Peer-Reviewed Articles
397 An Accurate and Automated Approach to Georectification
of HDF-EOS Swath Data
Wenli Yang and Liping Di
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HDF-EOS is the standard format for NASA's EOS data products. Data stored in
the HDF-EOS swath structure are in a raw sensor coordinate system and are
not rectified to a map projection or geographic coordinate system. The geolocation
information is stored as separate fields in a swath. Swath data have to be
georectified before they can be integrated and analyzed. Presently, no general
purpose remote sensing image processing or geographic information system
software has the capability to directly perform georectification of HDF-EOS
swath data by using the geolocation information in the file. Most software
cannot even import such data due to the relevant newness of the format, and
this has limited the public use of NASA EOS data products. This paper describes
algorithms that automatically georectify the swath data by using the geolocation
information in the swath. The algorithms have been implemented in a software
package which is freely available to data users.
405 Assessing the Accuracy of National Land Cover
Dataset Area Estimates at Multiple Spatial Extents
Jeffrey W. Hollister, M. Liliana Gonzalez, John F. Paul,Peter V. August, and
Jane L. Copeland
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Site-specific accuracy assessments evaluate fine-scale accuracy of land-use/land-cover
(LULC) datasets but provide little insight into accuracy of area estimates
of LULC classes derived from sampling units of varying size. Additionally,
accuracy of landscape structure metrics calculated from area estimates cannot
be determined solely from site-specific assessments. We used LULC data from
Rhode Island and Massachusetts as reference to determine the accuracy of
area measurements from the National Land Cover Dataset (NLCD) within spatial
units ranging from 0.1 to 200 km². When regressed on reference area,
NLCD area of developed land, agriculture, forest, and water had positive
linear relationships with high r², suggesting acceptable accuracy. However,
many of these classes also displayed mean differences (NLCD - REFERENCE),
and linear relationships between the NLCD and reference were not one-to-one
(i.e., low r², ?0 ? 0, ?1 ? 1), suggesting mapped area is different
from true area. Rangeland, wetland, and barren were consistently, poorly
classified.
415 Combining Non-Parametric Models for Multisource
Predictive Forest Mapping
Zhi Huang and Brian G. Lees
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Most models of forest type for predictive mapping cannot produce estimates
of confidence in the prediction of individual pixels, even where they provide
good overall accuracy. A new strategy that combines several models based
on different principles not only provides estimates of prediction confidence,
but also improves the mapping accuracy. In this study, the theoretical foundation
of Artificial Neural Networks, Decision Trees, and Dempster-Shafer's Evidence
Theory are briefly reviewed, compared, and applied to a common data set.
Two ways for integrating the results of the three models were then evaluated.
One method was to separately harden the probability results of the three
models, then combine them to make a single classification. In the second
method, the probabilities of the three models for each pixel were simply
averaged, then hardened to a single classification. Deferring the hardening
to the final stage produced the best results. The 3 percent increase in overall
accuracy for the second approach compared with the best individual model
is encouraging. More importantly, estimates of prediction confidence were
derived, based on a comparison between a combined model and the three models,
something that is impossible using a single model.
Please see these links for the figures: Figure 1. Figure 2. Figure 3. Figure 4.
427 An Initial Study on Automatic Reconstruction
of Ground DEMs from Airborne IfSAR DSMs
Yun Zhang, C. Vincent Tao, and J. Bryan Mercer
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The demands for ground digital elevation models (DEMs) have been increasing
rapidly. Many modern mapping technologies, such as lidar scanning, interferometric
SAR (IfSAR), and stereo image matching, are capable of generating digital
surface models (DSMs) directly. Research on deriving DEMs fromDSMs is a topical
area. Airborne IfSAR technology has been in commercial operation since 1996.
However, it is still technically challenging to automate the process of generating
DEMs fromIfSAR DSMs. The lack of scientific studies in this area is mainly
caused by the sensitivity and availability of commercial IfSAR data. In this
study, researchers have been working with the commercial IfSAR system developer
to perform an initial study on automatic reconstruction of ground DEMs fromIfSAR
DSMs. This paper presents a compact and yet efficient version of our developed
approach. This approach has been tested by using many IfSAR DSMs in different
hilly urban areas with light and moderate variations in relief. An accuracy
analysis of the reconstructed ground DEMs against the laser DSMs and DEMs
has also been performed to test the reliability and efficiency of our approach.
439 The Characteristics and Interpretability of
Land Surface Change and Implications for Project Design
Terry L. Sohl, Alisa L. Gallant, and Thomas R. Loveland
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The need for comprehensive, accurate information on land cover change has never
been greater. While remotely sensed imagery affords the opportunity to provide
information on land-cover change over large geographic expanses at a relatively
low cost, the characteristics of land-surface change bring into question
the suitability of many commonly used methodologies. Algorithm-based methodologies
to detect change generally cannot provide the same level of accuracy as the
analyses done by human interpreters. Results from the Land Cover Trends project,
a cooperative venture that includes the U.S. Geological Survey, Environmental
Protection Agency, and National Aeronautics and Space Administration, have
shown that land-cover conversion is a relatively rare event, occurs locally
in small patches, varies geographically and temporally, and is spectrally
ambiguous. Based on these characteristics of change and the type of information
required, manual interpretation was selected as the primary means of detecting
change in the Land Cover Trends project. Mixtures of algorithm-based detection
and manual interpretation may often prove to be the most feasible and appropriate
design for change-detection applications. Serious examination of the expected
characteristics and measurability of change must be considered during the
design and implementation phase of any change analysis project.
451 Validation and Calibration of Canada-WideCoarse-Resolution
Satellite Burned-AreaMaps
R.H. Fraser, R.J. Hall, R. Landry, T. Lynham, D. Raymond, B. Lee, and Z. Li
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Satellite-based mapping can provide a timely and efficient means of identifying
burned vegetation at continental scales for estimating greenhouse gas emissions
and impacts on the terrestrial carbon budget. In this study, we used a sample
of 55 Landsat Thematic Mapper (TM) scenes distributed across Canada to validate
and calibrate 1998 and 1999 national-level burned areas maps produced using
coarse resolution (approx. 1-km) SPOT VEGETATION and NOAA AVHRR imagery.
Commission and omissions errors, based on fire events greater than 200 ha,
were found to be small in the coarse resolution maps (4 percent and 1 percent,
respectively). However, the coarse resolution burned-area estimates were
72 percent larger than the crown fire burned area mapped at 30 m using Landsat
TM (11,039 versus 6,403 ha average area). This bias was attributed to spatial
aggregation effects in which the coarse resolution product included the tree
crown fire, partial burn, and unburned fractions of a pixel. A regression
calibration model (R² = 0.95, p < 0.0005, RMSE = 3,015 ha, n = 155)
based on a VGT/TM double sampling approach was derived to correct for the
aggregation bias and to provide Canada-wide estimates of crown fire burned
area.
461 Satellite Observations of the Seasonal Vegetation
Growth in Central Asia: 1982-1990
Fangfang Yu, Kevin P. Price, James Ellis, and Dietrich Kastens
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A new method called Derivation and Threshold Approach (DATA) was developed
to estimate the onset of vegetation green-up (OVG) for the Mongolian Plateau
from 1982 to 1990. The estimates of OVG date were highly correlated with geographic
patterns and climatic variations across time and space within our study area.
The date of OVG was earliest for the forest and meadow steppe, later for the
typical steppe, and latest for the desert steppe. Our findings also showed
that ecosystems in the Mongolian Plateau exhibited high spatiotemporal variability
in the date of OVG during the study period. The date of onset was found to
be most consistent for the forest ecosystem, while the desert steppe was characterized
by high year-to-year variations in onset date. A comparison of the derived
OVG date estimates with meteorological data showed a clear linkage between
onset date and the beginning of spring precipitation in the dry region of the
Mongolian Plateau.
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