Response of NDVI dynamics to precipitation in the Beijing–Tianjin sandstorm source region
[Date:2013-07-10]

Jinghui Liua,b,c, Jianjun Wua,b,c, Zhitao Wua,b,c, Ming Liua,b,c
a State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, Beijing 100875, China;
b Academy of Disaster Reduction and Emergency Management, Ministry of Civil Affairs (MOCA)/Ministry of Education (MOE), Beijing Normal University, Beijing 100875, China;
c Key Laboratory of Environment Change and Natural Disaster, Ministry of Education (MOE), Beijing Normal University, Beijing 100875, China.
 
Abstract: This article analysed the spatio-temporal changes in vegetation cover in the Beijing–Tianjin sandstorm source region in China and related these changes to vegetation types based on the Advanced Very High Resolution Radiometer (AVHRR) normalized difference vegetation index (NDVI) data set from 1982 to 2006. The annual maximum NDVI and peak time were identified. The different periods (1–12 months) of accumulated precipitation before the peak time were then calculated at the grid scale for each year. On this basis, the NDVI–rainfall relationship and the temporal responses in this area were studied by calculating the correlation coefficient between the annual maximum NDVI and different periods (1–12 months) of accumulated precipitation before the occurrence of the annual maximum NDVI for each pixel. The results show an upward trend in regional vegetation, a significant recovery efficiency for grassland, and the evident degradation of cropland. Peak plant growth is significantly related to precipitation and is strongly positively correlated with precipitation in the previous period (1 month) regardless of vegetation type. The regions showing the strongest correlations between peak plant growth and 1 month cumulative rainfall are the western desert grassland, grassland to forest in the transitional hill regions, the mountains of Yanshan, and the Greater Hinggan Mountains.
 
Published in International Journal of Remote Sensing. 2013, 34(15): 5331-5350.