide basis. Regionally, South-central Alaska is
mapped by 34% of users; the Interior, Southeast, and North Slope regions each by 16%; the Southwest
and Northwest each by 11%; and Aleutians and Bering Sea each by 4.5%.
Figure 15 Alaska Generalized Regions from SDMI User Survey
Users were also queried regarding area types that they commonly map, with results enumerated in
Table 6, below. Nearly half of the respondents indicated that they map all of the listed area types.
About one-third of users map river corridors. Villages, highway corridors and urban areas are each
mapped by more than one-quarter of users. Land management area categories are each mapped by
10—15% of users. Somewhat contradictory information is documented in Tables 4 and 5 (listing
common applications and target map features by user response rates). According to these tables, users
more commonly map management and natural areas than urban areas and infrastructure, a reversal of
the situation portrayed by Table 6.
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SDMI Imagery Workshop Whitepaper
Area Type
Response (%)
All of the types listed below
River corridors
Villages
Highway corridors (e.g. Parks highway, etc.)
Urban areas (i.e. cities)
Environmentally sensitive areas, e.g. wetland, floodplains, habitat zones
Coastal areas
Economic corridors (e.g. pipeline route, mine access road, etc.)*
Conservation management areas, e.g. parks, refuges, national monuments, etc.
Land management areas, e.g. Native corporate regions, National Petroleum
Reserve Alaska, etc.
Forest management areas
46.8
29.1
29.1
27
26.2
24.8
24.8
21.3
14.2
14.2
10.6
Table 6 Preferred Acquisition Area by Area Type
* Examples of economic corridors are gas and oil pipeline corridors such as the Trans Alaska Pipeline System (TAPS), and the
proposed ANGDA line, and Enstar BULLITT line.
As a follow-up activity to the survey we requested and received specific areas-of-interests (AOIs) from
respondents via shapefile and through the DataDoors™ application set up by i-cubed. A list of these AOI
providers is shown in Appendix 4.3 of the SDMI Task 1 Report. The AOIs were analyzed in GIS, using a
simple grid additive overlay to map overlapping respondents’ AOIs. Areas-of-interest generated through
the AGDC effort in 2005 were also incorporated into the analysis. Figure 16, below, illustrates the
mapped results, where darker red shades indicate the most requested areas.
Figure 16 Acquisition Areas for Digital Base map Data
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Spatial Resolution
The majority of surveyed users indicated that appropriate spatial resolution is one of the most
important characteristics of digital imagery for their applications. The user’s application determines the
scale of target features to be mapped, and therefore the optimal image resolution for the application.
The “best” image resolution for a project may not be the highest available, but the lowest resolution
that captures the target features at the desired level of detail. Data with a resolution that is several
factors higher than needed may complicate mapping procedures by introducing unnecessary image
detail and complexity, also taxing system resources. Conversely, image resolution that is lower than
needed may not deliver adequate results. Data sharing can pose challenges to selecting a single image
resolution appropriate for multiple applications at different scales.
SDMI survey participants were asked to indicate which features they commonly map (Table 5). In Tables
7, 8 and 9, we correlate these features with the resolution ranges required for feature capture (taken
from industry standards and a survey of Alaska users’ past dataset acquisitions). Incorporated in the
tables are a few additional, non-surveyed map feature types and corresponding resolution
requirements; these are included largely on the basis of information provided by users in the Task 1
SDMI User Survey Existing Imagery Inventory (Appendix 4.5).
Responses are grouped into scale of feature categories: Table 7 lists broad scale features (2.5-10
meters), Table 8 lists moderate scale features (1-2.5 meters) and Table 9 lists detailed scale features
(sub-meter). For a given target feature, the lower number in the Required Image Resolution range
represents the resolution requirement to achieve the most detailed level of feature extraction. The
higher number represents the limit at which the feature can still be resolved for general extraction
requirements, as well as for mapping applications, e.g., navigation, aesthetic/information backdrops.
Response:
Required Image
Resolution (m)
Scale of
Features
Survey
Response (%)
10+
very broad
55.3
10+
very broad
53.2
10+
2.5-5
2.5-10
2.5-10
2.5-10
very broad
broad
broad
broad
broad
31.9
47.5
26.2
*
2.8
2.5-10
broad
not surveyed
2.5-10
broad
not surveyed
2.5-10
2.5-10
broad
broad
not surveyed
not surveyed
General hydrographic features (e.g. broad outlines
of rivers, coastline)
Vegetation, land cover (e.g. general mapping of
forest stands, e.g., to NVCS Alliance level)
Geologic (e.g. unit mapping)
General wetlands, statewide
Mining (landform/feature delineation)
Parcel/property boundaries - Underlay
Pivot irrigation
Vegetation - detailed classification of small-area
canopy /understory to NVCS Assoc. or higher level
General broad urban cover (e.g., built-up areas,
parks, bare soil, etc.)
Land use boundaries - Underlay
Natural area boundaries - Underlay
Table 7 Broad Scale Features Mapped from Image Sources (2.5–10 meters)
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Response:
Required Image
Resolution (m)
Scale of
Features
Survey
Response (%)
1-2.5
moderate
75.2
1-2.5
moderatebroad
50.4
1-2.5
1-2.5
1-2.5
moderate
moderate
moderate
not surveyed
not surveyed
not surveyed
Required Image
Resolution (m)
Scale of
Features
Survey
Response (%)
Hydrographic features (discrete, e.g. river banks,
ponds, discrete coastlines, etc.)
Wetland boundaries within developed areas
(discrete, e.g. ¼ acre per COE, EPA)
Land use boundaries - Extraction
Natural area boundaries - Extraction
Rural cover
Table 8 Moderate Scale Features Mapped from Image Sources (1-2.5 meters)
Response:
Tree canopies
0.6-4
Agricultural Mapping & Monitoring
0.5-2.5
Parcel/property boundaries - Extraction
Pipelines
0.5
0.3-1
0.25-4
Protected areas trails, roads and infrastructure
Parking lots/impervious surface
Commercial buildings
Houses and building footprints
Military base mapping
Major roads & intersections
Road center lines
Utilities (e.g. hydrants, electric power pole)
0.25-2.5
0.25-1
0.25-1
0.25-0.6
0.25 to 1
0.25 to 1
0.075-0.3 (0.25
foot to 1 foot)
detailed moderate
detailedmoderate
detailed
detailed
detailedmoderate
detailedmoderate
detailed
detailed
detailed
detailed
detailed
detailed
27
10.6
*
30.5
not surveyed
24.1
24.8
34.8
not surveyed
48.9
46.1
31.2
Table 9 Detailed Scale Features Mapped from Image Sources (sub-meter to 1 meter)
* Survey question did not distinguish between Extraction and Underlay, More common use would be Underlay.
In the very broad scale feature category (10+ meters), general hydrographic features and
vegetation/land cover features are mapped by over 50% of respondents. These features have
generalized boundaries and are mapped at a small scale over large areas. Thus, imagery with large
swath width and lower resolution would be appropriate for users mapping at this scale.
In the broad scale feature category (2.5-10 meters) general wetlands are mapped by nearly 50% of
respondents. Given that most regions of Alaska, including mountainous areas, contain extensive areas of
wetlands, a large portion of the state would need to be imaged at 2.5-10 meters to meet users’ needs.
In the moderate scale feature category (1-2.5 meters), discrete hydrographic features are mapped by
75% of users. Since hydrographic features are ubiquitous in Alaska, imagery within this resolution range
may be required for much of the state.
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The detailed scale feature category (sub-meter) contains the largest number of feature types. These
features are primarily concentrated in urban areas and transportation corridors; thus high resolution
requirements correspond to small scene-area requirements. Discrete wetland boundaries (e.g. ¼ acre
per COE, EPA) are mapped by half the respondents in this category; these features are also very small,
but distributed throughout the state, posing more of a challenge for imagery collection.
Spectral Properties
As with image resolution, the specific user application determines the optimal spectral parameters to
achieve the mapping goal. In the real world, however, there is often a necessary trade-off between
more accurate spectral information and higher resolution, due to constraints such as available sensor
characteristics, data-sharing, and budget. For example, a user may need to choose between 30 m.
resolution Landsat imagery with seven spectral bands, and higher resolution SPOT-5 imagery with only
four spectral bands. Most SDMI Survey respondents indicated that they prefer natural color imagery;
many users however rely on multi-spectral (MS) or color-infrared imagery for their remote sensing
applications.
Imagery vendors offer various spectral products to satisfy the requirements of different mapping
applications. Aerial photography options include black-and-white film, color film, and color-infrared
film; aerial digital options include panchromatic (PAN) and multispectral (MS). Many satellite imagery
vendors such as SPOT, GeoEye and Digital Globe offer three digital products: high resolution
panchromatic (PAN), lower resolution multispectral (MS) and a pan-sharpened multispectral (PSM)
product, which merges the detail of the panchromatic image with the multispectral bands.
PAN imagery can be used in urban or engineering applications. The crisp detail at high contrast is useful
for discrete feature extraction, such as mapping of roads and infrastructure, although PSM is more
commonly used for its aesthetic value as a backdrop to proposals/presentations. Because PAN imagery
is only a single bandwidth (covering the range of the visible spectrum) it is not useful for spectral
analysis.
MS imagery is conventionally used for thematic mapping - classifying an image into discrete segments
corresponding to surface characteristics, such as land cover types. Various natural resource
applications, agricultural mapping, and some urban applications such as impervious surface mapping,
employ classification of multispectral imagery. Considerations when evaluating multispectral products
with similar resolution capabilities include the number of spectral bands, band width, and dynamic
range of each band. PSM imagery (sometimes referred to as “color” or “color-infrared (CIR)” depending
on band selection) is commonly used for map navigation, aesthetic and informative backdrops, 3-D
visualization, area calculations, and discrete feature extraction. There is some debate whether the PSM
product is appropriate for image classification purposes. Purists argue that the spectral signals of
surface features on which classification relies may become distorted by the pan-sharpening algorithm,
reducing the quality of the classification. Also, the high resolution of PSM imagery may render
traditional broad classification categories meaningless; for example, a 30-meter Landsat pixel classified
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SDMI Imagery Workshop Whitepaper
as “Urban,” when pan-sharpened to a higher resolution, may segment into multiple classes such as
“Grass,” “Open Forest,” “Bare Soil” and “Impermeable Surface” xlix. However, current research also
indicates that use of PSM may improve classification results in situations where a significant proportion
of classification features are too small to be resolved in MS imagery. In these cases, care should be
taken to ensure that the pan-sharpening algorithm is one that minimally modifies the multispectral data
number values, that all available multispectral bands are processed, and that dynamic range adjustment
(to improve the aesthetic quality of a scene, or create appearance of uniformity with other scenes) is
avoided. l
SDMI queried survey participants regarding their spectral preferences, with results given in Table 10
below. Users were also queried regarding their general form of usage of digital imagery and elevation
data (Table 11 below). The two tables correlate well: the highest-use spectral type is Natural Color
(61%), which correlates with the highest use category, Basic Mapping, (89%) as well as Visualization
(55%). The second highest-use spectral type is Multispectral, which correlates with the second-highest
use category, Advanced Mapping (73%), as well as Remote Sensing (42%). Several users also noted the
utility of multispectral imagery for deriving a variety of other image types (e.g., pan-sharpened
multispectral, natural color and color-infrared) as well as map products. Color-infrared (CIR) received
relatively low responses, indicating that most users prefer to use multispectral imagery over CIR film or
PSM CIR imagery for vegetation mapping applications. The lowest use spectral type is Panchromatic,
which correlates with less prevalent Surveying and Design use categories. In summary, almost all users
require natural color imagery, and two-fifths require multi-spectral imagery.
Spectral Type:
Response %
Natural Color
Multispectral
No Preference
Color-Infrared (CIR)
Panchromatic
61.1
29.0
24.4
22.1
9.2
Table 10 User Preferences by Spectral Type of Imagery
Use Category
Response %
Basic mapping (simple base map, navigation, other)
Advanced mapping (analysis, other)
Visualization (3D, other)
Remote sensing
Surveying
Design (in CAD, other)
N/A
88.7
72.7
55.3
42
18
16
3.3
Table 11 Use level and form of usage of digital imagery and elevation
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Geometric Accuracy
The majority of surveyed users indicated that high geometric accuracy is one of the most important
characteristics of digital imagery for their applications.
Specific accuracy requirements were not
surveyed; however, Dewberry (2008) conducted interviews with key Alaska user groups regarding their
DEM accuracy requirements, and states: “All DEM user groups were consistent in their stated needs for
DEMs to have horizontal accuracy equivalent to 1:24,000-scale topographic quad maps, i.e., about 14
meter horizontal (radial) accuracy at the 95% confidence level, defined as Accuracy by the FGDC.” This
indicates that most users would be satisfied by 1:24,000 NMAS image accuracies for regional-scale
applications. These conclusions coincides with SDMI’s stated goal of acquiring imagery base map data
for Alaska with sufficient registration accuracy to meet the National Map Accuracy Standards (NMAS) at
1:24,000 statewide and at other scales for designated areas.
NMAS MAP SCALE
1:12,000
1:24,000
1:50:000
1:63,360
NMAS CE90 (m)
10.16
12.19
25.40
32.19
NSSDA CE95 (m)
11.588
13.906
28.970
36.700
RMSE (m)
6.695
8.035
16.74
21.21
Table 12 Approximate NMAS Map Scale Equivalencies
Terrain Source
Among other applications, DEMs are used in the image orthorectification process by supplying the base
elevations to remove distortion due to terrain effects and sensor look angle. To reach the SDMI target
image accuracy of 1:24,000 requires higher-accuracy DEMs than currently exist statewide for Alaska.
Researching and identifying appropriate technology options to satisfy user requirements is thus of
paramount importance to achieving the goals of SDMI. To this end, SDMI and the Alaska Geographic
Data Committee (AGDC) sponsored the Alaska DEM Workshop on July 22-23, 2008, the outcome of
which was a whitepaper summarizing user requirements, available technology options, and workshop
sponsor and attendee recommendations (Dewberry 2008). Out of the DEM Workshop, a distinction was
made between the need for terrain as a source for orthophoto generation, and the need for terrain to
meet requirements for applications such as aviation safety and coastal erosion monitoring. It is worth
noting that the Mid-Accuracy terrain model, required to satisfy the majority of application needs
statewide, can meet the requirements for orthophoto generation at a scale of 1:24,000 and better.
However, the inverse is not necessarily true. A terrain model capable of producing suitable
orthophotos, will not meet all application uses for terrain statewide. For the purpose of this Imagery
Whitepaper, the focus of terrain requirements will be for suitable ortho-image base map production.
A Low Accuracy DTED Level 2 type terrain model could be sufficient to achieve the SDMI's primary goal
of providing a DEM suitable for orthophoto generation (Conclusion 9, Dewberry 2008) provided the
image and DEM both have the horizontal accuracy required for that scale, and provided the incidence
angle of the imagery and the slope of the terrain do not cause excessive displacement. Various satellite
sensor systems are capable of producing DEMs meeting or exceeding the required accuracy, including
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the ASTER Global DEM (G-DEM), GeoEye's IKONOS, Digital Globe's WorldView-1, Spot Image Corp's
SPOT-5 HRS, ASRC's Cartosat-1, and MDA's Radarsat-2. The GCP requirements to produce these
datasets, and accuracies achievable are highly variable (please refer to Table 5R of Dewberry 2008 DEM
Whitepaper). Note that projects requiring sub-meter imagery at 1:4,800 or better accuracies would
need more accurate DEMs; these projects often utilize aerially-acquired stereo-imagery, from which
DEMs are extracted through photogrammetry.
Cloud Cover Restrictions
Satellite imagery archives of Alaska show significant data loss due to cloud cover and cloud shadows.
For example, filtering IKONOS and QuickBird scenes for 0-10% cloud cover eliminates 70% of the Alaska
archives. Surprisingly, extending the cloud cutoff to 20% increases the available selection by just 2-3%
of the total archive, whereas reducing the cloud cutoff to 5% restricts the available archive by only an
additional 3% of the total. The SPOT-5 HRG archive shapefile was delivered to SDMI pre-filtered to
remove scenes with greater than 20% cloud cover. Of this archive, two-thirds of the scenes have
virtually no cloud cover, one-third of the scenes have 2-10% cloud cover, and only a small fraction have
11-20% cloud cover.
Given these statistics, SDMI’s stipulation of 0-10% cloud cover per contiguous order area is very
supportable. The AHAP statewide imaging program (1978-86) also used a 10% cloud cover cutoff. The
implications of this restriction are a prolonged timeframe to complete imaging of the state compared to
imaging of non-cloudy geographies of comparable area.
For perpetually cloudy areas of Alaska such as Ketchikan Gateway Borough, satellite imagery collection
may be less viable than aerial photography, which has been used in the past to aggressively capture
imagery on rare, cloud-free days. Synthetic aperture radar (SAR), a non-optical source of remote
sensing, offers another solution for imaging perpetually cloudy areas. SAR captures the textural
characteristics of the landscape and is useful for differentiating land cover types and linear features.
SAR imagery can be merged with a lower resolution optical data source to produce a natural color
product (color orthorectified radar image) thereby rendering the imagery more interpretable. Given the
users’ requirements for multispectral imagery, color orthorectified radar Imagery would be a fallback
solution applied only to limited areas with chronic cloud cover.
Temporal, Seasonal, and Update Requirements
The SDMI Executive Committee has decided on a requirement of in-season acquisition of source data,
defined for SDMI purposes as May 1 through September 30. This restriction is in place in order to
maximize sun angle and vegetative cover, and minimize shadow, snow and cloud cover in the source
data. Some satellites, such as SPOT-5 and ALOS, have historically only collected imagery over Alaska only
during this time frame. Other satellites, for example, IKONOS and QuickBird have collected year-round.
The seasonality restriction eliminates about one-third of the available archives (pre-filtered for 10% or
less cloud cover) for the latter two sensors. In terms of application requirements, users have reported
that they require occasional access to winter imagery, e.g., to resolve class confusion in spectral analysis
for land cover mapping.li
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One suggestion at the workshop from Gene Dial of GeoEye was that a more flexible collection window
be applied. With a longer season, especially in the southern parts of the state, statewide collection could
be achieved more rapidly. It is recommended that, rather than a strict date window, more flexible
criteria based on sun angle, snow cover, and vegetation senescence be used for the statewide
collection.
A key finding, shown in Table 13, indicates that most survey respondents (70%) want a minimum fiveyear refresh rate on imagery; 46% favor refresh of three years or better, citing the following reasons.
Land surface features are dynamic, changing over relatively short time frames (in human terms).
Hydrographic features, such as stream banks, are particularly in flux.
Urban landscapes change at a rapid rate; for example, the addition of roadways.
Ability to respond to new events is important: e.g. fire, flood, and earthquake response
management.
Frequency
Response %
Annually
Every three years
Every five years
At least every five to ten years
18.4
27.7
24.8
12.1
Table 13 Desired Imagery Refresh Rates
Despite user preferences, a statewide refresh rate of three years will be challenging to complete, given
the huge state area to be imaged, limited acquisition season, and cloud cover restrictions. Sensors with
lower resolutions and larger swath widths will be more capable of imaging Alaska statewide within the
three to five year requested refresh period. Most users have indicated that more frequent refresh rates
are required for key areas undergoing rapid change, e.g. urban areas, environmentally sensitive areas,
volcanically active areas, etc.
Sensor Platform
Sensor platforms include airborne transport (airplanes, helicopters) and space-borne (satellites).
Satellite sensors increasingly provide more viable, economic options for imaging large or remote
geographic areas than their aerial counterparts. Existing sensors offer resolutions as high as 0.5 meters
for unclassified use, appropriate for most urban applications. For specific infrastructure mapping
applications that require ultra-high resolution, and for areas beset with frequent cloud cover, aerial
photography remains the best option.
Dynamic Range
Dynamic range refers to storage allocation per pixel of information. Sensors with high dynamic range
are able to capture spectral information even at low light intensities, for example within shadowed
portions of an image. The USDA Forest Service is one agency that requires the use of sensors with high
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dynamic range to image coastal areas with steep topography, enabling extraction of land cover
information within shadowed areas.lii
Datum, Geoid Model and Projection Requirements
SDMI Survey respondents were not queried regarding these requirements, however based on
discussions with users and the author’s familiarity with usage in Alaska the most common datums and
projections in use in Alaska in order are:
Alaska Albers, NAD 27
Alaska Albers, NAD 83
State Plane Zones (varies from Zone 1 to 10 across the state)
UTM (varies)
There has been a major effort on the part of the Alaska mapping community to standardize on NAD83
datums for mapping, but in some agencies the migration from NAD27 to NAD83 has been slow (Jennifer
Dowling, USCGS, pers.comm., 2009). Due to the number of state plane and UTM projection zones in
Alaska (10 and 10 respectively), most users on a statewide basis use Alaska Albers, for example
ADOT&PF. liii
Based on congruity with the Alaska DEM user group preferences [Dewberry, 2008] the NAD83 datum
and the GEOID06 geoid are preferred by most users, and are current standards for all Alaska geospatial
data.
The DEM user group survey indicated that most groups (ten of fourteen) prefer geographic coordinates
(latitude and longitude), at least for elevation data. Three DEM user groups, all federal (BLM, NRCS and
USGS) also prefer the Alaska Albers Equal Area projection. Two DEM user groups (DCCED and DOT),
working at the infrastructure level, use State Plane coordinates.
Requirements for Data Format, Delivery and File Storage
Imagery scenes are large, requiring significant computing resources to store, view, and manipulate.
Several SDMI Survey respondents indicated that large file size can be an impediment to their use of
imagery for their applications. Thus most users benefit by working with the lowest pixel resolution that
meets the requirements of their analysis. For example, when mapping large wilderness areas, a higherthan-required pixel resolution may unnecessarily slow image loading, viewing and processing. Larger file
sizes are also more difficult to download by ftp. Most image processing, GIS and cartographic
applications cannot open files over 2 GB in sizeliv. Image vendors require tiling of image strips or scenes
that exceed a given size. Vendors offer pixel-based and map-coordinate-based tiling schemes. Mapbased tiling may be available only for projected data (i.e., excludes data in geographic coordinates). The
problem of viewing large areas of high resolution imagery at low zoom is commonly solved by use of
reduced resolution datasets (R-Sets). With the approach, a user needing ½ x zoom would load the R-Set
at ½ x resolution. Use of R-Sets is automatic with ERDAS, Remote View, SOCET SET and other commonly
used remote sensing software packages.lv
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Fifty percent of SDMI Survey respondents reported that they prefer to work with data that is served
over the Internet (e.g., Web Map Service). Thirty-eight % of respondents prefer local storage. Two-thirds
of respondents prefer working with full-resolution image formats (e.g., geotiff), whereas 46% prefer
compressed formats (e.g., MrSid).
SDMI has devised the following data service solution: Imagery delivered to SDMI from vendors will be
ingested into the SDMI storage and service system. The data will be accessible to users via seamless web
services for user-selected regions. Users who need broader areas will benefit either from SDMI
mosaicking work, then extraction via web services call, or from delivery of full scenes or swaths of raw
data of consistent radiometric properties. Users will also have the option to receive uncompressed or
compressed image files dependant on their storage capabilities or intended use of the data.
Metadata
More than half the SDMI Survey respondents ranked adequate metadata/documentation as a highpriority aspect of base map data. Fourteen percent of users are content with bare minimum metadata;
52% want additional details about the data, including how it was developed; and 27% want
comprehensive metadata, with very detailed information such as camera models. SDMI intends to
require a high standard of metadata for new product deliveries.
Licensing
Ninety-nine percent of SDMI Survey respondents indicated that having a publicly available and updated
digital base map would strengthen their organization’s GIS or mapping program. A key improvement to
facilitating their use of digital base map data would be to “make the data more available.” Thus, SDMI
hopes to maintain the goal of obtaining the broadest possible licensing options for stakeholders, within
their budget.
Summary of SDMI Imagery Requirements
SDMI’s broad goals are to acquire new and better digital base map data for Alaska and to make existing
datasets more accessible to users. The more specific objective - “to ultimately provide an accurate,
current, seamless, single source, statewide base map, made available over the internet, through open
standards, free of charge, to all” is attractive in the simplicity of a one-size-fits-all approach. The SDMI
User Survey has provided the raw data against which to examine and refine this objective.
Analysis of the SDMI Survey results from the standpoint of application use-cases and user requirements
leads to the following conclusions regarding the required attributes of imagery dataset(s) to fulfill the
needs of all stakeholders:
Applications and target features-to-be-mapped drive key imagery requirements such as spatial
resolution, geometric accuracy and spectral properties. Respondents to the SDMI User Survey
use imagery for a very wide array of applications (See Application Requirements section), and to
map features of widely ranging scales and levels of detail. It is thus clear that a single imagery
type dataset will not meet the application needs of all users. However, many of the use cases
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have expressed a need for a statewide imagery coverage that is consistent in terms of vintage
and reasonably accurate.
Users are most concerned with image resolution and geometric accuracy. Users’ image
resolution requirements are derived from their target map features (Tables 7-9). Three main
scales of features mapped are identified to encompass all of the users’ needs:
Broad scale features:
2.5-10 meters
Moderate scale features:
1-2.5 meters
Detailed scale features:
sub-meter to 1 meter
Commonly mapped features (primarily hydrographic as identified from the survey) are identified
in each of these resolution categories; thus, again, it is clearly not possible to select a single
target resolution range to meet the needs of all Alaska imagery users. A tiered approach is
therefore proposed here.
Tier 1 accommodates the need of users for a small-scale, statewide imagery base map from which broad
scale features can be extracted. This tier would also provide the necessary data to refresh USGS
topographic maps, which form the basis for so many users’ mapping applications.
Tier 2 accommodates the needs of users who require regional geographic coverage at moderate
resolution.
Tier 3 provides imagery of compact areas – primarily urban environments, villages, and transportation
corridors – at detailed resolution.
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One meter imagery down-sampled to 5 m
Potential Tier 1 – broad scale features
One meter imagery down-sampled to 2.5 m
Potential Tier 2 – moderate scale features
One meter imagery level of detail
Potential Tier 3 – detailed scale features
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To address users’ requirements for higher geometric accuracy, SDMI has previously established
the goal of acquiring imagery base map data with sufficient registration accuracy to meet the
National Map Accuracy Standards (NMAS) at 1:24,000 scale statewide and at other scales for
designated areas (e.g., Tier 3 detailed scale mapping areas).
To achieve the statewide target accuracy of 1:24,000 scale, a DTED Level 2 DEM could be used
to meet the requirements for image orthorectification to 1:24,000 NMAS accuracy, provided the
image and DEM both have the horizontal accuracy required for that scale, and provided the
incidence angle of the imagery and the slope of the terrain do not cause excessive displacement.
Low-accuracy DEMs, which may meet the necessary accuracy requirements, can be produced
from various satellite sensor datasets, including the public domain ASTER G-DEM to be delivered
in 2009 (Dewberry, 2008).
If a mid- accuracy DEM is available in the future, it would offer several advantages over a lowaccuracy DEM. Ortho-imagery derived with the use of a mid-accuracy DEM will have significantly
improved geometric accuracies. In addition, its usage will enable increased image collection
capacity due to less restrictive source data collection parameters.
Consistency of regional coverage is also a concern in terms of vintage and consistency of
methodologies used for analysis.
User Areas of Interest vary, but are notably concentrated in areas coinciding with economic
corridors, airports, and generally more inhabitated areas. Programs are underway by various
agencies to collect some of these areas, e.g. the Profiles program and DOT Aviation
Required spectral properties of imagery are driven by user applications. Most users prefer to
work with natural color imagery; this preference could be met with MS data, PSM data or
scanned color film products. A smaller, but significant percentage of users require MS data to
achieve their application objectives (e.g., spectral classification). There is some debate whether
PSM data can be substituted for MS data for such applications, and consideration will need to be
given to product resolution and number of spectral bands to determine the best product.
SDMI has set a target acquisition window of May 1 through September 30 (Alaska in-season).
Although this restricts some vendor archives, the payback is higher quality imagery with more
relevant information.
Most users request a 3-5 year imagery refresh rate for statewide base map coverage (e.g., Tier
1). Users mapping dynamic natural or urban features, or involved in applications such as
disaster management, require higher refresh rates, generally on the order of every one to two
years. More information regarding refresh rates will be provided at the Imagery Workshop, and
help in decision making.
A 10% cloud cover cutoff does not significantly restrict the available satellite archives compared
to a 20% cloud cover cut off. Rates of new acquisition and desired refresh will highly influence
meeting this requirement.
Datum and projection requirements should conform to state standards, and to those identified
for DEM datasets by Dewberry (2008), as well as users’ preferences.
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The Geographic Information Network of Alaska (GINA) delivers data via seamless web services
to enable user selected regions. For users who need broader areas, they will benefit either from
SDMI mosaicking work, then extraction via web services call, or from full scenes or swaths of
raw data of consistent radiometric properties.
Most users desire moderately specific metadata.
compliant metadata.
The current federal standard is FGDC-
All users agree that having a publicly available and updated digital base map would strengthen
their organization’s GIS or mapping program.
Display Scale vs. Accuracy Scale
The stated accuracy goal for the SDMI is to create a statewide ortho-image that meets 1:24,000 scale
NMAS. When scale is used as a measure of accuracy there are associated horizontal accuracy standards
that must be met. The following table lists the horizontal accuracy equivalent s that would need to be
met to achieve various scales of mapping:
NMAS MAP SCALE
NMAS CE90
NSSDA CE95
RMSE
1:50,000
25.4 m
29.0 m
16.7 m
1:24,000
12.2 m
13.9
8.0 m
1:12,000
10.2 m
11.6 m
6.7 m
1:4,800
4.1 m
4.7 m
2.7 m
1:2,400
2m
2.3 m
1.3 m
Table 14 NMAS scale accuracy
To generalize using NMAS CE90 standards, digital imagery that is described as meeting a 1:24,000 scale
accuracy requirement must meet the accuracy requirement criteria where tested. That is to say, the
tested location within the digital imagery must fall within 12.2 m of its actual location on the ground, for
at least 90% of the locations tested.
When scale is used to refer to a level of display it is different than when it is used as an accuracy
measurement. A display scale of 1:24,000 means that 1 of any unit, represents 24,000 of that same unit
within the softcopy or hardcopy display. Display scale refers to the identification of features and the
measurement of distance, not the measurement of accuracy.
It is important to understand that imagery of varying resolutions can both be orthorectified to the same
scale of accuracy, but when viewed at the same display scale, reveal different levels of feature
identification (i.e. spatial resolution).
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The following examples illustrate two images of varying resolutions at display scales of 1:12,000,
1:24,000. In both examples, the left image is a 1 m dataset, and the images on the right are 2.5 m
imagery. Notice that while both datasets meet a 1:24,000 scale accuracy standard, the features
identifiable from each dataset vary from each other, even at the same display scale:
Figure 17 Display Scale 1:12,000 - Image on Left: 1 m. IKONOS courtesy of GeoEye Image on Right: 2.5 m SPOT5 Courtesy of SPOT Image
Figure 18 Display Scale 1:24,000 - Image on left 1 m. IKONOS courtesy of GeoEye Image on Right: 2.5 m SPOT5 Courtesy of SPOT Image
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Orthorectification Considerations
The technical evaluation of the solutions proposed at the SDMI Imagery Workshop, was implemented
using an error budgeting tool. Building on an existing in-house program, the error budget tool was
developed by Yusuf Siddiqui of i-cubed, in support of SDMI objectives. The tool is referred to as the
Sensor Ortho-accuracy Estimation Worksheet (or Error Budget Tool), and was delivered as part of SDMI
Task 4: Research of Horizontal and Vertical Control Alternatives. This interactive worksheet helps to
identify the major components of an error budget for any orthorectification project involving satellite
imagery, based on user inputs. The worksheet is supported by a detailed primer that documents each
field in the spreadsheet, presents mathematical calculations, and explains the scientific basis behind
each calculation (see Appendix 11.3 of the Task 4 – Control Requirements Report).
Without going into all of the detail that is available in the supporting document for the Error Budget
tool, the following discussion will briefly review the components that contribute to ortho-positional
error, and discuss how error contributed by one factor may be mitigated by modification of another.
Factors that introduce error into an ortho-image product include:
Image sensor geolocation or native accuracy if GCPs are not utilized
Image sensor controlled accuracy specifications if GCPs are utilized
Image sensor Incidence (off-nadir) angle of imagery acquisition
Terrain source vertical accuracy
Terrain source horizontal accuracy and slope
Accuracy of horizontal control (ground control points)
Image pixel resolution as it relates to photo-identifiable precision of ground control
The minimum requirements for producing an ortho-image are:
A source image with a known native accuracy (geolocation)
A vertical control source (Digital Elevation Model)
GCPs are not required, if the source imagery has a high enough native accuracy. If horizontal control
(GCPs) are not utilized the satellite sensor’s geolocation, or native accuracy specification will be utilized
in determining overall ortho-positional accuracy.
The ortho-positional accuracy of an ortho-image can be improved by utilizing horizontal control (GCPs):
If horizontal control (GCPs) are utilized the satellite sensor’s corrected (improved) accuracy
specification will be utilized in determining overall ortho-positional accuracy.
The horizontal accuracy of the GCPs will influence the overall ortho-positional accuracy. The
higher the accuracy of the horizontal control, the more accurate will be the ortho-image.
The image pixel resolution will also influence the overall ortho-positional accuracy. The lower
the pixel resolution the more error is introduced due to photo-identifiable precision of the
ground control.
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Terrain is an essential component for producing ortho-imagery. The vertical error of the terrain data
can be a significant contributor to ortho-positional error.
The influence of the vertical accuracy of the terrain
model can be mitigated by decreasing the
incidence angle at which the source imagery is
collected.
Imagery acquired at nadir is collected at an
incidence angle of 0°.
Horizon angle is also commonly referred to as
elevation angle.
The look angle is also commonly referred to as
oblique or off-nadir angle.
Figure 19 Satellite Angles
The following graph illustrates how an increase in the vertical error of the terrain model increases the
positional error introduced. In order to illustrate how the error introduced can be minimized by
decreasing the incidence angle, the error introduced by vertical error is illustrated with the use of four
different incidence angles.
Figure 20 Error introduced by vertical error of terrain model, can be mitigated by decreased incidence angles
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Horizontal accuracy of the terrain model (DEM) is also a component of error introduction to overall
ortho-positional accuracy. The error introduced by the horizontal error of the terrain model is
increased as the slope of the project area increases. The following graph illustrates how an increase in
the horizontal error of the terrain model increases the positional error introduced. This relationship is
illustrated with the use of four different slope percentages, to illustrate how the error introduced by
horizontal error of the terrain model is magnified by increases in the slope.
Error Introduced by Horizontal Error of Terrain Model:
increase as slope increases
10
9
Error Introduced (m)
8
7
6
40% slope
5
30% slope
4
20% Slope
3
10% Slope
2
1
0
0
5
10
15
20
25
30
35
Horizontal Error (CE90) of Terrain Model (m)
Figure 21 Error introduced by horizontal error of the terrain model increases as the terrain relief increases
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Overall ortho-positional error
In calculating the overall ortho-positional error, the cumulative error is not additive, but rather derived
from the root-sum-square (RSS) function. The RSS function is explained in the field of statistics, and is
discussed in further detail in the Circular Predicted Error section of Appendix 11.3 of the Task 4 – Control
Requirements Report. Summing all errors will derive the worst case scenario for accuracy, while utilizing
the RSS function will arrive at the statistical expectation for overall accuracy.
In Figure 22 we see the amount of source error from various factors:
LE90 DEM = Vertical Accuracy of Terrain Model
CE90 DEM = Horizontal Accuracy of Terrain Model
CE90 SAT = Horizontal Accuracy of the Satellite
CE90 GCPS = Horizontal Accuracy of GCPs utilized
Figure 22 also illustrates the overall ortho-postitional error after those factors are combined using the
RSS function:
CE90 NMAS = Ortho-positional Error reported in NMAS CE90 standard
Figure 22 Contribution of individual components, in comparison to overall ortho-postional error as calculated by the RSS function
The summation of the error components is higher than the overall ortho-positional error reported in the
CE90 NMAS number. This is because overall error is a derivative of the root-sum-square function.
Ortho-positional error is not a simple summation of the various error components.
Another way of visualizing why circular errors combine in this unique way is to imagine a vector
representing the first source of error. In Figure 23, this is the red arrow. Next, imagine another vector
that starts at the tip of this first error, representing a second source of error; this is the blue arrow.
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Figure 23 visualization of combined errors
If the second vector has exactly the same direction as the first, they add up. If it has the opposite
direction, they subtract. If it is perpendicular, they add up by the Pythagorean Theorem, which is the
same as a root sum square. If in any other direction, the vectors add up by the Law of Cosines; the
green arrow represents the summation for the case of an arbitrary direction for the second error. If all
directions of the second vector are equally likely (which they should be, given the assumptions of
circularity and independence), then the weighted average of all possible directions will be the
perpendicular case, which adds with the first as a root sum square. This analogy can be extended to any
number of independent error vectors.
It is useful to note with the sum of squares that the largest error tends to dominate the result. For
example, if the error due to the vertical inaccuracy of the DEM is 10 meters and the only other error is 5
meters, the result is
, or 11.18 meters, which is only 11.8% larger than the largest
contributing error of 10 meters.
Therefore, overall ortho-positional accuracy is not largely dependent upon one given factor all the time.
The largest contributor to error can vary depending on what factor is the largest source of error for a
given scenario.
Each bar in Figure 24 illustrates the overall orthopositional error for a scenario that utilizes a lowaccuracy terrain model, and survey grade GCPs for control.
Each bar is divided into a representation of how much each individual error component (satellite model,
pixel resolution, ground control, DEM) contributes to overall error. Please note that pixel resolution and
controlled accuracy specifications are not proportionally related, and that each sensor will have a
unique specification for both items.
While the accuracy of the DEM does not change between the four bars represented in Figure 24, the
DEM’s contribution to overall orthopositional error diminishes as the satellite model and pixel
resolution’s contribution to overall error increases.
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Figure 24: Overall ortho-positional error & contributing factors using low-accuracy terrain and survey grade ground control
Figure 25 illustrates a scenario that utilizes a mid-accuracy terrain model, and survey grade GCPs for
control. The pixel resolution and satellite model accuracy specifications are the same as the four bars
represented in Figure 24. However, the contribution to overall error that can be assigned to the satellite
model and pixel resolution increase in Figure 25, as the accuracy of the terrain model is greatly
improved, and therefore a less significant contributor to overall error. As you can see from a
comparison of these two graphs, a more accurate terrain model can produce a more accurate ortho.
Figure 25 Overall ortho-positional error & contributing factors using mid-accuracy terrain model and survey grade ground control
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Horizontal Control Considerations
Horizontal control quality and distribution recommendations for various solutions are discussed in the
Imagery Workshop section of this whitepaper. Additional horizontal control considerations are
discussed in more detail in the SDMI Task 4 Report – Control Requirements. As part of Task 4, a GAP
Analysis of existing control statewide was conducted. Discussions at the Imagery Workshop revealed
several potential new sources for control, which are listed below. At the Imagery Workshop several
vendors propose employing a processing methodology referred to as block bundle adjustment for orthoimagery production. This methodology is introduced in a brief description below.
Block Bundle Adjustments
Bundle Block Adjustment is a process that employs a rigorous mathematical model of spatiotriangulation to correct the georeferencing of multiple overlapping images (a "block" of images)
simultaneously as opposed to a single image adjustment. There are several advantages to block
adjustment:lvi
To reduce the number of ground control points (GCPs)
To obtain a better relative accuracy between the images
To obtain a more homogeneous and precise mosaic over large areas
To generate homogeneous GCP network for future geometric processing
Control Sources Statewide
A gap analysis of horizontal control datasets statewide was conducted as part of the SDMI Task 4 –
Control Requirements. In addition to the content available in the Task 4 Report, the following additional
sources were identified during the SDMI Imagery Workshop:
62.5 cm Orthorectified Radar Imagery (ORI) which is a bi-product of IFSAR Terrain
Figure 26 ORI on left, courtesy of Intermap Technologies Inc. compared with DigitalGlobe optical imagery on right courtesy of Google Earth
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Aero-Metric Reference Base Stations
Figure 27 Distribution of Aero-Metric base stations. Graphic courtesy of Aero-Metric
Leveraging NextView to obtain imagery for use as control
Aerial photography produced as part of National Resources Inventory (NRI) Small Area
Photography Program.
Figure 28 NRI Alaska site locations for Small Area Photography Program
FCC Tower Points
IFR Airport control
RSAC Control (Chris Noyles)
USFS Chugach NF control
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Alaska DEM Analysis
DTM vs. DSM for Terrain Modeling
Definitions and images of various forms of digital elevation data are explained in detail in “Digital
Elevation Model Technologies and Applications; the DEM Users Manual,” second edition, published by
the American Society for Photogrammetry and Remote Sensing (ASPRS, 2007).
A Digital Elevation Model (DEM) is a generic term used for digital topographic data in all its various
forms, including a Digital Terrain Model (DTM) of the bare-earth terrain, void of vegetation and
manmade features, and a Digital Surface Model (DSM) which is similar to a DEM or DTM except that it
depicts elevations of the top reflective surface of buildings, trees, towers, and other features elevated
above the bare earth. Figure 29 shows a DSM and DTM, both needed for different applications.
Raw elevation data, whether of the top reflective surface or the terrain beneath, normally consists of
irregularly spaced elevation mass points, sometimes supplemented with linear breaklines to depict
streams. This combination of irregularly-spaced elevation mass points and linear breaklines is called a
DTM and can be represented in different file formats including a Triangulated Irregular Network (TIN).
For hydrologic and hydraulic modeling of hydrographic features, a DTM is typically hydro-enforced to
ensure the downward flow of water, but this is not necessary for orthorectification. Unless specifically
referenced as a DSM, the generic DEM normally implies a uniformly-gridded DTM consisting of x/y
coordinates and z-values of the bare-earth terrain at regularly spaced intervals in x and y, where ∆x and
∆y are normally measured in feet or meters to even units. A DEM is normally stored in the form of 3-D
pixels called voxels, as shown at Figure 30.
Figure 29 DSM of the tree tops and DTM of the bare-earth terrain
Figure 30 3-D volumetric pixels are called voxels
Uniformly-gridded DTMs (DEMs) are best for orthorectification. Because such DTMs (DEMs) are
smoother than DSMs they are less likely to cause artifacts in the orthoimagery.
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Image Artifacts Caused by Terrain Artifacts
Image artifacts in orthoimagery can result from terrain artifacts, and such artifacts are noteworthy
primarily for manmade features such as roads, bridges, and buildings which are readily recognizable
when they appear warped on orthoimagery.
Image artifacts are most likely to result from terrain artifacts when there is a large difference in
elevations between neighboring voxel cells. For example, a DSM records the first reflective surface and
thus will contain elevations that do not reflect the bare ground. This is a major reason why DSMs are
generally not used for orthorectification. With DSMs, there would be large differences in elevations
between neighboring voxel cells, for example, when in the case of vegetation along a road, one cell
depicts the elevation of vegetation (e.g. a tree top) and the adjoining cell depicts the elevation of the
road elevation (bare-earth terrain). The same is true for buildings which often appear warped when
orthorectified to a DSM instead of a DTM. It is generally recommended that orthorectification be
performed using a DTM, instead of a DSM.
One such example of using a DSM (instead of a DTM) is shown at Figures 31 and 32. The Alaska
Department of Natural Resources (DNR) demonstrated how a 12-meter difference in adjoining voxel cell
elevations caused an orthoimage of a road to be unacceptably warped, requiring the DSM along the
road right-of-way to be smoothed in order to straighten the road in the orthoimage. Figure 31 shows a
QuickBird 0.6-meter panchromatic image, orthorectified using a 10-meter DSM. A close inspection
showed that the road appeared distorted when in reality the road was smooth. Recognizing that terrain
artifacts cause image artifacts, the DSM was reviewed in detail. Although not visible at this scale, the
DSM at Figure 32 showed a 12-meter drop in elevation between adjoining DSM cells, distorting the
image during orthorectification. The DSM was smoothed along the road and surrounding buffer (similar
to a DTM) and the orthorectification was redone, resulting in a smooth road in the revised image. If such
terrain artifacts existed in natural terrain, the resulting image artifacts would probably be unnoticeable.
This problem can occur whether using DSMs from LiDAR, IFSAR, or automated stereo-correlation of
optical imagery.
Figure 31 Quickbird orthoimage shows road distorted because of
irregular/unsmoothed DEM along the road
Figure 32 The largest distortion occured where there was a 12-meter
offset between neighboring voxel cells
In the case of bridges, it is beneficial to use a DSM in the orthorectification process because bridges
often appear distorted on orthoimagery. This is not as a result of any terrain artifact; it is because the
height of the bridge structure is not maintained in the DTM. Figure 33 shows an “hourglass”
bridge caused by orthorectifying the bridge to the DTM elevation far below the elevation of the
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bridge deck. To correct the orthophoto at Figure 34, the image was orthorectified to the DSM,
but only for the area immediately surrounding the bridge. An alternative method is to stereocompile breaklines across the top of the bridge deck on both sides of the bridge. Both methods
are effective in correcting bridge distortions.
Figure 33 Bridges & overpasses are often distorted by
orthorectification to the DTM beneath the bridge, causing "hour
glass" or other distortion
Figure 34 Bridge distortions are corrected by local orthorectification
to the DSM or breaklines at the elevation of the bridge deck
Slope Analysis
Orthoimagery is most likely to appear smeared or stretched on steep slopes, especially when those
slopes include trees of considerable height above the terrain and/or when the imagery is off-nadir. The
slope and aspect of the terrain model, as well as the incidence angle of the imagery, have an impact on
image smear. Figure 35 shows a color infrared (CIR) image, orthorectified to the DTM, showing both the
distorted bridge as well as the smeared image of the tall trees on the steep slopes beside the river.
Figure 36 shows a natural color image from the same sensor, orthorectified to the DSM, showing the
corrected image of the bridge as well as an un-smeared image. This is a severe example, caused by the
large incidence angle of the source imagery. In some cases, trees are smeared, regardless of whether a
DTM or DSM is used for orthorectification. In aerial and satellite sensors, this problem is minimized by
using nadir imagery as opposed to off-nadir imagery from a sensor with a high incidence angle.
Figure 35 CIR image of steep slope, from a sensor with high
incidence angle, orthorectified to the DTM
Figure 36 Natural color image from the same sensor, but
orthorectified to the DSM
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Terrain Options & Availability
The Alaska DEM Whitepaper (Dewberry, 2008) evaluated various terrain options, indicating that DEMs
of low vertical accuracy (40-foot contour accuracy or worse) are normally usable for orthorectification,
assuming that the DEM’s horizontal accuracy is suitable for mapping at a scale of 1:24,000, but also
depending on other factors such as the horizontal accuracy and incidence angle of the source imagery.
The target accuracy scale for Alaska statewide imagery produced through the SDMI is 1:24,000. When
producing digital orthophotos at a scale of 1:24,000 (1” = 2,000 feet), the circular error at 90%
confidence level (CE90) must be 12.2 meters or less from all error sources; this is the same as CE95 of
13.9 meters or less. When producing 1-meter orthophotos to proposed standards of the Imagery for
the Nation (IFTN) program, for example, CE90 should be 7.6 meters or less, or CE95 of 8.7 meters or
less. These are the total horizontal error budgets from all sources. CE95 is horizontal (radial) accuracy
at the 95% confidence level, the criterion recommended by the National Standard for Spatial Data
Accuracy (NSSDA) and defined therein as “Accuracyr.” The international geospatial community still refers
to CE90 as its primary reference to horizontal accuracy.
Three basic terrain options are available, again classified as high-, mid-, and low-accuracy for vertical
accuracy, which does not necessarily equate to high-, mid-, and low- horizontal accuracy:
High-accuracy elevation data from LiDAR or airborne photogrammetry.
LiDAR or airborne photogrammetry produces the most accurate DTM. LiDAR is best able to
penetrate dense forest canopy to map the bare earth terrain and produce a DTM, but this
option would be very expensive and is justified only when the high accuracy LiDAR DTM is
required for a project application. It is not deemed a necessary source for statewide
orthorectification requirements.
Mid-accuracy elevation data from IFSAR.
Should Alaska succeed in obtaining a statewide IFSAR DEM, as recommended in the Alaska DEM
whitepaper (Dewberry, 2008); this would be the preferred source of DEMs for creating orthoimages statewide. The improved accuracy of this terrain model, and the IFSAR orthorectified
radar imagery (ORI), would have several added benefits:
o
Improve geometric accuracies obtainable for all potential ortho image sources.
o
Reduce the accuracy requirements of, or eliminate the need for, GCPs for several
sensors.
o
Reduce restrictions on off-nadir imaging, which will improve collection agility.
o
Provide pan-sharpening of lower-resolution satellite imagery (5-10 m pixels), using the
ORI imagery with 0.625 m or 1.25 m resolution, as shown at Figure 37, to produce
ortho-imagery with higher resolution, and to produce ortho-imagery using satellite
imagery with partial cloud cover when such imagery is the best available.
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Figure 37. Top Image Pair: Example of 5 m pixel satellite image (RAPID EYE) that was orthorectified using IFSAR data (left image) and after
pan-sharpening with IFSAR ORI image (right image). Bottom Image Pair: Example of 10 m pixel satellite image (ALOS PRISM) that was
orthorectified using IFSAR data (left image) and after pan-sharpening with IFSAR ORI image (right image). Orthorectification using the IFSAR
ORI and DTM also allows satisfaction of horizontal CE95 criteria, lacking in the satellite image.
Low-accuracy elevation data from satellites.
There are several satellites that have the ability to create terrain models suitable for producing
ortho-images that can meet a geometric accuracy equivalent to NMAS at 1:24,000-scale, but to
do so, those with CE95 horizontal accuracy values larger than 13.9 meters would need to use a
rigorous least-squares bundle block adjustment for aerial triangulation of large blocks of images,
preferably with some ground control points (GCPs). SPOT 5 can produce a DTED Level 2 terrain
model using its HRS sensor (17.11 m CE95 horizontal and 11.91-35.74 m LE95 vertical,
depending on slope), and it has demonstrated that it can exceed CE95 of 13.9 meters with
large bundle block adjustments. GeoEye’s IKONOS can produce a DTED Level 2 DEM (29 m CE95
horizontal and 14 m LE95 vertical). Digital Globe’s WorldView-1 can produce a higher accuracy
DEM (8 m CE95 horizontal and <6 m LE95 vertical) without the use of GCPs for terrain
derivation. ASRC’s Cartosat-1, MDA’s Radarsat-2, and RapidEye can all produce DTED Level 2
terrain models, but require GCPs to do so. The vertical accuracy of all terrain data derived from
optical stereo pairs will decrease as the steepness of the terrain increases. Please refer to the
DEM Whitepaper (Dewberry, 2008) for details regarding the slope specifics for which the above
accuracy specifications were provided. The timeframes for acquisition and production of the
above terrain datasets range from 12-60 months. Please note that while some DTED Level 2
terrain models can be produced from satellite imagery without the use of GCPs, this does not
imply that 1:24,000-scale ortho-imagery can be generated without the use of GCPs.
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Of particular interest to the AK SDMI is the upcoming release of the ASTER Global DEM (G-DEM). The
Ministry of Economy, Trade and Industry of Japan (METI), NASA, and the USGS have collaborated to
develop the ASTER G-DEM. This global DEM data was acquired by the satellite-borne sensor “ASTER”
and covers all the land on earth. The anticipated accuracies are 30 m CE95 horizontal and 20 m LE95
vertical. The G-DEM has been produced and is currently undergoing validation. Anticipated public
release of this terrain product is summer of 2009. This product will be available free of charge. If this
DEM meets anticipated accuracy standards, its vertical accuracy could be suitable for image
orthorectification, but additional processing would be required to improve the horizontal accuracy to a
CE95 value of less than 13.9 m. The ASTER G-DEM’s greatest contribution to the SDMI may be in
providing ancillary data that could be geo-registered to an IFSAR DTM of Alaska, when produced, to fill
data voids caused by IFSAR shadow and layover that could not be resolved from alternate looks (e.g.
orthogonal or secondary flight lines). An alternative to ASTER is the use of stereo-radar (e.g. from
TerraSAR-X or RADARSAT) for IFSAR void fills.
Advantages & Disadvantages of Competing Technologies
Panchromatic vs. Mutli-spectral Imagery
Panchromatic is black and white, single band imagery that has limited application usage
Multispectral enables natural color imagery, spectral analysis for vegetation health and land
cover mapping.
Over three quarters of users surveyed require natural color or multi-spectral imagery.
Aerial vs. Satellite Imagery
Satellite sensors collect data continuously, with relatively quick revisit rates. Aerial sensors must
be utilized on a planned mission, which requires more logistical planning.
Aerial sensors can provide better targeted collection of special project areas. Taking advantage
of any opportunity gaps that arise in the weather.
Although satellites can be tasked to collect specific areas as a priority upon request. If necessary,
specialized tasking of a satellite is usually an added expense.
Both can collect panchromatic or multispectral.
Traditionally, aerial sensors provided higher geolocation accuracy than satellite sensors, but this
gap is narrowing with the development of new high resolution, high geolocation accuracy
sensors like GeoEye-1 and WorldView.
The orbital altitude of satellites reduces the variation in incidence angle across the swath width
(field of view) of a scene, thus minimizing artifacts like building lean, and image smear.
Satellite imagery is the solution that can meet statewide mapping refresh of 3-5 years as supported by
the SDMI user survery. Higher resolution, optical, multispectral, airborne solutions can be considered
for specific areas within the state (urban centers and special infrastructure projects).
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Optical vs. Radar Imagery
Radar imagery acquisition is both day/night and all-weather, meaning that you can acquire data
any time of the day or night, and that the data can be collected through cloud cover.
Optical acquisition is limited to daylight hours with high enough sun angle, and requires cloudfree conditions for usable data.
Optical imagery can be collected in panchromatic or multispectral modes. Multispectral data
enables extraction of natural color imagery (the most desired user product according to the
SDMI user survey), as well as detailed spectral analysis for a broad range of applications.
Radar imagery is not in the visible spectrum. It is created from the backscatter intensity of radio
waves. Since this is not a portion of the electromagnetic spectrum that human eyes operate in,
there are challenges in interpretability of radar imagery that are not present in optical imagery.
Similar to optical imagery, radar imagery can be fused with color imagery to produce a pansharpened affect.
Figure 37 on the left is 1.25 m radar image compared to 30 m. mutlispectral Landsat imagery on the right. (Intermap Technologies Inc.)
Figure 38 on the left is an example of merged 30 m. Landsat with 1.25 m radar, compared with aerial photography on the right (Intermap
Technologies Inc.)
While the day/night and all-weather imaging capabilities could be useful for mapping areas of Alaska
that experience persistent cloud cover, radar data does not meet the functional requirement of
multispectral data that is supported by the users. In addition, if a statewide airborne IFSAR terrain
solution is sought, a bi-product of that process will be a high resolution orthorectified radar image (ORI).
For these reasons, the Alaska SDMI is not seeking radar solutions at this time for a statewide orthoimagery base map.
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Imagery Workshop – Technology Options Presented
Questions Posed
Vendors presentations for the SDMI sponsored Imagery Workshop were asked to address the following
list of 15 questions that were provided in advance of the workshop:
1. Sensor: What sensor do you propose to use for acquisition of source data to produce orthoimagery? If you would like to propose more than one sensor option, please address this list of
questions separately for each sensor.
2. Product Characteristics: Please state the characteristics of the ortho-imagery product you
intend to provide, including spatial resolution, spectral bands, bit depth, signal to noise ratios,
and other notable features. Please describe advantages of these characteristics in the context
of typical applications you serve.
3. Application uses of the product: Please provide a list of the applications that your product is
intended to serve, and provide examples of these; and why your product is uniquely suited to
these applications.
4. Native Horizontal Accuracy: Without the use of ground control, what is the native horizontal
accuracy that can be achieved from the proposed sensor, independent of the terrain model, and
assuming nadir viewing? Please respond with NSSDA CE95 confidence levels. Please specify the
native geolocation accuracy based on your product specification as well as number based on
operational experience if they differ. Are rational polynomial coefficients (RPCs) provided with
imagery? Is there any additional cost to obtain this information?
5. Improved Horizontal Accuracy: With the use of ground control, what is the improved horizontal
accuracy that can be achieved from the proposed sensor? Please report this improved
horizontal accuracy as the uncertainty in the model of sensor position, attitude and cameral
model, independent of the terrain model, and assuming nadir viewing. Please respond with
NSSDA CE95 confidence levels.
6. Horizontal Control Distribution: Assuming survey grade, 0.6 m. CE95 accuracy of each ground
control point, what is your minimum required distribution of points? Please address efficiencies
gained through strips or blocks of imagery and constraints related to the distribution of the
points relative to the collection pattern (areas of scene overlap, ends of strips etc. Based on this
minimum requirement, what is your estimate of the total number of ground control points you
would need statewide? Would you require this control to be provided, or could you obtain this
control? If yes, please state the expected cost of obtaining this control, independent of imagery
and processing costs.
7. Digital Elevation Model Requirement: What is the minimum accuracy of a terrain model you
would require to achieve the SDMI specification of 1:24,000 NMAS? How would you acquire this
minimum accuracy DEM?
8. Ortho-Production: If you provide data to generate ortho-imagery, rather than a finished orthoimage product, do you have established processing partners that produce the ortho-image?
What are the timeframes / throughputs associated with typical production workflows?
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9. Revisit: Provide detailed specifications regarding sensor revisit frequency for Alaska. Since lower
off-nadir angles are required to achieve higher product geometric accuracies, please address a
range of revisit rates as they apply to mapping accuracy levels in your response.
10. Swath Width: What is the width at nadir of the imaging swath of the sensor you propose to use?
What is the longest swath length your sensor could capture over Alaska? Describe the
advantages and considerations for this imaging swath and duty cycle. For dual-axis scanning
sensors (e.g. WorldView-1 and GeoEye-1), also describe the imaging capacity on a single 1000km-long pass over Alaska in order to compute an “equivalent swath width” for your sensor.
11. Rapid Delivery and Ground Receiving Station Options: Describe options for rapid delivery of data
for emergency response or monitoring. Do you offer local downlink to partners, a ground
receiving station option, or other means to deliver products in near-real-time? Does your
current global configuration of ground receiving stations pose any constraint or offer
advantages for collection of data?
12. Production volume: SDMI requires in-season acquisition of source data, which for SDMI is
defined as May through September. This restriction is in place in order to maximize the sun
angle and minimize shadow and snow cover in the source data collected. Please state your
gross image collection capabilities in square kilometers for the state of Alaska during this five
month imaging window. When calculating this capability, please take into consideration any
look angle restrictions that your sensor would be under to achieve a 1:24,000 scale orthoimage
product, utilizing a DTED level 2 terrain model. Please contact Jill Mamini [mailto:
[email protected]] if you require guidance on calculating look angle restrictions that may apply to
your sensor. Please provide answers ignoring the impacts of cloud cover, but also provide any
information considering cloud constraints that you have based on modeling and / or practical
experience (for example <10% cloud cover).
13. Delivered Products: Please list all products that can be delivered to SDMI, i.e. raw source
scenes, ortho-rectified images, supporting terrain model; image and file formats to be delivered;
metadata; and suggestions for ingestion into an SDMI repository. In this list, please also
describe whether or not the images are archive or new (to be collected).
14. License: What license options are available for your delivered product options?
15. Pricing: Please provide rough order of magnitude (ROM) cost estimates for statewide coverage
of all product and licensing options available. If you are unable to provide cost estimates for
statewide coverage, you have the option of providing cost estimates on a per square kilometer
basis for high priority areas. If your proposal includes the generation of a supporting terrain
model please providing separate pricing for both the imagery and the terrain model. If the
pricing is confidential, please be sure it is marked as such. Please include pricing for Alaska
Agencies (and contractors) only, add Federal Agencies, add Alaska citizens for private use, add
commercial companies, and finally, assume it is in the public domain. If you want to add time
constraints, (such as OK for the public domain after 1 year) then please do.
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Vendor Responses
The following section includes a summary table of each vendor’s responses to the SDMI Imagery
Workshop questionnaire. All summary tables have been reviewed and authorized for submission in the
Imagery Whitepaper by the source vendor.
For confidentially, full written submissions, including proprietary pricing information will be posted on
the SDMI password protected group hub only.
Power Point presentations submitted by the vendors will be requested to be posted on the public
Alaska Mapped (www.alaskamapped.org) site. If vendors decline posting to this site, then presentations
will be posted to the password protected group hub
Digital Globe
DigitalGlobe’s response includes a combination of satellite and aerial based solutions, including
QuickBird, WorldView-1, WorldView-2*, and a Leica ADS40 aerial sensor. An overview of the Leica
ADS40 aerial sensor capabilities is covered in the Aerial Sensors section, and will not be summarized
here. QuickBird currently supports the SDMI foundation requirements of providing multi-spectral image
bands. WorldView-2 will also support that requirement when it is operational in 2010. WorldView-1
imagery is panchromatic only, but may be used to help augment collection of base imagery, and can be
seamlessly integrated into a base data layer.
* scheduled for launch in late 2009
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Vendor
Name of Satellite
Source Pixel Size at nadir
Spectral Characteristics
Dynamic Range
Signal to Noise Ratio
Native Horizontal Accuracy
Improved Horizontal Accuracy
Horizontal Control
Requirements
Recommended Quantity of
GCPs
Vertical Control Source
Ortho Production
Incidence Angle Range
Revisit Rate
Swath Width at nadir
Strip Length
Recording Capacity
Ground Receiving Station (GRS)
DigitalGlobe
QuickBird
PAN 0.61 m / MSI 2.44 m
Pan = 450-900 nm
MSI 1 (NIR 1) = 760-900 nm
MSI 2 (R) 630-690 nm
MSI 3 (G) = 520-600 nm
MSI 4 (B) = 450-520 nm
11-bits
sun elevation angles better than 15 for non-pan-sharpened
sun elevation angles better than 30o for pan-sharpened
23 m CE90
4 m CE90
In areas where control can be impossible to collect the native
accuracies of WorldView1 and WorldView2 will be sufficient to
produce 1:24,000 map data
Employing a block bundle adjustment across strips, 6 control
points per 1 degree cell, with 2-3 control points reserved for
accuracy assessment.
DTED Level 2 DEM required. Stated 30 m LE90 or better
Ortho-satellite products generated in DG production facilities
Typically a 1 degree cell takes 3 weeks to process after
acquisition
Max. 45°
Licensing
<1 - 10 days depending on off-nadir angle
16.8 km
Up to 220 km
128 Gbits
2 GRS in Alaska. Basic Imagery can be produced in Longmont, CO
within 5-6 hours of acquisition
For collection at 0-25 degrees off-nadir, 0-20% cloud cover,
sensor will collect at 75% capacity: for Alaska, this equals
280,000 square km over a two-month growing season.
pan/ms/psm; 8 or 11 bit;
Basic, Standard, ortho-ready standard imagery;
Orthorectified imagery:
1:12,000 – 10 m CE90
1:5,000 – 4.2 m CE90
1:4,800 – 4.1 m CE90
Not discussed
Pricing
Not discussed
Collection Volume
Delivered Products
Table 15 Digital Globe QuickBird Sensor
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Vendor
Name of Satellite
Source Pixel Size at nadir
DigitalGlobe
WorldView-1
Acquired 0.46 m / Delivered 0.5 m
NIIRS
Capacity of greater than 5.0
Spectral Characteristics
Pan = 400-900 nm
Dynamic Range
Signal to Noise Ratio
11-bits
sun elevation angles better than 15 for non-pan-sharpened
sun elevation angles better than 30o for pan-sharpened
Production specification: 6.5m CE90
Operational specification: 4.0 – 5.5m CE90
2 m CE90
Native Horizontal Accuracy
Improved Horizontal Accuracy
Horizontal Control Requirements
Recommended Quantity of GCPs
Not required to meet AK SDMI objective of 1:24,000 scale
mapping
Not Required
Vertical Control Source
DTED Level 2 DEM required. Stated 30 m LE90 or better
Ortho Production
Ortho-satellite products generated in DG production facilities
Typically a 1 degree cell takes 3 weeks to process after
acquisition
Max. 45°
Incidence Angle Range
Revisit Rate
Swath Width at nadir
Strip Length
Recording Capacity
Collection Volume
Licensing
<1 - 10 days depending on off-nadir angle
17.6 km
Up to 110 km
2199 gigabits solid state with EDAC
For collection at 0-25 degrees off-nadir, 0-20% cloud cover,
sensor will collect at 75% capacity: for Alaska, this equals 280,000
square km over a two-month growing season.
8 or 11 bit pan;
Basic, Standard, ortho-ready standard imagery;
Orthorectified imagery:
1:12,000 – 10 m CE90
1:5,000 – 4.2 m CE90
1:4,800 – 4.1 m CE90
Not discussed
Pricing
Not discussed
Delivered Products
Table 16 DigitalGlobe WorldView-1 Sensor
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Vendor
Name of Satellite
Source Pixel Size at nadir
Spectral Characteristics
DigitalGlobe
WorldView-2 (scheduled for launch in late 2009)
PAN 0.46 m / MSI 1.84 m
Native Horizontal Accuracy
Pan = 450-800 nm
MSI 1 (NIR 1) = 770-895 nm MSI 5 (Red Edge) = 705-745 nm
MSI 2 (R) 630-690 nm
MSI 6 (Yellow) 585-625 nm
MSI 3 (G) = 510-580 nm
MSI 7 (Coastal) 400-450 nm
MSI 4 (B) = 450-510 nm
MSI 8 (NIR 2) 860-1040 nm
Planned geolocation accuracy of 6.5 m CE90
Improved Horizontal Accuracy
TBD
Horizontal Control Requirements
Recommended Quantity of GCPs
Given geolocation accuracy of sensor, horizontal control points
are not needed to meet the AK SDMI objective of 1:24,000 scale
mapping
Not required
Vertical Control Source
DTED Level 2 DEM required. Stated 30 m LE90 or better
Ortho Production
Ortho-satellite products generated in DG production facilities
Typically a 1 degree cell takes 3 weeks to process after
acquisition
Max. 45°
Incidence Angle Range
Revisit Rate
Swath Width at nadir
Strip Length
Recording Capacity
Collection Volume
Licensing
<1 – 8 days
16.4 km
Pending determination of orbit elevation
TBD
Collecting at 100% capacity, 10% cloud cover and 25 degrees
off nadir, 33 months for state completion. Collection at 75%
capacity, 10% cloud cover and 25 degrees off nadir, 39
months for state completion.
All current DigitalGlobe product offerings will be supported by
WV2, including Basic, Basic Stereo, Standard, Ortho-ready
Standard and Ortho (1:12,000, 1:5,000, 1:4,800 CE90);
Band combinations: MS1 Bands – red, green, blue, NIR1
MS2 Bands – coastal, yellow, red edge, NIR2
All MS Bands (MS1 + MS2)
Pan, MS1 + MS2 Bundle (Pan + All 8 MS bands)
Not discussed
Pricing
Not discussed
Delivered Products
Table 17 DigitalGlobe WorldView-2 Sensor
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GeoEye
The proposed solution presented by GeoEye is a combination product called GeoProfessional that is
compiled from two different sensors: IKONOS and GeoEye-1. The summary table below provides some
details specific to the IKONOS platform. Fields that relate to the overall solution are completed in the
GeoEye-1 specific table that follows on the next page:
Vendor
GeoEye
Name of Satellite
IKONOS
Source Pixel Size
Acquired at 0.82 m pan/3.28 m ms
Delivered product 1 m pan / 4 m ms
NIIRS
4.5
Spectral Characteristics
Pan = 526-929 nm
MSI 1 (B) = 445-516 nm
MSI 2 (G) 506-595 nm
MSI 3 (R) = 632-698 nm
MSI 4 (NIR) = 757-853 nm
Dynamic Range
11 bits
Native Horizontal Accuracy
Mono Product Specification: 15 m CE90 / 17 m CE95
Mono Operational Specification: 7 m CE90 / 8 m CE95
Stereo 10 m CE90 10 m LE90 / 11.4 m CE95 11.9 LE95
Improved Horizontal Accuracy
1.75 m CE90 / 2 m CE95
Incidence Angle Range
Max. 30°
Revisit Rate
2-3 days at > 60°N
Swath Width
11.3 km
Recording Capacity
80 gigabits
Collection Volume
300,000 km2 per day in both pan / ms
Table 18 GeoEye IKONOS Sensor
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Vendor
Name of Satellite
Source Pixel Size
GeoEye
GeoEye-1
Acquires at 0.41 m pan / 1.64 m ms
delivered product 0.5 m PSM
5.5
Pan = 450-800 nm
MSI 1 (B) = 450-510 nm
MSI 2 (G) 510-580 nm
MSI 3 (R) = 655-690 nm
MSI 4 (NIR) = 780-920 nm
11 bits
Mono <5 m CE90 / <5.7 m CE95
Stereo 4 m CE90 6 m LE90 / 4.6 m CE95 7.2 LE95
0.88 m CE90 / 1 m CE95
NIIRS
Spectral Characteristics
Dynamic Range
Native Horizontal Accuracy
Improved Horizontal Accuracy
Horizontal Control Requirements
Recommended Quantity of GCPs
Vertical Control Source
Ortho Production
Incidence Angle Range
Revisit Rate
Swath Width
Strip Length
Recording Capacity
Ground Receiving Station (GRS)
Collection Volume
Delivered Products
Licensing
Pricing
Given geolocation accuracy of sensor, horizontal control points
are not needed to meet the AK SDMI objective of 1:24,000 scale
mapping
GeoProfessional Product: Horizontal control not required
GeoProfessional Precision Level Product: one survey grade point
spaced every 50 km down an image strip
DTED Level 2 DEM required.
Stated 19 m. LE90 or better.
GeoProfessional Product generated in GeoEye production
facilities. Local Alaska production partnerships available as well.
Max. 30°
5 days
15.2 km
112 km
3-4 strips per pass
1.0 Terabit solid state recorders
Multiple, including GRS in Barrow & Fairbanks, AK
5,400,000 km2 during 150 day in-season, excluding cloud cover
~791 in-season days (5+years) to collect mainland AK cloud-free
GeoProfessional product: pan/ms/psm ; 8 or 11 bit;
15 m CE90 at 1 m/ 10 m CE90 at 0.5 m
GeoProfessional Precision Level Product: 4 m CE90
Flexible. Pricing below is based on single user licensing
GeoProfessional Product is recommended solution for SDMI:
Single User License: $25/km2 for 1 m product; $30/km2 for 0.5
m product
Uplift would be applied for a broader license
Table 19 GeoEye GeoEye-1 Sensor
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SPOT
SPOT Image proposes primary image data collection by the SPOT-5 HRG instruments, augmented with
data from three other satellites (listed below) to address specific requirements or supplement the
HRG acquisitions:
1) Kompsat‐2, which is capable of collecting 1-m pan and 4-m multispectral data.
2) Formosat‐2, which is capable of collecting 2-m pan and 8-m multispectral data.
3) SPOT4, High Resolution Visible – Infrared (HRV‐IR) instruments capable of collecting 10-m
pan and 20-m multispectral data.
Summary tables were not produced for these additional sensors, but all relevant information pertaining
to them can be found in SPOT Image’s full response to the SDMI questionnaire.
Vendor
Name of Satellite
Source Pixel Size (m)
SPOT Image
SPOT5
2.5 m pan and psm; 5 m pan and psm; 10 m ms psm
Spectral Characteristics
2.5 m pan and psm; 5 m pan and psm; 10 m ms
Pan (0.48‐0.71 μm)
G(0.50‐0.59 μm)
R (0.61‐0.68 μm)
NIR (0.78‐0.89 μm)
SWIR (1.58‐1.75 μm)
Dynamic Range (bits)
Signal to Noise Ratio
Native Horizontal Accuracy
(m) CE90/CE95
8
177,188,213, SWIR
Product Specifications: 39 m CE90 / 41 m CE95
Operational Specifications: 35.8 m CE90
Improved Horizontal
Accuracy (m) CE90/CE95
Production Specifications (using Ref3d): 10.52 m CE90/ 12 m CE95
Operational Specifications (using Ref3d): 8.8 m CE90
Horizontal Control
Requirements
Survey grade GCPs either by scene or fewer if block bundle adjustment
is performed. No GCPs is an option if the DTED Level 2 DEM and Ortho
Image from Reference3d product is utilized as vertical control during a
block bundle adjustment
Recommended Quantity of
GCPs
Option_1: Single scene ortho - 10 points per scene
Option_2: Block Bundle Ortho Production: ~100 points statewide
Option_3:Block Bundle without GCPs using Ref3d product
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Vendor
SPOT Image
Name of Satellite
SPOT5
Vertical Control Source
DTED Level 2 (30 m. postings) or better. Possible sources include:
Aster G-DEM (Spring 2009)
SPOT HRS stereo pair generated DTED Level 2 Terrain Model
(Statewide source HRS stereo pair coverage currently available)
Future use of SDMI/AGDC NED DEM Solution (unknown availability)
Ortho Production
Local Alaska Production Partner can be utilized. Production capacity
ranges from 500,000 km2 / month to 3.5 Million km2 / month.
Independent of collection of data, and using the basic production
configuration, statewide seamless ortho mosaic can be produced within
a 6 month timeframe
Incidence Angle Range
Revisit Rate (days)
Max. 31°
In general every 1-2 days. During 26 day orbital cycle, can revisit the
same location 22 times (incidence angles up to 31°), and 12 times
(incidence angles up to 15°)
Swath Width (km)
60 km
Please Note: SPOT 5 contains 2 identical High Resolution Geometry
(HRG) sensors sensor. Both are needed to produce the 2.5 m pan
data, while each HRG can be used independently to collect 2.5 m
pan, or 5 m pan and 10 m ms data. This increases the length of
the scene size collected at the lower resolutions.
Strip Length
Recording Capacity
Ground Receiving Station
(GRS)
Collection Volume
Delivered Products
(6-7 scenes) 350-400 km
When collecting data with both instruments, 2 strips of 350-400km,
can be collected, doubling the amount of data collected
90 Gbit (~200 scenes)
Proposed GRS located in Alaska (2 hours until data is available) or SPOT
Image owned GRS (6-48 hours until data is available)
Ref3d / HRS 10 m. ORI: 12-18 months (from DEM Workshop) with
Alaska based GRS: 3 seasons to collect 95% of State with <10% cloud
and snow (accounting for some areas that might experience persistent
cloud coverage)
SPOT Standard Processing Levels include: Level 1A Raw Data; Level 2A
Georeferenced Data; Level 3A orthorectified data; Data/Metadata
Format: DIMAP-GeoTiff
Licensing
Various Options Available. See Appendix for full details. Combined
RBU/APU licensing suggested in ROM estimates.
Regional Broad Use (RBU) and Alaska Public Use (APU)
Pricing
SPOT Image has provided detailed confidential pricing to the SDMI
executive committee.
Table 20 SPOT Image SPOT5 Sensor
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RapidEye
Vendor
Name of Satellite
Source Pixel Size (m)
Spectral Characteristics
RapidEye
RE 5 Constellation
6.5 m native at nadir/ 5 m orthorectified
Blue: 440 - 510 nm
Green: 520 - 590 nm
Red: 630 - 685 nm
Red Edge: 690 - 730 nm
NIR: 760 - 850 nm
Up to 12 bits
Depends on illumination
44.85 CE 90/51.18 CE 95
Dynamic Range (bits)
Signal to Noise Ratio
Native Horizontal Accuracy (m)
CE90/CE95
Improved Horizontal Accuracy (m)
CE90/CE95
Horizontal Control Requirements
Adequate density of GCPs with 7 m or better RMSE
Recommended Quantity of GCPs
Vertical Control Source
1 GCP / 1000 sq. km
DTED Level 2 DEM (19.2 m LE90)
Ortho Production
Swath Width (km)
Level 1B strip length
in-house with RapidEye Product Processing System System
can orthorectify >2 million sq km/day
No price uplift for orthorectified data
Max. 20° for imaging, 25° for stereo pairs
RapidEye can image 90% of AK in ~3 days at less than 20
degree look angle. 10 days for complete coverage of AK, not
accounting for clouds. <5 day revisit at nadir.
77 km
270 km
Strip Length
Recording Capacity
Up to 1500 km
48 Gbit
Ground Receiving Station (GRS)
Collection Volume
1 in Svalbard, Norway
86,625,000 km2 / 150 in-season days
not accounting for cloud cover
~24 in-season days to collect mainland Alaska
Sensor Level (L1B) and Orthorectified (L 3A) products.
Browse Images, Unusable data mask, Metadata
All Alaska Fed, state, tribal Government. Emergency license
for Commercial and Military use (no charge). Public Domain
and standard licensing (single user, multi user, enterprise)
available.
Base price US$1.20 per km2 for single use license
Incidence Angle Range
Revisit Rate (days)
Delivered Products
Licensing
Pricing
10.66 m CE90 / 12.16 CE95
Table 21 RapidEye RE5 Constellation of Sensors
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ALOS
Questionnaire response has yet to be received as of 05-04-2009.
Vendor Comparison
This section gives an overview of competing technologies and how they compare with regards to some
of the major concerns:
Spatial resolution
Spectral characteristics
Accuracy potential
Collection capacity and refresh potential
Lifespan of sensors
Spatial Resolution
As previously discussed in the user requirements section of this whitepaper, user’s applications
determine the features that are required to be mapped. The “best” image resolution for a project may
not be the highest available, but the lowest resolution that captures the target features at the desired
level of detail. The following tables summarize the available spatial resolutions of various sensors:
Vendor
GeoEye
GeoEye
DigitalGlobe
DigitalGlobe
DigitalGlobe
Name of Satellite
GeoEye-1
IKONOS
QuickBird
WorldView-1
WorldView-2*
Source Pixel Size (m)
PAN 0.41 m
MS 1.64 m
PAN 0.82 m
MS 3.28 m
PAN 0.61 m
MS 2.44 m
PAN 0.46 m
PAN 0.46 m
MS 1.80 m
Delivered:
PSM 0.5 m
Delivered:
PSM 1.0 m
Delivered:
PSM 0.61 m
Delivered:
PAN 0.50 m
Delivered:
PSM 0.5 m
Table 22 Acquired spatial resolution and delivered Pan & Pan-sharpened multispectral (PSM) products from DigitalGlobe and GeoEye
* scheduled for launch in late 2009
Vendor
SPOT Image
RapidEye
ALOS
ALOS
Name of Satellite
SPOT5
RE 5 Constellation
Prism
Avnir-2
Source Pixel Size (m)
PAN 5 m
MS 10 m
MS 6.5 m
PAN 2.5 m
MS 10 m.
Delivered:
PAN 2.5 m
PSM 2.5 m
PSM 5.0 m
Delivered:
MS 5 m
Delivered:
PSM 2.5 m
When fused with
PRISM data*
Table 23 Acquired spatial resolution and delivered MS & PSM products from SPOT5, RapidEye & ALOS
*The ALOS satellite acts as a platform for AVNIR-2, as well as two other sensors. One of those sensors is the 2.5 m.
panchromatic PRISM sensor. A 2.5 m. fused product can be produced, but it is worth noting that this fused product is based
on data acquired independently from two different sensors.
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The user requirements are driving the process of determining a solution for the SDMI. From the analysis
of these requirements it has been determined that no single solution can meet the needs of all users. A
three tiered approach was established to illustrate the variation in user requirements for application
feature based mapping. Figure 40 illustrates how various sensors can address the spatial resolution
requirements within each tier.
Figure 39 Three Tiers and Applicable Sensors
It might be assumed that an image resolution capable of meeting the needs of Tier 3 detailed scale
features would also satisfy the needs of the other two tiers. However, from user feedback it has been
determined that several characteristics of higher resolution data can create problems when trying to
map broader features over larger areas of interest:
Excessive detail and shadowing create confusion for automated analysis
Higher resolution does not offer the same level of spectral continuity over larger areas
Larger data volume can hamper speed at which data can be analyzed and viewed
Down-sampling of data can solve data volume issue, but does not always resolve confusion
issues related to excessive detail and shadow.
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Spectral
Most satellites image bands within the Blue (B), Green (G) and Red (R) visible spectrum of light. These
bands can be combined into RGB composites that produce a natural color image. This band combination
is very useful for underlay applications, as it provides a spectral view that humans are familiar with
seeing and interpreting.
Most multi-spectral sensors also collect a band within the near-infrared (NIR) portion of the
electromagnetic spectrum. Composite images that include this band are referred to as color infrared
images. The NIR band is particularly useful in vegetation mapping and plant health analysis, as it is the
portion of the spectrum that is most highly reflected by green vegetation.
Some satellites collect data with a dynamic range of 8 bit while others offer higher ranges of 11 or 12
bits. Higher bit data collection allows for a greater range of pixel values, which is useful for image
enhancement or spectral analysis. However, higher bit data does need more storage capacity, and may
require a conversion back to 8 bit data for use as backdrops, in some GIS and graphic applications.
Vendor
GeoEye
GeoEye
DigitalGlobe
DigitalGlobe
Name of
Satellite
Spectral
Characteristics
GeoEye-1
IKONOS
QuickBird
WorldView-2*
Pan = 450-800 nm
MSI 1 (B) = 450-510 nm
MSI 2 (G) 510-580 nm
MSI 3 (R) = 655-690 nm
MSI 4 (NIR) = 780-920 nm
Pan = 526-929 nm
MSI 1 (B) = 445-516 nm
MSI 2 (G) 506-595 nm
MSI 3 (R) = 632-698 nm
MSI 4 (NIR) = 757-853 nm
Pan = 450-900 nm
MSI 4 (B) = 450-520 nm
MSI 3 (G) = 520-600 nm
MSI 2 (R) 630-690 nm
MSI 1 (NIR 1) = 760-900 nm
R/G/B/NIR
4 additional
bands:
coastal
red_edge
yellow
nir
11 bit
11 bit
11 bit
11 bit
Dynamic
Range (bits)
Table 24 Spectral Characteristics of GeoEye and DigitalGlobe Sensors
* scheduled for launch in late 2009
The above satellites, which are comparable in resolution, have very similar spectral properties at a
dynamic range of 11 bit. Each sensor typically offers one higher resolution panchromatic band, and then
four lower resolution multispectral bands. The multi-spectral bands cover the Blue, Green, Red, and
Near-Infrared portions of the spectrum of light.
The one exception will be the planned WorldView-2 sensor from DigitalGlobe. This sensor will be able
to serve a more diverse set of specific applications, with the addition of four extra bands:
Coastal Band: Bathymetric mapping & Chlorophyll absorption
Yellow Band: Vegetation & Turbidity, as well as natural color enhancement
Red Edge: Vegetation mapping for plant health assessment & chlorophyll production
NIR2: Atmospheric correction as well as vegetation bio-mass analysis
DigitalGlobe’s Worldview-1 and the ALOS PRISM sensor are panchromatic sensors only.
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Vendor
SPOT Image
RapidEye
ALOS
Name of
Satellite
Spectral
Characteristics
SPOT5
RE 5 Constellation
AVNIR-2
Pan = 480 -710 nm (0.48‐0.71 μm)
Blue: 440 - 510 nm
Green: 520 - 590 nm
Red: 630 - 685 nm
Red Edge: 690 - 730 nm
NIR: 760 - 850 nm
4 visible/near IR bands similar to
Landsat 7 equivalent bands:
Band 1 (B): 420-500nm
Band 2(G): 520-100nm
Band 3(R): 610-690 nm
Band 4(NIR): 760-890 nm
Up to 12 bits
8 bit
G = 500 – 590 nm (0.50‐0.59 μm)
R= 610-680 nm (0.61‐0.68 μm)
NIR=780-890 nm 0.78‐0.89 μm)
SWIR= 1580-1750 nm (1.58‐1.75 μm)
Dynamic Range
(bits)
8 bit
Table 25 Spectral Characteristics of SPOT5, RapidEye and AVNIR-2
Both AVNIR-2 and the RapidEye Constellation of satellites offer bands covering the Blue, Green, Red and
NIR portion of the spectrum of light.
RapidEye offers an additional band that covers the red edge portion of the spectrum, which is useful in
mapping vegetation stress. RapidEye images at 12 bit dynamic range.
SPOT 5 is unique in that it does not image the Blue band in the visible spectrum, but does image a band
within the mid-infrared (SWIR) portion of the spectrum of light. The mid-infrared portion can be useful
for lithologic mapping of rock types. Since SPOT5 does not collect a Blue band, an algorithm is used to
create a simulated natural color product.
Accuracy
It is understood that all of the reviewed satellites can meet the SDMI goal of 1:24,000 scale mapping
based on varying degrees of:
Vertical control accuracy
Horizontal control accuracy
Horizontal control quantity and distribution
Restricted incidence angle of acquired imagery
Overall ortho-positional accuracy is based on a combination of:
Satellite Accuracy
Terrain (DEM) Accuracy
Horizontal Control Accuracy
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Satellite Accuracy: The following table summarizes the satellite accuracies for each sensor as they were
provided by the vendors. Some vendors provided operational specifications (OS) for their native
accuracy that were better than the stated product accuracies (PS). These numbers are displayed in
brackets underneath their production specification:
Sensor
GeoEye-1 WV-1
QB
IKONOS SPOT5 RapidEye PRISM AVNIR-2
Native
6.5
15
39
Accuracy
<5
23
44.85*
<20
<100
(4.0-5.5)*
(7.0)*
(35.8)*
(CE90) (m)
Controlled
Accuracy
0.88
2
4
1.75
10.52
10.66
**
**
(CE90) (m)
Table 26 Native & Corrected Accuracy specifications provided by vendors
*Operational specifications for some sensors have been independently validated to be better than stated accuracy. RapidEye’s
basic product has been controlled to a global control database and their stated accuracy of this basic product is 44.85 m CE90.
**Controlled accuracy numbers were not provided for the ALOS sensors
Terrain Accuracy: In the evaluations that follow, each sensor has been evaluated utilizing two different
terrain accuracies. These accuracies are associated with the Low- and Mid-accuracy definitions utilized
in the SDMI DEM Whitepaper. The following table lists some potential sources for low and mid accuracy
terrain data as reported in the DEM Whitepaper.
Terrain
Category
Low-Accuracy
Mid-Accuracy
Mid-Accuracy
Dataset used for Analysis
ASTER G-DEM
Fugro EarthData GeoSAR*
Intermap IFSAR*
Vertical Accuracy
meters LE95
20
1.8-8.78
1.8- 12
Horizontal Accuracy
meters CE95
30
10.4-17.30
3.46
Table 27 Terrain Source Accuracies *based on a range of slopes. See DEM Whitepaper for details
The Vertical and Horizontal accuracy numbers utilized for the Low Accuracy terrain category are taken
from anticipated accuracy of the ASTER G-DEM product that is scheduled for release in spring 2009.
These numbers have not yet been validated for the ASTER G-DEM product.
The Vertical and Horizontal accuracy numbers utilized for the Mid Accuracy terrain category are taken
from the numbers submitted for the GeoSAR IFSAR Terrain Product, as part of the SDMI DEM Workshop
held in August of 2008. FUGRO submitted a range for both vertical and horizontal accuracy. For the
purpose of the evaluations that follow, the lowest anticipated accuracy numbers were utilized.
Therefore, when looking at accuracies achieved using mid-accuracy terrain, realize that these accuracies
could most likely be improved upon.
Horizontal Control Accuracy: Each of the following evaluation tables state the assumed accuracy of the
horizontal control, if utilized. If horizontal control is utilized the vendor provided controlled accuracy
specifications are utilized. If no horizontal control is utilized the vendor provided native accuracy
specifications are utilized.
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Table 30 below lists accuracies achievable for optical sensor products using either the ASTER G-DEM
(Low-Accuracy) or an IFSAR DEM (Mid-Accuracy) with no ground control to improve native image
accuracy. GeoEye-1 and WorldView-1 can currently exceed the 1:24,000 scale requirement based on
the native accuracy of the sensor alone, when coupled with either a DTED Level 2 Low-Accuracy ASTER
G-DEM or a Mid-Accuracy IFSAR DEM.
Using the operational specifications (OS) of its native accuracy, and a Mid Accuracy terrain model,
indicate that IKONOS may also be able to exceed the 1:24,000 scale requirement, independent of
horizontal control. All other sensor options are not capable of meeting the geometric accuracy
requirement without the use of some form of horizontal control.
Horizontal Control Source: Native Accuracy of Satellite Only (No GCPs Utilized)
Incidence Angles up to 30° and 90th percentile of slope set to 20%
Sensor
Achievable Accuracy
Equivalent
Achievable Accuracy
Equivalent
(CE90 m) using Aster NMAS Scale
(CE90 m) using
NMAS Scale
G-DEM
IFSAR
Low Accuracy Terrain
Mid Accuracy Terrain
GeoEye-1
WorldView-1 OS
WorldView-1 PS
IKONOS OS
IKONOS PS
ALOS PRISM
QuickBird
SPOT5
RapidEye
ALOS AVNIR-2
10.59
11.55
12.06
12.33
18.11
22.43
25.14
40.30
46.08
100.51
20,840
22,730
23,731
24,276
35,655
44,153
49,490
79,330
90,704
197,862
6.93
7.29
8.07
8.48
15.74
20.56
23.49
39.29
44.86
100.11
8,187
8,605
9,528
10,010
30,989
40,478
46,242
77,346
88,308
197,075
Table 28 Error Budget Analysis - based on native accuracies (no GCPs utilized)
One option for horizontal control is to utilize another satellite image dataset that has a high geolocation
or native accuracy as the control source. Table 31, below, lists accuracies achievable for selected optical
sensor products using the same DEM options as in the table above, plus ground control (accurate to 5-m
CE90) to improve native image accuracy. This is the equivalent of using GeoEye-1 imagery as control.
Worldview-1, with a geolocation accuracy of 6.5 m could also be a suitable source of control.
Please note that only the vendors that provided corrected accuracy numbers for their sensors are
included in this table. GeoEye-1 and WorldView-1 are also excluded from this table, as their native
accuracies alone are high enough to be the sole source of horizontal control.
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Horizontal Control Source: 5.0 m CE90 GCPs (Equivalent to using Geolocation Accuracy of GeoEye-1)
Incidence Angles up to 30° and 90th percentile of slope set to 20%
Sensor
Achievable Accuracy
Equivalent
Achievable Accuracy
Equivalent
(CE90 m) using Aster NMAS Scale
(CE90 m) using
NMAS Scale
G-DEM
IFSAR
Low Accuracy Terrain
Mid Accuracy Terrain
IKONOS
QuickBird
SPOT5
RapidEye
11.46
12.01
15.50
15.88
22,564
23,636
30,516
31,266
7.15
8.00
12.65
13.11
8,447
9,443
24,905
25,818
Table 29 Error Budget Analyis - based on 5 m. CE90 GCPs
The above tables both are based on imagery acquisition at a maximum incidence angle of 30°. Vendors
often apply sensor angle restrictions in order to improve the geometric accuracy of a product. Several
vendors recommend restrictions for achieving specific accuracies within their product guides. These are
recorded in the table below:
Name of Satellite
QuickBird
"3A Ortho"
"3D Ortho"
"3D Ortho" High Relief Terrain
"3G Ortho"
IKONOS
Geo™
GeoProfessional™
GeoProfessional™ Precision
GeoStereo™
GeoStereo™ Precision
GeoEye-1
Geo™
GeoProfessional™
SPOT 5
"1A, 1B, 2A"
"SPOTView 2B,3a"
“SPOTView 3a”
RapidEye
1B RE Basic
3A RE Ortho
ALOS
AVNIR-2
Maximum Incidence Angle
Recommendation
Scale
25°
25°
15°
15°
1:50,000
1:12,000
1:12,000
1:4,800
30°
24°
18°
30° with stereo collection
30° with stereo collection
n/a
1:12,000
1:5,000
1:20,000
1:5,000
30°
24°
n/a
1:12,000
27°
27°
15°
1:100,000
1:50,000
1:24,000
20°
20° (can be limited to 10° over high terrain)
1:24,000
1:24,000
Mandatory for ortho production: 0°
Table 30- Vendor recommended incidence angle restrictions for achievable accuracies
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The following tables list calculated ortho-image accuracies when survey grade horizontal control points
are used in the orthorectification process. All of the listed satellite ortho-imagery products can exceed
the 1:24,000 scale accuracy requirement with the use of survey grade control, when coupled with a
statewide mid-accuracy terrain model. For all sensors, except SPOT5 and RapidEye, the incidence angle
can be as high as 30 degrees, and the 1:24,000 scale accuracy requirement can still be meet, even when
ortho-production is done with the lower accuracy terrain model (Table 33).
Horizontal Control Source: 0.5 m CE90 GCPs (Equivalent to using survey grade GCPs)
Incidence Angles up to 30° and 90th percentile of slope set to 20%
Sensor
Achievable Accuracy
Equivalent
Achievable Accuracy
Equivalent
(CE90 m) using Aster NMAS Scale
(CE90 m) using
NMAS Scale
G-DEM
IFSAR
Low Accuracy Terrain
Mid Accuracy Terrain
GeoEye-1
WorldView-1
IKONOS
QuickBird
SPOT5
RapidEye
10.21
10.36
10.33
10.93
14.68
15.08
20,091
20,400
20,328
21,512
28,902
29,693
4.91
5.21
5.14
6.26
11.63
12.14
5,803
6,154
6,068
7,392
22,899
23,889
Table 31 Error Budget Analysis - based on 0.5 m CE90 GCPs maximum incidence angle 30°
According to the Error Budget Analysis, the incidence angle at which imagery is acquired should be
limited to 18° for SPOT5, and 15° for RapidEye to achieve 1:24,000 scale with the use of the ASTER GDEM product, and survey grade horizontal control.
Horizontal Control Source: 0.5 m CE90 GCPs (Equivalent to using survey grade GCPs)
Incidence Angles up to 15° and 90th percentile of slope set to 20%
Sensor
Achievable Accuracy
Equivalent
Achievable Accuracy
Equivalent
(CE90 m) using Aster NMAS Scale
(CE90 m) using
NMAS Scale
G-DEM
IFSAR
Low Accuracy Terrain
Mid Accuracy Terrain
GeoEye-1
WorldView-1
IKONOS
QuickBird
SPOT5
RapidEye
4.83
5.15
5.08
6.21
11.61
12.11
5,700
6,082
5,995
7,333
22,845
23,838
2.46
3.04
2.91
4.61
10.84
11.37
2,906
3,588
3,439
5,446
21,329
22,390
Table 32 Error Budget Analysis - based on 0.5 m CE90 GCPs maximum incidence angle 15°
Additional information supporting the ability of SPOT5 to meet or exceed the 1:24,000 scale
requirement are the results from a 2006 Commercial and Civil Applications Project (CCAP) evaluation of
SPOT5 data conducted by the National Geospatial-Intelligence Agency (NGA). The NGA evaluation
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concluded that the accuracy of ortho‐rectified SPOT‐5 imagery meets or exceeds the vendor
stated accuracy specification. The NGA determined the accuracy to be 8.8m CE90. The ortho-image
product that NGA validated was generated using SPOT’s Reference3D product as control. Reference3D
is a packaged dataset produced from SPOT’s High Resolution Stereo (HRS) sensor. The product includes
both a DTED level 2 DEM and a 5m Control Image Base (CIB) ortho-image.
Although not validated by NGA, SPOT Image reports that similar accuracies have been achieved
with Kompsat‐2, Formsat‐2 and SPOT‐4 when Reference 3D was used as DEM and horizontal
control source. SPOT Images also reports that clients using survey grade GCPs have reported
achieved horizontal accuracies of better than 5 meter absolute accuracy.
Additional information supporting the ability of RapidEye to meet or exceed the 1:24,000 scale
requirement are the results of the 2009 evaluation of of RapidEye data conducted by USGS and reported
in the paper by Chandler, Hayes , Rengaragan and Haque, March 31 2009 at the Joint Agency for
Commercial Imagery Evaluation (JACIE) conference. The USGS evaluation concluded that the accuracy
of ortho-rectified RapidEye imagery over their Railroad Valley test area to be 7.31 CE90 which exceeds
the vendor stated accuracy specification. The orthoimage product was created by RapidEye using
DOQQ imagery for ground control (~7m RMSE) and SRTM DEMs. NGA positional accuracy validation of
RapidEye data is in progress.
ALOS: Questionnaire response has yet to be received. Evaluation is pending. AVNIR-2 has geometry
issues that require imagery to be collected at nadir only to support ortho-production.
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Distribution of Ground Control
Vendor Notes on GCP requirements from Imagery Workshop:
GeoEye’s satellite, GeoEye-1, does not require GCPs to meet 1:24,000 accuracy specification
GeoEye recommends using 1 GCP every 50 km along an 11km wide strip of IKONOS data.
GeoEye-1 data can be used as control for IKONOS to meet NMAS 1:24,000 accuracy
specification. However, as mentioned previously, the Error Budget Analysis shows IKONOS
could meet the above accuracy specification without the use of control (when the operational
specifications provided for IKONOS native accuracy is utilized).
DigitalGlobe’s WorldView-1 does not require GCPs to meet 1:24,000 accuracy specification
DigitalGlobe’s stated requirement is 6-10 GCPs per QuickBird scene to achieve NMAS 1:24,000
scale accuracy. WorldView-1 imagery could be used as the control source.
SPOT Image requires ~100 well distributed ground control points statewide for use in their block
bundle adjustment. They recommend using the SPOT Reference3D product as control. This
includes a DTED Level 2 terrain model and a 5 meter Control Image Base (CIB) ortho-image. Spot
Image is currently using a production software solution called Pixel Factory, developed
by their production partner Infoterra France. Pixel Factory is a hardware and software
package which is designed for large scale ortho-image production. It utilizes block
bundle processing techniques in order to ensure high accuracy with a minimal amount
of GCPs. Spot Image estimates that less than 100 well distributed GCPs are sufficient to
meet the stated SDMI accuracy specifications. Spot Image has proven this technique
with results in many large scale SPOTMaps™ mapping projects worldwide. Other COTS
software solutions offer a similar block bundle processing methodology that could be utilized
during production.
RapidEye’s recommended distribution of ground control is 1 GCP (7 m RMSE or better) every
1000 km2.
Acquisition
The state of Alaska covers over 1.5 million square kilometers. It is almost 2.5 times the size of the
second largest state, Texas. Alaska comprises almost 17.5% of the total land mass of the entire United
States alone. In addition to its sheer size, another set of unique characteristics makes imaging the state
in its entirety a particular challenge:
Alaska is located at a northerly latitude ranging from 51°20’N to 71°50’N. Therefore Alaska is
subject to low sun angles (resulting in deep shadowing), and extensive snow coverage for much
of the year. This affords a relatively short seasonal imaging window.
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Alaska encompasses the Aleutian Islands. This chain is composed of over 300 islands, and
covers an extent of approximately 1900 km. This is a longer distances than Seattle to San Diego
Figure 40 Alaska size comparison to conterminous U.S. courtesy of Aero-Metric
Sub-arctic climate, extensive coastline, and mountainous terrain combine to bring adverse
weather conditions and cloud cover over much of the state for extensive periods throughout the
year, further reducing imaging opportunities. Almost a third of Alaska’s land mass is above the
Arctic Circle.
The ability of any sensor to image this extent of land, with this number of unique challenges will be
dependent on sensor swath width and revisit rates:
Vendor
GeoEye
Satellite
GeoEye-1
Swath Width (km)
15.2
GeoEye
DigitalGlobe
IKONOS
QuickBird
11.3
16.5
2-3 days at > 60°N
1-3.5 days depending on latitude
(at maximum look angle 30°)
DigitalGlobe
DigitalGlobe
SPOT Image
WorldView-1
WorldView-2*
SPOT5
17.6
16.4
60
1.7-5.4 days
1.1 -3.7 days
1-2 days
RapidEye
RE 5
Constellation
77
daily off-nadir
<5 days at nadir
ALOS
Prism
35
Daily for Latitudes > 55°N
ALOS
Avnir-2
70
Daily for Latitudes > 55°N
105
Revisit Rate
10° off -nadir 8.3 days /
35° off-nadir 2.1 days
SDMI Imagery Workshop Whitepaper
Table 33 Swath width & revisit rate statements for various sensors
* scheduled for launch in late 2009
Swath Width (km)
Swath Width Comparison
80
70
60
50
40
30
20
10
0
Sensors in MS mode except for WorldView-1 and Prism
Figure 41 Swath width comparison
Vendor collection capacity statements:
Digital Globe put their combined Quickbird/WorldView-1 collection capacity at an estimated
280,000 sq. km every 2 months. If this is extrapolated over a 5 month in-season acquisition the
estimated acquisition would be 700,000 sq. km. per season. This would require at least 3
seasons to acquire and is based on 0-20% cloud cover. If a 0-10% cloud cover restriction was
applied, this would lengthen the time to acquire statewide coverage. (see further discussion
under User Requirements, Cloud Cover section).
GeoEye specified their combined IKONOS/GeoEye-1 collection capacity at an approximate 791
in-season days to collect mainland Alaska cloud-free. This would translate into at least five
seasons of required acquisition for complete coverage.
SPOT specified a collection capacity that would provide 95% of the state with <10% cloud and
snow cover within three collection seasons. 5% was factored in for areas that may experience
persistent cloud coverage, or other climatic challenges.
RapidEye has the collection capacity to image 90% of Alaska in approximately three days. They
estimate 10 days for complete coverage of AK, but gave no concessions to account for cloud
coverage. Given the improved collection capacity provided by a constellation of five satellites, it
is estimated that Alaska could potentially be imaged cloud-free in a single collection season.
ALOS – The SDMI have yet to receive a response for ALOS based sensors. However, for suitable
geometric accuracy, AVNIR-2 can only be collected at nadir (0° incidence angle). This restriction
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would eliminate any collection agility, and would impede the sensors ability to meet the refresh
requirements.
Figure 42 Swath width comparison over Alaska imagery
Revise in-season requirement to improve collection capacity:
In assessing the satellite vendor archives, an in-season range from May 1st through September 30th was
established for consistent comparison. For responses to the Imagery Workshop questionnaire, vendors
were asked to estimate collection capacity within this five month period. However, it has since been
recommended that the SDMI adopt collection requirements that are based on sun elevation angle
restrictions, which would produce variable in-season collection time-frames by varying ranges of
latitude.
Satellite imagery is typically collected with sun elevations that are greater than 15°.lvii The following
figure illustrates date ranges for variable latitudes where the sun elevation angle is less than 15°.
If this recommendation is
followed:
The imaging window for
the northern most
latitudes of Alaska could
be opened up from
March 30th to
September 30th
For the southern most
latitudes of Alaska, the
imaging window could
be greatly improved
upon by being opened
up from mid-January
through to late
Figure 43 Date Ranges for Alaska, by latitude, where sun angle is less than 15°. Graphic
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November.
Courtesy of GeoEye
Sensor Lifespan
When considering potential space-borne solutions, it is important to be aware of the expected lifespan
of that sensor, as well as the plans for any supporting future generation sensors. The following table
illustrates operational expectations for all the satellite platforms proposed to support the SDMI, as well
as mention of future generation planned launches and life expectancy of the those proposed satellites:
Vendor
Satellite
DigitalGlobe QuickBird
WorldView-1
WorldView-2
GeoEye
IKONOS
SPOT Image
RapidEye
ALOS
GeoEye-1
GeoEye-2
SPOT5
SPOT4
Formosat-2
Kompsat-2
Pleiades-1
Pleiades-2
SPOT6
SPOT7
Constellation of 5
Satellites
PRISM*
AVNIR-2*
*on same
platform
Operational through
Planned Launch
2010
2018
Currently operational; exceeded
life expectancy of 7 years and
has received verification and
certification for continued
insurance; continues to operate
in a fully functional manner.
2018
2022
2014
2012
2014
2012
2015
Sept./Oct. 2009
Expected
Lifespan
7-8 years
-
-
2012
2010
2011
2012
2013
-
10 years
5
5
7
7
-
Currently operational; recently
extended operations for an
additional 5 years.
-
-
Table 35 Sensor lifespan and future generation planned launches and life expectancy
The specifications for DigitalGlobe’s WorldView-2 have been discussed throughout the document.
GeoEye-2 will be of the same general class as GeoEye-1, with a higher source pixel resolution of
panchromatic data in the range of 0.25 meters. SPOT Image will be the official and exclusive distributor
for the Pleiades family of satellites, which will have similar capabilities (spectral, radiometry, temporal;
collection capacity, swath size, accuracy, etc) to the current high resolution satellites offered by
DigitalGlobe and GeoEye (including spatial sub-meter resolution). SPOT6 and SPOT7 will provide data
similar to what SPOT5 currently offers.
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Conclusions
1) The five main factors characterizing imagery specifications as they relate to applications are:
a. spatial resolution – refers to the size of each pixel of imagery which relates to the size
and kind of features that can be characterized or extracted from the image. Higher
resolutions are generally preferred for visualization applications, but lower resolutions
are often more suitable for land use / land cover classifications. Map scales – such as
1:24,000 – are often used as a proxy for pixel resolution but this can cause confusion as
different applications can require varying resolutions while mapping at the same scale.
b. location accuracy – refers to the location of a pixel on the image as it relates to the
actual location on the ground. No map or image is locationally perfect and the error is
often irregularly distributed over the map or image. The error is often referred to as a
linear or circular error at a statistical confidence level e.g. CE90. The error is also
sometimes correlated with a map scale – such as NMAS 1:24,000 – which can be
correlated to the other error characterizations as shown in the table below. Note that
positional accuracy and resolution can easily be confused or misused when referred to by
map scale.
There are several factors that influence the positional accuracy including (1) the
accuracy of the DEM used during orthorectification (the process used to convert the raw
image into an image map), (2) the availability of ground control points (to tie the image
to the ground), (3) the design of the sensor and the number of GCPs required, (4) the
angle between the sensor and the point on the ground where the image is acquired, (5)
the knowledge of and accuracy of the location and orientation of the sensor when the
image was obtained and (6) the number of overlapping scenes (or images) available.
Map Scale
1:50,000
1:24,000
1:12,000
1:4,800
1:2,400
CE90
25.4m
12.2m
10.2m
4.1m
2.0m
Table 36 CE90 accuracy and associated NMAS scale
c. collection & revisit characteristics – refers to (1) the time between application
definition and image acquisition, i.e. a short time for wild-fire applications and a longer
time for climate change applications, (2) the time of day and the season that an image
can be acquired along with (3) the time between successive images. These factors
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impact the goals of SDMI users in many ways including (1) responsiveness, especially to
public safety applications, (2) uniformity of sun angle and shadow, (3) some applications
need imagery acquired during specific times of the year i.e. leaf-on vs. leaf-off, and (4)
the span of time required to acquire multiple images to end up with a cloud-free
composite or mosaic.
d. spectral coverage – refers to the number, size and location of bands. For example a
natural color image requires blue, green and red bands whilst a traditional CIR image
requires the green, red and near infra-red bands. Different combinations of bands make
an image more or less suitable for a particular application.
e. scene footprint size – refers to the size of the image that is collected over a short
duration of time. Typically relatively higher resolution images have smaller scene sizes
and relatively lower resolution images have larger scene sizes. The scene size matters
because (1) often the amount of ground control (and thus the cost to acquire it) is
related to the number of scenes rather than the size of the scene, so less ground control
would be required for the entire state if the scene size was larger, (2) a larger scene size
means more uniform imagery (spectral continuity) which is important to visualization
applications and critical to classification applications.
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2) There is a broad range of requirements that no single image specification meets. In fact, the
applications and supporting imagery requirements needed to be grouped in order to make
the decision making process manageable. This leads to the inverted pyramid concept as
depicted below for both applications and corresponding image resolution.
Figure 44 Applications by tiers
Figure 45 Sensor resolutions by tiers
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3)
Alaska has unique challenges for uniform collection based on (1) the size of the state, (2) the
short collection season where the sun angle is high and there is minimal snow cover, (3)
cloud cover, (4) the lack of a digital elevation model (DEM) suitable for orthorectification at
map accuracy scales of NMAS 1:24,000 or better, and (5) the lack of consistent, well-spaced
image-identifiable ground control points.
4)
Given the varying application requirements, a statewide image map of NMAS 1:24,000
accuracy and 1:24,000 feature identification / display scale suitability is the sweet spot.
Higher accuracy and resolution imagery will be required but likely only on a regional or
project basis rather than on a statewide basis. The following maps depict the distribution of
resolution requirements as garnered during the end-user survey. The supporting
spreadsheet allows for SDMI staff to change the weight by applications or by agency and
thus change the color density on the maps. (All are weighted equally in the graphics below).
Imagery Area Requirements by Scale/Resolution
Agency/Organization
Broad (2.5-10
meter)
Moderate (1-2.5
meter)
Detailed (sub- to 1-meter)
Area (sq.
km.)
% of
State
Area (sq.
km.)
% of
State
Area (sq.
km.)
% of State
Federal Aviation Administration,
ADOT Aviation
1,493,266
100
-
-
155,245
10
US Department of Defense
-
-
8,498
1
234,523
16
National Wetlands Inventory
(USFWS)
1,493,266
100
-
-
-
-
USFWS
1,493,266
100
290,343
19
-
-
Bureau of Land Management
1,493,266
100
209,758
14
11,764
1
National Park Service
203,342
14
203,342
14
-
-
USDA Forest Service
1,493,266
100
-
-
85,942
6
State of Alaska (including ADOT),
Alaska Railroad Corp.
1,493,266
100
413,180
28
305,912
20
Native Corpsorations &
Organizations
1,493,266
100
-
-
174,650
12
-
-
528,234
35
104,990
7
Populated Places (Municipalities,
Cities, Villages) - Buffered 10 km
-
-
-
-
229,299
15
Major Roads & Railroads Buffered 5 km
-
-
-
-
58,020
4
Trans-Alaska Pipeline & Other
Pipelines - Buffered 10 km
-
-
-
-
25,223
2
-
-
-
-
55,541
4
1,493,266
100
1,205,591
81
409,062
27
Boroughs, Municipalities, Cities
Utilities & Infrastructure
NON-OVERLAPPING AREA
TOTALS
Table 34 Imagery area requirements by Agency
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Figure 46 Acquisition areas for broad resolution imagery
Figure 47 Acquisition areas for moderate resolution imagery
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Figure 48Acquisition areas for detailed resolution imagery
Discussion of Specific Options
Given the limited number of vendor sources, it is impossible to be completely generic while discussing
optional sources to meet SDMI requirements. All of the collection platforms and components have
design and operational pros and cons in regards to performance for various applications. The following
section is not meant to favor any vendor or technology, but rather to give SDMI decision makers a
starting point and context for RFP specifics.
Digital Elevation Model
This discussion of the DEM is for its utility as it relates to orthorectification of the imagery. The DEM
workshop and whitepaper discussed (and its author made conclusions in regards to) the other uses of
the DEM to directly support applications such as flight safety, hydrology, coastal erosion etc.
A DEM meeting the NGA DTED-2 accuracy specifications (http://www.nga.mil/ast/fm/acq/89020B.pdf)
is required and is sufficient for all of the satellite imagery vendors who responded to calls for
participation in this project to meet NMAS 1:24,000 accuracy (given some other collection and ground
control constraints discussed in the body of this whitepaper). The DTED-2 specification is very similar to
the specs of the USGS DEM / NED that is available for the conterminous US. (Note that airborne vendors
would likely create their own DEMs for orthorectification, but could use the DTED-2 for the areas
requiring only broad resolution imagery.)
The existing Alaska USGS DEMs are too coarse and inaccurate to be used.
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A joint US / Japanese DEM project known as the ASTER G DEM is creating a DEM that claims to be close
to the DTED-2 accuracy specification and will have no acquisition cost to SDMI. As the DEM has not
been released and any early evaluations are subject to non-disclosure, it is unknown at this point if it will
be sufficient. This DEM is being built from auto-correlated pairs of visible satellite imagery that is
uncontrolled. Each pair is independently correlated and the results composited with no bundle
adjustment. It is anticipated that there will be artifacts in the DEM that will create noticeable artifacts
in subsequent orthorectified imagery (from wobbly roads to sharp offsets or “tears” in the imagery).
Therefore, it is anticipated that the DEM will have to be “cleaned-up” in order to be usable for NMAS
1:24,000 orthorectification processes. A ROM estimate for cleanup is $500,000; however, since the
DEM has not been released for evaluation, this is a very rough number. It is possible that the DEM will
be usable as delivered or that it will never be suitable for some of the referenced applications.
An additional consideration is that as of the date of this publication, distribution of the DEM from other
than the NASA DAC or the Japanese equivalent is currently prohibited. Distribution of a “cleaned-up” or
enhanced DEM may also be subject to this distribution restriction. Therefore, the enhanced DEM could
be used for orthorectification purposes only by the organization that did (or sponsored) the
enhancement and subsequent use by SDMI members or members of the public would be prohibited.
This restriction was imposed at the request of the Japanese partner and would have to be negotiated
with them (perhaps through the USGS).
It is anticipated that the first version of this DEM will be released in the summer of 2009, but it is also
possible that they will decide that the errors are such that the entire DEM needs to be regenerated
pushing an availability date to mid-2010 before clean-up.
An alternative would be to take the input Aster scenes and create a more accurate, controlled, bundleadjusted DEM, called Aster-A DEM in this paper. This approach was not examined in the DEM
whitepaper so details of cost and time would need to be determined at a later date. A very rough
estimation of cost would be $1 million - $2 million but that could vary significantly based on control
requirements.
SPOT Ref 3D DEM is a product that is created from a set of purpose built sensors on the SPOT-5 satellite.
This DEM has not been built for Alaska, but SPOT Image has verified collection of the required imagery
for approximately 85% of the state and could finish collection this summer (2009). According to SPOT
Image, creation of this DEM requires only minimal ground control and the NGA has determined that it
does meet DTED-2 specifications. NGA has written studies comparing ASTER DEMs to SPOT DEMs and
has certified SPOT DEMs for use as SRTM infill and has not certified ASTER DEMs for that purpose.
Additional products in the Ref 3D bundle include an ortho-image layer that would be viable for ground
control for 2.5 meter imagery and a confidence layer. A DEM only layer is also available from SPOT
Image for a lower cost.
Note that NGA has expressed interest in having a DTED-2 DEM of Alaska and collaboration with them
would likely result in a lower total cost from SPOT Image and perhaps co-funding. (However, see the
section on IFSAR below.) It is estimated that production of this DEM would take approximately two
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years from contract initiation. The cost would need to be quoted from SPOT. An unofficial estimate of
the cost would be $5m - $10m.
An IFSAR DEM created from airborne radar systems was recommended in the DEM white paper. This
DEM significantly exceeds the DTED-2 specs and would support orthorectification of not only the broad
and moderate resolution image requirements, but also some of the detailed resolution requirements
along with the DEM-only requirements for aviation, public safety, hydrology etc. There appears to be
significant federal agency interest (including reportedly NGA, USGS & FAA) in this level of resolution and
accuracy which could lead to co-funding. The estimated timeline for a finished product is two to four
years after contract signing and ROM costs are $45 million to $80 million.
Imagery
RapidEye is a new constellation of five satellites that delivers 5 meter imagery with a large footprint that
is suitable for natural color and CIR applications. The advantage of the five satellites is that there are
lots of opportunities to collect imagery over the same piece of ground and thus a shorter calendar time
to the completion of a “cloud-free” mosaic. RapidEye would therefore be the only system capable of
collecting the entire state in one summer. Such frequent revisit could also be suitable to leaf-on / leafoff and monitoring applications.
The resolution would be at the outside edge of what would be desired / required for 1:24,000 display /
interpretation but it would be fairly uniform across the state. The company believes that about 1500
control points would be required for the state (along with a DTED-2 DEM of course) to meet the NMAS
1:24,000 accuracy requirements.
The risk with RapidEye is that it is a brand new system (although it is operational today) and doesn’t
have a track record yet. Costs have not been quoted, but an estimate would be $1 million to $2 million /
year depending on licensing and the length of the contract.
SPOT Image is offering data from 4 satellites, SPOT 4 and 5, Formosat-2, Kompsat-2. However SPOTs
offer of a GRS would provide for 2 satellites imagery options – SPOT5 2.5 meter pan/psm, 5 meter
pan/psm and 10 meter ms & HRS stereo sensors and SPOT4 10 meter pan and 20 meter ms. SPOT
Imagery offers well-tested and proven imagery solutions. The resolution would support the broad and
perhaps some of the moderate resolution requirements. SPOT Image states they can meet the 1:24000
accuracy standard using ~100 GCP statewide, when production is completed us a block bundling
technique. Current collection parameters would indicate that it takes about three years of collection to
cover the state given normal cloud and snow patterns. Spot has acquired and archived imagery over
Alaska for the 2007 and 2008 collection seasons. At the end of the 2009 collections season, SPOT
expects to have imagery, with 10% cloud coverage or less, for 95% of the State of Alaska.
Costs have not been quoted, but an estimate would be $2 million to $4 million before orthorectification
and mosaicking costs. SPOT is also offering a ground receiving option with telemetry costs rather than
per scene or per square kilometer costs that will be discussed below.
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ALOS, the Alaska State Facility (ASF), has not provided the SDMI with a full written response to the
Imagery Workshop Questionnaire as of March 26, 2009. Prior to final release of this paper, it is hoped
that the ALOS solution of PRISM (2.5 m panchromatic) and AVNIR-2 (10 m. multi-spectral) can be further
explored. For the time being, the main concerns about this solution are that AVNIR-2 cannot support
ortho-image generation except if the data is acquired at nadir, and that the active tasking of AVNIR-2
has been limited. It should be noted that ALOS did just extend its operation for an additional five years,
and there has been mention that an effort will be made to increase the nominal collection capacity of
the sensor.
GeoEye offers two satellite imagery options – the 1 meter resolution IKONOS system and the brand new
0.5m resolution GeoEye-1. Both can collect visible and NIR bands. Many participating agencies have
experience with the IKONOS system. GeoEye-1 not only has higher resolution, but has such accurate
location and orientation information that the imagery meets NMAS 1:24,000 at nadir with no ground
control! This is accurate enough that GeoEye-1 imagery can actually be used as ground control for some
of the other, coarser systems.
GeoEye-1 is more agile than IKONOS, which means that it doesn’t just collect in strips, but can acquire
multiple targets in a single pass. For example, it can collect a 1 degree tall by ½ degree wide patch
virtually simultaneously. This is a dramatic improvement and will impact SDMI by having new
collections completed much more rapidly than may have been experienced with IKONOS. This improved
agility will also improve the spectral continuity of collection for some large area collects.
No RFPs have been issued and no quotes have been received, but estimates would be a collection
timeframe of three years for most of the state and a cost of $10m - $20m depending on licensing etc. A
DTED-2 DEM and limited ground control would be necessary. Note that it is rumored that the NGA has
requested that many areas including Alaska be collected by GeoEye-1 and that the NextView license has
some capacity for transfer of the imagery license to the state. There are many rumors as to under what
circumstances this transfer may apply. It is recommended that an official request for clarification be
issued by the SDMI Executive Committee directly to senior NGA officials. (The same holds true for
Digital Globe content.)
DigitalGlobe currently offers two satellite systems – the 0.6 meter QuickBird visible and CIR system and
the 0.5 meter B&W WorldView-1 system – and is expecting a third - WorldView-2 0.5 meter visible, CIR,
plus 4 other bands, to be available before the end of 2009. WorldView-1 accuracies are (and
WorldView-2 is designed to be) capable of meeting NMAS 1:24,000 at nadir with no ground control!
They also can actually be used as ground control for some of the other, coarser systems. The WorldView
systems are also very agile which means that they don’t just collect in strips, but can acquire multiple
targets in a single over-pass. For example, they can collect a 1 degree tall by ½ degree wide patch
virtually simultaneously. This is a dramatic improvement and will impact SDMI by having new
collections done in much more rapidly than may have been experienced with QuickBird.
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No RFPs have been issued and no quotes have been received, but estimates would be a collection
timeframe of three years for most of the state and a cost of $10m - $20m depending on licensing etc. A
DTED-2 DEM and limited ground control would be necessary. (See the note about the NGA NextView
license above.)
Please note, that while the newer DigitalGlobe and GeoEye sensors have improved their collection
capacities over their predecessors, sensors with greater swath widths still offer considerable advantages
in terms of collection capacity, and spectral continuity over larger areas.
Aerial Vendors offer collection with digital sensors that are mounted in airplanes. The dynamics are
well understood both from large area collects (e.g. NAIP) in the lower 48 and from smaller area collects
in Alaska. Natural color and infrared bands are available. The resolution depends on the flight height,
but reasonable ranges are from 2 meters down. Certainly this option makes sense for resolutions higher
than 0.5 meters which is the best that the satellites can offer. For statewide coverage at 1 meter
resolution, an estimate would be $10 million to $30 million and would take three to five years.
Ground Receiving Stations
Ground Receiving Station (GRS) and telemetry contract (a fixed all-you-can-eat annual fee) offer the
following advantages (1) the ability for SDMI to have total control over collection priorities (perhaps
tailored to specific applications), (2) a significantly lower comparative cost vs. buying the same number
of scenes individually, (3) no additional budget requirements for recurring emergencies such as response
to public safety incidents such as fires and (4) utilization of state resident labor pools for operation of
the system and processing of the data.
Several vendors at the Imagery Workshop proposed establishing new, or leveraging existing GRSs. While
other vendors offer benefits through subscription programs or “virtual” GRSs. All vendors at the
Imagery Workshop have expressed a willingness to tailor a program to SDMI requirements. It is possible
that costs could escalate with the number of scenes delivered in a subscription program (as opposed to
a true GRS) because the vendor has costs related to the handling of each scene; however, the total cost
may be significantly lower given a time or volume commitment.
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References
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SDMI Imagery Workshop Whitepaper
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Tom Brigham, ADOT&PF
Tom Duncan, FNSB
Tom Knox, MOA
Wny Menefee, ADNR Div. Mining, Land and Water
120
SDMI Imagery Workshop Whitepaper
i
Paul Brooks, 2009
ii
Available from www.alaskamapped.org
iii
Paul Brooks, pers.comm, 2009
iv
Ted Cox, pers.comm, 2009
v
Craig Seaver, USGS, 2009
vi
http://www.blm.gov/ak/st/en.html
vii
Joni Piercy, pers. comm. 2008
viii
http://www.fs.fed.us/r10/ro/ak_overview/table_of_contents/intro.shtml
ix
Mark Riley, pers. comm., 2009
x
Mark Riley, pers. comm., 2009
xi
George Plumley, pers.comm., 2009; Alaska State Division of Community and Regional Affairs, Community Mapping (IAID)
program document.
xii
Bill Holloway, pers. comm. 2008
xiii
Charles Park, pers. comm., 2008
xiv
http://alaska.usgs.gov/announcements/news/highlights.php?hmthday=1019&&hyear=2006
xv
http://www.grsgis.com/
xvi
http://www.grsgis.com/
xvii
http://www.abrinc.com/projects/ecological-land-classification-and-mapping.htm
xviii
http://alaska.fws.gov/fisheries/nwi/what.htm
xix
http://alaska.fws.gov/fisheries/nwi/what.htm; Julie Michaelson, pers. comm., 2008-2009
xx
Jerry Tande, Julie Michaelson, pers. comm., 2008-2009
xxi
http://www.abrinc.com/projects/wetland.htm
xxii
Jeff Schively, pers. comm., 2008
xxiii
Doug Lalla, JPO
xxiv
Lisa Saperstein, pers. comm., March 2009
xxv
Rebecca Strauch, Carol Barnhill, pers.comm., 2009
xxvi
http://www.uaf.edu/water/faculty/nolan/glaciers/McCall/index.htm
xxvii
Robert Ruffner, pers. comm., 2008
121
SDMI Imagery Workshop Whitepaper
xxviii
Robin Beebee, pers. comm., 2008
xxix
http://www.nps.gov/akso/
xxx
Parker Martyn, Sara Wesser, NPS.
xxxi
US Forest Service Pacific NW Research Stateion: http://www.fs.fed.us/pnw/about/programs/index.shtml
xxxii
Mark Riley, pers. comm., 2009
xxxiii
Ken Winterberger, pers. comm., 2009
xxxiv
Ken Winterberger, pers. comm., 2009; Joel Calderwood, USDA Forest Service.
xxxv
Aaron Ritchins, pers.comm., 2008
xxxvi
Gary Friedmann, pers.comm., 2008
xxxvii
Erik Kenning, ASRC; Larry Clamp, ASRC Energy Services
xxxviii
Kyle Cunningham, CIRI
xxxix
Rick Perkins, Sealaska Corporation
xl
Currently published ICAO guidance for the use of imagery for the Etod requirements is contained in the document 9881,
which is subject to change: International Civil Aviation Organization, July 2004, Annex 15 Aeronautical Information Services
Report, Appendix 8.
xli
Ryan Anderson, pers. comm., 2009
xlii
Ryan Anderson, pers. comm., 2009
xliii
Tom Brigham, pers. comm., 2009
xliv
Marko Radonich, pers. comm., 2008; Jeff Schively, pers. comm., 2008
xlv
Andrew Frasier, pers. comm, 2008
xlvi
http://fire.ak.blm.gov/afs/
xlvii
http://www.fema.gov/plan/prevent/hazus/index.shtm
xlviii
http://www.grsgis.com
xlix
Sheet 057-01, May 2001: : http://egsc.usgs.gov/isb/pubs/factsheets/fs05701.html
l
refs-QB user guide, other
li
http://www.grsgis.com
lii
Ken Winterberger, pers. comm., 2009
liii
David Oliver, pers.comm., 2008
liv
QB Product guide http://www.digitalglobe.com/file.php/589/QuickBird_Imagery_Products-Product_Guide.pdf
USSGS DOQ Fact Sheet 057-01, May 2001: : http://egsc.usgs.gov/isb/pubs/factsheets/fs05701.html
122
SDMI Imagery Workshop Whitepaper
lv
Tara Byrnes, GeoEye
lvi
Toutin, Th., and Carbonneau Y., Chenier R.., 2000. Block Adjustment of Landsat-7 ETM Images
http://www.photogrammetry.ethz.ch/general/persons/jana/isprs/tutmapup/ISPRS_tutorial_Toutin_hannover3.pdf, p.1
lvii
Horizontal Control Consideration Presentation, Gene Dial, GeoEye. SDMI Imagery Workshop March 3, 2009.
123