ORIGINAL RESEARCH article
Front. For. Glob. Change
Sec. Forests and the Atmosphere
Revealing the Driving Forces of Vegetation Dynamics in China's Mountainous Regions: Integrating Climatic and Anthropogenic Factors
Zhengwen Niu 1
Shuchun Wang 1
Feng Yu 1
Binjie Li 1
Dejin Dong 2
Shenghong Zheng 1
Chunju Peng 1
Ke Zhang 1
Guanghui Zeng 1
Qi Huang 1
1. Wenzhou Vocational College of Science and Technology, Wenzhou, China
2. Faculty of Social and Cultural Studies, Kyushu Daigaku, Fukuoka, Japan
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Abstract
Vegetation growth is influenced by both natural factors and human activities. However, many studies assessing the impacts of temperature and precipitation on vegetation fail to account for the influence of human activities, thereby introducing significant uncertainty into their evaluations. Focusing on mountainous regions in China, this study utilized Google Earth Engine (GEE) to obtain MODIS NDVI data during the growing seasons from 2000 to 2020. Spatial transfer matrices, Sen's slope estimator, the Mann–Kendall test, and multiple linear regression were employed to analyze the spatiotemporal variation of vegetation and its driving forces. The results revealed that: (1) NDVI across China's mountainous regions exhibits marked spatial heterogeneity, increasing gradually from northwest to southeast. Vegetation cover changes followed a distinct progression pattern, with the transition from moderate to high coverage accounting for 52.18% of the total transformed area. (2) The fastest NDVI increases occurred in the central and northern mountainous regions (0.004/a and 0.0039/a, respectively), while southwestern regions showed slower growth (0.002/a). Overall, 83.02% of mountainous areas experienced improving NDVI trends, with 44.34% showing significant improvement; only 7.55% of areas experienced degradation. (3) An improved multiple linear regression model revealed that climate variables alone could not adequately capture vegetation responses to precipitation. The effects of precipitation on NDVI varied significantly by region. In the humid southeastern mountains, precipitation negatively impacted NDVI, while in most areas, its effect was positive. When human activity and topographic variables were incorporated, the regression coefficient of precipitation decreased from 0.170 in the climate-only model to 0.088 in the full model, indicating that omitting these factors led to an approximately 48% overestimation of precipitation's effect on NDVI. This highlights the combined influence of precipitation dependency, topographic complexity, and human activities on vegetation dynamics. Future studies should incorporate these interacting factors to more comprehensively understand the mechanisms driving NDVI changes.
Summary
Keywords
Driving Mechanisms, Google Earth Engine (GEE), Mountainous China, NDVI, spatiotemporal dynamics
Received
03 December 2025
Accepted
17 February 2026
Copyright
© 2026 Niu, Wang, Yu, Li, Dong, Zheng, Peng, Zhang, Zeng and Huang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Qi Huang
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