229
The remote sensing method required less time to collect the data and yielded
useful information to describe and map the phenomenon. Depending on the information, computers and technology available to individual local administrations, this
method could be applied easily and quickly to the whole of the Veneto region.
Remote sensing analyses require LiDAR data and high resolution orthophotos
(0.2–0.5 m per pixel), preferably including the infrared band, for the entire administrative area.
For each selected area, this methodology allowed us to find out the sqm of vegetation (divided by height), the ratio between permeable and impermeable surface,
the incident solar irradiation, and the sky view factor (Berdahl and Bretz 1997).
Technically, the analysis involved the creation of three-dimensional digital models
of the terrain, DSM (Digital Surface Models) and DTM (Digital Terrain Models),
which made it possible to identify and inventory the composition of urban surfaces.
By adding the DEM (Digital Elevation Models) obtained by processing the LiDAR
data with the multispectral orthophotos, we also got to an automatic breakdown of
the horizontal surfaces of the city by type and height, resulting in an atlas of surfaces
composed of green spaces, with their respective heights, and impermeable spaces
(buildings, roads, parking lots).
Next, we used software like LAStools, Saga Gis and eCognition to produce sky
view factor and solar irradiation maps, which most importantly provide essential
information to determine the specific areas that require intervention, in addition to
which interventions should be performed to mitigate the UHI phenomenon and
adapt the urban environment to climate changes.
The key strength of these innovative analysis techniques is that they can be replicated over very extensive urban areas, whose level of detail would require months
to obtain with traditional topographical detection methods. However, we must realize that not all areas are equipped with LiDAR or similar detection devices, which
means this methodology is still used in limited areas despite the fact that it is innovative and efficient (Figs. 8.3, 8.4, 8.5, 8.6, 8.7, 8.8 and 8.9).
The collected information, converted into vector format, can be queried using
height and covering type data. Breaking down the city in all its three dimensions, we
can identify the areas that are most vulnerable to heat waves, and also adapt portions
of the city to the extreme weather phenomena, suggesting some possible strategies
to achieve that goal. So as to test and evaluate the efficacy of the interventions, we
then proceeded to build four different transformation scenarios of the area under
study.
The four scenarios, and their specific interventions, which resulted from the integration of accurate temperature readings and the indicators research, were then processed using the ENVI-met software, which simulates air temperature changes
based on the physical changes proposed within a selected area. It can therefore
verify and indicate mitigation strategies for the UHI phenomenon by showing the
results of the proposed interventions. For example, this simulator can show what
benefits would be derived from adding trees to an actual area or modifying the
albedo of some of its surfaces. ENVI-met can not only verify the effectiveness of an
intervention but also the optimal location for its application.
8 Mitigation of and Adaptation to UHI Phenomena: The Padua Case Study
The remote sensing method required less time to collect the data and yielded
useful information to describe and map the phenomenon. Depending on the information, computers and technology available to individual local administrations, this
method could be applied easily and quickly to the whole of the Veneto region.
Remote sensing analyses require LiDAR data and high resolution orthophotos
(0.2–0.5 m per pixel), preferably including the infrared band, for the entire administrative area.
For each selected area, this methodology allowed us to find out the sqm of vegetation (divided by height), the ratio between permeable and impermeable surface,
the incident solar irradiation, and the sky view factor (Berdahl and Bretz 1997).
Technically, the analysis involved the creation of three-dimensional digital models
of the terrain, DSM (Digital Surface Models) and DTM (Digital Terrain Models),
which made it possible to identify and inventory the composition of urban surfaces.
By adding the DEM (Digital Elevation Models) obtained by processing the LiDAR
data with the multispectral orthophotos, we also got to an automatic breakdown of
the horizontal surfaces of the city by type and height, resulting in an atlas of surfaces
composed of green spaces, with their respective heights, and impermeable spaces
(buildings, roads, parking lots).
Next, we used software like LAStools, Saga Gis and eCognition to produce sky
view factor and solar irradiation maps, which most importantly provide essential
information to determine the specific areas that require intervention, in addition to
which interventions should be performed to mitigate the UHI phenomenon and
adapt the urban environment to climate changes.
The key strength of these innovative analysis techniques is that they can be replicated over very extensive urban areas, whose level of detail would require months
to obtain with traditional topographical detection methods. However, we must realize that not all areas are equipped with LiDAR or similar detection devices, which
means this methodology is still used in limited areas despite the fact that it is innovative and efficient (Figs. 8.3, 8.4, 8.5, 8.6, 8.7, 8.8 and 8.9).
The collected information, converted into vector format, can be queried using
height and covering type data. Breaking down the city in all its three dimensions, we
can identify the areas that are most vulnerable to heat waves, and also adapt portions
of the city to the extreme weather phenomena, suggesting some possible strategies
to achieve that goal. So as to test and evaluate the efficacy of the interventions, we
then proceeded to build four different transformation scenarios of the area under
study.
The four scenarios, and their specific interventions, which resulted from the integration of accurate temperature readings and the indicators research, were then processed using the ENVI-met software, which simulates air temperature changes
based on the physical changes proposed within a selected area. It can therefore
verify and indicate mitigation strategies for the UHI phenomenon by showing the
results of the proposed interventions. For example, this simulator can show what
benefits would be derived from adding trees to an actual area or modifying the
albedo of some of its surfaces. ENVI-met can not only verify the effectiveness of an
intervention but also the optimal location for its application.
8 Mitigation of and Adaptation to UHI Phenomena: The Padua Case Study