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| DOI：10.13522/j.cnki.ggps.2025298 |
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| Spatiotemporal responses of habitat quality to land use changes and the underlying driving factors in the Shiyang River Basin |
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Hou Huimin, Guo Feng, Wang Pengquan, Wang Hui, Zhang Wenjing, Lu Di
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1. Lanzhou University of Technology, Lanzhou 730050, China; 2. Qinghai Minzu University, Xining 810007, China;
3. Shiyang River Basin Water Resources Utilization Center of Gansu Provincial Department of Water Resources, Wuwei 733000, China
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| Abstract: |
| 【Objective】The Shiyang River Basin is an ecologically fragile arid basin in Northwest China and faces habitat degradation under the dual influence of human activities and climate change. Understanding future habitat quality change and its riving mechanisms is critical for ecological conservation and territorial spatial planning. This study predicts the spatiotemporal variation in habitat quality under different land use scenarios in the basin and identifies its driving forces.【Method】The FLUS model was used to simulate spatial changes in land use in 2035 under three scenarios: cultivated land protection, natural development, and ecological protection. The InVEST model was then applied to predict the spatiotemporal variation in habitat quality; the random forest classification algorithm was used to quantify the importance of the driving factors in affecting the spatial differentiation of habitat quality.【Result】From 2020 to 2035, the spatial distribution of habitat quality remained generally consistent across different land use scenarios. In 2020, areas with low-grade habitat quality and high-grade habitat quality accounted for 47% and 35% of the total area of the region, respectively, with the remaining classified as medium-grade habitat quality areas.【Conclusion】Under all three land use change scenarios from 2020 to 2035, habitat quality in the Shiyang River Basin decreases from the southwest to the northeast, but the overall ecological habitat quality shows an improving trend. |
| Key words: Shiyang River Basin; habitat quality; FLUS model; InVEST model; scenario prediction |
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