Peer-Reviewed Articles
1257 Aerial Photography at the Agricultural Adjustment
Administration: Acreage Controls, Conservation Benefits, and Overhead
Surveillance in the 1930s
Mark Monmonier
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Aerial photography played an important but largely unsung role in New Deal
efforts to improve farm income. Established in 1933, the Agricultural Adjustment
Administration (AAA) promoted agriculture secretary Henry Wallace's "ever-normal
granary" with production controls (1934-1935) and conservation programs (1936-1937)
before Congress adopted a combined strategy in 1938. To administer these
programs and ensure performance, the AAA set up an innovative hierarchy of
state, county, and local committees. Experiments in 1935 and 1936 demonstrated
that aerial photography provided cost-effective, adequately precise measurements
and led to a concerted effort to extend photographic coverage. In 1937, 36
photographic crews flew 375,000 square miles (970,000 square km), and by
late 1941 AAA officials had acquired coverage of more than 90 percent of
the country's agricultural land. From its initial goal of promoting compliance,
the Agriculture Department's aerial photography program became a tool for
conservation and land planning as well as an instrument of fair and accurate
measurement. Local administration and a widely perceived need to increase
farm income fostered public acceptance of a potentially intrusive program
of overhead surveillance.
1263 Incorporating Surface Emissivity into a Thermal
Atmospheric Correction
Nathaniel A. Brunsell and Robert R. Gillies
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The issue of incorporating surface emissivity into a thermal atmospheric correction
of thermal remotely sensed data is addressed. The Normalized Difference Vegetation
Index (NDVI) is derived using atmospherically corrected surface reflectance
values, which is subsequently used to estimate the percent of vegetation
cover. Surface emissivity is approximated by a linear interpolation between
a minimum bare soil emissivity and a maximum vegetation value of emissivity.
An application of the method to an image over the Southern Great Plains 1997
(SGP97) Hydrology Experiment for the Advanced Very High Resolution Radiometer
(AVHRR) band 4 demonstrates temperature corrections up to 8°C, with a
mean correction of 3.7°C. The temperatures within the fully vegetated
pixels show good agreement with air temperature measurements at the time
of satellite overpass.
1271 Supervised and Unsupervised Spectral Angle
Classifiers
Youngsinn Sohn and N. Sanjay Rebello
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We report that the cosine of the angle ? (spectral angle) can be utilized as
a metric for measuring distances in feature space for multispectral image
classification and clustering. Due to the invariant nature of the cosine
of the angle ? to the linearly scaled variations, when two spectra are exactly
linearly scaled variations of one another by distance r, the cosine of the
angle ? becomes zero while spectral distance is scaled by r. The fact that
the cosine of the angle ? becomes zero when two spectra are exactly linearly
scaled variations of one another implies that if we only have spectral patterns
that are exactly linearly scaled variations of one another, then we will
not be able to define distances between pairs of signatures for classification
and clustering. For this reason, the cosine of the angle has never been considered
before as a metric for multispectral image classification. According to our
study, however, the fact that spectra of the same type of surface objects
are approximately linearly scaled variations of one another due to the atmospheric
and topographic effects allows the spectral angle to be used as a metric
for measuring "angular distances" in feature space. Our test results indicate
that the new spectral angle classifier is robust and provides better results
than do the existing major image classifiers. The spectral angle classifiers
do not require the data to be normally distributed, and they are insensitive
to data variance and the size of the training data set. A major difference
between the spectral angle classifier and conventional classifiers (ISODATA,
minimum distance, maximum likelihood, decision trees, neural nets, etc.)
is that the spectral angle classifier rests on the spectral shape pattern,
i.e., the "identity: of the spectral pattern, while conventional classifiers
rest on the statistical distribution pattern. Even though it is true for
all the classifiers, especially when the spectral angle classifiers are used,
the analyst's ability to relate field information to spectral characteristics
and spectral shape patterns of different land-cover/land-use types is an
important factor for acquiring accurate and adequate mapping results. We
believe that the spectral angle classifier can potentially be one of the
most accurate classifiers and a valuable tool for land-cover/land-use mapping
using remotely sensed multispectral image data.
1283 Urban Growth Detection Using Texture Analysis
on Merged Landsat TM and SPOT-P Data
Renee Gluch
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This study illustrates an automated technique in which urban growth is effectively
mapped and monitored. The methodology utilized digitally merged TM and SPOT-P
data, resampled to 10 meters, for each of two years, 1990 and 1995. For each
date, a two-step texturing analysis resulted in a binary "built/non-built" map
defining urban versus non-urban super pixels. The results from the study
clearly defined "growth" pixels for the five-year time interval. An accuracy
of 92 percent was achieved. These "growth" pixels were then compared to a
growth "potential" map produced by a GIS analysis based on environmental
inducements and constraints to growth. Portions of the study area that were
rated highest in growth potential in fact experienced the largest amount
of urban expansion, both in total area and in percent of class.
1289 Radar and Optical Data Comparison/Integration
for Urban Delineation: A Case Study
Barry N. Haack, Elizabeth K. Solomon, Matthew A. Bechdol, and Nathaniel D.
Herold
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This study compared spaceborne radar and radar-derived products with optical
data for urban delineation. RADARSAT and Landsat Thematic Mapper (TM) multispectral
data were assessed independently and in combination. The primary methodology
was supervised spectral signature extraction and the application of a maximum-likelihood
statistical decision rule to classify surface features in the Kathmandu Valley,
Nepal. Relative accuracy of the resultant classifications was established
by comparison to ground-truth information. Both radar post-classification
smoothing and Variance texture measures were improvements over the poor results
achieved with the unfiltered, original radar data. Speckle reduction procedures
were found to be very advantageous. Combinations of radar-derived products
greatly improved results, achieving an overall accuracy of nearly 90 percent.
The best overall accuracy was achieved with the merger that included a texture
image derived from despeckled radar and the despeckled original radar. The
radar and radar-derived combination achieved much better results than did
the TM and were comparable to a combined radar and TM data classification.
The systematic strategy of this study, determination of the best individual
method before introducing the next procedure, was effective in managing a
very complex, almost infinite set of analysis possibilities.
1297 Classifying and Mapping General Coral-Reef
Structure Using Ikonos Data
Jill Maeder, Sunil Narumalani, Donald C. Rundquist, Richard L. Perk, John Schalles,
Kevin Hutchins, and Jennifer Keck
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Monitoring coral reef, seagrass, and sand features using contemporary remotely
sensed data may prove to be a cost-effective and time-efficient tool for
reef surveys, change detection, and management. Previous attempts at subsurface
feature discrimination with satellite remote sensing have been limited in
accuracy due to the effects of pixel mixing associated with poor spatial
resolutions. While aerial reconnaissance may offer higher spatial resolutions
than satellite sensors, it is often limited by the high costs of planning
and implementing the missions, image rectification, area that can be covered,
and repeat coverage. In this study, the Ikonos satellite with a 4- by 4-m
spatial resolution in the multispectral bands was used as a tool for subsurface
feature identification. The Single-Image Normalization Using Histogram Adjustment
was used for atmospheric corrections on the imagery. Classification was performed
using bands 1, 2, and 3 (blue, green, and red) to maximize the water-penetration
capabilities of the sensor. An accuracy assessment of the classification
results was performed using in situ data collected at 62 points one day prior
to the image being acquired. It was concluded that the Ikonos data were useful
for discriminating sand, coral reef (at two depth intervals), and seagrass
features (providing overall accuracies of 89 percent each for the two study
areas). However, error still remained in discriminating small, diverse patch-reef
features. This error (producers accuracy 67 percent) was found in the "Reef
? 5 m" class and was primarily attributed to the diversity of this spectral
class, which may lead to a spectral signature based on the dominant cover
type in a given pixel.
1307 Creation of Digital Terrain Models Using an
Adaptive Lidar Vegetation Point Removal Process
George T. Raber, John R. Jensen, Steven R. Schill, and Karen Schuckman
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Commercial small-footprint lidar remote sensing has become an established tool
for the creation of digital terrain models (DTMs). Unfortunately, even after
the application of lidar vegetation point removal algorithms, vertical DTM
error is not uniform and varies according to land cover. This paper presents
the results of the application of an adaptive lidar vegetation removal process
to a raw lidar dataset of a small area in North Carolina. This process utilized
an existing lidar vegetation point removal algorithm in which the parameters
were adaptively adjusted based on a vegetation map. The vegetation map was
derived through the exclusive use of the lidar dataset, making the process
independent of ancillary data. The vertical error and surface form of the
resulting DTM were then compared to DTMs created using traditional techniques.
The results indicate that the adaptive method produces a superior DTM.
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