Peer-Reviewed Articles
1279 Assessment of 2001 NLCD Percent Tree and
Impervious Cover Estimates
Eric J. Greenfield, David J. Nowak, and Jeffrey T. Walton
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The 2001 National Land Cover Database (NLCD) tree and
impervious cover maps provide an opportunity to extract
basic land-cover information helpful for natural resource
assessments. To determine the potential utility and limitations
of the 2001 NLCD data, this exploratory study compared
2001 NLCD-derived values of overall percent tree and
impervious cover within geopolitical boundaries with aerial
photo interpretation-derived values for the same areas.
Results of the comparison reveal that NLCD underestimates
tree cover and to a lesser extent, underestimates impervious
cover. The underestimate appears to be consistent across the
conterminous United States with no statistical differences
among regions. However, there were statistical differences in
the degree of underestimation of tree cover among mapping
zones and of impervious cover by population density class.
1287 Large Area Scene Selection Interface (LASSI)
Methodology of Selecting Landsat Imagery for the
Global Land Survey 2005
Shannon Franks, Jeffrey G. Masek, Rachel M. K. Headley,
John Gasch, and Terry Arvidson
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The Global Land Survey (GLS) 2005 is a cloud-free, orthorectified
collection of Landsat imagery acquired during the
2004 to 2007 epoch intended to support global land-cover
and ecological monitoring. Due to the numerous complexities
in selecting imagery for the GLS2005, NASA and the U.S.
Geological Survey (USGS) sponsored the development of
an automated scene selection tool, the Large Area Scene
Selection Interface (LASSI), to aid in the selection of imagery
for this data set. This innovative approach to scene selection
applied a user-defined weighting system to various scene
parameters: image cloud cover, image vegetation greenness,
choice of sensor, and the ability of the Landsat-7 Scan Line
Corrector (SLC)-off pair to completely fill image gaps, among
others. The parameters considered in scene selection were
weighted according to their relative importance to the data
set, along with the algorithm’s sensitivity to that weight.
This paper describes the methodology and analysis that
established the parameter weighting strategy, as well as the
post-screening processes used in selecting the optimal data
set for GLS2005.
1297 Adaptive Registration of Remote Sensing Images
using Supervised Learning
Line Eikvil, Marit Holden, and Ragnar Bang Huseby
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This paper describes a system for co-registration of time series
satellite images which uses a learning-based strategy. During a
training phase, the system learns to recognize regions in an
image suited for registration. It also learns the relationship
between image characteristics and registration performance for
a set of different registration algorithms. This enables intelligent
selection of an appropriate registration algorithm for each
region in the image, while regions unsuited for registration can
be discarded. The approach is intended for co-registration of
sequences of images acquired from identical or similar earth
observation sensors. It has been tested for such sequences
from different types of sensors, both optical and radar, with
varying resolution. For images with moderate differences in
content, the registration accuracy is, in general, good with an
RMS error of one pixel or less.
1307 Analysis of Dynamic Thresholds for the Normalized
Difference Water Index
Lei Ji, Li Zhang, and Bruce Wylie
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The normalized difference water index (NDWI) has been
successfully used to delineate surface water features. However,
two major problems have been often encountered: (a) NDWIs
calculated from different band combinations [visible, nearinfrared,
or shortwave-infrared (SWIR)] can generate different
results, and (b) NDWI thresholds vary depending on the
proportions of subpixel water/non-water components. We need
to evaluate all the NDWIs for determining the best performing
index and to establish appropriate thresholds for clearly
identifying water features. We used the spectral data obtained
from a spectral library to simulate the satellite sensors Landsat
ETM, SPOT-5, ASTER, and MODIS, and calculated the simulated
NDWI in different forms. We found that the NDWI calculated
from (green – SWIR)/(green SWIR), where SWIR is the shorter
wavelength region (1.2 to 1.8 mm), has the most stable
threshold. We recommend this NDWI be employed for mapping
water, but adjustment of the threshold based on actual
situations is necessary.
1319 A Matching Algorithm for Detecting Land Use
Changes Using Case-Based Reasoning
Xia Li, Anthony Gar-On Yeh, Jun-ping Qian, Bin Ai, and
Zhixin Qi
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The paper deals with change detection using time series
SAR images. SAR provides a unique opportunity for detecting
land-use changes within short intervals (e.g., monthly) in
tropical and sub-tropical regions with cloud cover. Traditional
change detection methods mainly rely on per-pixel
spectral information but ignore per-object structural information.
In this study, a new method is presented that integrates
object-oriented analysis with case-based reasoning (CBR) for
change detection. Object-oriented analysis is carried out to
retrieve a variety of features, such as tone, shape, texture,
area, and context. An incremental segmentation technique is
proposed for deriving change objects from multi-temporal
Radarsat images. Feature selection based on genetic algorithms
is carried out to determine the optimal set of features
for change detection. A CBR matching algorithm is developed
to identify the temporal positions and the kind of changes.
It is based on the weighted k-Nearest Neighbor classification
using an accumulative similarity measure. The comparison
of the four combinations of change detection methods,
object-based or pixel-based plus case-based or rule-based, is
carried out to validate the performance of this proposed
method. The analysis shows that this integrated approach
has provided an efficient way of detecting land-use changes
at monthly intervals by using multi-temporal SAR images.