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在过去二十年中,中国的年内植被变化和空间变化基于遥感和时间序列聚类

Xi Cheng¹, Mingliang Luo¹, Ke Chen¹

¹School of Geographical Sciences, China West Normal University, Nanchong, 637009, China.

Environmental monitoring and assessment

|July 1, 2024

概括

中国中国中国中国.

科学领域:

生态监测和远程传感
环境科学和气候变化研究

背景情况:

植被动态对于陆地-大气-水相互作用至关重要.
现有的研究忽略了年内植被变异性,专注于长期趋势.
了解年内变化对于生态洞察和保护至关重要.

研究的目的:

通过使用增强植被指数 (EVI) 数据,分析中国的年内植被变化模式.
探索空间变化,并确定这些变化的关键驱动因素.
为生态保护和管理策略提供数据.

主要方法:

使用了2000-2022年16天间的增强植被指数 (EVI) 数据.
应用的k-means时间序列聚类以区域化年度内植被模式.
使用回归和随机森林模型来确定驱动因素.

主要成果:

观察到明显的年内植被模式:夏季高,冬季低,在中国北部变化更大.
确定了四个不同的区域集群的年内植被变化,与气候区相关.
发现一年内植被变化和气象因素 (露点温度,降水,最高温度,海平面压力) 之间存在显著的相关性.

结论:

这项研究揭示了中国各地具有特色的年内植被动态.
阐明了这些动态的空间模式和关键气象驱动因素.
研究结果为生态研究,保护和管理策略提供了宝贵的见解.

关键词:

动态时间扭曲.植被的年内变化.遥感是一种远程传感.坡回归的回归方法时间序列聚类时间序列聚类.

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