Spatiotemporal Dynamics and Response of Land Surface Temperature and Kernel Normalized Difference Vegetation Index in Yangtze River Economic Belt, China: Multi-Method Analysis
As global climate change intensifies, its impact on the ecological environment is becoming increasingly pronounced. Among these, land surface temperature (LST) and vegetation cover status, as key ecological indicators, have garnered widespread attention. This study analyzes the spatiotemporal dynami...
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MDPI AG
2025-03-01
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| author | Hongjia Zhu Ao Wang Pengtao Wang Chunguang Hu Maomao Zhang |
| author_facet | Hongjia Zhu Ao Wang Pengtao Wang Chunguang Hu Maomao Zhang |
| author_sort | Hongjia Zhu |
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| description | As global climate change intensifies, its impact on the ecological environment is becoming increasingly pronounced. Among these, land surface temperature (LST) and vegetation cover status, as key ecological indicators, have garnered widespread attention. This study analyzes the spatiotemporal dynamics of LST and the Kernel Normalized Difference Vegetation Index (KNDVI) in 11 provinces along the Yangtze River and their response to climate change based on MODIS Terra satellite data from 2000 to 2020. The linear regression showed a significant KNDVI increase of 0.003/year (<i>p</i> < 0.05) and a LST rise of 0.065 °C/year (<i>p</i> < 0.01). The Principal Component Analysis (PCA) explained 74.5% of the variance, highlighting the dominant influence of vegetation cover and urbanization. The K-means clustering identified three regional patterns, with Shanghai forming a distinct group due to low KNDVI variability. The Generalized Additive Model (GAM) analysis revealed a nonlinear LST–KNDVI relationship, most evident in Hunan, where cooling effects weakened beyond a KNDVI threshold of 0.25. Despite a 0.07 KNDVI increase, high-temperature areas in Chongqing and Jiangsu expanded by over 2500 km<sup>2</sup>, indicating limited LST mitigation. This study reveals the complex interaction between LST and the KNDVI, which may provide scientific basis for the development of regional ecological management and climate adaptation strategies. |
| format | Article |
| id | doaj-art-4773a412bf8c4d4b85d989a09f463f78 |
| institution | OA Journals |
| issn | 2073-445X |
| language | English |
| publishDate | 2025-03-01 |
| publisher | MDPI AG |
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| spelling | doaj-art-4773a412bf8c4d4b85d989a09f463f782025-08-20T01:48:41ZengMDPI AGLand2073-445X2025-03-0114359810.3390/land14030598Spatiotemporal Dynamics and Response of Land Surface Temperature and Kernel Normalized Difference Vegetation Index in Yangtze River Economic Belt, China: Multi-Method AnalysisHongjia Zhu0Ao Wang1Pengtao Wang2Chunguang Hu3Maomao Zhang4School of Urban Construction, Chengdu Polytechnic, Chengdu 611433, ChinaSchool of Architecture and Urban Planning, Chongqing University, Chongqing 400030, ChinaSchool of Tourism, Xi’an International Studies University, Xi’an 710128, ChinaSchool of Urban Planning and Design, Peking University, Shenzhen 518055, ChinaCollege of Public Administration, Huazhong University of Science and Technology, Wuhan 430079, ChinaAs global climate change intensifies, its impact on the ecological environment is becoming increasingly pronounced. Among these, land surface temperature (LST) and vegetation cover status, as key ecological indicators, have garnered widespread attention. This study analyzes the spatiotemporal dynamics of LST and the Kernel Normalized Difference Vegetation Index (KNDVI) in 11 provinces along the Yangtze River and their response to climate change based on MODIS Terra satellite data from 2000 to 2020. The linear regression showed a significant KNDVI increase of 0.003/year (<i>p</i> < 0.05) and a LST rise of 0.065 °C/year (<i>p</i> < 0.01). The Principal Component Analysis (PCA) explained 74.5% of the variance, highlighting the dominant influence of vegetation cover and urbanization. The K-means clustering identified three regional patterns, with Shanghai forming a distinct group due to low KNDVI variability. The Generalized Additive Model (GAM) analysis revealed a nonlinear LST–KNDVI relationship, most evident in Hunan, where cooling effects weakened beyond a KNDVI threshold of 0.25. Despite a 0.07 KNDVI increase, high-temperature areas in Chongqing and Jiangsu expanded by over 2500 km<sup>2</sup>, indicating limited LST mitigation. This study reveals the complex interaction between LST and the KNDVI, which may provide scientific basis for the development of regional ecological management and climate adaptation strategies.https://www.mdpi.com/2073-445X/14/3/598dynamicsKNDVILSTclimate changeYangtze River Economic Belt |
| spellingShingle | Hongjia Zhu Ao Wang Pengtao Wang Chunguang Hu Maomao Zhang Spatiotemporal Dynamics and Response of Land Surface Temperature and Kernel Normalized Difference Vegetation Index in Yangtze River Economic Belt, China: Multi-Method Analysis Land dynamics KNDVI LST climate change Yangtze River Economic Belt |
| title | Spatiotemporal Dynamics and Response of Land Surface Temperature and Kernel Normalized Difference Vegetation Index in Yangtze River Economic Belt, China: Multi-Method Analysis |
| title_full | Spatiotemporal Dynamics and Response of Land Surface Temperature and Kernel Normalized Difference Vegetation Index in Yangtze River Economic Belt, China: Multi-Method Analysis |
| title_fullStr | Spatiotemporal Dynamics and Response of Land Surface Temperature and Kernel Normalized Difference Vegetation Index in Yangtze River Economic Belt, China: Multi-Method Analysis |
| title_full_unstemmed | Spatiotemporal Dynamics and Response of Land Surface Temperature and Kernel Normalized Difference Vegetation Index in Yangtze River Economic Belt, China: Multi-Method Analysis |
| title_short | Spatiotemporal Dynamics and Response of Land Surface Temperature and Kernel Normalized Difference Vegetation Index in Yangtze River Economic Belt, China: Multi-Method Analysis |
| title_sort | spatiotemporal dynamics and response of land surface temperature and kernel normalized difference vegetation index in yangtze river economic belt china multi method analysis |
| topic | dynamics KNDVI LST climate change Yangtze River Economic Belt |
| url | https://www.mdpi.com/2073-445X/14/3/598 |
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