期刊
LAND
卷 10, 期 9, 页码 -出版社
MDPI
DOI: 10.3390/land10090990
关键词
landscape metrics; soil erosion; sediment yield; PLSR
资金
- National Key Research and Development Program of China [2019YFC1510503]
- National Natural Science Foundation of China [41867016]
- Natural Science Foundation of Jiangxi Province [20202BABL203029]
- Postdoctoral Research Merit Foundation of Jiangxi Province [2019KY46]
- Water Conservancy Science and Technology Project of Jiangxi Province [201922ZDKT17, 201821ZDKT15, 202124ZDKT25, 202124ZDKT24, 201821ZDKT18, 201922ZDKT08, 202123TGKT07, 201921TGKT12]
Landscape patterns are influenced by both natural and social factors. Quantifying the relationships between landscape pattern changes, soil erosion, and sediment yield in river basins is essential for decision-making. This study focused on the upper Ganjiang Basin, using a hydrological model to simulate soil erosion and sediment yield, leading to the establishment of new landscape metrics that were significantly correlated with soil erosion and sediment yield in the river basin.
Landscape patterns are a result of the combined action of natural and social factors. Quantifying the relationships between landscape pattern changes, soil erosion, and sediment yield in river basins can provide regulators with a foundation for decision-making. Many studies have investigated how land-use changes and the resulting landscape patterns affect soil erosion in river basins. However, studies examining the effects of terrain, rainfall, soil erodibility, and vegetation cover factors on soil erosion and sediment yield from a landscape pattern perspective remain limited. In this paper, the upper Ganjiang Basin was used as the study area, and the amount of soil erosion and the amount of sediment yield in this basin were first simulated using a hydrological model. The simulated values were then validated. On this basis, new landscape metrics were established through the addition of factors from the revised universal soil loss equation to the land-use pattern. Five combinations of landscape metrics were chosen, and the interactions between the landscape metrics in each combination and their effects on soil erosion and sediment yield in the river basin were examined. The results showed that there were highly similar correlations between the area metrics, between the fragmentation metrics, between the spatial structure metrics, and between the evenness metrics across all the combinations, while the correlations between the shape metrics in Combination 1 (only land use in each year) differed notably from those in the other combinations. The new landscape indicator established based on Combination 4, which integrated the land-use pattern and the terrain, soil erodibility, and rainfall erosivity factors, were the most significantly correlated with the soil erosion and sediment yield of the river basin. Finally, partial least-squares regression models for the soil erosion and sediment yield of the river basin were established based on the five landscape metrics with the highest variable importance in projection scores selected from Combination 4. The results of this study provide a simple approach for quantitatively assessing soil erosion in other river basins for which detailed observation data are lacking.
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