期刊
FORESTS
卷 12, 期 5, 页码 -出版社
MDPI
DOI: 10.3390/f12050594
关键词
extreme precipitation events; vegetation activity; normalized difference vegetation index; performance assessment; the middle and lower reaches of the Yangtze River
类别
资金
- Qinglan Project of Jiangsu Province in China
- Project of Philosophy and Social Science Re-search in Colleges and Universities in Jiangsu Province [2019SJA0915]
- Priority Academic Program Development of Jiangsu Higher Education Institutions
Remote sensing has been used to monitor extreme climatic events, particularly droughts, on a regional to global scale by identifying vegetation activity anomalies. However, limited research has focused on the performance of remote sensing in detecting extreme precipitation events. This study in the Middle and Lower Reaches of the Yangtze River in China found that the maximum NDVI is better suited to capture the response of vegetation to extreme precipitation events.
Remote sensing has frequently been employed to monitor extreme climatic events, especially droughts, by identifying the anomalies of vegetation activity from the regional to global scale. However, limited research has addressed the performance of remote sensing on detecting extreme precipitation events. By using the Middle and Lower Reaches of the Yangtze River (MLR-YR) in China as an example, this paper examines the application of the satellite-derived normalized difference vegetation index (NDVI) for detecting the change of extreme precipitation events from 1982 to 2012. The performances of three NDVI-based indices, including minimum, mean, and maximum NDVIs, were examined to capture the sensibility of vegetation activity to changes in extreme precipitation events. The results show not only common enhanced trends, but also obvious spatial discrepancies between the intensity and frequency of extreme precipitation events in the MLR-YR. As to its application on terrestrial vegetation, changes in extreme precipitation intensity coincided with that of the vegetation activity, which was represented as the maximum and the minimum NDVIs, especially the maximum NDVI. In addition, similar patterns were found between the standard deviation of the maximum NDVI and the trend of extreme precipitation intensity. Furthermore, the correlation coefficients were relatively greater between the maximum NDVI and extreme precipitation intensity than that of the minimum NDVI. Our results support the hypothesis that maximum NDVI is more suited to capture the response of vegetation activity to extreme precipitation events in the MLR-YR region, in comparison to the other two NDVI indices.
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