{"title":"利用高分二号得出的城市绿地信息预测当地地表温度","authors":"","doi":"10.1016/j.ufug.2024.128463","DOIUrl":null,"url":null,"abstract":"<div><p>Urban green spaces (UGS) significantly influence the distribution of surface heat and play a crucial role in regulating surface temperature. However, the quantitative relationship between UGS and surface temperature remains unclear, necessitating further research. This study aims to predict surface temperature based on green space information from GaoFen-2 satellite data. To achieve this, GaoFen-2 data were utilized to obtain spatial distribution and vegetation growth status in Urumqi, Xinjiang. Three machine learning models such as Support Vector Machine (SVM), Random Forest (RF), and Gradient Boosting Regression Tree (GBRT) were constructed to predict surface temperature. Results indicated that UGS information extracted from GaoFen-2 data using the U-Net semantic segmentation model successfully predicted surface temperature. Among the three machine learning models, GBRT exhibited the highest predictive accuracy with an <span><math><msubsup><mrow><mtext>R</mtext></mrow><mrow><mtext>adj</mtext></mrow><mrow><mn>2</mn></mrow></msubsup></math></span> of 0.81, RMSE of 0.44, and RPD of 2.29, followed by RF (<span><math><msubsup><mrow><mtext>R</mtext></mrow><mrow><mtext>adj</mtext></mrow><mrow><mn>2</mn></mrow></msubsup></math></span> of 0.80, RMSE of 0.45, and RPD of 2.22), and SVM (<span><math><msubsup><mrow><mtext>R</mtext></mrow><mrow><mtext>adj</mtext></mrow><mrow><mn>2</mn></mrow></msubsup></math></span>of 0.79, RMSE of 0.47, and RPD of 2.15), In addition, a variable importance assessment reduced the original 44 variables to 28, maintaining predictive accuracy with the GBRT model achieving an <span><math><msubsup><mrow><mtext>R</mtext></mrow><mrow><mtext>adj</mtext></mrow><mrow><mn>2</mn></mrow></msubsup></math></span> of 0.81, RMSE of 0.43, and RPD of 2.3. Our study demonstrates the effectiveness of using vegetation information derived from GaoFen-2 to predict surface temperature. This approach provides valuable recommendations for the layout of UGS in urban areas and serves as a comprehensive reference for urban planning and real estate development.</p></div>","PeriodicalId":49394,"journal":{"name":"Urban Forestry & Urban Greening","volume":null,"pages":null},"PeriodicalIF":6.0000,"publicationDate":"2024-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Utilizing GaoFen-2 derived urban green space information to predict local surface temperature\",\"authors\":\"\",\"doi\":\"10.1016/j.ufug.2024.128463\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>Urban green spaces (UGS) significantly influence the distribution of surface heat and play a crucial role in regulating surface temperature. However, the quantitative relationship between UGS and surface temperature remains unclear, necessitating further research. This study aims to predict surface temperature based on green space information from GaoFen-2 satellite data. To achieve this, GaoFen-2 data were utilized to obtain spatial distribution and vegetation growth status in Urumqi, Xinjiang. Three machine learning models such as Support Vector Machine (SVM), Random Forest (RF), and Gradient Boosting Regression Tree (GBRT) were constructed to predict surface temperature. Results indicated that UGS information extracted from GaoFen-2 data using the U-Net semantic segmentation model successfully predicted surface temperature. Among the three machine learning models, GBRT exhibited the highest predictive accuracy with an <span><math><msubsup><mrow><mtext>R</mtext></mrow><mrow><mtext>adj</mtext></mrow><mrow><mn>2</mn></mrow></msubsup></math></span> of 0.81, RMSE of 0.44, and RPD of 2.29, followed by RF (<span><math><msubsup><mrow><mtext>R</mtext></mrow><mrow><mtext>adj</mtext></mrow><mrow><mn>2</mn></mrow></msubsup></math></span> of 0.80, RMSE of 0.45, and RPD of 2.22), and SVM (<span><math><msubsup><mrow><mtext>R</mtext></mrow><mrow><mtext>adj</mtext></mrow><mrow><mn>2</mn></mrow></msubsup></math></span>of 0.79, RMSE of 0.47, and RPD of 2.15), In addition, a variable importance assessment reduced the original 44 variables to 28, maintaining predictive accuracy with the GBRT model achieving an <span><math><msubsup><mrow><mtext>R</mtext></mrow><mrow><mtext>adj</mtext></mrow><mrow><mn>2</mn></mrow></msubsup></math></span> of 0.81, RMSE of 0.43, and RPD of 2.3. Our study demonstrates the effectiveness of using vegetation information derived from GaoFen-2 to predict surface temperature. This approach provides valuable recommendations for the layout of UGS in urban areas and serves as a comprehensive reference for urban planning and real estate development.</p></div>\",\"PeriodicalId\":49394,\"journal\":{\"name\":\"Urban Forestry & Urban Greening\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":6.0000,\"publicationDate\":\"2024-07-25\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Urban Forestry & Urban Greening\",\"FirstCategoryId\":\"93\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S1618866724002619\",\"RegionNum\":2,\"RegionCategory\":\"环境科学与生态学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENVIRONMENTAL STUDIES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Urban Forestry & Urban Greening","FirstCategoryId":"93","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1618866724002619","RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENVIRONMENTAL STUDIES","Score":null,"Total":0}
Utilizing GaoFen-2 derived urban green space information to predict local surface temperature
Urban green spaces (UGS) significantly influence the distribution of surface heat and play a crucial role in regulating surface temperature. However, the quantitative relationship between UGS and surface temperature remains unclear, necessitating further research. This study aims to predict surface temperature based on green space information from GaoFen-2 satellite data. To achieve this, GaoFen-2 data were utilized to obtain spatial distribution and vegetation growth status in Urumqi, Xinjiang. Three machine learning models such as Support Vector Machine (SVM), Random Forest (RF), and Gradient Boosting Regression Tree (GBRT) were constructed to predict surface temperature. Results indicated that UGS information extracted from GaoFen-2 data using the U-Net semantic segmentation model successfully predicted surface temperature. Among the three machine learning models, GBRT exhibited the highest predictive accuracy with an of 0.81, RMSE of 0.44, and RPD of 2.29, followed by RF ( of 0.80, RMSE of 0.45, and RPD of 2.22), and SVM (of 0.79, RMSE of 0.47, and RPD of 2.15), In addition, a variable importance assessment reduced the original 44 variables to 28, maintaining predictive accuracy with the GBRT model achieving an of 0.81, RMSE of 0.43, and RPD of 2.3. Our study demonstrates the effectiveness of using vegetation information derived from GaoFen-2 to predict surface temperature. This approach provides valuable recommendations for the layout of UGS in urban areas and serves as a comprehensive reference for urban planning and real estate development.
期刊介绍:
Urban Forestry and Urban Greening is a refereed, international journal aimed at presenting high-quality research with urban and peri-urban woody and non-woody vegetation and its use, planning, design, establishment and management as its main topics. Urban Forestry and Urban Greening concentrates on all tree-dominated (as joint together in the urban forest) as well as other green resources in and around urban areas, such as woodlands, public and private urban parks and gardens, urban nature areas, street tree and square plantations, botanical gardens and cemeteries.
The journal welcomes basic and applied research papers, as well as review papers and short communications. Contributions should focus on one or more of the following aspects:
-Form and functions of urban forests and other vegetation, including aspects of urban ecology.
-Policy-making, planning and design related to urban forests and other vegetation.
-Selection and establishment of tree resources and other vegetation for urban environments.
-Management of urban forests and other vegetation.
Original contributions of a high academic standard are invited from a wide range of disciplines and fields, including forestry, biology, horticulture, arboriculture, landscape ecology, pathology, soil science, hydrology, landscape architecture, landscape planning, urban planning and design, economics, sociology, environmental psychology, public health, and education.