Gis-Based Mapping of Local Climate Zones Using Fuzzy Logic and Cellular Automata

Abstract

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.

Application of GIS Tools in the Measurement Analysis of Urban Spatial Layouts Using the Square Grid Method

“…Various types of square grids are also used in other analyses, such as studies of the spatial development of cities [72][73][74][75][76][77][78], cities' landscape change and land use [79][80][81]. They are also used in studies on urban climate, build-up areas [76,[82][83][84][85] and on issues related to the quality of life of inhabitants [86,87] or population density [88]. In most of the cases cited, grids were created using GIS tools, including those available in ArcMap software.…”

Local Climate Zones: Lessons From Singapore and Potential Improvement With Street View Imagery

“…Efforts have been been exercised to standardize methods on using RS data; they have been criticized for being unjustified and unstandardized (Quan and Bansal, 2021;Geletič and Lehnert, 2016). GIS method requires a complete set of raster and vector-based planning data (Zheng et al, 2018;Lelovics et al, 2013) to conduct LCZ classification (Perera and Emmanuel, 2018;Chen et al, 2020;Rodler and Leduc, 2019;Hidalgo et al, 2019;Estacio et al, 2019;Kotharkar and Bagade, 2018).…”

Inference of Local Climate Zones from GIS Data, and Comparison to WUDAPT Classification and Custom-Fit Clusters

“…The raster of AHF is resampled to 100 m × 100 m to be further used to calculate the mean AHF in a grid tile. The roughness length (z 0 ) is calculated from the formula suggested by Oke (cited from Estacio et al [23]):…”

“…Its ISF range value is also modified from ISF ≥ 90 to ISF ≥ 60, so that it can better detect bare rock or paved surfaces. The zone properties are classified into LCZ classes by applying fuzzy logic with a trapezoidal membership function modified from Estacio et al [23]. The membership of every zone property for every LCZ class is determined as shown in Figure 4 (example case of LCZ 2 and its ISF property).…”

“…Another GIS-LCZ method was conducted by Estacio et al [23] for Quezon City, Philippines. The study employed seven zone properties: sky view factor, building height, roughness length, surface albedo, and the three usual surface fractions.…”