Vegetation change based on SPOT-VGT data from 1998 to 2007, northern China

摘要

Dynamic change of vegetation in northern China from 1998–2007 was explored, based on SPOT-VGT data. The results showed that the NDVI can effectively monitor vegetation change, but also the mean multi-year NDVI maximum of 0.10 basically can be considered as the threshold of vegetation cover in northern China, and those places with smaller than or equal to the threshold value mainly were covered by deserts, Gobi and lakes, salinization lands, glaciers, snow patches and bare mountains, etc. The change trends of vegetation where sub-region C and D were affected by east-Asia monsoon were similar with the characteristics of first decreasing, later increasing and finally slowly decreasing; the difference lies in that sub-region C maintained a lower vegetation state, and sub-region D reached a higher state. The vegetation change of sub-region A was anomalous with smaller fluctuation. The change trend of vegetation of sub-region B was not only extremely unstable, but also the change extent was huge. As far as the whole of northern China is concerned, vegetation has not demonstrated obvious improvement since these ecological construction and protection projects, and the area of vegetation degradation still is much larger than that of vegetation improvement from 1998 to 2007, but there was a bigger spatial difference. Field investigation and remote sensing monitoring reveal that vegetation has obviously been improved in the agro-grazing mixing zone and to the south, as well as many oases in the northwestern arid zone, while vegetation still has worsened in the steppes, especially the Ulanqab steppe, Hunshandake Sandy Land, the region along the Sino–Mongolia border within Xilingol Meng, Hulunbeir Sandy Land and the northern Tianshan mountain region in Xinjiang. Human activities only obviously showed success in local regions. The compelling challenges of restoring the eco-environment are still immense, especially in the northern grazing zone of China.