Peer-Reviewed Articles
1001 Landscape Dynamics of the Spread of Sudden
Oak Death
Maggi Kelly and Ross K. Meentemeyer
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Sudden Oak Death is caused by a newly discovered virulent pathogen (Phytophthora
ramorum) that is killing thousands of native oak trees in California. We present
a landscape-scale
study on the spatio-temporal dynamics of oak mortality. Second-order spatial
point-pattern analysis techniques (Ripley's K) were applied to the distribution
of dead tree crowns (derived from high-resolution imagery) in Marin County,
California to determine the existence and scale of mortality clustering in
two years (2000 and 2001). Both years showed clustering patterns between 100
and 300 m. A classification tree model was developed to predict spatial patterns
of risk for oak mortality based on several landscape-scale variables. Proximity
to forest edge was the most important explanatory factor, followed by topographic
moisture index, proximity to trails, abundance of Umbellularia californica,
and potential summer solar radiation. This research demonstrates the utility
of integrating remotely sensed imagery analysis with geographic information
systems and spatial modeling for understanding the dynamics of exotic species
invasions
1011 Characterizing Fire-Related Spatial Patterns
in the Arizona Sky Islands Using Landsat TM Data
Mary C. Henry and Stephen R. Yool
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This research investigates the use of Landsat Thematic Mapper data
to characterize spatial patterns in forests experiencing different
fire severities and frequencies
between 1943 and 1996. Spectral vegetation indices (SVIs) were used to compare
spectral characteristics and spatial patterns for four categories of fire
history: once burned, twice burned, multiple burned, and unburned.
We quantified spatial
patterns by calculating spatial statistics from several SVI s for each plot.
These statistics were used in Spearman's Rank Correlation Analysis with fire
history characteristics. We found significant relationships (p < 0.05)
between many of the spatial measures (mean patch size, patch size coefficient
of variation,
mean patch fractal dimension, Shannon's Diversity Index, and Shannon's Evenness
Index) and fire occurrence in the past ten, thirty, fifty, and fifty-four
years; average fire-free interval; most recent fire-free interval; and time
since
the most recent fire.
1021 Time Series Remote Sensing of Landscape-Vegetation
Interactions in the Southern Great Plains
Mark E. Jakubauskas, Dana L. Peterson, Jude H. Kastens, and David R. Legates
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The southern Great Plains may be one of the first areas in the United States
to show significant and detectable changes in vegetation cover as a result
of global climate change. The
objective of this project was to examine interactions between landscape environmental
factors and interannual variability of land-cover types in this region. Harmonic
analysis of a nine-year time series (1989-1997) of NOAA Advanced Very High
Resolution Radiometer (AVHRR) normalized difference vegetation index (NDVI)
biweekly composite data
was used to
quantify interannual changes in natural and managed vegetation. An index of
interannual landscape variability was developed based on the weighted circular
variance in phase values produced by the harmonic analysis. Results indicate
that landscape variability, as quantified by the weighted circular variance,
is significantly different among three soil texture classes and five land-use/land-cover
types. Harmonic analysis of time-series data offers considerable promise as
a tool for monitoring landscape change.
1031 Characterizing Landscape Dynamism Using Paneled-Pattern
Metrics
Kelley A. Crews-Meyer
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Pattern metrics were generated from four land-use/land-cover
(LULC) classifications derived from Landsat MSS scenes in 1972/1973, 1975/1976,
1979, and 1985. Patch-level statistics were longitudinally assembled into
a ``life history'' of patches in order to assess structural changes over
time in landscape composition, and further to infer process in an area reported
to have undergone rapid deforestation. Patches were defined using the 1972/1973
LULC classification and then applied as landscape partition boundaries for
calculating pattern metrics throughout the remaining time series. Pattern
metrics tested were double log fractal dimension, interspersion/juxtaposition
index, percentage area, and mean patch fractal dimension. The paneled metrics
produced mixed results: some panels exhibited strong association with ecological
gradients and anecdotal information regarding landscape processes in the
area, while others offered no insight into LULC dynamics at work in the area
due to their invariance. Recommendations are made for further assessing the
potential of a paneled-pattern metric approach for characterizing landscape
dynamics.
1041 Statistical Methods to Partition Effects
of Quantity and Location During Comparison of Categorical Maps at
Multiple Resolutions
R. Gil Pontius, Jr.
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New generalized statistical methods to measure agreement between two maps at
multiple-resolutions, where each cell in each map has a multinomial distribution
among any number
of categories, are presented. This methodology quantifies agreement between
any two categorical maps, where either map uses fuzzy or crisp classification.
The method measures the
agreement at various resolutions by aggregating neighboring cells into an increasingly
coarse grid. At each resolution, the method partitions the overall agreement
into correct due to
chance, correct due to quantity, correct due to location, error due to location,
and error due to quantity. In addition, the method computes six statistics
that are useful to interpret the
differences between maps, and shows how these statistics change with resolution.
This technique is particularly useful for characterizing land-cover change
and for validating land-cover change models. For illustration, this paper applies
these theoretical concepts to the validation of a land-use change model for
Costa Rica.
1051 Stochastic Simulation of Land-Cover
Change Using Geostatistics and Generalized Additive Models
Daniel G. Brown, Pierre Goovaerts, Amy Burnicki, and Meng-Ying Li
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An approach to simulating land-cover change based on pairs of classified images
is presented. The method conditions the simulations on three sources of information:
an initial land-cover
map, maps of the probabilities of each possible class transition, and a description
of the spatial patterns of changes (e.g., semivariograms). The method can produce
multiple simulated
land-cover maps that honor each of these sources of information. The approach
is demonstrated for data on forest-cover change near Traverse City, Michigan.
The discussion describes
extensions to the method and an approach to generating future land-cover scenarios
based on socioeconomic information.
1063 Assessment of the Urban Development Plan
of Beijing by Using a CA-Based Urban Growth Model
Jin Chen, Peng Gong, Chunyang He, Wei Luo, Masayuki Tamural, and Peijun
Shi
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We developed a CA-based urban growth simulation model to emulate the city growth
before 1997 and simulate possible change scenarios after that. An adaptive
Monte-Carlo method
was used to automate the calibration of factor weights used in the CA transitional
rules. We used one scene of Landsat MSS imagery from 1975 and three scenes
of TM imagery from 1984, 1991, and 1997 to classify the land-use patterns,
and we used the results to calibrate the CA model. We applied the model to
assess the general urban development plan entitled ``disperse polycentric urban
development plan'' of Beijing City and found that the plan failed to meet its
objectives.
1073 Drivers of Land-Use/Land-Cover Changes and
Dynamic Modeling for the Atlanta, Georgia Metropolitan Area
C.P. Lo and Xiaojun Yang
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Landsat images and census data were integrated in a zone-based cellular approach
to analyze the drivers of land-use/land-cover changes in Atlanta, Georgia,
a postmodern metropolis. Land-use/land-cover statistics, which were extracted
from Landsat MSS, TM, and ETM + images for 1973, 1979, 1987, 1993, and 1999
for the 13 metro counties of the Atlanta metropolitan area, revealed rapid
increases in high-density and low-density urban use at the expense of cropland
and forests during this period of rapid population growth. To understand the
underlying causes of all these changes, demographic and socio-economic data
from the censuses were integrated with the land-use/land-cover change data
and location data. A total of 17 themes comprising 78 variables were used in
the analysis at three different spatial levels: the whole metropolis, county,
and census tract, all unified at the 60-meter grid-cell level. It was found
that proximity to highways, nodes, and shopping malls tended to promote urban
development in Atlanta, and the increasing affluence of the population has
induced rapid suburbanization, with consequent adverse impact on the greenness
and fragmentation of the environment in recent years. The results of the driving
force analysis were incorporated into a dynamic model, namely, cellular automaton,
at the census tract level, which simulated the land-use/land-cover change of
Atlanta from 1999 to 2050. It predicted the continued growth of edge cities
and the loss of forest, if unchecked, within a time span of 10 to 20 years.
The limitations of the cellular automaton model as applied to Atlanta were
also discussed.
1083 Quantifying and Describing Urbanizing
Landscapes in the Northeast United States
Daniel L. Civco, James D. Hurd, Emily Hoffhine Wilson, Chester L. Arnold,
and Michael P. Prisloe, Jr.
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There is neither a single definition of nor a standardized process for performing
landscape characterizations. For more than a decade, researchers at the University
of Connecticut have used remote sensing technology to detect land-cover features
and provide information to municipal land-use officials. Recent research has
been directed at three dynamic elements of the
landscape that are critically important to land use officials: impervious surfaces,
forest fragmentation and urban growth. Techniques have been developed to extract
impervious surface
data directly from Landsat imagery to estimate non-point source pollution impacts
on watersheds. A model has been created to quantify and describe forest fragmentation
over various geographic areas and an urban growth model has been developed
that quantifies and categorizes urban change. Both of these models use land-cover
information as their source data.
These tools and the derived information are important educational components
of the University's recently created Center for Land use Education And Research.
1091 A Strategy for Estimating the Rates of
Recent United States Land-Cover Changes
T.R. Loveland, T.L. Sohl, S.V. Stehman, A.L. Gallant, K.L. Sayler, and
D.E. Napton
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Information on the rates of land-use and land-cover change is important in
addressing issues ranging from the health of aquatic resources to climate change.
Unfortunately, there is a
paucity of information on land-use and land-cover change except at very local
levels. We describe a strategy for estimating land-cover change across the
conterminous United States over
the past 30 years. Change rates are estimated for 84 ecoregions using a sampling
procedure and five dates of Landsat imagery. We have applied this methodology
to six eastern U.S. ecoregions. Results show very high rates of change in the
Plains ecoregions, high to moderate rates in the Piedmont ecoregions, and moderate
to low rates in the Appalachian ecoregions. This indicates that ecoregions
are appropriate strata for capturing unique patterns of land-cover change.
The results of the study are being applied as we undertake the mapping of the
rest of the conterminous United States.
1101 A Land-Cover Data Infrastructure for Measurement,
Modeling, and Analysis of Land-Cover Change Dynamics
Richard Aspinall
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This paper describes land-cover data as part of a broad management and analytical
infrastructure. This land-cover data infrastructure provides a flexible and
inclusive mechanism
for a full sequence of analyses of land-cover, including description, management,
analysis, and modeling of land-cover change dynamics. The land-cover data infrastructure
addresses the diversity of description and application of land-cover data,
the sources of land-cover data, and the sequence of processing for land-cover
mapping, analysis, and modeling. The design of the infrastructure produces
an integrated suite of databases and processing toolboxes that supports this
entire processing stream and that focuses on the data needs of the applications
of land-cover data, rather than the representation of a cartographic land-cover
product. The infrastructure integrates diverse data sources, including imagery,
field data, and existing land-cover maps with data processing operations; the
goal is to provide data for modeling and analysis of land-cover dynamics relevant
to application needs of different users. The opportunity for further development
of GIS, remote sensing, and spatial analysis to be used to conduct rigorous
scientific investigation of land-cover change is discussed in the context of
the data infrastructure.
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