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. © 2022 by the authors.
