Peer-Reviewed Articles
941 The Land-cover Change Mapper (LCM) and its Application
to Timber Harvest Monitoring in Western
Canada
Guillermo Castilla, Richard H. Guthrie, and Geoffrey J. Hay
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We introduce an automated change detection and delineation
tool for remote sensing images: the Land-cover Change
Mapper (LCM). LCM rapidly generates a polygon vector layer
(shapefile) of regions deemed to have undergone significant
change in land-cover. In its simplest usage, LCM requires two
single band or multi-band co-registered images of the same
scene acquired at different dates, and as the only user defined
parameter, the minimum size for change regions. The
main advantages of this tool are that (a) it is fully unsupervised,
(b) it is exceptionally fast, (c) it is robust to geometric
misregistration errors and variations in illumination, and
(d) it produces visually pleasing outlines that resemble those
obtained through manual digitization. We describe how the
tool works, illustrate its application to monitoring forest clearcuts
on a 1,000 km2 area in Western Canada using SPOT
imagery, compare it to a commercial tool, and report on its
thematic and spatial accuracy. A freeware LCM version is
available on the Internet.
951 Enhancing Binary Change Detection Performance
Using A Moving Threshold Window (MTW) Approach
Jungho Im, Jinyoung Rhee, and John R. Jensen
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This study introduced a new concept, the Moving Threshold
Window (MTW), for binary change detection. An automated
MTW-based calibration model was developed and evaluated
using a case study. The MTW-based model is free from the
assumption of symmetry for difference and ratio types of
change-enhanced images, unlike traditional binary change
detection methods. The MTW-based calibration model outperformed
the traditional binary change detection methods based
on the Symmetric Threshold Window (STW) for both single
and multiple change-enhanced images of the study area. In
most of the calibrations, the optimum thresholds resulting in
the highest Kappa coefficient were asymmetric. Three major
factors may explain the asymmetric characteristics of the
optimum thresholds, including: different atmospheric conditions
found in the two dates of imagery, different look angles
associated with the two dates of imagery, and the nature
of the change information. Multiple change-enhanced images
generally produced higher accuracies than single change enhanced
images using both the MTW- and STW-based models.
963 Range of Categorical Associations for Comparison of
Maps with Mixed Pixels
Robert Gilmore Pontius Jr. and John Connors
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This paper presents a method to compare maps that contain
pixels that have partial membership to multiple categories,
i.e., mixed or soft classified pixels. The method quantifies
ranges for associations among categories based upon
possible variations in sub-pixel spatial allocation. The paper
derives the mathematical equations for constructing the
range of associations based on three types of cross-tabulation
matrices, the greatest matrix, the random matrix, and
the least matrix. We demonstrate how the analysis can be
combined with multiple resolution map comparison to
specify the resolution at which clusters exist on a single
map or between two maps. The method produces a range
that reflects the amount of uncertainty in the categorical
associations. We illustrate the procedure with both a simple
example and data from the Plum Island Ecosystems study
site in Massachusetts, USA.
971 Projection of Land Use Change Patterns Using Kernel
Logistic Regression
Bo Wu, Bo Huang, and Tung Fung
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Change analysis is probably a natural step following the
detection of changes using remote sensing data. One significant
topic in change analysis is to model the changes in
relation to their driving factors and to project future land-use
patterns. While logistic regression (LR) has been widely used
in change modeling, this paper presents an improved method,
kernel logistic regression (KLR), to model the nonlinear relationship
between land-use change and various causal factors
such as population, distance to road and facilities, surrounding
land-use, and others. Traditional KLR models contain one
coefficient for each training sample, rendering it inappropriate
for applications of land-use change analysis with more than a
few thousand samples. A feature vectors selection method for
the KLR model has therefore been proposed to impose sparsity
and control complexity. To test the effectiveness of KLR, a
case study was implemented to model rural-urban land-use
conversion in the city of Calgary, Canada during the periods
1985 to 1990 and 1990 to 1999. The KLR model was compared
with a commonly used LR model in terms of the Percentage
of Correct Prediction (PCP), Area under Curve (AUC), and
McNamara’s test, and the results consistently demonstrated
the better performance of KLR.
981 A Disturbance Inventory Framework for Flexible and
Reliable Landscape Monitoring
J. Linke, G.J. McDermid, D.N. Laskin, A.J. McLane, A. Pape, J. Cranston, M. Hall-Beyer, and S.E. Franklin
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Remote sensing plays a key role in landscape monitoring, but
our handling of these data in a multi-temporal time series is
not yet fully developed. Of particular concern is the presence
of spatial and thematic errors in independently created maps
that distort measures of landscape pattern and constrain the
reliability of change analysis. In addition, there is a need to
incorporate continuous attributes of cover gradients for flexible
map representations that support a variety of applications. In
this paper, we present a framework for generating temporally
and categorically dynamic land-cover maps that provide such
a reliable and adaptable foundation. The centerpiece is a
spatio-temporal disturbance-inventory database, created
through semi-automated change detection and conditioned
with boundary-matching procedures, which can be used to
backdate and update both continuous and categorical reference
maps. We demonstrate our approach using multi-annual
Landsat imagery from a forested region in west-central
Alberta, Canada, between the years 1998 and 2005.
997 Quantifying Impacts of Land Ownership on Regional
Forest NDVI Dynamics: A Case Study at Bankhead
National Forest in Alabama, USA
Xiongwen Chen and Rory Fraser
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Identifying the impacts of human activities on local and
regional ecological processes is important for protecting
essential ecological functions. In this study, the spatial and
temporal dynamics of Normalized Difference Vegetation
Index (NDVI) at three adjacent areas with different proportions
of private land (6 percent, 20 percent and 55 percent) in the
Bankhead National Forest in Alabama, USA from 1998 to
2004 were examined. The phase coupling, synchrony, and
information entropy of NDVI at multiple temporal scales for
each of these areas were examined. A higher proportion
of private land (e.g., 55 percent) resulted in decrease of
annual mean, coefficient of variance, seasonal maximum,
and absolute value of rate of increase/ rate of decrease for
NDVI values as well as increase in seasonal minimum NDVI
and decrease in spatial coupling and synchrony of NDVI
dynamics. Thus, a higher proportion of private land could
affect regional NDVI dynamics in complex and ecologically
significant ways.
1005 Individual Object Change Detection for Monitoring the
Impact of a Forest Pathogen on a Hardwood Forest
Tim De Chant and Maggi Kelly
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Sudden oak death (SOD) has caused widespread mortality in a
number of tree and shrub species throughout coastal California.
As a result, canopy changes are directly visible from
remotely sensed imagery. To quantify changes in horizontal
canopy structure to the oak woodlands in China Camp State
Park, California, USA, a heavily hit area, we developed a
novel change detection technique that tracks changes to
individual objects. Using 4-band, 1 m spatial resolution aerial
photography, we classified four annual images (2000 to 2003)
with object-based image analysis (OBIA) and employed a GIS
for our change detection technique. We identified 352 gaps
that contained SOD mortality in 2000 and persisted through
2003. Their median areas and perimeters did not change
significantly in that time. However, those gaps that increased
in size tended to be smaller than those that decreased,
indicating increased mortality in newly infected areas. Our
new change detection method allowed us to monitor these
gaps one-by-one, revealing ecologically meaningful results that
would otherwise be obscured in a landscape-scale analysis.
1015 Spatial Change Optimization: Integrating GA with
Visualization for 3D Scenario Generation
Magesh Chandramouli, Bo Huang, and Lulu Xue
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Urban spatial analysis is becoming an increasingly complex
problem due to the overwhelming demands imposed by the
population and several other factors. Consequently, tools are
needed to solve complex urban spatial problems that are
multiobjective in nature. This study presents a multiobjective
optimization approach to generating alternative land
use scenarios and offers a visual evaluation tool for assessing
the Pareto solutions. Typically, with genetic algorithms
(GA), decision makers are finally left with alternative
solutions in the form of the Pareto set, from which one or a
few more will be chosen. Hence, a visualization tool is
employed in this study, whereby the decision makers can
better evaluate the alternative solutions from the Pareto set.
Modeling futuristic land uses is devised as an optimization
problem wherein spatial configurations are created through
the use of evolutionary algorithms. With the goal of sustainable
urban land use planning, the evolutionary algorithm is
designed for multiple objectives, such as maximization of
per capita green space, maximization of urban housing
density, maximization of public service space, and conflict
resolution among neighboring land uses. The results evince
the validity of the GA framework and also corroborate the
utility of the virtual scenarios.