Peer Reviewed Articles
667 C-Factor for Softcopy Photogrammetry
Donald L. Light
Abstract
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The C-factor is an empirical value based on the precision of
the photogrammetric instrumentation. The conventional C-factor
has been used successfully over the years to determine
the flying height required to produce a specified contour interval.
C-factors for conventional instruments range from 900
to 2200 with 2200 being typical of state-of-the-art analytical
plotters. Today's trend away from analog and computer-assisted
plotters to digital photogrammetric workstations calls
for a C-factor to use when the photogrammetry is to be accomplished
using softcopy photogrammetric workstations.
The soft C-factor will be based on conventional mathematics
developed for analog instruments and related to today's
cameras, scanners and soft copy workstations. Typical soft
C-factors for digital photogrammetric workstations, assuming
standard aerial photography and different scan spot sizes,
are in a range from 800 to 2200.
671 Nonlinear Trackers Using Image-Dependent Gains
D.D. Sworder, J.E. Boyd, G.A. Clapp, and R. Vojak
Abstract
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A maneuvering target is difficult to follow because its turns
are hard to identify from noisy position measurements. It is
shown here that, even at close range, an image-enhanced architecture
is effective in a tracking application. The performance
of a nonlinear, dual-sensor algorithm is contrasted with
sophisticated radar-only algorithms, and it is shown that sensor
utilization can be expanded significantly with a dual-sensor
estimation approach.
679 Comparison of NOAA/NASA PAL and NOAA GVI Data for Vegetation
Change Studies over China
Stephen S. Young and Assaf Anyamba
Abstract
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Remotely sensed data, especially that from the Advanced
Very High Resolution Radiometer (AVHRR), are increasingly
being used to analyze changes in the global environment.
This research analyzes two of the most commonly used remote
sensing data sets for global environmental change research,
the National Oceanic and Atmospheric Administration's
(NOAA) Global Vegetation Index (GVI) data and the new
NOAA/National Aeronautics and Space Administration's
(NASA) Pathfinder AVHRR Land (PAL) data set, to determine if
the new PAL data have successfully removed the major sensor-related problems found in the GVI data. Principal Components
Analysis of the data for the geographic region of China
is used with results indicating that sensor-related problems
remain in the PAL data, though not as severely as in the GVI
data. For the time period of 1982 to 1992, the GVI and PAL
data suffer from problems of spatial misregistration and radiometric
miscalibration. The problem of orbital drift, however,
has been minimized in the PAL data.
689 High Frequency Passive Microwave Radiometry over a Snow-Covered
Surface in Alaska
A.B. Tait, D.K. Hall, J.L. Foster, and A.T.C. Chang
Abstract
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Millimeter-wave Imaging Radiometer (MIR) data (ranging in
frequency from 89 to 325 GHz) were collected from NASA
ER-2 flights over Alaska in April 1995. This study determines
whether these data can be used to identify clouds, vegetation
type, and snow cover. The procedure used is as follows: (1)
determine whether a purely MIR-based cloud detection
scheme is possible over a snow-covered surface, (2) analyze
the influence of changing vegetation type on the brightness
temperatures, and (3) compare completely snow-covered
scenes with partially snow-covered and snow-free regions for
cloudy and clear-sky periods to determine whether varying
snow conditions affect the MIR data.
Results show that surface features con be identified using the less opaque channels at 89, 150, and 220 GHz, although the 150-GHz (2.0-mm wavelength) and 220-GHz (1.4-mm) channels are more sensitive to atmospheric phenomena compared with 89 GHz (3.4 mm), because the atmospheric contribution to the upwelling radiation is larger for shorter wavelengths. Statistical examination of the MIR data shows that the determination of cloudy pixels over a snow-covered surface is not possible using a simple brightness temperature threshold technique. Furthermore, it is concluded that, while no statistical discrimination between specific vegetation classes can be made, significance is obtained when the vegetation is grouped into two classes only, for example, vegetated and barren , It is also shown that the state of the snow cover (complete coverage, melting, or patchy) has a distinct effect on these results.
697 A Remote Sensing Strategy for Measuring Logging Road System
Length from Small-Format Aerial Photography
John P. Rowe, Timothy A. Warner, Darrell R. Dean, Jr., and Andrew F.
Egan
Abstract
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Small-format aerial photographs (SFAP) can be acquired at a
relatively low cost to obtain quantitative data for natural resource
applications. A remote sensing strategy was developed
for measuring lagging road system length from SFAP.
Aerial photography, digital image processing, and field work
procedures are described in sufficient detail for natural resource
managers to acquire their own SFAP with only basic
skills and equipment. An alternative field-based strategy was
developed and used to evaluate time, costs, and relative accuracy
of the remote sensing strategy. The field-based strategy
was assumed to consistently produce more accurate
measurements of road system length than did the remote
sensing strategy, The remote sensing strategy was less expensive
than the field-based strategy because logging road system
length can be measured in a shorter amount of time.
Error for the remote sensing strategy has an average of 11
percent and can be expected to range from 8 to 14 percent.
Accuracy is limited by use of uncontrolled mosaics, lens distortion,
tilt displacement, topographic displacement, and
scale variation.
705 Evaluation of Geostatistical Measures of Radiometric Spatial
Variability for Lithologic Discrimination in Landsat TM Images
Francisco Abarca-Hernández and Mario Chica-Olmo
Abstract
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Different measures of spatial variability (MSV) calculated
from several estimators of the veriogram function are used
for lithologic discrimination in the framework of digital image
classification. These measures are calculated in a local
context using moving windows, which characterize the spatial
variability of the radiometric data and represent textural
indices to be used in image classification. Before applying
this methodology, a spectral enhancement of the main geological
features of the image by principal camponent analysis
(PCA) has been necessary. The variographic analysis of
the selected PCs in the training areas has shown important
differences in the spatial behavior between lithologic classes.
The MSV assessment was carried out by discriminant analysis
in the training areas and supervised classification of the
Landsat TM image. The results have shown that the use of
TM radiometric data together with MSV improves the overall
accuracy of the lithologic discrimination.
713 A Method for the Automated Production of Digital Terrain
Models Using a Combination of Feature Points, Grid Points, and Filling
Back Points
Jung-Sheng Hsia and Ian Newton
Abstract
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This paper describes a new method that has been developed
to automatically generate by digital photogrammetry a digital
terrain model (DTM) using a combination of feature points,
grid points, and "filling back" points. Software to implement
the method is designed to run on a PC platform. Tests have
demonstrated the capability of the method to give encouraging
results.
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