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基于可解释机器学习的绿色基础设施对生态系统质量的影响:中国西省的案例研究
Yuxiang Zhang1, Wei Kong2, Guoqing Wang3
1College of Horticulture, Shanxi Agricultural University, Jinzhong, 030801, China. 13103540034@163.com.
Scientific reports
|August 25, 2025
概括
两十年来,绿色基础设施 (GI) 改善了洛斯高原的生态系统质量. 特定的地理标志特征,而不仅仅是覆盖面,对生态健康产生重大影响,指导未来的城市规划.
科学领域:
- 环境科学
- 城市生态
- 地理空间分析
背景情况:
- 快速的城市化威胁着生态安全和可持续发展.
- 绿色基础设施对于减轻这些影响至关重要.
- 洛斯高原是一个生态脆弱的地区,需要集中研究.
研究的目的:
- 分析洛斯高原绿色基础设施 (GI) 的变化.
- 研究地理标志的覆盖-特征形式对生态系统质量的影响.
- 了解包括恢复项目在内的人类活动对地理标志和生态系统健康的影响.
主要方法:
- 使用可解释的机器学习 (XGBoost模型).
- 分析了2000年至2022年的地理标志变化及其与生态系统质量的关系
- 研究了地理标志的覆盖面,特征和形式对生态指标的影响.
主要成果:
- 由于大规模的生态恢复,生态系统质量普遍得到改善.
- 核心地理标志区域扩大,但由于形态类型减少,景观碎片化增加.
- 微小的地理标志成分 (例如桥梁,小岛) 不成比例地影响了生态系统的质量.
结论:
- 生态系统的健康取决于GI的数量,具体特征和空间布局.
- 整合覆盖-特征-形式框架对于优化GI至关重要.
- 未来的城市规划和恢复工作应优先考虑微妙的地理标志策略,以提高抵御能力.
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