Objective This study aims to deconstruct the mechanisms of interaction between land use and ecosystem health status in the Yellow River Ω-shaped bend region, thereby providing scientific references for resolving the conflict between regional urban development and ecology. Methods Using the PLUS model and integrating multi-source data, combined with land use and ecosystem health data from 2000 to 2023, this study comprehensively analyzed the mutual feedback mechanisms between land use evolution and ecosystem health in 2025 and 2030. Results (1) Urban construction land surged by 103.84% over the past 23 years, mainly encroaching on farmland and grassland. However, a large area of unused land was converted into grassland, highlighting the effectiveness of ecological governance. Multi-scenario simulations indicated that the ecological protection scenario could effectively inhibit the expansion of construction land and protect the regional ecological environment. (2) The ecosystem health index continued to rise. Spatially, the “pathological/sub-pathological” areas decreased, but the northwestern desert zone remained a significant cold spot. (3) The analysis of the coupling effects between land use and ecosystem health in the Yellow River Ω-shaped bend region revealed that the dual tension between land use development pressure and ecological restoration benefits shaped a three-level gradient spatial pattern. The high coordination state was found in the Shanxi-Shaanxi core area, the Ningxia-Inner Mongolia transitional zone was in the middle, and the northwestern region remained trapped in low values. The construction of a cross-regional coordinated network could effectively bridge the regional development gap. Conclusion Overall, the ecosystem health in the Yellow River Ω-shaped bend region is improving, with significant results from ecological restoration measures such as returning farmland to forest. The coordination of multiple regions can promote the positive coordination between urban land use and ecosystem health.
值得关注的是,近年来人工智能与复杂系统建模技术的突破,为此类问题的解决提供了新工具。PLUS模型(Patch-generating Land Use Simulation Model)作为新一代土地利用模拟框架,通过耦合随机森林算法与土地扩张分析策略(LEAS),不仅能精准识别多维驱动因子的非线性作用,还可通过政策情景模块动态响应“双碳目标”“生态红线”等约束条件,弥补了传统模型在空间异质性与政策嵌入性上的不足[10]。然而,该模型在生态脆弱区的应用仍存在两大瓶颈:其一,沙漠化区域的土地利用竞争机制复杂,耕地、草地、未利用地之间的转换阈值受降水年际波动影响显著,现有模型参数化方案对此考虑不足;其二,模拟结果与生态系统服务功能的动态耦合尚不成熟,亟需构建“土地模拟-健康评估”的闭环分析框架。例如,Ye以安徽为例,利用PLUS模型揭示了城镇扩张侵占耕地的路径,但未能量化这一量化过程对粮食生产与土壤固碳功能的协同性损害[11]。
其中,生态系统服务价值(Ecosystem Service Value, ESV)测算采用谢高地等[23]基于Costanza等[24]的利益转移方法,在构建ESV当量权重因子时,结合黄河几字弯地区实际情况对各地类生态系统的单位面积ESV系数进行修正[25]。具体调整如下:耕地以旱地为基准,林地以灌木为基准,草地取草原、灌草丛、草甸的平均值,鉴于永久性冰川雪地占地面积相对较小,取水域当量因子时忽略其系数;建设用地ESV取0;未利用地取荒漠系数(表3)。其计算公式如下:
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