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[基于XGBoost-SHAP模型对北京生态系统服务的空间模式和驱动因素的分析]
Hui Zhao1, Qian Liu1, Min Zhang1
1Institute of Statistics and Data Science, Xinjiang University of Finance & Economics, Urumqi 830012, China.
Huan jing ke xue= Huanjing kexue
|February 9, 2026
概括
这项研究分析了北京的北京.
科学领域:
- 生态生态学 生态生态学
- 环境科学 环境科学
- 空间分析 空间分析
背景情况:
- 了解生态系统服务对于生态管理和可持续发展至关重要.
- 北京的动态环境需要对其生态系统服务进行研究.
- 之前的研究还没有完全整合多种生态系统服务的空间模式,权衡和驱动因素.
研究的目的:
- 分析2000年至2020年北京的息地质量,碳储存,水产和土壤保留的空间模式,相关性,权衡和协同作用.
- 确定影响这些生态系统服务的关键驱动因素.
- 为北京的生态保护战略提供科学支持.
主要方法:
- 生态系统服务评估的InVEST模型.
- 空间自相关性,冷/热点分析,以及用于空间模式分析的双变空间自相关性.
- XGBoost-SHAP模型用于识别关键驱动因素.
主要成果:
- 对于息地质量而言,高价值的地区是在较高,较少受到干扰的地区发现的. 碳储量在西北部高,在东南部低. 水产在城市地区达到顶峰,土壤保护集中在西南部.
- 在所有四种生态系统服务中都观察到显著的空间聚合 (高价值聚类).
- 息地质量,碳储存和土壤保护显示出协同作用的关系,而水产量显示出与这些服务的权衡.
- 高度被确定为所有四种生态系统服务的主要驱动因素,斜率,人口密度和年降水量也发挥着重要作用.
结论:
- 北京生态系统服务的空间模式是复杂的,受到多种因素的影响.
- 不同生态系统服务之间存在协同作用和权衡,需要综合管理方法.
- 高度是主要因素,但其他人为和气候因素显著影响了生态系统服务的提供.
- 这些发现为优化北京的空间规划和生态保护提供了关键的见解.
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