4.3 Article

Spatiotemporal Change of Vegetation Coverage and its Relationship with Climate Change in Freshwater Marshes of Northeast China

期刊

WETLANDS
卷 39, 期 3, 页码 429-439

出版社

SPRINGER
DOI: 10.1007/s13157-018-1072-z

关键词

Fractional vegetation cover; Freshwater marshes; Wetland; Climate change; Northeast China

资金

  1. National Natural Science Foundation of China [41601048]
  2. National Key Research and Development Program of China [2017YFC0212303, 2016YFC0500400]
  3. Excellent Young Scientists Foundation of the Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences [Y7H7041001]

向作者/读者索取更多资源

Based on the normalized difference vegetation index and climate data, this study investigated the spatiotemporal change of fractional vegetation cover (FVC) and its relationship with climate change in freshwater marshes of Northeast China from 2000 to 2016. Although freshwater marshes in Northeast China have undergone loss during the past nearly 20years, the FVC of unchanged marshes has increased by 0.34% per year over the study area, with the largest increases in Songnen Plain. Correlation analysis results showed that warm growing season temperatures could enhance the FVC of marshes in the Greater and Lesser Khingan Mountains, but reduce the FVC in arid and semi-arid grassland regions of Songnen Plain and Eastern Inner Mongolia. By contrast, the increased growing season precipitation could significantly enhance the FVC of marshes in semi-arid grassland regions of Northeast China, but reduce the FVC of marshes in the Lesser Khingan Mountains and Sanjiang Plain. Under the background of future climate change, we can predict lower FVC of marshes in Songnen Plain and Eastern Inner Mongolia, but higher FVC of marshes in the Changbai Mountains in the future. This research is expected to provide valuable implications for the protection and restoration of wetland vegetation in Northeast China.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.3
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据