Assessing Natural Resource Carrying Capacity in Xinjiang Using Remote Sensing: Spatiotemporal Patterns and Implications for Future Land Use

Abstract

Xinjiang has undergone rapid oasis expansion, leading to significant changes in land use and, consequently, to unsustainable issues regarding natural resources. Most studies on natural resource carrying capacity (NRCC) focus on single resource elements and rarely examine patterns at the pixel scale. Although remote sensing technologies and cloud platforms offer significant advantages in NRCC assessment, this potential remains underutilized. Meanwhile, existing land use simulation (LUS) research mainly emphasizes land suitability or land use patterns under specific development objectives, with a limited understanding of resolving the conflict between NRCC and sustainable land development. To address these gaps, this study constructs a spatiotemporal variation and driving factor analysis framework for NRCC, driven by remote sensing, based on the Google Earth Engine. It also integrates the PLUS model to simulate land use patterns in Xinjiang under different scenarios for 2030. The results show that the NRCC in Xinjiang exhibits a high value in the northwest and a low value in the southeast, with an overall increasing trend from 2001 to 2020. Precipitation and temperature difference are the primary driving factors. Under dynamic NRCC scenarios, the farmland and built‐up land will increase, whereas in static NRCC scenarios, the farmland and water will increase. The LUS framework based on NRCC constraints provides a new approach to alleviating the conflict between limited natural resources and land development in arid regions. This study expands the perspective on LUS in arid areas but also provides practical references for sustainable natural resources and land use management in other areas.