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探索自然和人类活动对植被变化的影响:基于趋势分析和机器学习的综合分析框架
Ying Chen1, Qian Zhao2, Yiming Liu1
1School of Urban Planning and Design, Peking University, Shenzhen, 518055, China.
Journal of environmental management
|January 18, 2025
概括
本研究介绍了TMXS框架,用于分析非线性植被变化. 人类活动和气候对植被产生重大影响,城市化导致成都-重庆地区的退化.
科学领域:
- 环境科学 环境科学
- 遥感 遥感 遥感 遥感
- 机器学习 机器学习
背景情况:
- 植被变化受到气候,人类活动和地形的影响.
- 这些因素与植被随着时间的变化之间的非线性关系的研究是有限的.
研究的目的:
- 提出和验证一种新的方法分析框架 (TMXS),用于探索植被变化与影响因素之间的非线性关系.
- 量化气候,人类活动和地形对2003-2022年成都-重庆城市聚集区植被变化的影响.
主要方法:
- 集成的趋势分析与机器学习和SHAP技术.
- 开发了Theil-Sen - Mann-Kendall - XGBoost - SHAP (TMXS) 的分析框架.
- 应用TMXS框架来分析植被变化 (LAI) 和影响研究区域因素.
主要成果:
- TMXS框架有效量化了因素对植被变化的非线性影响.
- 叶片面积指数 (LAI) 的整体增加观察到,但在城市化地区下降.
- 气候和人类活动对植被变化的贡献超过65%;人类活动导致了45.73%的植被退化,特别是在人口密地区.
- 斜坡梯度的变化显著影响了植被覆盖.
结论:
- TMXS方法可靠地阐明了植被变化和影响因素之间的非线性关系.
- 这些发现支持有针对性的区域植被恢复和生态政策的制定.
- 强调人类活动和温度在植被动态中的关键作用.
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