Prioritizing environmental determinants of urban heat islands: A machine learning study for major cities in China
The exacerbated thermal environment in cities, with the urban heat island (UHI) effect as a prominent example, has been the source of many adverse urban environmental issues, including the increase of health risks, degradation of air quality and ecosystem services, and reduced resiliency of engineer...
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| Format: | Article |
| Language: | English |
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Elsevier
2023-08-01
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| Series: | International Journal of Applied Earth Observations and Geoinformation |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1569843223002352 |
| _version_ | 1797738017568325632 |
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| author | Haoran Hou Qianqiu Longyang Hongbo Su Ruijie Zeng Tianfang Xu Zhi-Hua Wang |
| author_facet | Haoran Hou Qianqiu Longyang Hongbo Su Ruijie Zeng Tianfang Xu Zhi-Hua Wang |
| author_sort | Haoran Hou |
| collection | DOAJ |
| description | The exacerbated thermal environment in cities, with the urban heat island (UHI) effect as a prominent example, has been the source of many adverse urban environmental issues, including the increase of health risks, degradation of air quality and ecosystem services, and reduced resiliency of engineering infrastructure. Last decades have witnessed tremendous efforts and resources being invested to find sustainable solutions for urban heat mitigation, whereas the relative contributions of different UHI attributes and their patterns of spatio-temporal variability remain obscure. In this study, we employed the random forest (RF) method to quantify the relative importance of four categories of urban surface characteristics that regulate the surface UHI, namely the urban greenery fraction, land surface albedo, urban morphology, and level of human activities. We selected seventeen major cities from six megaregions in China as our study areas, with the RF training and test sets obtained from multi-sourced remote sensing and observational data products. It is found that the urban greenery coverage manifests as the most important environmental determinants of UHI, followed by surface albedo. The results are informative for urban planners, policymakers, and engineering practitioners to design and implement sustainable strategies for urban heat mitigation. |
| first_indexed | 2024-03-12T13:37:48Z |
| format | Article |
| id | doaj.art-e6391cc863514272ad4d4630fb0071fd |
| institution | Directory Open Access Journal |
| issn | 1569-8432 |
| language | English |
| last_indexed | 2024-03-12T13:37:48Z |
| publishDate | 2023-08-01 |
| publisher | Elsevier |
| record_format | Article |
| series | International Journal of Applied Earth Observations and Geoinformation |
| spelling | doaj.art-e6391cc863514272ad4d4630fb0071fd2023-08-24T04:34:13ZengElsevierInternational Journal of Applied Earth Observations and Geoinformation1569-84322023-08-01122103411Prioritizing environmental determinants of urban heat islands: A machine learning study for major cities in ChinaHaoran Hou0Qianqiu Longyang1Hongbo Su2Ruijie Zeng3Tianfang Xu4Zhi-Hua Wang5School of Sustainable Engineering and the Built Environment, Arizona State University, Tempe, AZ 85287, USA; Key Laboratory of Water Cycle & Related Land Surface Processes, Institute of Geographic Sciences and Natural Resources, Chinese Academy of Sciences, Beijing 100101, China; University of Chinese Academy of Sciences, Beijing 100049, ChinaSchool of Sustainable Engineering and the Built Environment, Arizona State University, Tempe, AZ 85287, USADepartment of Civil, Environmental and Geomatics Engineering, Florida Atlantic University, Boca Raton, FL 33431, USASchool of Sustainable Engineering and the Built Environment, Arizona State University, Tempe, AZ 85287, USASchool of Sustainable Engineering and the Built Environment, Arizona State University, Tempe, AZ 85287, USASchool of Sustainable Engineering and the Built Environment, Arizona State University, Tempe, AZ 85287, USA; Corresponding author.The exacerbated thermal environment in cities, with the urban heat island (UHI) effect as a prominent example, has been the source of many adverse urban environmental issues, including the increase of health risks, degradation of air quality and ecosystem services, and reduced resiliency of engineering infrastructure. Last decades have witnessed tremendous efforts and resources being invested to find sustainable solutions for urban heat mitigation, whereas the relative contributions of different UHI attributes and their patterns of spatio-temporal variability remain obscure. In this study, we employed the random forest (RF) method to quantify the relative importance of four categories of urban surface characteristics that regulate the surface UHI, namely the urban greenery fraction, land surface albedo, urban morphology, and level of human activities. We selected seventeen major cities from six megaregions in China as our study areas, with the RF training and test sets obtained from multi-sourced remote sensing and observational data products. It is found that the urban greenery coverage manifests as the most important environmental determinants of UHI, followed by surface albedo. The results are informative for urban planners, policymakers, and engineering practitioners to design and implement sustainable strategies for urban heat mitigation.http://www.sciencedirect.com/science/article/pii/S1569843223002352AlbedoLand surface temperatureNormalized difference vegetation index (NDVI)Random forestUrban heat islandUrban morphology |
| spellingShingle | Haoran Hou Qianqiu Longyang Hongbo Su Ruijie Zeng Tianfang Xu Zhi-Hua Wang Prioritizing environmental determinants of urban heat islands: A machine learning study for major cities in China International Journal of Applied Earth Observations and Geoinformation Albedo Land surface temperature Normalized difference vegetation index (NDVI) Random forest Urban heat island Urban morphology |
| title | Prioritizing environmental determinants of urban heat islands: A machine learning study for major cities in China |
| title_full | Prioritizing environmental determinants of urban heat islands: A machine learning study for major cities in China |
| title_fullStr | Prioritizing environmental determinants of urban heat islands: A machine learning study for major cities in China |
| title_full_unstemmed | Prioritizing environmental determinants of urban heat islands: A machine learning study for major cities in China |
| title_short | Prioritizing environmental determinants of urban heat islands: A machine learning study for major cities in China |
| title_sort | prioritizing environmental determinants of urban heat islands a machine learning study for major cities in china |
| topic | Albedo Land surface temperature Normalized difference vegetation index (NDVI) Random forest Urban heat island Urban morphology |
| url | http://www.sciencedirect.com/science/article/pii/S1569843223002352 |
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