The Upper Min River (UMR) watershed, with an area of 23,000 km 2 in the Upper Yangtze basin in Sichuan province, is ecologically and environmentally fragile. This situation is due to natural catastrophes and increasing anthropogenic disturbance. Forest cover has decreased dramatically, especially along the main Min River. Restoration of the vegetation in a watershed needs to consider the natural vegetation distribution and dynamics. In the UMR watershed, 625 sample plots were randomly placed to study the soil types, current vegetation distribution, vegetation dynamics, afforestation, and potential tree species for restoration. We investigated the relationship between vegetation types and soil orders, predicted the percentages of potential tree species for forest landscape restoration using logistic regression, identified priority areas for rapid restoration, and pinpointed areas for forest restoration where low precipitation is a constraint. The results showed that the vegetation types were well correlated with soil orders, and the latter could be used to deduce the potential vegetation for areas of degraded secondary forest. Priority areas for rapid restoration were demonstrated, and the difficult areas with precipitation as a limiting factor for vegetation restoration were specified. Suitable tree species were suggested for restoration on different soil orders at different elevations.
Soil erosion control requires a quantitative evaluation of potential soil erosion on a specific site. The Revised Universal Soil Loss Equation (RUSLE), Remote Sensing (RS), and Geographic Information System (GIS) were used to model soil erosion intensity for soil conservation and vegetation rehabilitation in an Upper Min River (UMR) watershed, which is in the Upper Yangtze River basin. Data used in this study to generate the soil loss were Landsat Enhanced Thematic Mapper (ETM) images, Digitized Elevation Model (DEM), soil erodibility, rainfall erosivity, and inventory data. The non-parametric k-nearest neighbor (k-NN) method was used to produce the cover management map by integrating the ETM images and vegetation coverage data measured in the 625 sample plots. The root mean square errors and significance of biases at pixel level were evaluated in order to find optimal parameters. Four raster maps have been produced for the soil erodibility, rainfall erosivity, slope length and steepness, and cover management factor, and the map with different soil loss risks has been produced for soil erosion potential. The result can be beneficial to the erosion control and ecological restoration in the degraded mountainous watershed.
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