Peer-Reviewed Article Abstracts
685 Using a Cartographic Modeling Language to Manipulate
Spectral Satellite Imagery
David Pullar and Samantha Sun
Abstract
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Land related information about the Earth's surface is commonly found
in two forms: (1) map information and (2) satellite image data. Satellite
imagery provides a good visual picture of what is on the ground but
complex image processing is required to interpret features in an
image scene. Increasingly, methods are being sought to integrate
the knowledge embodied in map information into the interpretation
task, or, alternatively, to bypass interpretation and perform biophysical
modeling directly on derived data sources. A cartographic modeling
language, as a generic map analysis package, is suggested as a means
to integrate geographical knowledge and imagery in a process-oriented
view of the Earth. Specialized cartographic models may be developed
by users, which incorporate mapping information in performing land
classification. In addition, a cartographic modeling language may
be enhanced with operators suited to processing remotely sensed imagery.
We demonstrate the usefulness of a cartographic modeling language
for pre-processing satellite imagery, and define two new cartographic
operators that evaluate image neighborhoods as post-processing operations
to interpret thematic map values. The language and operators are
demonstrated with an example image classification task.
693 Mapping Continuous Distributions of Land Cover:
A Comparison of Maximum-Likelihood Estimation and Artificial Neural
Networks
Brian G. Frizzelle and Aaron Moody
Abstract
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Both maximum-likelihood and neural network classifiers can be used
to characterize land cover as continuous fields that represent either
class proportions or classification certainty. We compared these
two approaches by examining the correspondence between their output
values and photointerpreted class proportions of 39 test regions
within a heterogeneous study area in southern California. The neural
network models consistently produced stronger correlations (1) between
output values for a given class and the proportions of that class
for all test regions combined and (2) between output values and proportions
for all classes and test regions combined. However, due to the discrete
nature of the response surface relative to the maximum-likelihood
classifier, maps produced using the neural networks did not represent
significant variability in the certainty of class labeling. Conversely,
the maximum-likelihood classifier produced membership likelihood
surfaces that varied considerably across the study areas. Differences
between the response functions of the two methods relate to the parametric
versus nonparametric nature of the maximum-likelihood and neural
network models, respectively. Visualization of the results from continuous
classifiers can be accomplished in several ways which help illustrate
the nature and spatial distribution of classification certainty.
707 An Assessment of Reference Data Variability
Using a “Virtual Field Reference Database”
Ross S. Lunetta, John Iiames, Joseph Knight, Russell G. Congalton,
and Thomas H. Mace
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A ``Virtual Field Reference Database VFRDB)'' was developed using field
measurement and digital imagery (camera) data collected at 999 sites
in the Neuse River Basin, North Carolina. The VFRDB was designed
to support detailed assessments of remote-sensor-derived land-cover/land-use
(LCLU) products by providing a robust database characterizing representative
cover types throughout the study area. The sampling frame incorporated
both systematic unaligned and stratified random design elements,
to provide both an even distribution of points and sufficient intensification
to account for rare classes. Numerous quality assurance procedures
were developed and incorporated to ensure both data consistency and
repeatability. Two independent interpreters assigned class labels
corresponding to a hierarchical classification system based on field
measurement and imagery data interpretation. Correspondence between
interpreters was analyzed at multiple classification levels. The
relatively high 91 percent overall correspondence of interpretations
was attributable to the application of the VFRDB, providing a high
quality source of measurement and imagery data to guide class assignments.
Confusion documented for rangeland and forest classes was consistent
with reported results for studies conducted in diverse biological
locations. Results demonstrate the requirement for reference data
with known variability, to support the quantitative assessments of
remote-sensor-derived LCLU products.
717 Boundary Uncertainty Assessment from a Single
Forest-Type Map
Tom De Groeve and Kim Lowell
Abstract
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The possibility and advantages of assessing the uncertainty of individual
boundary existence and location from statistical models relating
it to neighboring geometry (polygon area, shape, and line length)
and forest-type attributes is examined. Unlike traditional methods
of spatial uncertainty assessment that are based on a comparison
of multiple (costly) map realizations, the proposed method is based
on a single map. However, the reference spatial uncertainty for the
models is determined from an approach that employs multiple map realizations.
Results of the proposed method explain 27 percent of the variance
in boundary location and 61 percent of the boundary existence. The
best model for boundary location uncertainty predicts boundary width
both as a function of boundary length and the shape of neighboring
polygons, while the best model for boundary existence is based on
boundary length, polygon shape and area, and change in species composition
and height attributes across the boundary.
727 Statistical Rigor and Practical Utility in Thematic
Map Accuracy Assessment
Stephen V. Stehman
Abstract
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Although statistical rigor and practical utility have been advocated
as desirable features of map accuracy assessment protocols, specific
criteria defining these features have not been elucidated. Two criteria
are proposed for statistical rigor: probability sampling and consistent
estimation. Practical utility is synonymous with cost, and because
cost is directly related to quality, decisions regarding practical
utility may be evaluated in terms of their effect on quality. Four
criteria are proposed to define quality: the precision of the accuracy
estimates, the population to which sampling inference is justified,
the assumptions needed to justify inference, and the accuracy of
the reference data. The first step in planning a statistically rigorous,
practical accuracy assessment is to construct an efficient, probability-sampling-based
strategy permitting inference to the full map population. Modifications
of this strategy to enhance practical utility (i.e., reduce cost
of the assessment) should be evaluated using the criteria defined
for quality and statistical rigor.
735 Datum Conversion Issues with LIDAR Spot Elevation
Data
Richard C. Daniels
Abstract
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Light Detection and Ranging (LIDAR) elevation data are generally referenced
to the World Geodetic System of 1984 datum. To utilize these data
in a local or regional setting, it is often necessary to convert
the elevation data to a traditional vertical datum such as the North
American Vertical Datum of 1988. This datum conversion is done utilizing
a local geoid model developed through a detailed GPS survey covering
the area of interest or a model developed by the National Geodetic
Survey. Three techniques are described here for identifying systematic
errors that may be introduced into LIDAR elevation data during this
conversion process.
741 Enhancement of Image Resolution in Digital
Photogrammetry
John Fryer and Kerry McIntosh
Abstract
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In recent years, considerable developments have occurred in the field
of digital photogrammetry. These have been due mainly to increases
in computing power, the refinement of feature and area-based image
matching algorithms and the reduction in the cost of equipment capable
of producing near real-time images in digital format. A major limitation
to the widespread application of digital photogrammetry concerns
the small format size of the CCD sensor itself and, consequently,
the number of pixels on the sensor being limited in number. Much
time and effort has been expended trying to improve coverage through
hardware solutions such as producing imaging sensors with increased
numbers of pixels. An alternative software solution is offered in
this paper. An algorithm which combines several digital images, the
photogrammetric technique of area-based image matching, and a rigorous
mathematical solution to increase the effective number of pixels
is described. The resolution of the final composite image is enhanced
relative to its constituent images.
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