Vegetation Changes along the Qinghai-Tibet Plateau Engineering Corridor Since 2000 Induced by Climate Change and Human Activities

Abstract

Abstract:The Qinghai-Tibet (QT) Plateau Engineering Corridor is located in the hinterland of the QT Plateau, which is highly sensitive to global climate change. Climate change causes permafrost degradation, which subsequently affects vegetation growth. This study focused on the vegetation dynamics and their relationships with climate change and human activities in the region surrounding the QT Plateau Engineering Corridor. The vegetation changes were inferred by applying trend analysis, the Mann-Kendall trend test and abrupt change analysis. Six key regions, each containing 40 nested quadrats that ranged in size from 500 × 500 m to 20 × 20 km, were selected to determine the spatial scales of the impacts from different factors. Cumulative growing season integrated enhanced vegetation index (CGSIEVI) values were calculated for each of the nested quadrats of different sizes to indicate the overall vegetation state over the entire year at different spatial scales. The impacts from human activities, a sudden increase in precipitation and permafrost degradation were quantified at different spatial scales using the CGSIEVI values and meteorological data based on the double mass curve method. Three conclusions were derived. First, the vegetation displayed a significant increasing trend over 23.6% of the study area. The areas displaying increases were mainly distributed in the Hoh Xil. Of the area where the vegetation displayed a significant decreasing trend, 72.4% was made up of alpine meadows. Second, more vegetation, especially the alpine meadows, has begun to degenerate or experience more rapid degradation since 2007 due to permafrost degradation and overgrazing. Finally, an active layer depth of 3 m to 3.2 m represents a limiting depth for alpine meadows.

Monitoring the impact of climate change and human activities on grassland vegetation dynamics in the northeastern Qinghai-Tibet Plateau of China during 2000–2015

“…Our RDA results agree with recent studies which revealed that climate change, particularly the increase in temperature, has negatively affected plant growth, especially in alpine meadows in the Qinghai-Tibet Plateau (Xiong et al, 2016). The higher temperatures have enhanced permafrost thawing, which in turn inhibited the growth of plants with shallow root systems, causing the degradation of alpine meadows (Xue et al, 2009;Song et al, 2018). Throughout the entire study area (i.e., the northeastern Qinghai-Tibet Plateau), the NDVI was positively correlated with precipitation while negatively correlated with temperature (Fig.…”

Model Simulation and Prediction of Decadal Mountain Permafrost Distribution Based on Remote Sensing Data in the Qilian Mountains from the 1990s to the 2040s

“…The land coverage factor is used to present the distribution status of the ground surface. In this research, it is represented by the Normalized Difference Vegetation Index (NDVI), which is used to monitor the coverage degree and growth status of ground vegetation through band computation of remote sensing images [27]. The value range of NDVI is from −1 to 1.…”

On the Performances of Trend and Change-Point Detection Methods for Remote Sensing Data

“…Over the last few decades, many different methods have been developed to detect trend and change-points due to their important roles in model fitting and the prediction of time-ordered datasets [4][5][6][7][8][9][10][11][12]. The Mann-Kendall [13,14] and the Cox-Stuart methods [15] have been the most used non-parametric tests for trend detection.…”