Peer-Reviewed Articles
873 Geometric Processing of Ikonos Stereo Imagery
for Coastal Mapping Applications
Kaichang Di, Ruijin Ma, and Rongxing Li
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The results of photogrammetric mapping of a Lake Erie coastal area from 1-m-resolution
Ikonos Geo stereo images are presented. The nominal accuracy of ground point
de-termination with vendor-provided Rational Function (RF) coefficients is
evaluated and systematic errors are found when compared with ground control
points. A significant improvement in the accuracy is achieved by a refining
process that applies a three-dimensional affine transformation to the RF-calculated
3D ground points to correct the systematic errors. A DEM is automatically
generated by a chain of processes: area-based image matching, ground point
calculation, outlier elimination, TIN construction, and interpolation. Following
DEM generation, an orthoimage is produced using the DEM and the refined geometric
model. Accuracies of the DEM and the orthoimage as assessed from independent
checkpoints (ICPs) are approximately 2 m in planimetry and 3 m in height.
Finally, a 3D shoreline is extracted through manual digitization in one image
of a stereo pair and then automatic matching in the other image. Issues concerning
the production of digital tide-coordinated shorelines using instantaneous
observations are also discussed.
881 Fast Approximation of Visibility Dominance
Using Topographic Features as Targets and the Associated Uncertainty
Sanjay Rana
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An approach to reduce visibility index computation time and measure the associated
uncertainty in terrain visibility analyses is presented. It is demonstrated
that the visibility index computation time in mountainous terrain can be
reduced substantially, without any significant information loss, if the line
of sight from each observer on the terrain is drawn only to the fundamental
topographic features, i.e., peaks, pits, passes, ridges, and channels. However,
the selected sampling of targets results in an underestimation of the visibility
index of each observer. Two simple methods based on iterative comparisons
between the real visibility indices and the estimated visibility indices
have been proposed for a preliminary assessment of this uncertainty. The
method has been demonstrated for gridded digital elevation models.
889 Mapping Multiple Variables for Predicting
Soil Loss by Geostatistical Methods with TM Images and a Slope Map
Guangxing Wang, George Gertner, Shoufan Fang, and Alan B. Anderson
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Soil erosion is widely predicted as a function of six input factors, including
rainfall erosivity, soil erodibility, slope length, slope steepness, cover
management, and support practice. Because of the multiple factors, their
interactions, and their spatial and temporal variability, accurately mapping
the factors and further soil loss is very difficult. This paper compares
two geostatistical methods and a traditional stratification to map the factors
and to estimate soil loss. Soil loss is estimated by integrating a sample
ground data set, TM images, and a slope map. The geostatistical methods include
collocated cokriging and a joint sequential co-simulation model. With both
geostatistical methods, local estimates and variances at any location where
the factors and soil loss are unknown can be computed. The results showed
that the two geostatistical methods performed significantly better than traditional
stratification in terms of overall and spatially explicit estimates. Further
more, the cokriging led to higher accuracy of mean estimates than did the
co-simulation, while the latter provided decision makers with reliable uncertainties
of the local estimates as useful information to assess risk when making decisions
based on the prediction maps.
899 AVHRR-Based Spectral Vegetation Index
for Quantitative Assessment of Vegetaton State and Productivity: Calibration
and Validation
Felix Kogan, Anatoly Gitelson, Edige Zakarin, Lev Spivak, and Lubov Lebed
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The goal of the work was to estimate, quantitatively, vegetation state and
productivity using AVHRR-based Vegetation Condition Index (VCI). The VCI
algorithm includes application of post-launch calibration to visible channels,
calculation of NDVI from channels' reflectance, removal of high-frequency
noise from NDVI's annual time series, stratification of ecosystem resources,
and separation of ecosystem and weather components in the NDVI value. The
weather component was calculated by normalizing the NDVI to the difference
of the extreme NDVI fluctuations (maximum and minimum), derived from multi-year
data for each week and land pixel. The VCI was compared with wheat density
measured in Kazakhstan. Six test fields were located in different climatic
(annual precipitation 150 to 700 mm) and ecological (semi-desert to steppe-forest)
zones with elevations from 200 to 700 m and a wide range of NDVI variation
over space and season from 0.05 to 0.47. Plant density (PD) was measured
in wheat fields by calculating the number of stems per unit area. PD deviation
from year to year (PDD) was expressed as a deviation from median density
calculated from multi-year data. The correlation between PDD and VCI for
all stations was positive and quite strong (r² > 0.75) with the Standard
Errors of Estimates (SEE) of PDD less than 16 percent; for individual stations,
the SEE was less than 11 percent. The results indicate that VCI is an appropriate
index for monitoring weather impact on vegetation and for assessment of pasture
and crop productivity in Kazakhstan. Because satellite observations provide
better spatial and temporal coverage, the VCI-based system will provide efficient
tools for management of water resources and the improvement of agricultural
planning. This system will serve as a prototype in the other parts of the
world where ground observations are limited or not available.
907 An Explicit Index for Assessing the
Accuracy of Cover-Class Areas
Guofan Shao, Wenchun We, Gang Wu, Xinhua Zhou, and Jianguo Wu
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We present a new index, called Relative Errors of Area (REA), for assessing
the accuracy of cover-class areal percentage (%LAND) that is extracted from
thematic maps after classifying remotely sensed data. We demonstrate how
to derive REA from an error matrix and its relationship with user's and producer's
accuracy. We compare the REA index with other accuracy indices in a hypothetical
and two real case studies. The accuracy of cover-class areal estimates is
highly correlated with the REA index, but not with other classification accuracy
indices such as the overall classification accuracy. In general, users should
beware of using thematic maps with low REA values. Moreover, the estimates
of cover-class area can be revised by using REA if cell values of the major
diagonal in an error matrix are available.
915 Potential of Digital Color Imagery
for Censusing Haleakala Silverswords in Hawaii
Rick E. Landenberger, James B. McGraw, Timothy A. Warner, and Tomas Brandtberg
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Spatially explicit, high spatial resolution remotely sensed imagery offers
a largely untapped potential for censusing and monitoring rare plant populations
that exist in remote, exposed environments. Using digital color imagery acquired
over the Haleakala Crater on Maui, Hawai'i, we evaluated the accuracy of
photointerpretation and automated censuses by imaging nine silversword census
plots characterized by individuals of known size, life cycle status, and
location. Due to spatial resolution limitations, both methods tended to omit
small individuals, but omissions varied by size class and type of omission.
Omission rates were low for demographically important medium and large plants;
however, the automated method often failed to segment and census tightly
clustered plants. The photointerpreter commission error rate was lower than
that of the automated method, and both methods tended to overestimate mean
silversword size. These data outline the issues and challenges that will
likely emerge as spatially explicit, high spatial resolution aerial censuses
become more common.
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