Monitoring the Spatiotemporal Dynamics of Habitat Quality and Its Driving Factors Based on the Coupled NDVI-InVEST Model: A Case Study from the Tianshan Mountains in Xinjiang, China

Abstract

Globally, mountains have suffered enormous biodiversity loss and habitat degradation due to climate change and human activities. As an agent of biodiversity, evaluating habitat quality (HQ) change is an indispensable key step for regional ecological security and human well-being enhancement, especially for fragile mountain ecosystems in arid areas. In this study, we aimed to propose an integrated framework coupled with the Normalized Difference Vegetation Index (NDVI) and Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST)-HQ module to improve the effectiveness and accuracy of HQ estimation. We highlighted the Tianshan Mountains in Xinjiang as an example to validate the model, as it is a typical representative of mountain ecosystems in the temperate arid zone. Specifically, we aimed to assess the spatiotemporal dynamics of HQ over the past two decades and investigate its influencing factors using a geographical detector model. The results show that, first, grassland and unused land were the main land-use types in the study area. The land-use transitions were mainly concentrated in grassland, woodland, water body, and unused land. Second, the total area of very important habitats and general habitats accounted for over 70% of the Tianshan Mountains. The average HQ decreased from 0.5044 to 0.4802 during 1995–2015. Third, the HQ exhibited significant spatial differentiation, and the Ili River Valley and Kaidu River Basin were the hot spots, while the south and east of the Tianshan Mountains were the cold spots. Habitat quality was strongly related to the terrain gradient, with an inverted “U” type. Protected areas of different categories played a vital role in biodiversity conservation. Finally, soil type, land-use change, precipitation, temperature, and grazing intensity were the dominant factors in response to HQ change for both the total Tianshan Mountains and sub-regions, followed by elevation, the relief degree of the land surface, gross domestic product, population density, and distance to tourism attractions. The interaction effects of the influencing factors were improved compared to the effects of the individual factors in each zone. Furthermore, these results provide decision-making criteria for formulating a scientific and systematic protection of ecology and restoration planning systems to enhance the capacity to address climate change.

Construction of Multi-Level Ecological Security Pattern for World Natural Heritage Sites from the Perspective of Coupling and Coordination between Humans and Nature: A Case Study of Shilin Yi Autonomous County, China

“…Urban land, cultivated land, mining land, and unused land parameters were all considered stress factors in this study. It was determined that the maximum influence distance, weight, and spatial decay type of the InVEST Model are based on the User's Guide for the InVEST Model and other related literature (Table 1) [43,44]. As defined by the formula, it is as follows: In Equation ( 3), Q xj is the habitat quality index for land use type j for raster x, H j denotes habitat suitability for type j of land use, k denotes the constant of half-saturation, and z is a conversion factor inherent in the system, generally taken as 2.5.…”

Analyzing Spatiotemporal Variations and Driving Factors of Grassland in the Arid Region of Northwest China Surrounding the Tianshan Mountains

“…The Tianshan Mountains, often referred to as the "Water Tower of Central Asia" [31], connect the Junggar and Tarim basins and support the largest ecosystems in the arid Central Asian landscape [32]. In recent decades, with the deterioration of climate change due to rising global temperatures and frequent extreme weather events, this ecosystem has been increasingly threatened by the rapidly developing regional economy and urbanization, as evidenced by the continuous decline in productivity and environmental problems [33].…”