Mapping Local Climate Zones Using ArcGIS-Based Method and Exploring Land Surface Temperature Characteristics in Chenzhou, China

Abstract

The local climate zone (LCZ) has become a new tool for urban heat island research. Taking Chenzhou as the research object, eight urban spatial form elements and land cover elements are calculated respectively through ArcGIS, Skyhelios and ENVI software. The calculation results are then rasterized and clustered in ArcGIS to obtain the LCZ map at a resolution of 200 m. Afterwards, the land surface temperature (LST) of different local climate zones in the four seasons from 2017 to 2018 is further analyzed using one-way ANOVA F-test and Student’s t-test. The results suggest that: (1) by adding localized LCZ classes and applying the semi-automatic algorithm on the Arc-GIS platform, the final overall accuracy reaches 69.54%, with a kappa value of 0.67, (2) the compact middle-rise buildings (LCZ-2′) and open low-rise buildings (LCZ-6) heavily contribute to the high LST of the downtown area, while the large low-rise buildings (LCZ-8) cause the high LST regions in the eastern part of the town, (3) obvious land surface temperature differences can be detected in four seasons among different LCZ classes, with high LST in summer and autumn. Built-up LCZ classes generally revealed higher LSTs than land cover LCZs in all seasons. The findings of this study provide better understandings of the relationship between LCZ and LST, as well as important insights for urban planners on urban heat mitigation.

Mapping Local Climate Zones: A Bibliometric Meta-Analysis and Systematic Review

“…Many studies compared different machine learning algorithms for LCZ classification, and it can be concluded that their performance varies from city to city (Bechtel et al, 2016;Bechtel & Daneke, 2012;La et al, 2020;Mushore et al, 2019;Xu et al, 2017bXu et al, , 2018 Multi-source remote sensing and GIS data have been used for calculating LCZ parameters. For surface structure parameters, the sky view factor (SVF) can be calculated using digital surface model (DSM) (Bartesaghi Koc et al, 2017;Mitraka et al, 2015;Quan, 2019;Zhao et al, 2019a;Zheng et al, 2018), building data (Chen et al, 2020b;Nassar et al, 2016), and Google Street View (Middel et al, 2018). The aspect ratio can be calculated as the mean building height divided by the mean street width using building data (Chen et al, 2020b;Zheng et al, 2018), and as the maximum height divided by average width of ground surface based on the normalized DSM (Zhao et al, 2019a).…”

“…For surface structure parameters, the sky view factor (SVF) can be calculated using digital surface model (DSM) (Bartesaghi Koc et al, 2017;Mitraka et al, 2015;Quan, 2019;Zhao et al, 2019a;Zheng et al, 2018), building data (Chen et al, 2020b;Nassar et al, 2016), and Google Street View (Middel et al, 2018). The aspect ratio can be calculated as the mean building height divided by the mean street width using building data (Chen et al, 2020b;Zheng et al, 2018), and as the maximum height divided by average width of ground surface based on the normalized DSM (Zhao et al, 2019a). The height of roughness elements can be calculated as a mean height of buildings weighted by the building footprints using building data (Lelovics et al, 2014;Nassar et al, 2016;Quan, 2019;Zheng et al, 2018).…”

“…The height of roughness elements can be calculated as a mean height of buildings weighted by the building footprints using building data (Lelovics et al, 2014;Nassar et al, 2016;Quan, 2019;Zheng et al, 2018). The terrain roughness class can be determined by the Davenport classification method using aerial images, topographic maps, and building data (Lelovics et al, 2014), building data and Landsat 8 data (Chen et al, 2020b), and DSM and land cover data (Zhao et al, 2019a). For the calculation of surface cover parameters, the commonly used data are land cover and building data (Lelovics et al, 2014;Nassar et al, 2016;Quan et al, 2017).…”

“…(iii) Based on the semi-variogram model of building height. For example, the appropriate grid size determined by this method is m for Hong Kong (Zheng et al, 2018), 270 m for Dallas-Fort Worth, Austin, and San Antonio in Texas, US (Zhao et al, 2019a), and 200 m for Chenzhou, China (Chen et al, 2020b).…”

Application of Machine Learning Techniques to Delineate Homogeneous Climate Zones in River Basins of Pakistan for Hydro-Climatic Change Impact Studies

“…The regionalization of the precipitation statistics has numerous applications in various water resource management fields, such as agriculture practices, spatial and temporal rainfall patterns, hydrological analysis, extreme event forecasting [55] and watershed management. There are several methods available to delineate climate regions, for example, subjective and objective partitioning, geographical convenience and multivariate analysis [54,56,57]. The arbitrary and slightly misleading demarcation approach for a region, which is based on administrative boundaries and physical and geographical groupings, is referred to as the geographical convenience method.…”