Peer-Reviewed Articles (Click the linked titles to see the full abstract)
1021 Effects of Differential Single-and Dual-Frequency GPS and GLONASS Observations
on Point Accuracy under Forest Canopies
Erik Næsset
Abstract
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A 20-channel, dual-frequency receiver observing dual-frequency pseudorange
and carrier phase of both GPS and GLONASS was used to
determine the positional accuracy of 29 points under tree canopies.
The mean positional accuracy based on differential postprocessing
of GPS + GLONASS single-frequency observations ranged from
0.16 m to 1.16 m for 2.5 min to 20 min of observation at points with
basal area ranging from <20 m;s2/ha to 30 m;s2/ha. The mean positional
accuracy of differential postprocessing of dual-frequency GPS + GLONASS
observations ranged from 0.08 m to 1.35 m. Using the dual-frequency
carrier phase as main observable and fixing the initial integer phase
ambiguities, i.e., a fixed solution, gave the best accuracy. However,
searching for fixed solutions
increased the risk of large individual positional errors due to "false" fixed
solutions.The accuracy increased with decreasing density of forest, increasing
length of observation period, and decreasing a priori standard error as reported
by the postprocessing software.
1027 Sensitivity of Landscape Pattern Metrics to Map Spatial Extent
Santiago Saura and Javier Martínez-Millán
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Computation of landscape pattern metrics from spectrally classified
digital images is becoming increasingly common, because the characterization
of landscape spatial structure provides valuable information for
many applications. However, the spatial extent (window size) from
which pattern metrics are estimated has been shown to influence and
produce biases in the results of these spatial analyses. In this
study, the sensitivity of eight commonly used landscape configuration
metrics to changes in map spatial extent is analyzed using simulated
thematic landscape patterns generated by the modified random clusters
method. This approach makes it possible to control and isolate the
different factors that influence the behavior of spatial pattern
metrics, as well as taking into account a wide range of landscape
configuration possibilities. Edge Density is found to be the most
robust metric
and is recommended as a fragmentation index where the effect of spatial extent
is concerned. The metrics that attempt to quantify the irregularity and complexity
of the shapes in the pattern (Mean Shape Index, Area Weighted Mean Shape Index,
and Perimeter Area Fractal Dimension) are by far the most sensitive. In particular,
it is suggested that the Mean Shape Index should be avoided in further landscape
studies. For the eight analyzed pattern metrics, quantitative guidelines are
provided to estimate the systematic biases that may be introduced by the use
of a given
extent, so that the metric values derived from data of different spatial extents
can be properly compared.
1037 Modeling the Population of China Using DMSP Operational Linescan
System; Nighttime Data
C.P. Lo
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Radiance-calibrated DMSP-OLS nighttime lights data of China acquired
between March 1996 and January-February 1997 were evaluated for their
potential as a source of population data at the provincial, county,
and city levels. The light clusters were classified into six categories
of light intensity, and their areal extents were extracted from the
image. Mean pixel values of light clusters corresponding to the settlements
were also extracted. A light volume measure was developed to gauge
the three-dimensional capacity of a settlement. A density of light
cluster
measure known as percent light area was also calculated for each spatial unit.
Allometric growth models and linear regression models were developed to estimate
the Chinese population and population densities at the three spatial levels
using light area, light volume, pixel mean, and percent light area as independent
variables. It was found that the DMSP-OLS nighttime data produced reasonably
accurate estimates of non-agricultural (urban) population at both the county
and city levels using the allometric growth model and the light area or light
volume as input. on-agricultural population density was best estimated using
percent light area in a linear regression model at the county level. The total
sums of the estimates for non-agricultural population and even population overall
closely approximated the true values given by the Chinese statistics at all
three spatial levels. It is conÍcluded that the 1-km resolution radiance-calibrated
DMSP-OLS nighttime lights image has the potential to provide population
estimates of a country and shed light on its urban
population from space.
1049 Spatial Dynamic Modeling for Urban Development
Xinsheng Zhang and Yeqiao Wang
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A spatial dynamic model (SDM) in a GIS-based urban planning decision
support system has been developed for the city of Beihai in southern
China. The SDM was used to simulate the dynamic change of the urban
spatial structure by considering urban spatial growth as the result
of spatial interaction between demand and supply of urban resources.
The SDM acts as an analog machine. SDM modeling provides information
for decision support in urban planning and land use management. The
utility function of spatial choice and a methodology for the construction
of the utility function were developed to incorporate socioeconomic
factors into the modeling process. Multiple scenarios of urban planning
and the consequences of urban spatial growth, as well as the impacts
of planning schemes on traffic flow and on the environment, were
simulated. Comparisons of simulation results allowed planners to
evaluate the planning schemes in decision making.
1059 Blended Spectral Classification Techniques for Mapping Water Surface
Transparency and Chlorophyll Concentration
Perry LaPotin, Robert Kennedy, Timothy Pangburg, and Roberet Bolus
Abstract
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An innovative technique for estimating Secchi Disk Transparency and
Chlorophyll a concentration is examined using in situ samples and
coincidental satellite imagery for West Point Lake, Georgia. The
technique is divided into two main components: (1) unsupervised classification
to organize and reduce spectral variance, and (2) linear logarithmic
modeling to transfer class structure onto primary water quality measurements.
In component 1, clusters are derived using a non-parametric approach
that is computationally unique from the traditional ISODATA algorithm.
The method includes focused stratified sampling, non-parametric estimation,
and
blending of class structure using first-order principal components. In component
2, the class structure is tied to water quality estimation using primary band
ratios for visible, near infrared, and middle infrared as independent variables.
The results indicate a strong association between the Landsat TM middle infrared
band and observed measurements for Secchi Disk Transparency and Chlorophyll
a concentration. Logarithmic ratios for the visible green to the visible red
are shown to be the second most significant covariates. The resultant models
are shown to explain 98 percent of the variance in Secchi Disk Transparency,
and 93 percent of the
variance in Chlorophyll a concentration using pooled data from 59 sampling
stations acquired during two distinct periods: 08 June and 28 September 1991.
1067 Comparison of Change-Detection Techniques for Monitoring Tropical
Forest Clearing and Vegetation Regrowth in a Time Series
Daniel J. Hayes and Steven A. Sader
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The once remote and inaccessible forests of Guatemala's Maya Biosphere
Reserve (MBR) have recently experienced high rates of deforestation
corresponding to human migration and expansion of the agricultural
frontier. Given the importance of land-cover and land-use change
data in conservation planning, accurate and efficient techniques
to detect forest change from multi-temporal satellite imagery were
desired for implementation by local conservation organizations. Three
dates of Landsat Thematic Mapper imagery, each acquired two years
apart, were radiometrically normalized and pre-processed to remove
clouds, water, and wetlands, prior to employing the change-detection
algorithm. Three change-detection methods were evaluated: normalized
difference vegetation index (NDVI) image differencing, principal
component analysis, and RGB-NDVI change detection. A technique to
generate reference points by visual interpretation of color composite
Landsat images, for Kappa-optimizing thresholding and accuracy assessment,
was employed. The highest overall accuracy was achieved with the
RGB-NDVI
method (85 percent). This method was also preferred for its simplicity in design
and ease in interpretation, which were important considerations for transferring
remote sensing technology to local and international non-governmental organizations.
1077 Potential of Road Stereo Mapping with RADARSAT Images
Thierry Toutin
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Two stereo pairs generated with standard mode images (S1-S7 and S4-S7)
and one with fine mode images (F1-F5) are used to evaluate the potential
of RADARSAT-SAR for extracting planimetric features, such as roads,
on a PC-based stereo workstation. First, monoscopic and stereoscopic
plotting for the ground control points (GCPs) are performed to evaluate
the impact on the accuracy. It is prerequisite, especially for smaller
intersection angle stereo pairs, to acquire GCPs in stereoscopy because
the monoscopic collection mode degrades the relative and absolute
orientations of the stereo model by a ratio of two to four depending
of the stereo geometry. The roads are then interactively stereo extracted
by an operator and compared with the roads on the digital topographic
maps.
Statistical results over a large sample (more than 900 km) show an accuracy
of about 15 to 24 m for fine mode and 25 to 50 m for standard mode stereo-pairs
with 90 percent confidence levels, independently of the stereo configuration.
The impact of image sampling on the road positioning accuracy is also addressed.
Finally, comparisons with the orthorectification process show that the stereoscopic
method to extract planimetric features is slightly more accurate.
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