Vegetation cover changes and their relationship to climate variation in the source region of the Yellow River, China, 1990–2000

Abstract

The change history of vegetation cover and its relations to growing season precipitation (GSP) and average growing season temperature (AGST) in the source region of the Yellow River (SRYR) during 1990–2000 was retrieved based on the 1 km Advanced Very High‐Resolution Radiometer (AVHRR) Normalized Difference Vegetation Index (NDVI) data and meteorological records. The results show an overall warming and drying trend of the climate and a common degradation tendency of the ecosystem, with a greening trend in higher rugged regions. The pixel‐by‐pixel correlations between NDVI and climate factors indicate that a decrease in GSP mainly affects ecosystems with low precipitation and worse vegetation condition, and superimposes on the effects of increasing AGST which further deteriorate the climate background of these ecosystems. However, the positive correlations between AGST and NDVI in some higher/rugged regions suggest that the raising temperature can ameliorate vegetation growth conditions in these areas. Comparison and combination of the results of three change detection algorithms, i.e. post‐classification comparison (PCC), principal components analysis (PCA) and a newly developed multi‐temporal image difference (MTID) method, show that the integration of different methods can give a more comprehensive understanding of vegetation changes than any single method.

Acknowledgement

Financially support for this research was received from the National Natural Science Foundation of China (NSFC) Innovation Team Project (No. 40421001) and from the Excellent Young Teachers Programme of the Ministry of Education (No. 20022031). The 1 : 1 000 000 vegetation map of the Tibetan Plateau was vectorized by the Institute of Geographic Sciences and Natural Resource Research (CSA) and provided by Data‐Sharing Network of Earth System Science (National Scientific Data Sharing Project of China). We also sincerely thank NOAA CLASS for providing AVHRR data.