Machine-Learning-Assisted Characterization of Regional Heat Islands with a Spatial Extent Larger than the Urban Size
Surface urban heat islands (SUHIs) can extend beyond the urban boundaries and greatly affect the thermal environment of continuous regions over an agglomeration. Traditional urban-rural dichotomy depending on the built-up and non-urban lands is challenged in characterizing regional SUHIs, such as ho...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-02-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/16/3/599 |
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| author | Yin Du Zhiqing Xie Lingling Zhang Ning Wang Min Wang Jingwen Hu |
| author_facet | Yin Du Zhiqing Xie Lingling Zhang Ning Wang Min Wang Jingwen Hu |
| author_sort | Yin Du |
| collection | DOAJ |
| description | Surface urban heat islands (SUHIs) can extend beyond the urban boundaries and greatly affect the thermal environment of continuous regions over an agglomeration. Traditional urban-rural dichotomy depending on the built-up and non-urban lands is challenged in characterizing regional SUHIs, such as how to accurately quantify the intensity, spatial pattern, and scales of SUHIs, which are vulnerable to SUHIs, and what the optimal scale for conducting measures to mitigate the SUHIs. We propose a machine-learning-assisted solution to address these problems based on the thermal similarity in the Yangtze River Delta of China. We first identified the regional-level SUHI zone of approximately 42,328 km<sup>2</sup> and 38,884 km<sup>2</sup> and the areas that have no SUHI effects from the annual cycle of land surface temperatures (LSTs) retrieved from Terra and Aqua satellites. Defining SUHI as an anomaly on background condition, random forest (RF) models were further adopted to fit the LSTs in the areas without the SUHI effects and estimate the LST background and SUHI intensity at each grid point in the SUHI zone. The RF models performed well in fitting rural LSTs with a simulation error of approximately 0.31 °C/0.44 °C for Terra/Aqua satellite data and showed a good generalization ability in estimating the urban LST background. The RF-estimated daytime Aqua/SUHI intensity peaked at approximately 6.20 °C in August, and the Terra/SUHI intensity had two peaks of approximately 3.18 and 3.81 °C in May and August, with summertime RF-estimated SUHIs being more reliable than other SUHI types owing to the smaller simulation error of less than 1.0 °C in July–September. This machine-learning-assisted solution identified an optimal SUHI scale of 30,636 km<sup>2</sup> and a zone of approximately 23,631 km<sup>2</sup> that is vulnerable to SUHIs, and it provided the SUHI intensity and statistical reliability for each grid point identified as being part of the SUHI. Urban planners and decision-makers can focus on the statistically reliable RF-estimated summertime intensities in SUHI zones that have an LST annual cycle similar to that of large cities in developing effective strategies for mitigating adverse SUHI effects. In addition, the selection of large cities might strongly affect the accuracy of identifying the SUHI zone, which is defined as the areas that have an LST annual cycle similar to large cities. Water bodies might reduce the RF performance in estimating the LST background over urban agglomerations. |
| first_indexed | 2024-03-08T03:49:27Z |
| format | Article |
| id | doaj.art-17cb9da50e054ef0a47621e7de369429 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-08T03:49:27Z |
| publishDate | 2024-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-17cb9da50e054ef0a47621e7de3694292024-02-09T15:21:36ZengMDPI AGRemote Sensing2072-42922024-02-0116359910.3390/rs16030599Machine-Learning-Assisted Characterization of Regional Heat Islands with a Spatial Extent Larger than the Urban SizeYin Du0Zhiqing Xie1Lingling Zhang2Ning Wang3Min Wang4Jingwen Hu5Key Laboratory of Meteorological Disaster of Ministry of Education (KLME)/Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters (CIC-FEMD)/Joint International Research Laboratory of Climate and Environment Change (ILCEC), Nanjing University of Information Science & Technology, Nanjing 210044, ChinaJiangsu Climate Center, Nanjing 210019, ChinaJiangsu Climate Center, Nanjing 210019, ChinaJiangsu Climate Center, Nanjing 210019, ChinaKey Laboratory of Meteorological Disaster of Ministry of Education (KLME)/Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters (CIC-FEMD)/Joint International Research Laboratory of Climate and Environment Change (ILCEC), Nanjing University of Information Science & Technology, Nanjing 210044, ChinaKey Laboratory of Meteorological Disaster of Ministry of Education (KLME)/Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters (CIC-FEMD)/Joint International Research Laboratory of Climate and Environment Change (ILCEC), Nanjing University of Information Science & Technology, Nanjing 210044, ChinaSurface urban heat islands (SUHIs) can extend beyond the urban boundaries and greatly affect the thermal environment of continuous regions over an agglomeration. Traditional urban-rural dichotomy depending on the built-up and non-urban lands is challenged in characterizing regional SUHIs, such as how to accurately quantify the intensity, spatial pattern, and scales of SUHIs, which are vulnerable to SUHIs, and what the optimal scale for conducting measures to mitigate the SUHIs. We propose a machine-learning-assisted solution to address these problems based on the thermal similarity in the Yangtze River Delta of China. We first identified the regional-level SUHI zone of approximately 42,328 km<sup>2</sup> and 38,884 km<sup>2</sup> and the areas that have no SUHI effects from the annual cycle of land surface temperatures (LSTs) retrieved from Terra and Aqua satellites. Defining SUHI as an anomaly on background condition, random forest (RF) models were further adopted to fit the LSTs in the areas without the SUHI effects and estimate the LST background and SUHI intensity at each grid point in the SUHI zone. The RF models performed well in fitting rural LSTs with a simulation error of approximately 0.31 °C/0.44 °C for Terra/Aqua satellite data and showed a good generalization ability in estimating the urban LST background. The RF-estimated daytime Aqua/SUHI intensity peaked at approximately 6.20 °C in August, and the Terra/SUHI intensity had two peaks of approximately 3.18 and 3.81 °C in May and August, with summertime RF-estimated SUHIs being more reliable than other SUHI types owing to the smaller simulation error of less than 1.0 °C in July–September. This machine-learning-assisted solution identified an optimal SUHI scale of 30,636 km<sup>2</sup> and a zone of approximately 23,631 km<sup>2</sup> that is vulnerable to SUHIs, and it provided the SUHI intensity and statistical reliability for each grid point identified as being part of the SUHI. Urban planners and decision-makers can focus on the statistically reliable RF-estimated summertime intensities in SUHI zones that have an LST annual cycle similar to that of large cities in developing effective strategies for mitigating adverse SUHI effects. In addition, the selection of large cities might strongly affect the accuracy of identifying the SUHI zone, which is defined as the areas that have an LST annual cycle similar to large cities. Water bodies might reduce the RF performance in estimating the LST background over urban agglomerations.https://www.mdpi.com/2072-4292/16/3/599surface urban heat islandsurban agglomerationsrandom forestland surface temperatureMODIS |
| spellingShingle | Yin Du Zhiqing Xie Lingling Zhang Ning Wang Min Wang Jingwen Hu Machine-Learning-Assisted Characterization of Regional Heat Islands with a Spatial Extent Larger than the Urban Size Remote Sensing surface urban heat islands urban agglomerations random forest land surface temperature MODIS |
| title | Machine-Learning-Assisted Characterization of Regional Heat Islands with a Spatial Extent Larger than the Urban Size |
| title_full | Machine-Learning-Assisted Characterization of Regional Heat Islands with a Spatial Extent Larger than the Urban Size |
| title_fullStr | Machine-Learning-Assisted Characterization of Regional Heat Islands with a Spatial Extent Larger than the Urban Size |
| title_full_unstemmed | Machine-Learning-Assisted Characterization of Regional Heat Islands with a Spatial Extent Larger than the Urban Size |
| title_short | Machine-Learning-Assisted Characterization of Regional Heat Islands with a Spatial Extent Larger than the Urban Size |
| title_sort | machine learning assisted characterization of regional heat islands with a spatial extent larger than the urban size |
| topic | surface urban heat islands urban agglomerations random forest land surface temperature MODIS |
| url | https://www.mdpi.com/2072-4292/16/3/599 |
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