Fusion of Airborne Hyperspectral and LiDAR Remote Sensing Data to Study the Thermal Characteristics of Urban Environments - Computational Approaches for Urban Environments - page 278

from the simulations, the spatial models of each test site were slightly simplified
(cf. Figs. 11.5 band 11.6 b). Another point is that version 4 of ENVI-met does
not only require the input of certain meteorological framework conditions for a
proper model initialization but it also allows for constraining the diurnal profile
of specific climate variables during each simulation. This means that it is possible
to fit the model to known, temporally variable climatic conditions for a given date
and region of modeling. In the case of this study, the absolute values of modeled
parameters like air temperature and relative humidity were forced on the basis of
hourly measurements taken from a nearby climate station (John Dunn Helistop,
Houston, Texas; Lott et al. 2001 ). Based on these indications, it is reasonable to
assume that the obtained modeling results and the conclusions drawn from the
relative comparisons made are valid.
Despite some remaining uncertainties, the above examples clearly demonstrate
that classification-based modeling holds a large potential to capture, analyze, mon-
itor, and predict the urban microclimate under varying conditions. The simulations
enable the identification of hot spot areas that are exposed to increased heat stress
and decreased thermal comfort at day and night. In addition, dedicated what-
if scenarios facilitate the determination of critical land cover configurations that
should be avoided in urban design. The obtained mapping and modeling products
therefore represent promising sources of information which decision makers can
potentially incorporate into various urban planning activities to foster effective
management and to safeguard sustainable urban development.

11.6 Conclusions

Microclimate modeling is a powerful tool to study 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 that are usually
hard to obtain by traditional field surveys. This work aimed 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 data-driven microclimate modeling, two case studies were presented
for selected test sites in the City of Houston, Texas. The results of this study
highlight that a synergistic combination of hyperspectral and LiDAR data enables
reliable mapping of some of the key input parameters required for urban microcli-
mate modeling. Moreover, classification-based microclimate simulations can reveal
the thermal properties of urban neighborhoods under varying conditions and, thus,
facilitate the identification of hot spot areas and critical land cover configurations
which should be avoided in urban design. Given the ever-increasing availability
of hyperspectral and LiDAR data (e.g., NASA Jet Propulsion Laboratory 2014 ;
Cook et al. 2013 ; LiDAR Online 2014 ; OpenTopography 2014 ), it is concluded that
spatially explicit microclimate modeling should be an integral part of urban planning
to enable making more informed decisions about the future of urban environments.