Peer-Reviewed Articles
1021 Satellite Navigation Parameter-assisted Orthorectification for
Over 60° N Latitude Satellite Imagery
Guoqing Zhou and K. Jezek
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This paper presents a satellite navigation parameters-assisted
orthorectification method for the generation of a seamless,
full-coverage mosaic of the Greenland ice sheet using the over
60°N latitude Declassified Intelligence Satellite Photography
(DISP) imagery. The model integrates the photogrammetric
bundle adjustment model and satellite navigation parameters,
which are expressed by a third order polynomial solving for
the exterior orientation parameters of all images and satellite
navigation parameters simultaneously. This is because of the
fact that not each of DISP image contains sufficient Ground
Control Points (GCPs), and it is almost impossible to find out
or lay out the photogrammetrically targeted points prior to
1960s in the area of above 60°N latitude. The comparison of
three orthorectification methods (camera model, second order
polynomial, and the proposed method) demonstrated that the
proposed method could reach highest accuracy of the other
two methods. Finally, two full-coverage mosaics of Greenland
using 24 DISP images from the ARGON 9034A Mission and
36 images from of the 9058A/59A mission were assembled.
The average horizontal accuracy (relative to the SAR Mosaic)
is estimated to be 168 m in flat area, and 183 m in mountainous area.
The two mosaic products have been distributed for
use of research community via CD and internet through the
US National Snow and Ice Data Center (NSIDC) at no charge.
1031 Correction of Positional Errors and Geometric Distortions in
Topographic Maps and DEMs Using Rigorous SAR Simulation Technique
Hongxing Liu, Zhiyuan Zhao, and Kenneth C. Jezek
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In the history of surveying and mapping, a large volumes of
topographic maps and digital elevation models have been
created at various scales throughout the world. However,
positional errors and geometric distortions may exist in the
topographic contour maps and their derived DEMs due to
inaccurate ground control and poor navigation techniques in
the early years. In this paper, we present a new technique to
detect and correct positional errors and geometric distortions
in topographic data based on rigorous Synthetic Aperture
Radar (SAR) image simulation and mathematical modeling
of SAR imaging geometry. Our method has been successfully
applied to two USGS topographical data sets in Antarctica.
Using Radarsat SAR imagery, positional errors of these two
data sets have been reduced from 5 km to 200 m and from
200 m to 50 m, respectively.
1043 Urban Land Cover Change Analysisin Central Puget Sound
Marina Alberti, Robin Weeks, and Stefan Coe
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A methodology was developed to interpret and assess land
cover change between 1991 and 1999 in Central Puget Sound,
Washington at several scales (landscape, sub-basins, and
90 m grid window) relevant to regional and local decision
makers. Land cover data are derived from USGS Landsat
(Thematic Mapper and Enhanced Thematic Mapper +) images of Central Puget
Sound. Landsat data were registered, intercalibrated, and corrected
for atmosphere and topography
to ensure accuracy of land cover change assessment. We
apply a hybrid classification method to each image to address
the spectral heterogeneity of urbanizing regions. The method
combines a supervised classification approach with a spectral
unmixing approach to produce seven classes: >75 percent impervious,
15 to 75 percent impervious, forest, grass, clear cut,
bare soil, and water. Land cover change is identified using the
direct spatial comparison of classified images derived independently
for each time period. We assess that the overall accuracy of each classified
image was 91 percent for 1991 and
88 percent for 1999 respectively, which produces an accuracy
of 85 percent for the change analysis. Our results show that
urban growth over the last decade has produced an overall
6.7 percent increase in paved area.
1053 Spectral Mixture Analysis of the Urban Landscape in Indianapolis
City with Landsat ETM+ Imagery
Dengsheng Lu and Qihao Weng
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This paper examines characteristics of urban land-use and
land-cover (LULC) classes using spectral mixture analysis
(SMA), and develops a conceptual model for characterizing
urban LULC patterns. A Landsat Enhanced Thematic Mapper
Plus (ETM+) image of Indianapolis City was used in this research and
a minimum noise fraction (MNF) transform was
employed to convert the ETM+ image into principal components. Five
image endmembers (shade, green vegetation, impervious surface, dry
soil, and dark soil) were selected, and
an unconstrained least-squares solution was used to un-mix
the MNF components into fraction images. Different combinations of
three or four endmembers were evaluated. The best
fraction images were chosen to classify LULC classes based on
a hybrid procedure that combined maximum-likelihood and
decision-tree algorithms. The results indicate that the SMA-based approach
significantly improved classification accuracy
as compared to the maximum-likelihood classifier. The fraction images
were found to be effective for characterizing the
urban landscape patterns.
1063 Tree Cover Discrimination in Panchromatic Aerial Imagery of
Pinyon-Juniper Woodlands
Jesse Jacob Anderson and Neil Cobb
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Responding to an increasing interest in studying vegetation
changes over time, we review current methods of processing
black and white digital aerial photographs in order to classify
tree cover in pinyon-juniper woodlands. Besides applying commonly used
clustering and supervised maximum-likelihood
methods, we have developed a new classifier, nearest edge
thresholding, which is unsupervised and based on the principals of
edge detection and density slicing. Comparison of the
three methods' abilities to predict field values at plot scales of
100 m2 to 900 m2 shows this new method is better or comparable to others
at all scales, can be easily applied to digital imagery, and has
high correspondence with ground-truthed field
values of tree cover.
1069 Reflectance Modeling of Snow-Covered Forests in Hilly Terrain
Dagrun Vikhamar, Rune Solberg, and Klaus Seidel
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Seasonal snow covers large land areas of the Earth. Information about
the snow extent in these regions is important for
climate studies and water resource management. A linear
spectral mixture model for snow-covered forests (the SnowFor
model) has previously been developed for flat terrain. The
SnowFor model includes reflectance components for snow,
trees and snow-free ground. In this paper, the model is extended to
handle radiometric effects caused by topography on
mixed pixels of snow and trees through subpixel topographic
reflectance modeling. Empirical reflectance models for snow
and trees, based on the local solar incidence angle, are proposed (TopoSnow
and TopoTree models), and integrated into
the SnowFor model. Experiments with two Landsat Thematic
Mapper (TM) images are carried out in hilly, forested terrain
in Alptal, Switzerland with full snow cover. Results show that
the calibrated TopoSnow and TopoTree models enhance the
modeling of reflectance variability from snow-covered forests
for visible and near-infrared wavelengths. The performance of
four other topographic correction methods is evaluated for
snow-covered forests.
1081 Predicting Seafloor Facies from Multibeam Bathymetry and Backscatter
Data
Peter Dartnell and James V. Gardner
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An empirical technique has been developed that is used to
predict seafloor facies from multibeam bathymetry and
acoustic backscatter data collected in central Santa Monica
Bay, California. A supervised classification used backscatter
and sediment data to classify the area into zones of rock,
gravelly-muddy sand, muddy sand, and mud. The derivative
facies map was used to develop rules on a more sophisticated
hierarchical decision-tree classification. The classification
used four images, the acoustic-backscatter image, together
with three variance images derived from the bathymetry and
backscatter data. The classification predicted the distribution
of seafloor facies of rock, gravelly-muddy sand, muddy sand,
and mud. An accuracy assessment based on sediment samples shows the
predicted seafloor facies map is 72 percent
accurate.
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