ENERGY IN THE URBAN ENVIRONMENT: USE OF TERRA/ASTER IMAGERY AS A TOOL IN URBAN PLANNING N. CHRYSOULAKIS*

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1 ENERGY IN THE URBAN ENVIRONMENT: USE OF TERRA/ASTER IMAGERY AS A TOOL IN URBAN PLANNING N. CHRYSOULAKIS* Foundation for Research and Technology Hellas, Institute of Applied and Computational Mathematics, Regional Analysis Division, FORTH - IACM, Vassilika Vouton, P.O. Box 1527, GR-71110, Heraklion, Crete, Greece. (tel , fax , zedd2@iacm.forth.gr) Abstract. In this study Terra/ASTER imagery was analysed together with in-situ spatial data to examine the potential of multi-spectral remote sensing to support urban planning. The potential of ASTER imagery to support energy budget estimation was also examined by defining and mapping some microclimatic parameters for the centre of the city of Athens. Images in visible, near infrared and thermal infrared areas of the electromagnetic spectrum were processed to define the urban land cover and topographic characteristics as well as to estimate the spatial distributions of vegetation, visible reflected radiation and brightness temperature. It was founded that ASTER muli-spectral imagery enables a better understanding of energy aspects, their causes and effects, providing an important addition to conventional methods of monitoring the urban environment. INTRODUCTION It is well known that sustainable urban planning, which gives consideration to the environment and to the quality of life of the inhabitants, is a relatively new practice. Many cities around the world were not built as the result of overall planning but rather like a cancerous spread of the built up area outwards, from an old, sometimes ancient, nucleus. Among the sins of the city builders one may speak of high rise buildings along sea-fronts, narrow streets often perpendicular to the direction of the prevailing winds, insufficient or non-existent building installations and others. Furthermore some industries and the ever growing volume of motor vehicles entering and moving in the urban areas are direct source of heating through the thermal emissions of machines, furnaces and engines, as well as indirect sources by means of polluting gasses and particles released into the atmosphere. Thus, it becomes increasingly important to study urban climatic environments and to apply this knowledge to improve people s environment in cities. Urbanization leads to a very high increase of energy use. Increased urban temperatures have a direct effect on the energy consumption of buildings especially during the summer period. It is reported Key Words: Multi-spectral Remote Sensing, Urban Monitoring, Urban Planning, Energy Budget.

2 that the rate of change in energy use is twice the rate of change in urbanization. In fact it is found that higher urban temperatures increase the electricity demand for cooling and the production of carbon dioxide and other pollutants (Santamouris et al. 2000). The urban surfaces and atmosphere interactions are governed by the surface heat fluxes. The distribution of these fluxes is drastically modified by the urbanization. Main contributing factors are: changes in physical characteristics of the surface (albedo, thermal capacity, heat conductivity) owing to the surface replacement of vegetation by asphalt and concrete; the decrease of surface moisture available for evapotranspiration; changes in the relative fluxes and in the near surface flow, owing to the complicated geometry of streets and tall buildings and anthropogenic heat (Dousset and Gourmelon, 2001). Cooling in urban areas is highly affected by the temperature distribution in the city. Effective design of passively cooled urban buildings requires a good understanding of the urban climate characteristics and in particular on the temperature distribution. Satellite remote sensing is a relative new tool for monitoring the urban environment, enabling a better understanding of energy aspects, their causes and effects. Use of satellite imagery allows for instantaneous observation of large areas providing an important addition to conventional methods of monitoring the urban environment. Satellite sensors observing in the visible area of the electromagnetic spectrum with spatial resolution around 10 m are capable for monitoring the urban land cover characteristics. Sensors observing in the thermal infrared with spatial resolution better than 100 m may be used to produce thermal maps of urban areas. The 100 m spatial resolution enables work with average temperature (which include thermal emissions of buildings, streets, vegetation, etc), thus giving an overview of the urban area, which may be characterized by warmer and cooler zones caused by different types of building, roads, parks, bare ground or even archaeological sites. The main objective of this study is to present the potential of multi-spectral remote sensing to support urban planning by estimating the spatial distribution and intensities of physical parameters with relation to the urban energy budget. Within this framework the objectives are: Physical classification of the urban land cover and the heat sources in the study area; Estimation and mapping of radiation budget parameters such as topographic relief, densely built areas, roads, vegetated areas, as well as reflected radiation and temperature spatial distributions in a city centre; Study of the effects of different types of built-up area and different land uses on the urban radiation budget;

3 DATA AND METHODOLOGY The data used in this study are a Terra/ASTER image of October 30, 2001 (09.27 LST) over the region of Attica (60 x 60 Km scene) and the vectors of the road network and the spatial distribution of population for the city of Athens which have been derived from in-situ spatial information. A Transverse Mercator projection was applied (Projection System: Hellenic Geodetic Reference System 87 - HGRS87; Reference Ellipsoid: GRS80) in order to have all data in the same cartographic projection system. The Advanced Spaceborne Thermal Emission and Relfection Radiometer (ASTER) is an advanced multispectral imager that was launched on board NASA s Terra spacecraft in December, ASTER covers a wide spectral region with 14 bands from the visible to the thermal infrared with high spatial, spectral and radiometric resolution. ASTER consists of three separate instruments subsystems, each operating in a different spectral region, using separate optical system. These subsystems are the Visible and Near Infrared (VNIR), the Short Wave Infrared (SWIR) and the thermal infrared (TIR). The spatial resolution varies with wavelength: 15 m in the VNIR, 30 m in the SWIR and 90 m in the TIR. The VNIR subsystem consists of two telescopes one nadir looking with a there band detector (Channels 1, 2 and 3N) and the other backward looking (27.7 off-nadir) with a single band detector (Channel 3B). Because the nadir looking and the backward looking VNIR instrument can obtain data of the same area from two different angles, therefore stereo ASTER data can be used to produce Digital Elevation Models (DEMs). The spectral ranges of ASTER VNIR Channels are (Abrams and Hook 2001): Channel 1: µm; Channel 2: µm; Channel 3N: µm; Channel 3B: µm. The TIR subsystem consists of one telescope with a five band detector (Channels 10, 11, 12, 13, and 14). Only Channel 10 ( µm) is used in this study in order to estimate the brightness temperature spatial distribution for the centre of the city of Athens. Athens, the capital of Greece concentrates about half of the population of the Country. It is located in a basin, which is bounded by the mountains Egaleo to the West, Parnes to the north and Imitos to the east. To the south the basin is bounded by the Saronikos Gulf. The area under study is the centre of the metropolitan city of Athens. It is presented in Figure 1 (ASTER Channel 2), where the vegetated areas and the main road network are depicted in dark tones. Figure 2 presents the spatial distribution of population in the area of concern as has been derived from in situ-data. The sub-areas in which the population concentration is maximum have been separated and labelled in this Figure (polygons A - I). These sub-areas represent the most densely built areas which are of great importance for the definition of the energy balance in urban environment. The relative positions of these polygons as well as the topography of the whole area are also very important when environmental parameters should be estimated.

4 Terrain elevation data derived by applying photogrammetric processes to overlapping stereo pairs from ASTER. Channels 3N and 3B were used for the production of a DEM for the area of concern. Because geometric deviation between adjacent ASTER images were expected to be less than a single pixel, DEM was directly produced by applying a digital stereo correlation to calculate parallax differences. The net balance between the solar gains and the heat loss by emitted long wave radiation determines the thermal balance of urban areas. Temperature distribution in urban areas is highly affected by the urban radiation budget. Solar radiation incident on the urban surfaces is absorbed and then transformed to sensible heat. Most of the solar radiation impinges on roofs, and the vertical walls of the buildings, and only a relative small part reaches the ground level. Walls, roofs and the ground emit long wave radiation to the sky. The intensity of the emitted radiation depends to the view factor of the surface regarding the sky. Although the entire energy budget can not be estimated solely from remote sensed data, several important components can be derived from the data discussed here. The surface reflectance can be estimated from the visible ASTER Channel 1. The index of vegetation can be calculated from the normalized difference of the visible and near infrared channels (ASTER Channels 2 and 3N). The calibrated brightness temperature can be derived from the thermal infrared ASTER channel 10. Vegetation has various effects on urban environment. In order to dissipate the sensible heat from solar radiation and warm air, the vegetation transpire moisture. This phenomenon is called evapotranspiration and represents the main part of the thermal balance of vegetation. During the day, two-thirds of the net all wave radiation flux is used to evaporate water, the last third is dissipated in the sensible heat flux since the net energy storage is very small. Urban vegetation (mainly parks) is the mail source of latent heat in urban environment. For these reasons the relative position of vegetated areas in urban web must be taken into account in urban planning studies. In this study, the calculated Normalized Difference Vegetation Index (NDVI) was used for the classification of vegetated areas and finally for urban vegetation mapping. NDVI was calculated from Digital Numbers (DN) of ASTER Channels 2 and 3N using the formula: Channel 3N Channel 2 NDVI = (1) Channel 3N + Channel 2 An experimentally derived threshold was applied to NDVI values in order to separate pixels corresponding to vegetation in the original ASTER image. Following, a level slicing and pseudocolouring of these pixels took place to characterize and map urban vegetation. Surface emissivity is a measure of the inherent efficiency of the surface in converting heat energy into radiant energy above the surface. It depends on the composition, roughness and moisture content of the surface and on the observation conditions (wavelength, pixel resolution and

5 observation angle). Surface reflectance for each ASTER VNIR Channels can be obtained by applying an atmospheric correction to radiances reported by the ASTER sensor. The atmospheric correction removes effects due to changes in satellite-sun geometry and atmospheric conditions. The actual atmospheric correction is the process of retrieving the surface radiance and surface reflectance from the satellite radiances. To do this, the scattering and absorbing properties of the atmosphere are determined. Columnar amounts of absorbing gases are used to compute the sun-tosurface-to-satellite gaseous transmittance. The satellite radiances are divided by these transmittances to determine a satellite radiance for an atmosphere which does not contain gaseous absorption. The scattering optical depths, single scatter albedo, and size distribution are used along with the solar and view angles. In this study the spatial variations of surface reflectance were estimated on the basis of Radiance at the Sensor for each pixel of the satellite image which was calculated using the DN of the visible ASTER Channel 1. To convert from DN to Radiance at the Sensor, the unit conversion coefficients (defined as radiance per 1 DN) are used. Spectral Radiance is expressed in unit of W/(m 2 sr m). The radiance can be obtained from DN values as follows: Radiance at the Sensor = ( DN 1) UCC (2) where, UCC is the Unit Conversion Coefficient for each ASTER Channel in W/(m 2 sr m)/dn. For Channel 1 UCC values are for high gain, for normal gain and 2.25 for low gain (Abrams and Hook 2001). Estimating surface temperature through satellite thermal infrared data is accomplished at the precondition that the variability of the emission coefficients as well as atmospheric absorption are taken into account (Price 1984, Becker 1987, Becker and Li 1990, Vidal 1991, Sobrino et al. 1991, Kerr et al. 1992, Ottle and Vidal-Madjar 1992, Coll et al. 1994, Prata 1993, Prata et al. 1995, Casselles et al. 1997, Chrysoulakis and Cartalis 2002). Brightness Temperature (BT) differs from actual surface temperature due to several effects: partial absorption of blackbody radiation by the atmosphere; land surface emissivity being less than 1 and spatially and spectrally variable (Becker 1987); sub-pixel variations of surface temperature being averaged non-linearly due through Plank s law (Dousset et al. 1993); urban geometry trapping radiated and incident energy in urban canyon, effectively increasing the pixel average emissivity; non-vertical satellite viewing angles biasing towards vertical wall and hiding horizontal surfaces (Voot and Oke 1997). In this study, the spatial distribution of BT was calculated for the area of concern in order to show the potential of ASTER sensor for urban planning. ASTER Channel 10 was used to compute temperature as a function of spectral sensor radiance. Because Plank s equation cannot be inverted

6 explicitly the central wavelength method was used as the most straightforward approximation (Alley and Jentoft-Nilsen 1999). Under this approximation, BT was calculated as follows: c2 BT = c1 λc ln ( 5 λ πl c s (3) + 1) where, c 1 is the first radiation constant ( x W m 3 m -1 ), c 2 is the second radiation constant ( m K), λ c is the central wavelength for the ASTER Channel 10 (m), L s is the radiance observed by the sensor (W m -2 sr -1 m -1 ). L s was calculated for each pixel using the DN of the thermal infrared ASTER Channel 10. To convert from DN to Radiance at the Sensor the equation (2) was used with UCC for ASTER Channel 10 (6.822x10-3 W/(m 2 sr m)/dn). Equation (3) is accurate for the observed radiance that yields the standard temperature, but it becomes increasingly inaccurate for temperatures farther away from the standard temperature (Alley and Jentoft-Nilsen 1999). RESULTS Figure 3 presents the DEM for the centre of Athens as it has been derived from ASTER Channels 3N and 3B digital stereo correlation. The horizontal spatial resolution of this DEM has been set to 30 x 30 m, whereas its vertical resolution has been set to 10 m. 100 m contour lines are also shown. Imitos mountain (lower right corner), as well as Likabetus (centre right) and Tourkovounia (upper right corner) hills are clearly depicted. The spatial distribution of vegetation at the area of concern is presented in Figure 4 (white areas). As it has been already noticed, this urban vegetation mapping was based on NDVI values derived from ASTER Channels 2 and 3N. The main road network has been also added in Figure 4. Central Athens Park as well as National Garden are clearly depicted (areas 1 and 2 respectively). The massive vegetation area at the lower right park corresponds to Imitos mountain. Figure 5 presents the spatial distribution of Radiance at the Sensor for densely built areas of the centre of Athens, which was calculated using the DN values of ASTER Channel 1. The densely built areas were separated by applying a digital mask to the original image. This mask was produced with the use of ASTER derived DEM and NDVI in combination with the vectors of the road network and the spatial distribution of population. Thereby, during this special digital filtering process, the pixels corresponded to mountains, hills, vegetated areas, industrial or commercial areas and road network were set to zero value in the final image.

7 Visual inspection and comparison of Figures 2, 4 and 5 indicates that Radiance at the Sensor obtains minimum values ( 100 W/m 2 sr um) around the vegetated areas (i.e. upper left corner). This may happen for two reasons: The first reason is that these areas are medium densely built areas with lower albedo in Channel 1. The second reason is that some pixels containing vegetation have not been masked (the spatial resolution of ASTER Channel 1 is 15 m, thus sub-pixel isolated vegetated areas can not be detected). High densely built sub-areas (polygons A - I in Figure 2) obtain also low radiance values ( 140 W/m 2 sr um). This happens because in these areas most of the solar radiation impinges on roofs, and the vertical walls of the buildings, and only a relative small part reaches the ground level. Thus, these areas obtain greater albedo than areas in which asphalt or bare soil dominate. Therefore, taking also into account the ASTER image acquisition time (09:27 UTC), lower temperatures are expected in these areas than their surroundings medium densely built, commercial or industrial areas. Figure 6 presents the spatial distribution of BT for the centre of Athens, which was calculated using the DN values of ASTER Channel 10. The main road network has been also added. It is obvious in Figure 6 that the central commercial area, as well as the extended industrial area (left) obtain greater BT values than the densely built areas (polygons A I in Figure 2). As it has already explained this fact may be attributed to: the low solar height at the time of the satellite pass (09:27 LST); the thermal capacity of the buildings which retards their warming as compared to the respective warming of the industrial areas and the areas covered by bare soil; the mountains which surround Athens which acts as a shield to incoming solar radiation at the time of the satellite pass. The role of thermal capacity and absorptivity of asphalt can be seen in Figure 6 in medium densely built areas where the incoming solar radiation is most probably to reach the ground level. Thus, for example in areas X, Y and Z the BT values along the main roads are greater than the respective values in surrounding areas. The role of mountains and hills, which act as a shield to incoming solar radiation, can be seen in Figure 7 where the 100 m contour lines have been superimposed to the BT spatial distribution. The solar azimuth was about 110 during the ASTER image acquisition time. Thus, for example the BT values along the sunward slope of Imitos hill (area which is bounded by the circle in Figure 7) are greater that the respective values of the opposite slope.

8 CONCLUSIONS In this study, the potential of multi-spectral remote sensing to support urban planning was presented by estimating the spatial distribution and intensities of physical parameters with relation to the urban radiation budget. Sustainable urban planning demands the study urban climatic environments and the application of this knowledge to improve the quality of life of the inhabitants. ASTER mulispectral imagery enables a better understanding of energy aspects, their causes and effects providing an important addition to conventional methods of monitoring the urban environment. The optical characteristics of materials used in urban environments and especially the albedo to solar radiation and emissivity to long wave radiation have a very important impact to the urban energy balance. It was founded that ASTER images in the visible and thermal infrared areas of the electromagnetic spectrum can provide considerable information for the microclimatic conditions of the Athens area, whereas the perspective images in the visible and near infrared parts of spectrum allow the definition of land cover in terms of urban, industrial and vegetated areas. Although the entire energy budget can not be estimated solely from ASTER data, some important components which were derived from these data were presented here. Products as spatial distribution of BT are important for studying microclimate in urban areas and its variations, for defining the energy budget in a heavily populated areas such Athens, for assisting energy demand and management studies and for examining the links between the prevailing microclimatic conditions and the air pollution levels. The various thematic layers which were presented in this study can be integrated with other satellite or in-situ derived information in a GIS platform capable to support sustainable urban planning. Such a GIS platform will have the potential to be an effective support tool for urban planners and policy makers for taking energy aspects into consideration as an important factor in determining the quality of life. Finally, it may be used to assist researchers and engineers acting as a starting point for the definition of the real energy budget in urban environments on the basis of multi-spectral satellite imagery.

9 REFERENCES Abrams, M. and Hook, S. (2001). ASTER User Handbook. The Jet Propulsion Laboratory, California Institute of Technology, Los Angeles, USA. Alley, R. E. and Jentoft-Nilsen, M. (1999). Algorithm Theoretical Basis Document for Brightness Temperature. The Jet Propulsion Laboratory, California Institute of Technology, Los Angeles, USA. Becker, F. (1987). The impact of spectral emissivity on the measurement of land surface temperature from a satellite. Int. J Remote Sensing, 8: Becker, F. and Li, Z.-L. (1990). Towards a local split window method over land surfaces. Int. J Remote Sensing, 11: Caselles, V., Coll, C. and Valor, E. (1997). Land surface emissivity and temperature determination in the whole HAPEX - Sahel area from AVHRR data. Int. J Remote Sensing, 18(5): Chrysoulakis, N. and Cartalis, C. (2000). Improving the estimation of land surface temperature for the region of Greece: (adjustment of a split window algorithm to account for the distribution of precipitable water). Int. J Remote Sensing, 23: Coll, C., Caselles, V., Sorbino, J. A. and Valor, E. (1994). On the atmospheric dependence of the split-window equation for land surface temperature. Int. J Remote Sensing, 15: Dousset, B. and Gourmelon, F. (2001). Remote Sensing Applications to the Analysis of Urban Microclimates. Proc. IEEE/ISPRS Joint Workshop on Remote Sensing and Data Fusion over Urban Areas, held at Rome in Nov Dousset, B., Flament, P. and Bernstein, R. (1993). Los Angeles fires seen from space. EOS, Trans. AGU, 74:33, Kerr, Y. H., Lagouarde, J. P. and Imbernon, J. (1992). Accurate land surface temperature retrieval from AVHRR data with the use of an improved split-window algorithm. Remote Sensing Env., 41: Ottle, C. and Vidal-Madjar, D. (1992). Estimation of land surface temperature with NOAA -9 data. Remote Sensing Env., 40: Prata, A., Caselles, V., Coll, C., Ottle, C. and Sorbino, J. (1995). Thermal remote sensing of land surface temperature from satellites: current status and future prospects. Remote Sensing Rev., 12: Prata, A. (1993). Land Surface Temperatures Derived From the Advanced Very High Resolution Radiometer and the Along-Track Scanning Radiometer 1. Theory. J. Geophys. Res., 98: Price, J. C. (1984). Land surface temperature measurements from the split window channels of the NOAA -7 AVHRR. J. Geophys. Res., 89: Santamouris, M., Klitsikas, N. and Niahou, K. (2000). The heat island effect on passive cooling. Information Paper, Renewables in the City Environment Project, University of Liege, ( Sobrino, J. A.., Coll, C. and Caselles, V. (1991). Atmospheric correction for land surface temperature using NOAA -11 AVHRR channels 4 and 5. Remote Sensing of Env., 38: Vidal, A. (1991). Atmospheric and emissivity correction of land surface temperature measured from satellite using ground measurements or satellite data. Int. J Remote Sensing, 12: Voogt, J. A. and Oke, T. R. (1997). Complete Urban Surface Temperatures. J. Appl. Meteorology, 36:

10 CAPTIONS Figure 1. The study area (ASTER Channel 2, October 30, 2001 at LST). Three main components are easily detected: The vegetated areas and the main road network are depicted in dark tones, whereas the urban web is depicted in light tones. Figure 2. Spatial distribution of population at the centre of Athens. Polygons A - I represent the high densely built sub-areas. Figure 3. Digital Elevation Model and 100 m contours for the centre of Athens derived from ASTER stereo data. Imitos mountain, Likabetus and Tourkovounia hills are clearly depicted. Figure 4. Urban vegetation mapping for the centre of Athens based on NDVI which has been derived from ASTER Channels 2 and 3N. Central Athens Park and National Garden are clearly depicted (areas 1 and 2 respectively). The massive vegetation area at the lower right corner corresponds to Imitos mountain. Figure 5. Spatial distribution of Radiance at the Sensor for densely built areas of the centre of Athens. High densely built sub-areas obtain low radiance values because in these areas most of the solar radiation impinges on roofs, and the vertical walls of the buildings, and only a relative small part reaches the ground level. Figure 6. Spatial distribution of brightness temperature for the centre of Athens with the main road network superimposed. The central commercial area, as well as the extended industrial area (left) obtain greater brightness temperature values than the high densely built areas. In medium densely built areas X, Y and Z the brightness temperature values along the main roads are greater the respective values in surrounding areas. Figure 7. Spatial distribution of brightness temperature for the centre of Athens with the 100 m contours superimposed. Mountains and hills acts as a shield to incoming solar radiation as in can be seen in the area which is bounded by the circle, where the brightness temperature values along the sunward slope of Imitos hill are greater that the respective values of the opposite slope.

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