Disentangling the Response of Vegetation Dynamics to Natural and Anthropogenic Drivers over the Minjiang River Basin Using Dimensionality Reduction and a Structural Equation Model

Located at an average elevation of approximately 2000 m, the Minjiang River Basin (MJB), a key tributary of the Upper Yangtze River, straddles the Western Sichuan Plateau and the Sichuan Basin. Vegetation here is crucial for human life, providing oxygen and energy. However, the influence of climatic variables, human activities, and rugged terrain on vegetation vitality is still debated. This study mainly leverages data from the Normalized Difference Vegetation Index (NDVI), meteorological stations data, and land use data. Analytical techniques include trend analysis, partial correlation coefficient analysis (PCC), principal component analysis (PCA), and partial least squares structural equation modeling (PLS-SEM). Results indicate a stable upward trend in vegetation growth with minimal fluctuations, with a growth rate of 0.95 × 10−3/a (p < 0.01). PCC analysis shows a positive correlation between NDVI and key climatic elements in over 60% of the area. The areas with significant vegetation growth had the highest average PCC. PCA and PLS-SEM identify temperature and precipitation as primary growth drivers, while elevation and land use intensity hinder growth. The MJB landscape reveals thresholds and tipping points, with specific temperature and precipitation benchmarks varying by elevation, delineating the boundary between flourishing vegetation and growth inhibition.

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Publication Details

Journal
Forests
Published
2024-08-15
DOI
https://doi.org/10.3390/f15081438
Citations
7
Primary Topic
Remote Sensing in Agriculture
Type
article
Field-Weighted Citation Impact
3.21

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article

Disentangling the Response of Vegetation Dynamics to Natural and Anthropogenic Drivers over the Minjiang River Basin Using Dimensionality Reduction and a Structural Equation Model

Binni Xu, Tingbin Zhang, Jingji Li, Xiaohui Zhou et al.
7 citations
Forests
Remote Sensing in Agriculture
3.21
article

Disentangling the Response of Vegetation Dynamics to Natural and Anthropogenic Drivers over the Minjiang River Basin Using Dimensionality Reduction and a Structural Equation Model

Binni Xu, Tingbin Zhang, Jingji Li, Xiaohui Zhou, Guoyan Wang, Yanguo Liu, Yuling Jia, Yujie Kang, Ziqin Wang, Wenjie Shen, Yu Chen
article en
7 citations

Abstract

Located at an average elevation of approximately 2000 m, the Minjiang River Basin (MJB), a key tributary of the Upper Yangtze River, straddles the Western Sichuan Plateau and the Sichuan Basin. Vegetation here is crucial for human life, providing oxygen and energy. However, the influence of climatic variables, human activities, and rugged terrain on vegetation vitality is still debated. This study mainly leverages data from the Normalized Difference Vegetation Index (NDVI), meteorological stations data, and land use data. Analytical techniques include trend analysis, partial correlation coefficient analysis (PCC), principal component analysis (PCA), and partial least squares structural equation modeling (PLS-SEM). Results indicate a stable upward trend in vegetation growth with minimal fluctuations, with a growth rate of 0.95 × 10−3/a (p < 0.01). PCC analysis shows a positive correlation between NDVI and key climatic elements in over 60% of the area. The areas with significant vegetation growth had the highest average PCC. PCA and PLS-SEM identify temperature and precipitation as primary growth drivers, while elevation and land use intensity hinder growth. The MJB landscape reveals thresholds and tipping points, with specific temperature and precipitation benchmarks varying by elevation, delineating the boundary between flourishing vegetation and growth inhibition.

ForestsVol. 15(8)
Southwest University of Science and Technology (CN), Chengdu University of Technology (CN), Science and Technology Department of Sichuan Province (CN)
National Aeronautics and Space Administration, Chinese Academy of Sciences
Life in Land
Openalex Percentile: Top 9%
Remote Sensing in Agriculture
3.21
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