Environmental Engineering Reference
In-Depth Information
Part VI (Chs 22 - 26) examines the developments of remote
sensing and dynamic modeling techniques to simulate and pre-
dict urban growth and landscape changes. The first four chapters
deal with the three major types of dynamic modeling techniques,
i.e., cellular automata modeling, agent-based modeling, and
ecological modeling, while the last chapter shifts the discus-
sion from technical aspects to the underlying root metaphors
embedded in various modeling efforts. Chapter 22 explores how
the developments in remote sensing, together with advances
in physics, mathematics, chemistry and computer science, con-
tributed to the exploration of urban complexity. It discusses
the evolution from pixel-matrix structures towards cell and
agent-based models and the challenge of integrating spatial and
a-spatial data structures and models. The chapter then proposes
a new data structure and modeling approach in which remote
sensing can play an important role. Chapter 23 reviews the
developments in calibration and validation of urban cellular
automata models emphasizing on models tasked with simulat-
ing human - environment interactions. It discusses calibration
mechanisms and the derivation of calibration parameter val-
ues. It then moves to the consideration of model validation
routines and various procedures for sweeping the parame-
ter space of models. Chapter 24 introduces the agent-based
urban modeling technique, followed by a case study to demon-
strate the utilities of this type of modeling technique for urban
growth and landscape change simulation. Chapter 25 discusses
the utilities of ecological modeling for predicting changes in
biodiversity in response to future urban development, empha-
sizing an integrated modeling environment that can predict
future land cover, estimate biodiversity, and link the out-
put from land cover change models into models estimating
biodiversity.
The last chapter in Part VI (Ch. 26) provides a comprehensive
review on the progress in urban modeling during the past 50 years,
emphasizing the underlying root metaphors embedded in various
modeling efforts. It considers the four major urban modeling
traditions, i.e., spatial morphology, social physics, social biology,
and spatial events. The chapter argues that the root metaphors
embedded in these traditions correspond to those in Pepper's
world hypotheses - the world as forms, machines, organism,
and arenas. The author believes that urban modeling progress
is actually a shift of metaphors used for conceptualizing cities,
and we need to pay attention on the process whereby meaning
is produced from metaphor to metaphor, rather than between
model and the world. In this regard, we should not only check
the validity of our models from the technical perspective but also
examine the driving conceptual metaphors deeply embedded in
the models. Only then can we weave the insights gained from
the urban modeling efforts with other urban narratives to have a
more sensible urban future.
research in order to address the multidisciplinary needs. It dis-
cusses some major benefits and possible challenges in urban
remote sensing research. Moreover, the chapter provides an
overview on the topic structure and a topic-by-topic preview.
While many exciting progresses have been made in urban
remote sensing, as discussed in this volume, there are several
major conceptual or technical issues that deserve further
attentions. First, while the development of urban remote sensing
has been largely technology driven, urban remote sensing
professionals should be equipped with not only solid technical
skills but also essentials of intellectual knowledge on the urban
environment, including relevant core concepts, theoretical
debates, and emerging methods; such knowledge can help
better plan and implement an urban remote sensing project, as
indicated by some recent literatures (e.g., Yang and Lo, 2003; Lo,
2004; Dietzel et al ., 2005) and several chapters included in this
volume (Ch. 2, Ch. 11, Ch. 12, and Ch. 26).
Second, although the issue of remote sensor data resolu-
tions has been extensively discussed in various remote sensing
literatures, there is no consistent guidance on the choice of
image resolutions. While the current literature overwhelmingly
focusses on the issue of image spatial resolution, recent stud-
ies suggest the importance of image spectral characteristics in
urban feature mapping (e.g., Herold et al ., 2004; Ch. 2, Ch. 4).
Continuing research is needed to help acquire good and suffi-
cient in situ data for building comprehensive spectral libraries
of different urban features that can help improve urban fea-
ture mapping accuracy and to develop practical guidance on
the choice of image resolutions that should not only consider
the spatial component but also the spectral, radiometric, and
temporal characteristics.
Third, an emerging research effort is needed to balance the
different needs by remote sensing and urban planning com-
munities. Within the remote sensing community, there is an
increasing research demand to develop improved methods and
techniques for working with medium-resolution images covering
spectrally heterogeneous areas (such as urban areas) and with
high-resolution images; some exciting developments in these
aspects have been reported in this volume. Within the urban and
regional planning community, on the other hand, there is an
urgent need to operationalize the advanced information extrac-
tion techniques or procedures that have been recently developed
by the remote sensing community so that they can be widely used
to support various urban applications.
Fourth, data and technological integration play a key role in
urban remote sensing research, particularly for urban socioeco-
nomic and environmental analyses and predictive modeling of
urban growth and landscape changes. More efforts are needed to
develop innovative data models used for representing dynamic
processes, to identify improved methods and techniques that can
be used to deal with data incompatibility in terms of parameter
measuring and sampling schemes, and to develop more realistic
predictive models that can be used to support various urban and
regional planning activities.
Finally, with a broad vision on urban remote sensing research,
this topic advocates an interdisciplinary approach to the study
of urban environments. We need to understand not only
urban structure and patterns but also the underlying processes,
consequences, and possible feedbacks. To this end, conceptu-
alizing cities as a complex ecosystem can be very helpful. The
Summary and concluding
remarks
This chapter discusses the rationale and motivation leading to
the development of remote sensing for urban studies emphasiz-
ing the need to adopt a broad vision on urban remote sensing
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