Abstract:

Because Sanjiangyuan’s Yellow River Source Park flows into the Yellow River, its ecosystem is crucial to the entire Yellow River Basin of China. Of the land degradation problems observed in the study area from 2000 to 2020, this study focuses on the soil erosion modulus, sanding characteristic index and soil moisture index. To achieve a more in-depth analysis of spatial and temporal evolution of land degradation in the area, machine learning algorithms were used. The spatial Toeplitz inverse covariance-based clustering method was used to carry out the cluster analysis of three parameters as part of the spatio-temporal evolution analysis of land degradation in the Yellow River Source Park. The extreme gradient boosting method was used to analyze the factors influencing the spatio-temporal evolution. The results of the study showed that: (1) The most significant land degradation was observed in the central-southern and northeastern regions, followed by the northern region. (2) In the northeast, there are two categories of problems: soil erosion and land desertification. In the central-southern region, severe soil erosion is the main problem. In the northern region, land desertification is the main problem. (3) In the past two decades, the land degradation index has shown a fluctuating decline that land degradation overall tends to improve, and that the effects are more obvious in the remote southern region. (4) The problem of soil erosion is improving overall, and there is a risk of land sanding in the remote northern areas. This study is a critical tool for understanding the spatial and temporal trends in the distribution and development of land degradation in the Yellow River Source Park and, by extension, for the promotion of increased ecological protection in Yellow River Source Park.

Key words: land degradation, remote sensing, spatial Toeplitz inverse covariance-based clustering, extreme gradient boosting