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URBAN MORPHOLOGY CHARACTERISATION TO INCLUDE IN A GIS FOR
CLIMATIC PURPOSES IN LISBON. DISCUSSION OF TWO DIFFERENT
METHODS
Hugo Vieira*, João Vasconcelos*
*Centro de Estudos Geográficos, Faculdade de Letras da Universidade de Lisboa, Lisbon, Portugal
Abstract
In order to characterize Lisbon’s thermal patterns, maps for the physical features of the city are needed. Two
methods to construct an “urban morphology” layer are discussed: one of them using the “sky view factor” and the
other an empirical parameter characterizing urban structure, identified as “urban density”. Each map will be
converted into two layers of a GIS, and later used as independent variables of a regression model, where surface
temperature will be the dependent one, in order to validate the two methods.
Key words: urban morphology, urban density, sky view factor, GIS.
1. INTRODUCTION
For ongoing studies on Lisbon thermal patterns, in the frame of the project “CLIMLIS – Prescription of climatic
principles in urban planning - application to Lisbon” (Fundação Ciência e Tecnologia, ref.
POCTI/34683/GEO/2000), maps of the physical features of the city are needed to achieve the main goal
proposed by the project, that is to obtain climatic guidelines that can be easily understood and used by urban
planners in future urban plans for the city.
The continuous modifications in the surface geometry caused by the construction of buildings are usually
appointed as one of the main causes for the specificity of urban climate. The influence of the urban geometry in
processes such as the trapping of both incoming solar and outgoing long-wave radiation, and the reduction of the
turbulent transport due to wind shelter and the amount of the anthropogenic heat released, is largely well-known
(Oke, 1987). Therefore, the urban morphology plays an important role on the variation of air and street surface
temperature in the canopy layer within urban areas. Among other things, like increasing the aerodynamic
roughness and changing the wind fields in the constructed environment, these changes cause the reduction of the
sky view factor, changing the conditions of penetration, absorption and emission of the solar radiation at the urban
canyon level (Oke, 1988; Swaid, 1993). Therefore, and as the urban structure can be considered the main cause
for the formation of the urban heat island, characterizing the sky view factor and urban density is a way to
parameterize its intensity (Oke, 1981, 1991; Park, 1987; Sakakibara, 1996). Therefore, the elaboration of a map
containing information about the urban morphology of a city is of great importance to achieve the pretended
climatic guidelines.
In this paper, two methods to construct an “urban morphology” layer for the entire city are discussed: one of them
using the “sky view factor” and the other an empirical parameter characterizing urban structure, named “urban
density”. After comparing the results of the two methods and in order to validate them, each parameter will be
later converted into two layers of a GIS, which will be used as independent variables of a regression model,
where surface temperature will be the dependent one.
2. METHOD
2.1. Sky view factor
The sky view factor (SVF) expresses the ratio between the radiation received by a planar surface and that from
the entire hemispheric radiating environment (Watson and Johnson, 1987) and is mainly used in forest, road and
urban climatology to characterize radiative properties (Holmer et al., 2001). If the totality of the hemisphere is
visible, the SVF equals 1, hence the visual obstruction of the sky is null and the exposition to direct solar
radiation, in the absence of clouds, has the maximum possible duration during the day. When there are obstacles
that occult the hemisphere, the SVF value decreases (reaching the value of zero means that the entirety of the
sky is not visible) and depending on the position of the obstacles, the sun can be concealed in its trajectory.
In urban areas, the value of the SVF is primary dependent of the strong presence of buildings and its geometry.
Increasing the occultation of the hemisphere leads to lower values of the SVF, which will have an important effect
*
Corresponding author address: Hugo Vieira, Centro de Estudos Geográficos, Faculdade de Letras da
Universidade de Lisboa, Alameda das Universidades, 1600-214 Lisboa, Portugal; e-mail:
[email protected]
on the surface radiative budget. The geometry of an urban canyon is therefore important to the production of the
urban heat island, because it regulates the absorption and the emission of heat, modifying the air temperature
near the ground as well as the surface temperature (Oke, 1981, 1988; Bärring et al., 1985; Eliasson, 1990/91,
1992, 1996). Hence, the use of a parameter like the SVF is of great importance when studying the urban climate
of a city, but it is mainly applied in small scale studies in restricted areas, and not always as a parameter to
characterize urban morphology of an entire city.
The most popular way to estimate the SVF is by using the fish-eye images concept introduced by Steyn (1980)
and later developed by Holmer (1992), used as a complement of another method, where the SVF is calculated
geometrically using the angles measured to the tops and sides of the buildings (Watson and Johnson, 1987). As
an alternative, the RAYMAN software, developed by the Institute of Meteorology of the University of Freiburg
(Matzarakis, 2000; available to download at www.mif.uni-freiburg.de/rayman) allows the user to graphically
represent the celestial hemisphere and the obstacles, using a vector system where buildings and trees can be
drawn around a certain place of the surface. These obstacles are later projected in a solar diagram; the SVF is
calculated SVF and the trajectory of the sun as well its occultation during the day.
For the layer “sky view factor” a 500m by 500m grid was made for the city of Lisbon and the value of the SVF was
calculated for each cell, in a total amount of 330, using the RAYMAN software. The main problem encountered
was, where in the cell or in which street should the SVF be calculated? Having a prior knowledge of the city, one
should try to find a street that typified the entire cell. Knowing the exact place and using a CAD file supplied by the
city hall (where all the city buildings, by the year of 1999, are represented), the distances and heights of every
single obstacle that obstructed the hemisphere around the chosen place was measured, and after inserting it in
the software, the result was a 3D image reflected in a solar diagram and a single value of SVF. This procedure
was repeated to every other cell of the grid, except for the area occupied by the Monsanto City Park.
2.2. Urban density
Usually, the urban structure parameter is expressed as a measure of urban canyon width and height. For the
classification of Lisbon’s urban geometry, the “urban density” layer, the information about the percentage of area
occupied by constructions (PAOC) and the mean building height (MBH) was crossed. The same CAD file supplied
by the city hall was used to extract the necessary information, as well as another one, where only the blocks were
displayed. For this layer, a 250m by 250m grid was made and the value for each cell, except for the City Park,
was calculated using a GIS. The urban density layer resulted form the simple matrix, as shown on table 1:
0 m - 3 m (10)
3 m - 12 m (20)
12 m - 18 m (30)
higher than 18 m (40)
0% - 25% (1) 25% - 50% (2) 50% - 75% (3)
11
12
13
21
22
23
31
32
33
41
42
43
Table 1: Classification of the urban density
All pixels with a low PAOC (with less then 25% of its surface occupied by constructions) and low MBH (lower then
3 meters) formed the first class of the urban density; pixels with an occupied area between 25% and 50% and
with an MBH between 3 and 18 meters formed the second class; the third class included all the pixels with the
PAOC between 50% and 75% and with the MBH from 3 to 18 meters; and finally, all pixels with PAOC higher
then 18 meters and with MBH higher than 25% formed the fourth class. One should notice that there is no pixel
with a percentage of occupation higher than 75%.
One problem encountered was that despite the source of the information being the same, there was a
chronological gap between the two files. Therefore, in some areas there was information about the PAOC but
none about the MBH and vice versa. Therefore, the lack of information in one of the layers led to the exclusion of
that particular pixel in the urban density layer.
2.3 Urban morphology – Sky view factor and urban density
Both the SVF and the urban density layers were compared by means of the multiple regression equation using
the IDRISI software, so that it was possible to know if there was a strong correlation between the two. For this
equation, the logarithm of the SVF was used as the independent variable and the urban density as the dependent
variable. For a better understanding of the relation between both layers, several sample areas were selected.
2.4 Surface temperature
In order to validate the two layers of the urban morphology, both will be used as independent variables of a
regression model, where surface temperature will be the dependent one. The same sample areas were used
once again for the same purpose. The surface temperatures were estimated from the Landsat thermal band by
Lopes et al. (2001), who produced one thermal surface map at satellite overpass time, about 10 a.m., for the 19th
of August of 1994 (fig.2). In order to proceed with the regression, a mask eliminating all the unnecessary areas
from that image was made (the Tagus River, the Monsanto City Park and the surrounding municipalities).
3. RESULTS AND DISCUSSION
The sky view factor and the urban density layers are presented in figure 1. The city center is clearly the area of
the city where the urban density is high and the SVF is low (e.g. greater obstruction of the hemisphere). These
same values can be found as well to the North and to the West of the city center. As for each layer separately, the
SVF has lower values to the North / NE and also to the South of Monsanto, where for the urban density some
isolated pixels with high values can be found to the north of Monsanto and along the coastline.
Fig.1 – Sky view factor layer (left) and urban density (right)
The outcome for the regression statistics for the entire city for both methods obtained an R square value of 0.56.
The nature of the information; the different size of the pixels; the high amount of pixels covering a large area; the
importance of the city relief (especially where the values for the PAOC and MBH are low, which not always
corresponds to a small obstruction of the hemisphere, because the obstruction can be increased mainly due to
the relief, not taken into account by the urban density layer), may be the reasons for a not higher explanation in
the regression.
As for the sample areas (fig. 2), the better result was obtained in area A (corresponding to a section of the city
center), where R square was 0.75 and with a Pearson correlation coefficient with a high level of significance for
an error of 5%. Even though the R squares of the regressions for the areas B and C were not as high as area A
(0.46 and 0.59 respectively), the level of significance was still acceptable.
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
Airport
C
B
Monsanto
A
City Center
m
m
2000.00
2000.00
Tagus River
th
Fig. 2 – Sample areas (left); surface temperature derived from Landsat image, 10 August 1994 (right).
As for the validation with the surface temperatures, the results were not very significant. The regression applied to
the entire city did not reveal a clear relation between the independent variables and the temperature. As for the
sample areas, only area A could express this relation with a high level of significance: when using the SVF, the R
square is 0.84 and 0.77 for the urban density. On the other hand, the regression for area B and C was not
significant.
The reasons for the result obtained for the entire city are the same as explained before for the regression
between the SVF and the urban density, plus that the surface temperature variations observed during the morning
are not the same as the ones observed during the night (during the day, it is expected that open areas have
higher temperatures then highly constructed areas, leading to a more homogenous temperature distribution in the
city) and that the Landsat image is from 1994 when the city hall information is from 1999. These are the same
reasons for not having good results for sample areas B and C, since the variation found in the urban morphology
do not correspond to a variation in the surface temperature. Nevertheless, the good results obtained in area A
might be associated with the proximity of the Tagus River (that may justify a higher variation in the observed
temperature); and that the urban morphology in this area is more homogeneous, even though having a wide
range of SVF values (therefore the single SVF value is more likely to be representative of the 500 by 500m cell).
4. CONCLUSION
The main purpose for this research was to verify whether both methods described (the SVF and the urban
density) could be used to characterize the urban morphology, therefore influencing the surface temperature within
the city. Even though the results for the entire city were not very satisfactory, the good results obtained in the
sample areas, mainly in the city center, lead to a better understanding of the factors involved in the relation
between the two different methods proposed.
This relation can be further explained in upcoming researches by improving the nature of the information to be
used; by using more relevant surface temperature information (e.g. thermography during a winter night); by
decreasing the pixels dimension and by using the same grid for each layer.
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