International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Volume XLII-4/W19
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-4/W19, 199–206, 2019
https://doi.org/10.5194/isprs-archives-XLII-4-W19-199-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-4/W19, 199–206, 2019
https://doi.org/10.5194/isprs-archives-XLII-4-W19-199-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

  23 Dec 2019

23 Dec 2019

GIS-BASED MAPPING OF LOCAL CLIMATE ZONES USING FUZZY LOGIC AND CELLULAR AUTOMATA

I. Estacio1, J. Babaan1, N. J. Pecson1, A. C. Blanco1,2, J. E. Escoto2, and C. K. Alcantara2 I. Estacio et al.
  • 1Training Center for Applied Geodesy and Photogrammetry, University of the Philippines Diliman, Philippines
  • 2Department of Geodetic Engineering, University of the Philippines Diliman, Philippines

Keywords: Urban Heat Island, Sky View Factor, Surface fractions, Surface Albedo, Roughness Length

Abstract. Because of the vague distinction between urban and rural areas, the Local Climate Zone (LCZ) scheme was developed to better analyze the effect of Urban Heat Island. To map the LCZs in a city, the World Urban Database and Portal Tool is used as conventional method. However, this requires the assignment of training areas for each LCZ, which entails local knowledge of the area and may introduce errors, as distinction between LCZ types through visual inspection is inconclusive. This paper aims to develop a methodology and GIS tool to enhance and automate the mapping of LCZs using seven LCZ properties (sky view factor, building surface fraction, pervious surface fraction, impervious surface fraction, building height, roughness length, and surface albedo), and apply it in Quezon City, Philippines which comprises varying land use and land cover. Fuzzy Logic was used to determine the membership percentage of 100 m cells to an LCZ type based on these properties. Cellular Automata was implemented using Python to derive the LCZ map from the fuzzy layers. Results show that seven out of ten built-up LCZs and five out of seven land cover LCZs were identified. Through visual inspection on a basemap, the mapped LCZs was confirmed to match with the features of the city. Land Surface Temperature (LST) derived from Landsat 8 showed that each LCZ type displayed temperatures consistent with those observed from literature. The developed methodology and tool is ready to be used in other cities as long as the input layers are generated.