Geoscience Reference
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ever-growing percentage of global urban population comes at a high price. Many
cities all over the world are sprawling rapidly, and their spread is associated with
alarming rates of land consumption (Scalenghe and Marsan
2009
;Angeletal.
2011
; Taubenböck et al.
2012
). As a result of this process, the natural “skin” of our
planet is being successively replaced by man-made surfaces with distinct thermal
properties (Sobrino et al.
2012
).
It is generally agreed that urban areas and urban expansion affect the climate at
the local scale (Oke
1973
; Landsberg
1981
;Oke
1982
;Arnfield
2003
; Kalnay and
Cai
2003
). A very prominent example of this local climate change is a phenomenon
called the urban heat island (UHI). It refers to the observation that cities often feature
higher air and surface temperatures than their surroundings, especially at night
(Howard
1833
;Oke
1973
,
1982
; Voogt
2002
). The implications of the UHI effect are
diverse and range from changes in precipitation patterns (Changnon
1992
;Lowry
1998
; Yuan and Bauer
2007
) to raises in air pollution (Voogt
2002
; Yuan and Bauer
2007
), water use (Guhathakurta and Gober
2007
), energy consumption (Voogt
2002
;
Yuan and Bauer
2007
; Ewing and Rong
2008
), and mortality rates (Curriero et al.
2002
; Johnson and Wilson
2009
). Considering that the UHI intensity is expected to
increase in the future (McCarthy et al.
2010
) while, at the same time, more and
more people will be exposed to the living conditions in the cities of tomorrow
(United Nations
2008
), there is an urgent need for up-to-date, spatially explicit
urban climatological information which city planners can incorporate into decision-
making processes to foster effective management and to safeguard sustainable urban
development.
Microclimate modeling is a powerful tool to analyze the thermal characteristics
of urban environments at the local scale. However, it requires high spatial resolution,
area-wide information on urban surface materials, and object heights. Since these
information are still lacking in many urban areas, hypothetical scenarios often
represent the only way to parameterize microclimate models unless extensive field
surveys are up for debate. As an alternative way of data collection, hyperspectral
and light detection and ranging (LiDAR) remote sensing technologies are becoming
increasingly available (e.g., NASA Jet Propulsion Laboratory
2014
; Cook et al.
2013
; LiDAR Online
2014
; OpenTopography
2014
) and offer unique capabilities for
urban surface material and object height mapping. Thus, they hold a great potential
for microclimate modeling applications. This study aims at the derivation of an
urban surface material map to parameterize a 3D numerical microclimate model by
fusion of airborne hyperspectral and LiDAR remote sensing data. To demonstrate
the potential of classification-based microclimate modeling, two case studies are
presented for selected test sites in the City of Houston, Texas. This chapter is
structured as follows. Section
11.2
provides a brief review of the scientific literature
relatedtothisstudy.InSects.
11.3
and
11.4
, the data and methods used to achieve the
above goal are presented. Section
11.5
is dedicated to the description and discussion
of the study results, and the section “Conclusions” summarizes the findings of this
investigation.
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