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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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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:;
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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.

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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
1 KuangWenhui, Yang Tianrong, Liu Ailin, et al. An Eco-City model for regulating urban land cover structure and thermal environment: Taking Beijing as an example[J]. Science China Earth Sciences,2017,47(7):847-859.
1 匡文慧, 杨天荣, 刘爱琳, 等. 城市地表覆盖结构组分与热环境调控模型(EcoCity)研究——以北京城市为例 [J]. 中国科学: 地球科学, 2017, 47(7): 847-859.
2 Wang Yu, Tang Li, Zhu Haitao,et al. The study of urban thermal environment dynamicsand attribution analysis basedon multiple remote sensing dataset: In the case of Shenzhen[J]. Acta EcologicaSinica, 2021,41(22):8771-8782.
2 王煜,唐力,朱海涛,等.基于多源遥感数据的城市热环境响应与归因分析——以深圳市为例[J].生态学报,2021,41(22):8771-8782.
3 Xu H Q, Wang M Y, Shi T T, et al. Prediction of ecological effects of potential population and impervious surface increases using a Remote Sensing based Ecological Index(RSEI)[J]. Ecological Indicators, 2018, 93: 730-740.
4 Zhang Xiaodong, Zhao Yinxin, Chu Xiaodong, et al. Spatial and temporal evolution characteristics of thermal environment and its influencing factors in urban area of Yinchuan City based on remote sensing [J]. Research of Soil and Water Conservation, 2020, 27(6): 180-187.
4 张晓东, 赵银鑫,褚小东,等. 基于遥感的银川市城区热环境及其影响因素的时空演变特征 [J]. 水土保持研究, 2020, 27(6): 180-187.
5 Gao Yang, XiongJuhua, Wu Hao,et al. Frontier hotspotsand development directions of geographical science research:From a perspective of national natural science foundation application keywords in 2021[J/OL]. Scientia GeographicaSinica:1-16.
5 高阳,熊巨华,吴浩,等.2021年度自然科学基金申请书关键词透视地理科学研究前沿热点与发展方向[J/OL].地理科学:1-16..
6 Li Deren. Brain cognition and spatial cognition: On integration of geospatial big data and artificial intelligence[J]. Geomatics and Information Science of Wuhan University,2018,43(12): 1761-1767.
6 李德仁. 脑认知与空间认知—论空间大数据与人工智能的集成 [J]. 武汉大学学报: 信息科学版,2018,43(12):1761-1767.
7 Zhang Fei, Shao Yuan, Huang Hui,et al.Reviewof urban remote sensing researchinthe last two decades[J]. Acta Ecologica Sinica,2021,41(8):3255-3276.
7 张飞, 邵媛, 黄晖,等. 近20年城市遥感研究现状及其发展趋势[J]. 生态学报,2021,41(8):3255-3276.
8 Feyisa G L, Meilby H, Jenerette G D, et al. Locally optimized separability enhancementIndices for urban land cover mapping: Exploring thermal environmental consequences of rapid urbanization in Addis Ababa,Ethiopia[J].Remote Sen-sing of Environment,2016,175:14-31.
9 Yang Yuting, Tang Jiafa,BianJinhu,et al. Seasonal variations in the relationship between land sur‐face yemperature and impervious surface percentage in Kolkata[J]. Remote Sensing Technology and Application,2021,36(1):79-89.
9 杨玉婷,汤家法,边金虎,等.加尔各答市地表温度与不透水面比例季相相关性研究[J].遥感技术与应用,2021,36(01):79-89.
10 Zheng B J, Myint S W, Fan C. Spatial configuration of anthropogenic land cover impacts on urban warming[J]. Landscape and Urban Planning, 2014, 130: 104-111.
11 Chen Y J, Yu S X. Impacts of urban landscape patterns on urban thermal variations in Guangzhou, China[J]. International Journal of Applied Earth Observation and Geoinformation, 2017, 54: 65-71.
12 Kotharkar R, Bagade A. Evaluating urban heat island in the criticalLocal climate zones of an Indian city[J]. Landscape and Urban Planning, 2018, 169: 92-104.
13 Nguyen T, Yu X X, Zhang Z M, et al. Relationship betweentypes of urban forest and PM2.5 capture at three growth stages of leaves[J]. Journal of Environmental Sciences,2015,27:33-41.
14 Balcik F B. Determining the impact of urban components on land surface temperature of istanbul by using remote sensing indices[J]. Environmental Monitoring and Assessment, 2014, 186(2): 859-872.
15 Nsubuga F W N, Botai J O, Olwoch J M, et al. Detecting changes in surface water area of lake Kyoga sub-basin using remotely sensed imagery in a changing climate[J]. Theoretical and Applied Climatology, 2017, 127(1-2): 327-337.
16 XuHanqiu. Urban expansion process in the center of the fuzhou basin, Southeast China in 1976-2006[J]. Scientia Geographica Snica, 2011,31(3):351-357.
16 徐涵秋.近30 a来福州盆地中心的城市扩展进程[J]. 地理科学,2011,31(3):351-357.
17 Shen H F, Huang L W, Zhang L P, et al. Long-term and fine-scale satellite monitoring of the urban heat island effect by the fusion of multi-temporal and multi-sensor remote sensed data: A 26-year case study of the city of wuhan in China[J]. Remote Sensing of Environment, 2016, 172: 109-125.
18 Son N T, Thanh B X. Decadal assessment of urban sprawl and its effects on local temperature using landsat data in Cantho city,Vietnam[J].Sustainable Cities and Society,2018,36:81-91.
19 Van de Voorde T, Jacquet W, Canters F. Mapping form and function in urban areas: An approach based on urban metrics and continuous impervious surface data[J]. Landscape and Urban Planning, 2011, 102(3): 143-155.
20 Chander G, Markham B L, Helder D L. Summary of current radiometric calibration coefficients for landsat MSS, TM, ETM+, and EO-1 ALI sensors[J]. Remote Sensing of Environment, 2009, 113(5): 893-903.
21 CharvzJr P S. Image-based atmospheric corrections-revisited and revised[J]. Photogrammetric Engineering and Remote Sensing,1996,62(9):1025-1036.
22 Jiménez-Muñoz J C, Cristobal J, Sobrino J A, et al. Revision of the single-channel algorithm for land surface temperature retrieval from landsat thermal-infrared data[J]. IEEE Transactions on Geoscience and Remote Sensing,2009,47(1): 339-349.
23 Jiménez-Muñoz J C, Sobrino J A. A generalized single-channel method for retrieving land surface temperature from remote sensing data[J]. Journal of Geophysical Research Atmos-pheres, 2003,108(D22): 4688.
24 Jiménez-Muñoz J C, Sobrino J A, Skoković D, et al. Land surface temperature retrieval methods from Landsat8 thermal infrared sensor data[J]. IEEE Geoscience & Remote Sensing Letters, 2014, 11(10): 1840-1843.
25 Dematte J A M, Sayao V M, Rizzo R, et al. Soil class and attribute dynamics and their relationship with natural vegetation based on satellite remote sensing[J].Geoderma,2017,302:39-51.
26 Tulbure M G, Broich M, Stehman S V, et al. Surface water extent dynamics from three decades of seasonally continuous landsat time series at subcontinental scale in a semiarid region [J]. Remote Sensing of Environment,2016,178,142-157.
27 Li Deren, Luo Hui, Shao Zhenfeng. Review of impervious surface mapping using remote sensing technology and its application[J]. Geomatics and Information Science of Wuhan University, 2016, 41(5): 569-703.
27 李德仁, 罗晖, 邵振峰. 遥感技术在不透水层提取中的应用与展望[J]. 武汉大学学报(信息科学版), 2016, 41(5): 569-703.
28 XuH Q.Analysis of impervious surface and its impact on urban heat environment using the Normalized Difference Impervious Surface Index (NDISI)[J]. Photogrammetric Engineering & Remote Sensing, 2010,76(5):557-565.
29 Wentz E A.Getting started with geographic information systems, 3rd edition[J]. International Journal of Geographical Information Science, 2002, 16(2): 204-205.
30 Breiman L. Random forests[J]. Machine Learning, 2001, 45(1): 5-32.
31 JiaJiaqiong, Liu Wanqing, MengQingyan, et al. Estimation of maize leaf area index based on GF-1 WFV image and machine learning random algorithm[J]. Journal of Image and Graphics, 2018, 23(5):719-729.
31 贾洁琼, 刘万青, 孟庆岩, 等. 基于GF-1 WFV影像和机器学习算法的玉米叶面积指数估算[J]. 中国图象图形学报, 2018, 23(5): 719-729.
32 Du P J, Samat A, Waske B, et al. Random forest and rotation forest for fully polarized SAR image classification using polarimetric and spatial features[J]. ISPRS Journal of Photogrammetry and Remote Sensing,2015,105: 38-53.
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[5] . [J]. Remote Sensing Technology and Application, 1994, 9(2): 67 -68 .
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