Environmental Engineering Reference
In-Depth Information
spatial, and temporal features and ancillary information in the
image classification (e.g., Tottrup, 2004; Chapter 10); and (vi)
the use of artificial intelligence technology, such as rule-based
classifiers (e.g., Schmidt
et al
., 2004), artificial neural networks
(see Chapter 8) and support vector machines (Yang, 2011), for
pattern classification. These developments are quite promising.
Nevertheless, further efforts will certainly be maintained and will
probably intensify in order to adopt these techniques to solve
practical problems in a productive fashion.
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In this chapter, we discussed the utilities of satellite remote sens-
ing for the observation and measurement of urban spatial growth
and landscape changes emphasizing the use of archival Landsat
data. We targeted the data acquired by the three generations
of Landsat imaging sensors because they provide the principal
source of data for urbanization studies at the regional, national
and global scales. After a review on the past, present and future
of the Landsat program and its imaging sensors, we present a
case study focusing on a rapidly suburbanizing metropolis to
demonstrate the usefulness of time-sequential Landsat imagery
for monitoring decades-long urban spatial growth. The Atlanta
metropolitan area as the case study site is an ideal city to study the
postmodern urban dynamics and environmental consequences
of accelerating urban growth. The technical procedures used
were designed and implemented after a careful examination of
the image characteristics, landscape complexity, and the status of
technological development. Central to this study was a time series
of satellite imagery acquired by the three Landsat imaging sen-
sors, which has been used to produce accuracy-compatible land
use/cover maps through unsupervised classification and spatial
reclassification procedures. The time series of classified maps was
further used to analyze urban spatial growth and the nature of
change. This study reveals a rampant urban land growth during
a period of nearly four decades. Urban spatial form experienced
a transition from linear concentration to multinucleated pat-
tern for high-density urban use and from concentration mixed
with some degree of dispersal to more dispersed pattern for
low-density urban use in Atlanta.
Lastly, based on the case study and other literature, we further
formulated a generic workflow for urban spatial growth monitor-
ing, and discussed some conceptual and technical issues emerging
when using archival satellite images acquired by different sensors
and perhaps during different seasons. Conceptually, we strongly
suggested remote sensing professionals should be equipped with
some essential knowledge on urban geography and landscape
ecology that can help identify appropriate remote sensor data
or information extraction techniques in an urban study project.
Technically, we emphasized a thorough understanding of the
spatial, spectral, radiometric and temporal characteristics of the
satellite time series being used, a mandatory radiometric normal-
ization procedure to help restore a common radiometric response
among the multi-date, multi-sensor data set, and a carefully-
tailored change detection procedure with the research objectives
and scope in mind. We believe such discussions can help iden-
tify the outstanding issues that must be addressed in order to
implement an urban growth monitoring protocol effectively.
to
detect
and
monitor
land
cover
and
land
use
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