Peer-Reviewed Articles
825 Detection of Positional Errors in Systems Utilizing Small-Format
Digital Aerial Imagery and Navigation Sensors Using Area-Based Matching
Techniques
Amr Abd-Elrahman, Leonard Pearlstine, Bon A. Dewitt, and Scot E.
Smith
Abstract
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Integration of small-format aerial photographs with navigation systems
has been widely used in many remote sensing applications. Low cost
systems that employ onboard small-format digital cameras, GPS receivers,
and attitude and heading measuring devices can be efficiently utilized
as a point sampling technique. These systems are, however, subject
to many potential sources of positional error. In this research,
a method that uses area-based image matching techniques was developed
to detect positional errors in the image center point locations.
The aerial images were matched with lower resolution georeferenced
images. An Indian Remote Sensing (IRS) image and a Digital Orthophoto
Quadrangle (DOQ) were used as reference images. The matching process
succeeded in 70 percent and 50 percent of the tested aerial images
when using the IRS and the DOQ as reference images, respectively.
Limited success, however, was achieved where tree coverage was a
prominent feature in the image. Positional errors in the system were
detected by applying this technique on images within the actual flight
line and/or over a test area before and after taking the main flight
line.
833 Remote Sensing and Cast Shadows in Mountainous Terrain
Philip T. Giles
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In mountainous environments with high relief, topography may cause
cast shadows due to the blocking of direct solar radiation. Optical-infrared
remote sensing images of these landscapes display reduced values
of reflectance for shadowed areas compared to non-shadowed areas
with similar surface cover characteristics. Different approaches
to dealing with cast shadows are possible, although a common step
in various active approaches is first to delineate the shadows using
an automated algorithm and a digital elevation model. This article
demonstrates a common confusion caused by cast shadows and describes
a quantitative spatial evaluation of a cast shadow delineation algorithm
in comparison to human interpretation of a Landsat TM image. It is
shown that 86 percent of cast-shadow pixels were correctly marked
by the algorithm. The causes of differences between the algorithm
and human interpretation are discussed, and alternatives are considered
for dealing with cast shadows in classification studies using optical-infrared
images of mountainous terrain.
841 Monitoring the Magnitude of Land-Cover Change around the Southern
Limits of the Sahara
G.M. Foody
Abstract
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Studies of land-cover change using satellite remote sensing are often
constrained to depict land-cover conversions only, with the equally
important modifications undetected or misrepresented, resulting in
significant error. Desert fluctuations within the Sahel were examined
using an approach that indicated the magnitude of land-cover changes.
This showed that the conventional post-classification comparison
method of change detection appeared to underestimate the area of
land-cover change and, where a change was detected, typically overestimate
its magnitude. At the regional scale, the land-cover changes detected
were strongly related to rainfall variability. This relationship
did not, however, explain changes at a finer spatial scale and indicated
that dryland degradation, and its causes, may remain far from understood.
849 Using Spatial Co-Occurrence Texture to Increase Forest Structure
and Species Composition Classification Accuracy
S.E. Franklin, A.J. Maudie, and M.B. Lavigne
Abstract
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The analysis of forest structure and species composition with high
spatial resolution (< 1 m) multispectral digital imagery is described
in an experiment using spatial co-occurrence texture analysis and
maximum-likelihood classification. The objective was to determine
if higher forest species composition classification accuracies would
result in comparison to the use of spectral response patterns alone.
Increased accuracy was obtained when using texture at all levels
of a classification hierarchy. At the stand level, accuracies were
on the order of 75 percent in agreement with field surveys, an improvement
of 21 percent over the accuracy obtained using spectral data alone;
in stands grouped according to species dominance/co-dominance, the
accuracy improved still further to 80 percent. The overall classification
accuracy in a highly generalized lifeform classification was 100
percent. This represented a 33 percent increase in accuracy over
that which could be
obtained, in a classic spectral "signature" classification approach, using
spectral response patterns alone.
857 Mapping Seasonal Flooding in Forested Wetlands Using Multi-Temporal
Radarsat SAR
Philip A. Townsend
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Eleven Radarsat scenes imaged between 22 September 1996 and 28 February
1998 were analyzed to delineate flood inundation in the forests of
the Roanoke River floodplain, North Carolina. Threshold values distinguishing
flooded from nonflooded forests were identified using classification
trees. Data from 13 U.S. Geological Survey (USGS) wells located throughout
the floodplain were used to validate the flood mapping with an overall
accuracy of 93.5 percent. Images from both leaf-on and leaf-off periods
were acceptable for detecting flooding, although the leaf-off scenes
were classified with higher accuracy than were the leaf-on scenes
(98.1 percent versus 89.1 percent). In addition, threshold values
were lower for leaf-on scenes. The results also indicate that Radarsat
data can be used to detect minimal flood levels-sites with water
stages between 10 cm below and 10 cm above the forest floor were
classified with 90.6 percent accuracy. Radarsat data are effective
and appropriate for flood inundation mapping in forests, regardless
of season or water level.
865 Combining Location and Classification Error Sources for Estimating
Multi-Temporal Database Accuracy
Yohay Carmel, Denis J. Dean, and Curtis H. Flather
Abstract
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Detection and quantification of temporal change in spatial objects
is the subject of a growing number of studies. Much of the change
shown in such studies may be an artifact of location error and classification
error. The basic units of these two measures are different (distance
units for location error and pixel counts for classification error).
The lack of a single index summarizing both error sources poses a
constraint on assessing and interpreting the apparent change. We
present an error model that addresses location and classification
error jointly. Our approach quantifies location accuracy in terms
of thematic accuracy, using a simulation of the location error process.
We further develop an error model that combines the location and
classification accuracy matrices into a single matrix, representing
the overall thematic accuracy in a single layer. The resulting time-specific
matrices serve to derive indices for estimating the overall uncertainty
in a multi-temporal dataset. In order to validate the model, we performed
simulations in which known amounts of location and classification
error were introduced into raster maps. Our error model estimates
were highly accurate under a wide range of parameters tested. We
applied the error model to a study of vegetation dynamics in California
woodlands in order to explore its value for realistic assessment
of change, and its potential to provide a means for quantifying the
relative contributions of these two error sources.
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