Peer-Reviewed Article Abstracts
147 Total Ground-Cover Estimates from Corrected Scene Brightness
Measurements
Eric M. San den, Carlton M. Britten, and James H. Everitt
Abstract
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Total ground-cover estimates from spectral reflectance data were studied using
a scene brightness factor, corrected for variations in soil mosture content.
Red and near-infrared radiometer readings were collected from ground level
on a bermudagrass rangeland in south Texas. A soil line was developed to
obtain brightness factor and correction factor values that were correlated
to total ground-cover measurements. Data were collected under two conditions:
(1) when the soil was dry and vegetation senescent, and (2) under wet soil
and green vegetation conditions. Although the correction factor did not improve
the correlation between brightness measurements and total ground cover, overall
correlations were high. The best correlation (r = 0.87) was obtained when
vegetation was near peak greenness and the soil was saturated. This study
suggests that under certain conditions scene brightness measurements can
provide relatively accurate total ground-cover estimates.
151 Landsat TM-Based Forest Damage Assessment:
Correction for Topographic Effects
Sam Ekstrand
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Detection of forest damage is one of various remote sensing applications complicated
by topographic effects. This paper describes the response of Landsat Thematic
Mapper Data to the topography in Norway spruce forest, and the possibility
to assess forest damage in rugged terrain. The effect at the examined medium
and low solar elevations was non-Lambertian. Two new models were developed;
one based on Minnaert constants changing with the cosine of the incidence
angle, and the other based on an empirical relationship. Both models gave
satisfactory results although the empirical model performed better for nearly
shadowed northern slopes. With a model accounting for terrain and canopy
inhomogeneity effects using digitized stand data and digital elevation models,
healthy to slightly defoliated spruce forest could be separated from moderately
defoliated forest.
163 Comparison of Three Methods for Mapping Tundra
with Landsat Digital Data
Peter E. Joria and Janet C. Jorgenson
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Documenting the distribution of wildlife habitat on the coastal plain in northeast
Alaska is essential for determining potential impacts and mitigation of oil
exploration and development. Landsat Thematic Mapper (TM) and ancillary data
were used to map 14 cover types on a 13 000-km "SUP 2" portion
of the Arctic coastal plain. Three classification approaches were compared:
supervised, unsupervised, and modeling. The model used ancillary layers representing
elevation, slope, solar illumination, riparian zones, and terrain type in
a postclassification sorting of the unsupervised spectral classes. Modeling
resulted in the highest overall agreement with training areas (68%), but
agreement with an independent data set was 48%, only slightly better than
the other two approaches. Training data from an additional field season helped
increase the overall agreement between the model and the independent data
set to 52%.
171 Application of Remote Sensing and GIS Technologies
with Physiological Crop Models
Gregory J. Carbone, Sunil Narumalani, and Michelle King
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This study investigates how remote sensing and geographic information system
(GIS) technology can be used with a physiological crop model to examine spatial
variability in country soybean yield. Results show that spatial variability
in simulated country yield is often large and corresponds closely with soil
moisture availability. This availability is influenced primarily by soil
properties and by the timing and amount of precipitation, both of which vary
greatly across space. Examination of the spatial patterns of simulated yield
can improve production estimates and highlight vulnerable areas during droughts.
181 Combining Spectral and Texture Data in the
Segmentation of Remotely Sensed Images
Soren Ryherd and Curtis Woodcock
Abstract
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Addition of a spatial attribute, ie, image texture, improves the segmentation
process in most areas where there are differences in texture between classes
in the image. Such areas include sparsely vegetated areas and highly textured
human-generated areas, such as the urban-suburban interface. A simple adaptive-window
texture program creates a texture channel useful in image segmentation. Both
the weighting of textural data relative to the spectral data, and the effects
of the degree of segmentation, are explored. The use of texture improves
segmentations for most areas. Results indicate that, for most uses, segmentation
schemes should include both a minimum and maximum region size to insure the
greatest accuracy.
195 Remote Measurement of Algal Chlorophyll in
Surface Waters: The Case for the First Derivative of Reflectance Near 690 nm.
Donald C. Rundquist, Luoheng Han, John F. Schalles, and Jeffrey S. Peake
Abstract
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Remote sensing is an important technology for measuring algal-chlorophyll concentrations
in surface waters. Our paper provides hyperspectral signatures, in the visible
and near-infrared, associated with two experiments conducted outdoors in
large water tanks; one involving relatively low amounts of chlorophyll over
a narrow range and a second involving relatively high amounts over a wide
range. The principal finding was that the commonly used near-infrared/red
ratio is best for estimating pigment amounts when the concentration of chlorophyll
is relatively low, and the first derivative of reflectance around 690 nm
is best when the concentration is relatively high.
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