{"title":"基于遥感的城市热岛驱动因素分析:地方气候区视角","authors":"Zhi Qiao;Ruoyu Jia;Jiawen Liu;Huan Gao;Qikun Wei","doi":"10.1109/JSTARS.2024.3462537","DOIUrl":null,"url":null,"abstract":"This study utilized multisource remote sensing data and advanced technology to investigate the potential driving factors of urban heat island (UHI) effects from the perspective of local climate zones (LCZs), including natural, social, and urban three-dimensional (3-D) structural factors. Using MODIS land surface temperature remote sensing data products and supplementary datasets, the simplified urban-extent algorithm was employed to identify UHI areas and quantify UHI Intensity (UHII). The stepwise multiple linear regression method and SHapley Additive exPlanations-explained eXtreme gradient boosting machine learning method were then applied to attribute UHII to 15 selected driving factors across 17 LCZ types in 369 Chinese cities. The findings indicate that large UHI areas are predominantly associated with low-rise LCZ types, where compact building arrangements intensify UHII, and increased building heights exacerbate this effect. During daytime, the UHI effects are largely driven by urban 3-D structures, particularly within LCZ 1-6 areas. Conversely, at night, the UHI effect is more significantly impacted by natural environmental factors. These insights offer a robust scientific foundation for urban planners to craft LCZ-specific strategies aimed at fostering the development of sustainable cities and communities.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":null,"pages":null},"PeriodicalIF":4.7000,"publicationDate":"2024-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10681269","citationCount":"0","resultStr":"{\"title\":\"Remote Sensing-Based Analysis of Urban Heat Island Driving Factors: A Local Climate Zone Perspective\",\"authors\":\"Zhi Qiao;Ruoyu Jia;Jiawen Liu;Huan Gao;Qikun Wei\",\"doi\":\"10.1109/JSTARS.2024.3462537\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This study utilized multisource remote sensing data and advanced technology to investigate the potential driving factors of urban heat island (UHI) effects from the perspective of local climate zones (LCZs), including natural, social, and urban three-dimensional (3-D) structural factors. Using MODIS land surface temperature remote sensing data products and supplementary datasets, the simplified urban-extent algorithm was employed to identify UHI areas and quantify UHI Intensity (UHII). The stepwise multiple linear regression method and SHapley Additive exPlanations-explained eXtreme gradient boosting machine learning method were then applied to attribute UHII to 15 selected driving factors across 17 LCZ types in 369 Chinese cities. The findings indicate that large UHI areas are predominantly associated with low-rise LCZ types, where compact building arrangements intensify UHII, and increased building heights exacerbate this effect. During daytime, the UHI effects are largely driven by urban 3-D structures, particularly within LCZ 1-6 areas. Conversely, at night, the UHI effect is more significantly impacted by natural environmental factors. These insights offer a robust scientific foundation for urban planners to craft LCZ-specific strategies aimed at fostering the development of sustainable cities and communities.\",\"PeriodicalId\":13116,\"journal\":{\"name\":\"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":4.7000,\"publicationDate\":\"2024-09-16\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10681269\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://ieeexplore.ieee.org/document/10681269/\",\"RegionNum\":2,\"RegionCategory\":\"地球科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENGINEERING, ELECTRICAL & ELECTRONIC\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","FirstCategoryId":"5","ListUrlMain":"https://ieeexplore.ieee.org/document/10681269/","RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
Remote Sensing-Based Analysis of Urban Heat Island Driving Factors: A Local Climate Zone Perspective
This study utilized multisource remote sensing data and advanced technology to investigate the potential driving factors of urban heat island (UHI) effects from the perspective of local climate zones (LCZs), including natural, social, and urban three-dimensional (3-D) structural factors. Using MODIS land surface temperature remote sensing data products and supplementary datasets, the simplified urban-extent algorithm was employed to identify UHI areas and quantify UHI Intensity (UHII). The stepwise multiple linear regression method and SHapley Additive exPlanations-explained eXtreme gradient boosting machine learning method were then applied to attribute UHII to 15 selected driving factors across 17 LCZ types in 369 Chinese cities. The findings indicate that large UHI areas are predominantly associated with low-rise LCZ types, where compact building arrangements intensify UHII, and increased building heights exacerbate this effect. During daytime, the UHI effects are largely driven by urban 3-D structures, particularly within LCZ 1-6 areas. Conversely, at night, the UHI effect is more significantly impacted by natural environmental factors. These insights offer a robust scientific foundation for urban planners to craft LCZ-specific strategies aimed at fostering the development of sustainable cities and communities.
期刊介绍:
The IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing addresses the growing field of applications in Earth observations and remote sensing, and also provides a venue for the rapidly expanding special issues that are being sponsored by the IEEE Geosciences and Remote Sensing Society. The journal draws upon the experience of the highly successful “IEEE Transactions on Geoscience and Remote Sensing” and provide a complementary medium for the wide range of topics in applied earth observations. The ‘Applications’ areas encompasses the societal benefit areas of the Global Earth Observations Systems of Systems (GEOSS) program. Through deliberations over two years, ministers from 50 countries agreed to identify nine areas where Earth observation could positively impact the quality of life and health of their respective countries. Some of these are areas not traditionally addressed in the IEEE context. These include biodiversity, health and climate. Yet it is the skill sets of IEEE members, in areas such as observations, communications, computers, signal processing, standards and ocean engineering, that form the technical underpinnings of GEOSS. Thus, the Journal attracts a broad range of interests that serves both present members in new ways and expands the IEEE visibility into new areas.