Peer-Reviewed Articles
771 A Comparative Study of Australian Cartometric and
Photogrammetric Digital Elevation Model Accuracy
Jeffrey P. Walker and Garry R. Willgoose
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This paper explores the accuracy of Digital Elevation Models
(DEMs), with particular reference to Australian published
DEMs. Direct comparisons were made between cartometric
and automatically measured photogrammetric DEMs at
various grid spacings with an accurate and dense set of
ground truth data. The cartometric DEMs were found to
be more accurate than the photogrammetric DEMs for the
small study site in this paper, with RMS errors in elevation
of approximately 3.5 m and 4.5 m, respectively, and maximum
absolute errors in elevation of approximately 12 m
and 28 m, respectively. An important factor for environmental
prediction studies is slope, and RMS errors in slope were
approximately 6 percent and 20 percent for the cartometric
and photogrammetric DEMs, respectively, with maximum
absolute errors in slope of approximately 75 percent and
290 percent, respectively. However, use of suitable postprocessing
such as filtering may reduce the errors in photogrammetric
DEMs to at least the same magnitude as cartometric
DEMs. The cartometric DEMs were found to satisfy
the USGS specifications for Level 2 data.
781 Evaluating Ecoregions for Sampling and Mapping
Land-cover Patterns
Kurt H. Riitters, James D. Wickham, and Timothy G. Wade
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Ecoregional stratification has been proposed for sampling
and mapping land-cover composition and pattern over time.
Using a wall-to-wall land-cover map of the United States, we
evaluated geographic scales of variance for nine landscapelevel
and eight forest pattern indices, and compared stratification
by ecoregions, administrative units, and watersheds.
Ecoregions accounted for 65 percent to 75 percent of the total
variance of percent agriculture and percent forest because
dominant land-cover is included in ecoregional definitions. In
contrast, ecoregions explained only 13 percent to 34 percent
of the variance of the other seven landscape-level pattern
indices. After accounting for differences in amount of forest,
ecoregions explained less than 5 percent of the variance of
the eight forest pattern indices. None of the stratifications
tested would be effective mapping units for land-cover pattern
because within-unit variance of land-cover pattern is typically
two to four times larger than between-unit variance.
789 Evaluation of Eelgrass Beds Mapping Using a
High-Resolution Airborne Multispectral Scanner
Haiping Su, Duane Karna, Eric Fraim, Michael Fitzgerald, Rose
Dominguez, Jeffrey S. Myers, Bruce Coffl and, Lawrence R.
Handley, and Thomas Mace
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Eelgrass (Zostera marina) can provide vital ecological
functions in stabilizing sediments, influencing current
dynamics, and contributing significant amounts of biomass
to numerous food webs in coastal ecosystems. Mapping
eelgrass beds is important for coastal water and nearshore
estuarine monitoring, management, and planning. This
study demonstrated the possible use of high spatial (approximately
5 m) and temporal (maximum low tide) resolution
airborne multispectral scanner on mapping eelgrass beds
in Northern Puget Sound, Washington. A combination of
supervised and unsupervised classification approaches were
performed on the multispectral scanner imagery. A normalized
difference vegetation index (NDVI) derived from the red
and near-infrared bands and ancillary spatial information,
were used to extract and mask eelgrass beds and other
submerged aquatic vegetation (SAV) in the study area. We
evaluated the resulting thematic map (geocoded, classified
image) against a conventional aerial photograph interpretation
using 260 point locations randomly stratified over five
defined classes from the thematic map. We achieved an
overall accuracy of 92 percent with 0.92 Kappa Coefficient
in the study area. This study demonstrates that the airborne
multispectral scanner can be useful for mapping eelgrass
beds in a local or regional scale, especially in regions for
which optical remote sensing from space is constrained by
climatic and tidal conditions.
799 Object-based Detailed Vegetation Classification with
Airborne High Spatial Resolution Remote Sensing
Imagery
Qian Yu, Peng Gong, Nick Clinton, Greg Biging, Maggi Kelly,
and Dave Schirokauer
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In this paper, we evaluate the capability of the high spatial
resolution airborne Digital Airborne Imaging System (DAIS)
imagery for detailed vegetation classification at the alliance
level with the aid of ancillary topographic data. Image objects
as minimum classification units were generated through
the Fractal Net Evolution Approach (FNEA) segmentation
using eCognition software. For each object, 52 features were
calculated including spectral features, textures, topographic
features, and geometric features. After statistically ranking
the importance of these features with the classification and
regression tree algorithm (CART), the most effective features for
classification were used to classify the vegetation. Due to the
uneven sample size for each class, we chose a non-parametric
(nearest neighbor) classifier. We built a hierarchical classification
scheme and selected features for each of the broadest
categories to carry out the detailed classification, which
significantly improved the accuracy. Pixel-based maximum
likelihood classification (MLC) with comparable features was
used as a benchmark in evaluating our approach. The objectbased
classification approach overcame the problem of salt-and-
pepper effects found in classification results from traditional
pixel-based approaches. The method takes advantage
of the rich amount of local spatial information present in
the irregularly shaped objects in an image. This classification
approach was successfully tested at Point Reyes National
Seashore in Northern California to create a comprehensive
vegetation inventory. Computer-assisted classification of high
spatial resolution remotely sensed imagery has good potential
to substitute or augment the present ground-based inventory
of National Park lands.
813 Urban Land-use Classification Using Variogram-based
Analysis with an Aerial Photograph
Shuo-sheng Wu, Bing Xu, and Le Wang
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In this study, a variogram-based texture analysis was tested
for classifying detailed urban land-use classes, such as
mobile home, single-family house, multi-family house,
industrial, and commercial from a digital color infrared
aerial photograph. Spectral classification was first carried
out to separate the building class from non-building classes.
Then, a building-presence binary image was generated
so that building pixels were assigned a value of “1” and
non-building pixels were assigned a value of “0.” Multiple
texture bands were further generated employing a variogram-based texture analysis and used for land-use classification.
The generation of the building presence binary
image allowed us not only to fully explore the capability of
variogram-based analysis on spatial pattern detection, but
also to prevent the variogram-based analysis from being
disturbed by the natural fluctuation of spectral signals.
The result from using a mosaic test image was considered
satisfactory with a kappa coefficient of 0.72.
823 An Agreement Coeffi cient for Image Comparison
Lei Ji and Kevin Gallo
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Combination of datasets acquired from different sensor
systems is necessary to construct a long time-series dataset
for remotely sensed land-surface variables. Assessment of
the agreement of the data derived from various sources is
an important issue in understanding the data continuity
through the time-series. Some traditional measures, including
correlation coefficient, coefficient of determination,
mean absolute error, and root mean square error, are not
always optimal for evaluating the data agreement. For this
reason, we developed a new agreement coefficient for
comparing two different images. The agreement coefficient
has the following properties: non-dimensional, bounded,
symmetric, and distinguishable between systematic and
unsystematic differences. The paper provides examples
of agreement analyses for hypothetical data and actual
remotely sensed data. The results demonstrate that the
agreement coefficient does include the above properties,
and therefore is a useful tool for image comparison.
835 Automated Techniques for Environmental Monitoring
and Change Analyses for Ultra High-resolution Remote
Sensing Data
Manfred Ehlers, Monika Gaehler, and Ronald Janowsky
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For monitoring environmental changes, new digital remote
sensors have become available that allow monitoring and
change detection analyses at resolutions and scales that were
deemed impossible just a few years ago. The advent of airborne
stereo scanners of ultra high spatial resolution offers the
possibility of a complete digital remote sensing processing
system. Current sensors include the High-resolution Stereo
Camera (HRSC), the ADS-40, and the Digital Mapping Camera
(DMC). For automated analysis, however, the new sensors
require also new processing techniques. This paper presents
results of change monitoring analyses for areas along the
shorelines of the Elbe and Weser rivers in North Germany
using integrated HRSC and GIS datasets. An automated procedure
for highly accurate mapping was developed which is
based on a hierarchical stepwise approach integrating GIS
methods and digital surface information in this process. This
approach allows the production of GIS maps that are more
detailed and accurate than those that were previously produced
by conventional means. Within the GIS environment, the
multitemporal analysis also allows the exact quantification
and location of changes of the protected biotope types.