Spatial and temporal variations in vegetation coverage observed using AVHRR GIMMS and Terra MODIS data in the mainland of China
Spatial and temporal variations in vegetation coverage observed using AVHRR GIMMS and Terra MODIS data in the mainland of China
复制标题
利用AVHRR GIMMS和Terra MODIS数据观测中国大陆植被覆盖度时空变化
DOI：
10.1080/01431161.2020.1714781
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发表时间：
2020-02
影响因子：
3.4
通讯作者：
Aizhong Ye
中科院分区：
文献类型：
--
作者：
Yahai Zhang;Aizhong Ye
ABSTRACT Human activities and climate change have changed the vegetation in China. The analysis of the changes in vegetation that have occurred over the past 30 years in China remains a great challenge due to intense human activity and lack of field observations. The use of various Normalized Difference Vegetation Index (NDVI) datasets to study vegetation coverage changes has received much attention. In this paper, we selected the early versions of Advanced Very High Resolution Radiometer (AVHRR) Global Inventory Monitoring and Modelling Studies (GIMMS), GIMMS3g (third generation GIMMS NDVI from AVHRR sensors) and Moderate Resolution Imaging Spectroradiometer (MODIS) NDVI data including the fusion data (GIMMS+MODIS). We analysed spatial and temporal changes in vegetation cover in different ecosystems and basins in the mainland of China. Different contributions of ecosystems and variations in NDVI trends exist in different ecosystems in 17 basins. The results show that different NDVI from different data sources yield different results: (1) Vegetation increased in 74.62–77.7% of the area of the Chinese mainland during 1982–2015, mainly in the Yellow River and the middle reaches of the Yangtze River basin; (2) 2000–2017 MODIS NDVI in mainland China has increased more area (79.67%). (3) Farmland and Forest ecosystems were significantly enhanced in the eastern monsoon region; (4) High-resolution NDVI can provide more information than domain average NDVI. GIMMS and MODIS NDVI data have complementary spatial and temporal distributions. Our study improves the understanding of vegetation dynamics over long time periods and large areas and, moreover, has potential for supporting ecological managers in mainland China.
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影响因子：
13.5
作者：
Yulong Zhang;C. Song;L. Band;G. Sun;Junxiang Li
通讯作者：
Yulong Zhang;C. Song;L. Band;G. Sun;Junxiang Li
DOI：
10.1177/030913339902300207
发表时间：
1999-06
期刊：
Progress in Physical Geography
影响因子：
--
作者：
Daniel N.M. Donoghue
通讯作者：
Daniel N.M. Donoghue
影响因子：
2.8
作者：
Shulin Liu;Tao Wang;Jian Guo;J. Qu;P. An
通讯作者：
Shulin Liu;Tao Wang;Jian Guo;J. Qu;P. An
DOI：
10.1007/978-0-387-35973-1_1114
发表时间：
2008
期刊：
2008 IEEE/OES 9th Working Conference on Current Measurement Technology
影响因子：
--
作者：
通讯作者：
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影响因子：
--
作者：
L. Zhong;Yan Cao;Wen‐ying Li;W. Pan;K. Xie
通讯作者：
L. Zhong;Yan Cao;Wen‐ying Li;W. Pan;K. Xie