Peer-Reviewed Articles
957 Mapping Urban Extent Using Satellite Radar Interferometry
William Grey and Adrian Luckman
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Phase coherence between pairs of ERS SAR images is investigated as a method
for mapping urban extent in South Wales, United Kingdom. Separability indices
show that image pairs with time delays of greater than 2 months and baseline
separations of less than 300 m can discriminate effectively between urban
and non-urban land. Classification kappa coefficients greater than 90 percent
are achieved, and there is evidence to suggest that a single coherence threshold
is applicable for mapping urban extent in any similar landscape.
963 A Comparison of Urban Mapping Methods Using
High-Resolution Digital Imagery
Nancy Thomas, Chad Hendrix, and Russell G. Congalton
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Recent advances in digital airborne sensors and satellite platforms make spatially
accurate, high-resolution multispectral imagery readily available. These
advances provide the opportunity for a host of new applications to address
and solve old problems. High-resolution imagery is particularly well suited
to urban applications. Previous data sources (such as Landsat TM) did not
show the spatial detail necessary to provide many urban planning solutions.
This paper provides an overview of a project in which one-meter digital imagery
was used to produce a map of pervious and impervious surfaces to be used
by the city of Scottsdale, Arizona for storm-water runoff estimation. The
increased spatial information in one meter or less resolution imagery strains
the usefulness of image classification using traditional supervised and unsupervised
spectral classification algorithms. This study assesses the accuracy of three
different methods for extracting land-cover/land-use information from high-resolution
imagery of urban environments: (1) combined supervised/ unsupervised spectral
classification, (2) raster-based spatial modeling, and (3) image segmentation
classification using classification tree analysis. A discussion of the results
and relative merits of each method is included.
973 Synergistic Use of Lidar and Color Aerial Photography
for Mapping Urban Parcel Imperviousness
Michael E. Hodgson, John R. Jensen, Jason A. Tullis, Kevin D. Riordan, and
Clark M. Archer
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The imperviousness of land parcels was mapped and evaluated using high spatial
resolution digitized color orthophotography and surface-cover height extracted
from multiple-return lidar data. Maximum-likelihood classification, spectral
clustering, and expert system approaches were used to extract the impervious
information from the datasets. Classified pixels (or segments) were aggregated
to parcels. The classification model based on the use of both the orthophotography
and lidar-derived surface-cover height yielded impervious surface results
for all parcels that were within 15 percent of reference data. The standard
error for the rule-based per-pixel model was 7.15 percent with a maximum
observed error of 18.94 percent. The maximum-likelihood per-pixel classification
yielded a lower standard error of 6.62 percent with a maximum of 14.16 percent.
The regression slope (i.e., 0.955) for the maximum-likelihood per-pixel model
indicated a near perfect relationship between observed and predicted imperviousness.
The additional effort of using a per-segment approach with a rule-based classification
resulted in slightly better standard error (5.85 percent) and a near-perfect
regression slope (1.016).
981 Comparing ARTMAP Neural Network with the Maximum-Likelihood
Classifier for Detecting Urban Change
Karen C. Seto and Weiguo Liu
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Urbanization has profound effects on the environment at local, regional, and
global scales. Effective detection of urban change using remote sensing data
will be an essential component of global environmental change research, regional
planning, and natural resource management. This paper presents results from
an ARTMAP neural network to detect urban change with Landsat TM images from
two periods. Classification of urban change, and, in particular, conversion
of agriculture to urban, was statistically more accurate with ARTMAP than
with a more conventional technique, the Bayesian maximum-likelihood classifier
(MLC). The effect of different levels of class aggregation on the performance
of change detection was also explored with ARTMAP and MLC. Because ARTMAP
explicitly allows "many-to-one" mapping, classification using coarse
class resolution and fine class resolution training data generated similar
results. Together, these results suggest that ARTMAP can reduce labor and
computational costs associated with assembling training data while concurrently
generating more accurate urban change-detection results.
991 Spatial Metrics and Image Texture for Mapping
Urban Land Use
Martin Herold, XiaoHang Liu, and Keith C. Clarke
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The arrival of new-generation, high-spatial-resolution satellite imagery (e.g.,
Ikonos) has opened up new opportunities for detailed mapping and analysis
of urban land use. Drawing on the traditional approach used in aerial photointerpretation,
this study investigates an "object-oriented" method to classify
a large urban area into detailed land-use categories. Spatial metrics and
texture measures are used to describe the spatial characteristics of land-cover
objects within each land-use region as derived from interpreted aerial photographs.
In assessing how land-use categories vary in their spatial configuration,
spatial metrics were found to provide the most important information for
differentiating urban land uses. A detailed land-use map with nine categories
was derived for the Santa Barbara South Coast Region area. Results from our
work suggest that the region-based method exploiting spatial metrics and
texture measurements is a potential new avenue to extract detailed urban
land-use information from high resolution satellite imagery.
These links will open the color figures ina new window:
Figure 1.; Figure 2.; Figure 3.; Figure 4.; Figure 5a.; Figure 5b.; Figure 6.
1003 Urban Land-Cover Change Detection through
Sub-Pixel Imperviousness Mapping Using Remotely Sensed Data
Limin Yang, George Xian, Jacqueline M. Klaver, and Brian Deal
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We developed a Sub-pixel Imperviousness Change Detection (SICD) approach to
detect urban land-cover changes using Landsat and high-resolution imagery.
The sub-pixel percent imperviousness was mapped for two dates (09 March 1993
and 11 March 2001) over western Georgia using a regression tree algorithm.
The accuracy of the predicted imperviousness was reasonable based on a comparison
using independent reference data. The average absolute error between predicted
and reference data was 16.4 percent for 1993 and 15.3 percent for 2001. The
correlation coefficient (r) was 0.73 for 1993 and 0.78 for 2001, respectively.
Areas with a significant increase (greater than 20 percent) in impervious
surface from 1993 to 2001 were mostly related to known land-cover/land-use
changes that occurred in this area, suggesting that the spatial change of
an impervious surface is a useful indicator for identifying spatial extent,
intensity, and, potentially, type of urban land-cover/land-use changes. Compared
to other pixel-based change-detection methods (band differencing, rationing,
change vector, post-classification), information on changes in sub-pixel
percent imperviousness allow users to quantify and interpret urban land-cover/land-use
changes based on their own definition. Such information is considered complementary
to products generated using other change-detection methods. In addition,
the procedure for mapping imperviousness is objective and repeatable, hence,
can be used for monitoring urban land-cover/land-use change over a large
geographic area. Potential applications and limitations of the products developed
through this study in urban environmental studies are also discussed.
1011 Measuring the Physical Composition of Urban
Morphology UsingMultiple Endmember Spectral Mixture Models
Tarek Rashed, John R. Weeks, Dar Roberts, John Rogan, and Rebecca Powell
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The application of multiple endmember spectral mixture analysis (MESMA) to
map the physical composition of urban morphology using Landsat Thematic Mapper
(TM) data is evaluated and tested. MESMA models mixed pixels as linear combinations
of pure spectra, called endmembers, while allowing the types and number of
endmembers to vary on a per-pixel basis. A total of 63 two-, three-, and
four-endmember models were applied to a Landsat TM image for Los Angeles
County, and a smaller subset of these models was chosen based on fraction
and root-mean-squared error (RMSE) criteria. From this subset, an optimal
model was selected for each pixel based on optimization for maximum area
coverage. The resultant endmember fractions were then mapped into four main
components of urban land cover: Vegetation, Impervious surfaces, Soil, and
Water/Shade. The mapped fractions were validated using aerial photos. The
results showed that a majority of the image could be modeled successfully
with two or three endmember models. The validation results indicated the
robustness of MESMA for deriving spatially continuous variables quantified
at the sub-pixel level. These parameters can be readily integrated into a
wide range of applications and models concerned with physical, economic,
and/or socio-demographic phenomena that influence the morphological patterns
of the city.
1021 A Housing-Unit-Level Approach to Characterizing
Residential Sprawl
John Hasse and Richard G. Lathrop
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Five spatial metrics are developed at the housing-unit level for analyzing
spatial patterns of urban growth in order to better identify the characteristics
and qualities of urban sprawl. A multi-temporal land-use/land-cover dataset
for Hunterdon County, New Jersey is utilized to measure new housing units
developed between Time 1 (1986) and Time 2 (1995) for five traits defined
as "sprawl" in the planning and policy literature: (1) density,
(2) leapfrog, (3) segregated land use, (4) accessibility, and (5) highway
strip. The resulting housing-unit sprawl indicator measurements are summarized
by municipality to provide a "sprawl report card." The analysis
provides a new direction in sprawl research that addresses sprawl at the
atomic level, captures the temporal nature of urban growth, and provides
measures that are potentially useful to planners addressing sprawl.
1031 Modeling Urban Population Growth from Remotely
Sensed Imagery and TIGER GIS Road Data
Fang Qiu, Kevin L. Woller, and Ronald Briggs
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We modeled population growth from 1990 to 2000 in the north Dallas-Fort Worth
Metroplex using two different methods: a conventional model based on remote
sensing land-use change detection, and a newly devised approach using GIS-derived
road development measurements. These methods were applied at both city and
census-tract levels and were evaluated against the actual population growth.
It was found that accurate population growth estimates are achieved by both
methods. At the census-tract level, our models yielded a comparable result
with that obtained from a more complex commercial demographics model. At
both city and census-tract levels, models using road development were better
than those using land-use change detection. In addition to being efficient
in cost and time, our models provide direct visualization of the distribution
of the actual population growth within cities and census tracts when compared
to commercial demographic models.
These links will open the color figures in a new window:
Figure 1.; Figure 2a.; Figure 2b.; Figure 3a.; Figure 3b.; Figure 4a.; Figure4b.; Figure 5.; Figure 8a.; Figure 8b.
1043 Simulation of Development Alternatives Using
Neural Networks, Cellular Automata, and GIS for Urban Planning
Anthony Gar-On Yeh and Xia Li
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This study integrates neural networks and cellular automata (CA) to simulate
development alternatives for planning purposes. Most of the existing CA just
focus on simulating realistic urban dynamics. This paper demonstrates that
development alternatives can be simulated by incorporating planning objectives
in CA. It is important to define appropriate parameter values for simulating
development alternatives according to the planning objectives of planners
and decision makers. Training neural networks can automatically yield the
parameter values for urban simulation. GIS and remote sensing provide the
training data for calibrating the model. However, the simulation can inherit
past land-use problems if the original training data are used to calibrate
the model. The original data should be assessed and modified so that the
model can remember the past " failure" in land development. Planning
objectives can thus be embedded in the model by properly modifying the training
data sets. The training is robust because it is based on the well-defined
back-propagation algorithm. Experiments were carried out by using the city
of Dongguan, China as an example to test the model.
1053 Land-Use and Land-Cover Change, Urban Heat
Island Phenomenon, and Health Implications: A Remote Sensing Approach
C.P. Lo and Dale A. Quattrochi
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Land-use and land-cover maps of Atlanta Metropolitan Area in Georgia were produced
from Landsat MSS and TM images for 1973, 1979, 1983, 1987, 1992, and 1997,
spanning a period of 25 years. Dramatic changes in land use and land cover
have occurred, with loss of forest and cropland to urban use. In particular,
low-density urban use, which includes largely residential use, has increased
by over 119 percent between 1973 and 1997. These land-use and land-cover
changes have drastically altered the land surface characteristics. An analysis
of Landsat images revealed an increase in surface temperature and a decline
in NDVI from 1973 to 1997. These changes have forced the development of a
significant urban heat island effect at both the urban canopy and urban boundary
layers as well as an increase in ground level ozone production to such an
extent that Atlanta has violated EPA's ozone level standard in recent years.
Using canonical correlation analysis, surface temperatures and NDVI, extracted
from Landsat TM images, were found to correlate strongly with volatile organic
compounds (VOC) and nitrogen oxides (NOx) emissions, the two ingredients
that form ozone by reacting with sunlight, but only weakly with the rates
of cardiovascular and chronic lower respiratory diseases, which also did
not exhibit strong correlation with VOC and NOx emissions, possibly because
other factors such as demographic and socio-economic may also be involved.
Further research is therefore needed to understand the health geography and
its relationship to land- use and land-cover change. This paper illustrates
the usefulness of a remote sensing approach for this purpose.
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