基于改进的剩余趋势方法对中国的植被变化进行归因
Rong Pan1,2,3, Jian-Guo Sun1,2,3, Bo-Yang Hu1,2,3
1Faculty of Geomatics, Lanzhou Jiaotong University, Lanzhou 730070, China.
Ying yong sheng tai xue bao = The journal of applied ecology
|August 31, 2025
概括
一个新的空间整合模型通过减少人类干扰来改善植被变化的归因. 这种方法准确地量化了气候和人类的影响,显示人类因素在2003年至2022年期间推动了中国87.9%的植被变化.
科学领域:
- 地球和环境科学
- 生态与保护
- 遥感和地理空间分析
背景情况:
- 剩余趋势方法对于归因植被变化至关重要.
- 准确的归因需要在植被气候模型中尽量减少人类干扰信号.
- 传统的像素对像素模型仅依赖于时间引用而受到限制.
研究的目的:
- 开发一个空间整合的植被气候关系模型,以增强残留趋势方法.
- 准确区分气候驱动和人类驱动的植被变化.
- 分析2003年至2022年中国的植被动态.
主要方法:
- 构建一个空间集成的植被气候关系模型.
- 开发一个代方案来选择空间引用以抑制人类的干扰.
- 应用增强的残留趋势方法来分析中国的植被变化.
主要成果:
- 从2003年到2022年,中国的植被总体上呈上升趋势 (0.002·a-1).
- 在植被分布中观察到显著的空间变化,受海河 - 忠线的影响.
- 人类因素占植被变化的87.9%,生态政策和农业管理推动了改善,而过度放牧和城市化导致了退化.
结论:
- 新的空间集成模型显著提高了残余趋势方法的准确性.
- 该模型有效量化了气候和人类因素对植被变化的相对贡献.
- 人类活动是中国近期植被动态的主要驱动因素.
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