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Article

Spatiotemporal Dynamics and Multiple Drivers of Vegetation Cover in the Jinsha River Basin: A Geodetector-Based Analysis

by
Ran Zhai
1,
Jun Luan
2,
Juanru Yang
3,
Zhi Xu
1,
Liwen Xu
2,
Jin Tian
3,
Zhenyu Lv
1,
Xiao Chen
2 and
Yuping Bai
3,*
1
Science and Technology Research Institute, China Three Gorges Corporation, Beijing 101199, China
2
Luquan Wudongde Power Plant, Three Gorges Jinsha River Yunchuan Hydropower Development Co., Ltd., Kunming 651511, China
3
School of Land Science and Technology, China University of Geosciences, Beijing 100083, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2025, 17(16), 2783; https://doi.org/10.3390/rs17162783
Submission received: 11 June 2025 / Revised: 5 August 2025 / Accepted: 7 August 2025 / Published: 11 August 2025

Abstract

Under intensified global climate change and complex land use transitions, the Leaf Area Index (LAI) serves as a key ecological indicator to monitor vegetation responses to natural and anthropogenic factors. This study provided a comprehensive spatiotemporal diagnosis of the LAI and uniquely integrated remote sensing data with the Geodetector model to quantitatively assess both individual and interactive effects of natural and human drivers. Specifically, we analyzed LAI dynamics in the Jinsha River Basin from 2000 to 2023 using Sen’s Slope and Mann–Kendall tests, combined with Geodetector modeling to identify drivers and their interactions. Furthermore, ARIMA-based forecasting offered forward-looking insights to support land use planning and ecosystem resilience. Results revealed a fluctuating upward trend in LAI, with larger areas improving than degrading, and distinct seasonal and spatial patterns, with a notably higher LAI in southern regions. Elevation and temperature were the primary drivers, explaining 57% and 54% of spatial variation, respectively, with their combined effects further enhancing explanatory power. The future LAI trend appeared stable without significant changes. These findings demonstrated LAI’s utility for assessing land use change impacts and ecological sustainability, providing a scientific basis for land use optimization, ecological restoration, and sustainable regional development under the human–earth system framework.
Keywords: leaf area index (LAI); spatiotemporal dynamics; geodetector; interaction effect; Jinsha River Basin leaf area index (LAI); spatiotemporal dynamics; geodetector; interaction effect; Jinsha River Basin

Share and Cite

MDPI and ACS Style

Zhai, R.; Luan, J.; Yang, J.; Xu, Z.; Xu, L.; Tian, J.; Lv, Z.; Chen, X.; Bai, Y. Spatiotemporal Dynamics and Multiple Drivers of Vegetation Cover in the Jinsha River Basin: A Geodetector-Based Analysis. Remote Sens. 2025, 17, 2783. https://doi.org/10.3390/rs17162783

AMA Style

Zhai R, Luan J, Yang J, Xu Z, Xu L, Tian J, Lv Z, Chen X, Bai Y. Spatiotemporal Dynamics and Multiple Drivers of Vegetation Cover in the Jinsha River Basin: A Geodetector-Based Analysis. Remote Sensing. 2025; 17(16):2783. https://doi.org/10.3390/rs17162783

Chicago/Turabian Style

Zhai, Ran, Jun Luan, Juanru Yang, Zhi Xu, Liwen Xu, Jin Tian, Zhenyu Lv, Xiao Chen, and Yuping Bai. 2025. "Spatiotemporal Dynamics and Multiple Drivers of Vegetation Cover in the Jinsha River Basin: A Geodetector-Based Analysis" Remote Sensing 17, no. 16: 2783. https://doi.org/10.3390/rs17162783

APA Style

Zhai, R., Luan, J., Yang, J., Xu, Z., Xu, L., Tian, J., Lv, Z., Chen, X., & Bai, Y. (2025). Spatiotemporal Dynamics and Multiple Drivers of Vegetation Cover in the Jinsha River Basin: A Geodetector-Based Analysis. Remote Sensing, 17(16), 2783. https://doi.org/10.3390/rs17162783

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