Identification of impact factors for differentiated patterns of NDVI change in the headwater source region of Brahmaputra and Indus, Southwestern Tibetan Plateau

Identifying the driving forces and mechanisms underlying vegetation change is critical to globally adapting to future climate change. However, this topic remains unclear for ecosystems within the Headwater Source Region of Brahmaputra and Indus (HSRBI), southwestern Tibetan Plateau. In this study, v...

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Autores principales: Qionghuan Liu, Linshan Liu, Yili Zhang, Zhaofeng Wang, Jianshuang Wu, Lanhui Li, Shicheng Li, Basanta Paudel
Formato: article
Lenguaje:EN
Publicado: Elsevier 2021
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Acceso en línea:https://doaj.org/article/55788d8653ef4f569890f2e283a9d177
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Sumario:Identifying the driving forces and mechanisms underlying vegetation change is critical to globally adapting to future climate change. However, this topic remains unclear for ecosystems within the Headwater Source Region of Brahmaputra and Indus (HSRBI), southwestern Tibetan Plateau. In this study, vegetation changes, in terms of the Normalized Difference Vegetation Index (NDVI) from the Moderate Resolution Imaging Spectroradiometer (MODIS), were detected for breakpoints for the period of 2000–2017 by segment regression analysis. The effects of 14 potential environmental variables on vegetation dynamics were estimated and quantified via random forest modelling. Our results showed that monotonic increases (MI) and abrupt increases (AI) in NDVI accounted for 39.87% and 22.55% of vegetation change, respectively. In contrast, monotonic decreases (MD) and abrupt decreases (AD) only accounted for 19.38% and 7.65% of vegetation change, respectively. Our findings also confirmed that climate change was the most crucial driver of vegetation change in the HSRBI, and human disturbance was responsible for abrupt changes in vegetation. The primary MI pattern was affected by distance to the replenishment water source, which contributed to 21.36% of vegetation change. The distance to lakes was an essential factor associated with vegetation greening. AI was mainly affected by the interaction between human disturbance and climate change, which contributed to 27.12% and 26.89% of vegetation changes, respectively. Distance to residential areas was the most significant factor that linked the human disturbance to abrupt vegetation changes. AD and MD were inclined to be affected by the variation in climate variables. Finally, this study will help to identify multidimensional mechanisms of vegetation change and aid ecosystem stability in the headwater zones.