Spatiotemporal Variations in Grassland Vulnerability on the Qinghai-Tibet Plateau Based on a Comprehensive Framework

Abstract

Grasslands are globally important for providing essential ecosystem services and maintaining ecological security. Monitoring and assessing grassland vulnerability are critical for developing long-term grassland management policies and strategies. The grassland vulnerability on the Qinghai-Tibet Plateau (QTP) is considered high, but its spatial and temporal variations in response to human activities and climate change are not well understood. In this study, a comprehensive grassland vulnerability index (GVI), which includes natural factors (VNF), environmental disturbances (VED), and socioeconomic impacts (VSI), was developed by using the analytic hierarchy process (AHP), principal component analysis (PCA), and environmental vulnerability distance index (EVDI). Our results showed that the spatial distribution of GVI had obvious heterogeneity, decreasing from northwest to southeast; the regions with serious and extreme vulnerability were mainly concentrated in the north-western alpine steppe and desert steppe. From 2000 to 2018, GVI decreased from 0.61 in 2000 to 0.60 in 2010 and then to 0.59 in 2018, demonstrating a healthy tendency. The normalized difference vegetation index (NDVI), land desertification, and population were the factors that had the most significant impact on VNF, VED, and VSI, respectively. The global Moran’s I index of grassland vulnerability was greater than 0, with a significant positive spatial correlation. The number of High-High and Low-Low units decreased, indicating that the High-High and Low-Low cluster regions tended to be discrete. Moreover, our results suggest that understanding the variations in grassland vulnerability on the QTP is important for regional sustainable development in the context of intensified climate change and human disturbances.

Spatiotemporal Patterns and Driving Factors of Ecological Vulnerability on the Qinghai-Tibet Plateau Based on the Google Earth Engine

“…Local spatial autocorrelation analysis is used to determine the local spatial agglomeration, which is mainly reflected by spatial agglomeration map [5]. To ensure the accuracy of the quantitative evaluation and the integrity of information within the scale, combining the internal characteristics of the study area, hexagons with a side length of 10 km were created to analyze the spatial autocorrelation characteristics of the RSEVI [28]. Hexagons are considered more efficient than square grids for continuously partitioning a two-dimensional space, but square grids are more commonly used.…”

“…First, most of the studies that have conducted vulnerability assessments for the QTP have mentioned that ecological conservation measures have had a positive impact on the ecological environment of the QTP [32,42,71,72,74]. These programs supported the conversion of farmland to grassland, biodiversity conservation, and environmental improvement [28]. Additionally, as a result of climate change, the QTP is becoming warmer, wetter, and greener [22].…”

“…Therefore, there are uncertainties in the data quality, analytical methods, and indicators. In the future, as ecological indicators and models develop and evolve, various, accurate and appropriate indicators or models can be used to reflect ecological processes [28]. Third, ecosystems are characterized by complexity, self-organization, and facilitation [78].…”

“…Vulnerability is an indicator that integrates multiple multidimensional and multivariate attributes [8]. As the concept of ecological structure, limited resilience, and vulnerability to environmental changes, is one of the most sensitive and fragile regions in China [27,28]. Therefore, an ecological vulnerability assessment for the QTP is essential for sustainable regional development.…”