Quantifying Climate-Anthropogenic Forcing on Arid Basin Vegetation Dynamics Using Multi-Vegetation Indices and Geographical Detector

Understanding the spatiotemporal dynamics of vegetation and their driving mechanisms is essential for ecological assessment and management. However, current assessments of the Heihe River Basin (HRB) vegetation dynamics remain uncertain due to reliance on single indices without cross-validation and oversimplified attribution of residual variations. Here, we integrated four complementary vegetation indices (NDVI, EVI, kNDVI, and NIRv) with trend and abrupt change detection analyses to establish a framework for assessing vegetation changes in the HRB from 2004 to 2023. Given that the dominance of non-climatic factors is widely attributed to human water management and land use policies, land use change and other anthropogenic factors were incorporated together with topographic/edaphic factors into the optimal parameter-based geographical detector (OPGD), where vegetation changes induced by non-climatic factors were first isolated through residual trend analysis, thereby quantifying their explanatory power on vegetation index variations. The results demonstrate that vegetation in the HRB experienced a fluctuating upward trend (0.0013/yr) from 2004 to 2023, with significant improvement in 43% and degradation in 3% of the region. Climatic and non-climatic factors explained 26% and 74% of spatial variation, dominated by precipitation and land use change, respectively. Notably, the interaction of land use change and elevation accounted for 56% of NIRv variation, markedly exceeding single factors, as determined by the interaction detector in the OPGD. Additionally, large-scale ecological restoration projects and effective water resource management policies have played a pivotal role in facilitating vegetation recovery across the basin. This study enhances insight into vegetation dynamics and supports both sustainable restoration and development in the HRB.

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Publication Details

Journal
Remote Sensing
Published
2025-10-21
DOI
https://doi.org/10.3390/rs17203496
Primary Topic
Remote Sensing in Agriculture
Type
article
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article

Quantifying Climate-Anthropogenic Forcing on Arid Basin Vegetation Dynamics Using Multi-Vegetation Indices and Geographical Detector

Xiufang Song, Lan Wang, Xiang Li, Xinzhe Li et al.
Remote Sensing
Remote Sensing in Agriculture
article

Quantifying Climate-Anthropogenic Forcing on Arid Basin Vegetation Dynamics Using Multi-Vegetation Indices and Geographical Detector

Xiufang Song, Lan Wang, Xiang Li, Xinzhe Li, Mengran Yu, Qiuli Yang
article en

Abstract

Understanding the spatiotemporal dynamics of vegetation and their driving mechanisms is essential for ecological assessment and management. However, current assessments of the Heihe River Basin (HRB) vegetation dynamics remain uncertain due to reliance on single indices without cross-validation and oversimplified attribution of residual variations. Here, we integrated four complementary vegetation indices (NDVI, EVI, kNDVI, and NIRv) with trend and abrupt change detection analyses to establish a framework for assessing vegetation changes in the HRB from 2004 to 2023. Given that the dominance of non-climatic factors is widely attributed to human water management and land use policies, land use change and other anthropogenic factors were incorporated together with topographic/edaphic factors into the optimal parameter-based geographical detector (OPGD), where vegetation changes induced by non-climatic factors were first isolated through residual trend analysis, thereby quantifying their explanatory power on vegetation index variations. The results demonstrate that vegetation in the HRB experienced a fluctuating upward trend (0.0013/yr) from 2004 to 2023, with significant improvement in 43% and degradation in 3% of the region. Climatic and non-climatic factors explained 26% and 74% of spatial variation, dominated by precipitation and land use change, respectively. Notably, the interaction of land use change and elevation accounted for 56% of NIRv variation, markedly exceeding single factors, as determined by the interaction detector in the OPGD. Additionally, large-scale ecological restoration projects and effective water resource management policies have played a pivotal role in facilitating vegetation recovery across the basin. This study enhances insight into vegetation dynamics and supports both sustainable restoration and development in the HRB.

Remote SensingVol. 17(20)
Xinjiang Agricultural University (CN), Wuhan University (CN), State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing (CN), National Science Library (CN), University of Chinese Academy of Sciences (CN), Xinjiang University (CN)
Openalex Percentile: Top 71%
Remote Sensing in Agriculture
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