Identification of impact factors for differentiated patterns of NDVI change in the headwater source region of Brahmaputra and Indus, Southwestern Tibetan Plateau. (June 2021)

• Record Type: Journal Article
• Authors: Liu, Qionghuan; Liu, Linshan; Zhang, Yili; Wang, Zhaofeng; Wu, Jianshuang; Li, Lanhui; Li, Shicheng; Paudel, Basanta
• Is Part Of: Ecological indicators. Volume 125(2021)
• Publication Date: 2021-06
• Subjects: Vegetation change -- Random forest -- Climate change -- Human activities -- Asian water tower; Environmental monitoring -- Periodicals; Environmental management -- Periodicals; Environmental impact analysis -- Periodicals; Environmental risk assessment -- Periodicals; Sustainable development -- Periodicals; 333.71405
• DOI: 10.1016/j.ecolind.2021.107604
• Languages: English
• ISSNs: 1470-160X

Abstract

Highlights: Both pattern and driving factors behind NDVI change were identified in HSRBI, Tibetan Plateau. Random Forest Modelling was helpful to quantify diver's contribution to NDVI change. NDVI monotonically increased in 39.87% of grasslands, mainly affected by natural drivers. NDVI abruptly increased in 22.55% of grasslands, likely related to human disturbance. Abstract: 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.