Simulation of urban temperatures from very high resolution satellite imagery

In this work we present a new approach for simulating and estimating temperatures in urban environments solely from very high resolution satellite stereo imagery. For this first a dense digital surface model (DSM) is calculated from two or more satellite stereo images together with a digital terrain model (DTM). The difference gives high objects like buildings and trees. Using also the spectral information from the satellite imagery allows for classification of the scene to different surface materials. The main classes derived for a urban scene contain water, tree, grass, soil, road, adobe tiles and concrete. For each of the classes physical constants like the heat-capacity c in [kJ/kg/K], the heat-transport λ in [W/m/K], the density of the material ϱ in [kg/dm3], the absorption α (in %) and the diffusion resistance rl in [s/m] are derived and used for the further simulation. Also for preparing the simulation the sky-view of each pixel in the resulting DSM is calculated together with shadows every 300 seconds for a whole day. Afterwards the simulation runs several times for the day until stability is reached. The results are temperature maps for every 5 minutes for a whole day of 24 hours. Finally the results are compared with independent temperature measurements.