Simulating the Relationship between Land Use/Cover Change and Urban Thermal Environment Using Machine Learning Algorithms in Wuhan City, China

Abstract

The changes of land use/land cover (LULC) are important factor affecting the intensity of the urban heat island (UHI) effect. Based on Landsat image data of Wuhan, this paper uses cellular automata (CA) and artificial neural network (ANN) to predict future changes in LULC and LST. The results show that the built-up area of Wuhan has expanded, reaching 511.51 and 545.28 km2, while the area of vegetation, water bodies and bare land will decrease to varying degrees in 2030 and 2040. If the built-up area continues to expand rapidly, the proportion of 30~35 °C will rise to 52.925% and 55.219%, and the affected area with the temperature >35 °C will expand to 15.264 and 33.612 km2, respectively. The direction of the expansion range of the LST temperature range is obviously similar to the expansion of the built-up area. In order to control and alleviate UHI, the rapid expansion of impervious layers (built-up areas) should be avoided to the greatest extent, and the city’s “green development” strategy should be implemented.

A GIS and remote sensing approach for measuring summer-winter variation of land use and land cover indices and surface temperature in Dhaka district, Bangladesh

“…The regression results were strong enough to justify the correlations and researchers such as Kafy et al. (2020) and Zhang et al. (2022) also evaluated such correlation results.…”

Modeling Land Use Change in Sana’a City of Yemen with MOLUSCE

“…To forecast future changes in LULC and LST, Zhang and colleagues used cellular automata (CA) and artificial neural networks (ANN). Their findings demonstrated the growth of Wuhan's built-up area [52].…”

Impact of COVID-19 Lockdown on Vegetation Indices and Heat Island Effect: A Remote Sensing Study of Dhaka City, Bangladesh

“…An LST analysis with satellite thermal data necessitates a number of techniques, such as sensor radiometric alignment, the correction of air and surface reflectance, and the spatial variation of LULC. The LST calculation procedure is explained below in Equations ( 5)-( 9) by following the method of Zhang et al, (2022) and Kafy et al, (2020) [40,41].…”

“…Many studies were undertaken for detecting the relationship between thermal change and vegetation pattern. For example, Kafy et al, (2020) and Zhang et al, (2022) identified the spatiotemporal change in the LST and the normalized difference vegetation index (NDVI). They also quantified the relationship between the LST and the NDVI [40,41].…”

“…For example, Kafy et al, (2020) and Zhang et al, (2022) identified the spatiotemporal change in the LST and the normalized difference vegetation index (NDVI). They also quantified the relationship between the LST and the NDVI [40,41]. For detecting the change in vegetation more effectively, a vegetation index named the modified soil-adjusted vegetation index (MSAVII2) was used by different researchers [42][43][44].…”