Study on the Random Forest Regression Model of Land Cover and Thermal Environment in Megacities

doi:10.11873/j.issn.1004-0323.2022.2.0379

Remote Sensing Technology and Application

2022, Vol. 37

Issue (2): 379-388 DOI: 10.11873/j.issn.1004-0323.2022.2.0379

Study on the Random Forest Regression Model of Land Cover and Thermal Environment in Megacities

Meiya Wang¹(

),Hanqiu Xu²(

)

^1.School of History and Geography，Minnan Normal University，Zhangzhou 363000，China
^2.College of Environment and Resources； Institute of Remote Sensing Information Engineering； Fujian Provincial Key Laboratory of Remote Sensing of Soil Erosion and Disaster Prevention，Fuzhou University，Fuzhou 350116，China

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Abstract

Rapid urbanization has led to rapid change in land cover and the landsurface heat balance in megacities. Due to the complex potential nonlinear relationship between land surface temperature and surface biophysical components in megacities， the quantitative models and the response mechanism between land cover and thermal environment in megacities is not yet clear. Takesix Chinese and foreign megacities （Beijing， Shanghai， Guangzhou， London， New York and Tokyo） as the study area， Landsat images were used to comprehensively analyze the quantitative relationship between urban land cover factors and thermal environment. The single-channel algorithm was used to retrieve the land surface temperature of thesix megacities.The random forest regression model was used to establish the quantitative relationship （LCT） model between land cover types and urban thermal environment （LST）. The quantitative relationship between land cover type and LST showed that the LST was closely related to urban land surface types. The spatial pattern of the urban thermal field depends to a great extent on the spatial distribution pattern of the urban land surface types. The impervious surface will lead to the accumulation of high LST fields， while vegetation and water had a significant cooling effect. The land cover compositionin six megacities had different heating/cooling effects. In urban areas， such as Beijing， Shanghai， New York， and Tokyo， the cooling effects of vegetation and water were more pronounced than thosein Guangzhou and London. The established LCTmodel between the three land cover types， NDVI， MNDWI， and NDISI， and the urban thermal environment showed that the LCT model had higher precisionthan that was based on the multiple linear regression method. The R² value of the LCT_RF model is 0.021~0.074， which is higher than that of the LCT_MLR model. The RMSE is 0.07℃~0.35℃， which is lower than that of the LCT_MLR model.It will be helpful for future construction of eco-cities by studying the interaction mechanism between the land cover and the urban thermal environment in megacities.

Key words: Megacities Land cover Urban heat environment Remote sensing Random forest regression model

Received: 21 October 2021 Published: 17 June 2022

ZTFLH:

P237

Corresponding Authors: Hanqiu Xu E-mail: 286097145@qq.com;hxu@fzu.edu.cn

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Cite this article:

Meiya Wang,Hanqiu Xu. Study on the Random Forest Regression Model of Land Cover and Thermal Environment in Megacities. Remote Sensing Technology and Application, 2022, 37(2): 379-388.

URL:

http://www.rsta.ac.cn/EN/10.11873/j.issn.1004-0323.2022.2.0379 OR http://www.rsta.ac.cn/EN/Y2022/V37/I2/379

Fig.1 Landsat images of the study area

Fig.2 Process of random forest regression model

Fig.3 LST maps of the six megacities

Table 1 Results comparison of retrieve accuracy of LST

Fig.4 Relationship of NDVI， MNDWI， NDISI with LST

Table 2 Results comparison of LCT_RF and LCT_MLR model based on LST prediction

Fig.5 Results comparison of LCT_RF（3 indices）of the six megacities

Fig.6 Results comparison of LCT_MLR（3 indices）of the six megacities

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