Spatiotemporal variation and sustainability of NDVI in the Yellow River basin

IRRIGATION AND DRAINAGE

引用 4|浏览2
暂无评分
摘要
Accurately determining vegetation coverage and its changes is very important to detect the effectiveness of the Grain for Green Project (GGP) in vegetation construction in the Yellow River basin. Moreover, an understanding of the impact of human activities and the future trends of vegetation cover allows for more effective maintenance of the achievements of ecological construction. Based on the normalized difference vegetation index (NDVI) of SPOT/VEGETATION satellite remote sensing data from 2000 to 2017, the spatiotemporal patterns of the vegetative cover in the Yellow River basin in the last 18 years were studied using linear regression, residual analysis, and the Hurst index. The vegetation coverage of the Yellow River basin was higher in the southeast and lower in the northwest, with a relatively slow growth trend. The whole basin NDVI was relatively stable, and only approximately one-fifth of the region had a large fluctuation, mainly distributed in Ningxia and northern Shaanxi. The residual trend of the NDVI was positive in 87% of the areas in the basin, and the overall impact of human activities on the increase in NDVI was mainly promoted. The characteristics of vegetation change in the future with the same directionality were stronger than the reverse characteristics, but the area showing continuous degradation in the future still accounted for 7%, mainly distributed in Xining City, Lanzhou City, Ningxia Plain, Hetao Plain, etc., which needs further strengthening of ecological construction and protection. This study is helpful for predicting and evaluating the vegetation dynamics of the Yellow River basin under the background of GGP implementation and provides a theoretical basis for regional environmental protection and the future adjustment of GGP work.
更多
查看译文
关键词
human disturbance, Hurst index, NDVI, spatiotemporal variation, Yellow River basin
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要