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Spatiotemporal Dynamics and Response of Land Surface Temperature and Kernel Normalized Difference Vegetation Index in Yangtze River Economic Belt, China: Multi-Method Analysis

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  • Hongjia Zhu

    (School of Urban Construction, Chengdu Polytechnic, Chengdu 611433, China
    School of Architecture and Urban Planning, Chongqing University, Chongqing 400030, China)

  • Ao Wang

    (School of Architecture and Urban Planning, Chongqing University, Chongqing 400030, China
    Key Laboratory of New Technology for Construction of Cities in Mountain Area, Chongqing University, Chongqing 400044, China)

  • Pengtao Wang

    (School of Tourism, Xi’an International Studies University, Xi’an 710128, China)

  • Chunguang Hu

    (School of Urban Planning and Design, Peking University, Shenzhen 518055, China)

  • Maomao Zhang

    (College of Public Administration, Huazhong University of Science and Technology, Wuhan 430079, China)

Abstract

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 ( p < 0.05) and a LST rise of 0.065 °C/year ( p < 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 2 , 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.

Suggested Citation

  • Hongjia Zhu & Ao Wang & Pengtao Wang & Chunguang Hu & Maomao Zhang, 2025. "Spatiotemporal Dynamics and Response of Land Surface Temperature and Kernel Normalized Difference Vegetation Index in Yangtze River Economic Belt, China: Multi-Method Analysis," Land, MDPI, vol. 14(3), pages 1-20, March.
    • Handle: RePEc:gam:jlands:v:14:y:2025:i:3:p:598-:d:1610941
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    References listed on IDEAS

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    1. Wang, Shaojian & Shi, Chenyi & Fang, Chuanglin & Feng, Kuishuang, 2019. "Examining the spatial variations of determinants of energy-related CO2 emissions in China at the city level using Geographically Weighted Regression Model," Applied Energy, Elsevier, vol. 235(C), pages 95-105.
    2. Maomao Zhang & Abdulla-Al Kafy & Bing Ren & Yanwei Zhang & Shukui Tan & Jianxing Li, 2022. "Application of the Optimal Parameter Geographic Detector Model in the Identification of Influencing Factors of Ecological Quality in Guangzhou, China," Land, MDPI, vol. 11(8), pages 1-20, August.
    3. Maomao Zhang & Enqing Chen & Cheng Zhang & Chen Liu & Jianxing Li, 2024. "Multi-Scenario Simulation of Land Use Change and Ecosystem Service Value Based on the Markov–FLUS Model in Ezhou City, China," Sustainability, MDPI, vol. 16(14), pages 1-23, July.
    4. Roman Hoffmann & Anna Dimitrova & Raya Muttarak & Jesus Crespo Cuaresma & Jonas Peisker, 2020. "A meta-analysis of country-level studies on environmental change and migration," Nature Climate Change, Nature, vol. 10(10), pages 904-912, October.
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