Ecological Vulnerability in the Red Soil Erosion Area of Changting under Continuous Ecological Restoration: Spatiotemporal Dynamic Evolution and Prediction

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Abstract

Sustainable restoration of degraded ecosystems is a major environmental concern in several regions of China. Changting is one of the severely affected water- and soil-loss areas in southern China that have been under continuous management for the last 30 years. Taking the typical red soil erosion area in Changting, Fujian, as the research object, an evaluation index system with 30 m resolution was developed based on the Sensitivity–Resilience–Pressure (SRP) model. Spatial principal component analysis, Global Moran’s I, the LISA cluster map, and the CA-Markov model were employed to dynamically evaluate and predict the ecological vulnerability of the red soil erosion area in Changting. The findings revealed that the ecological vulnerability of the red soil erosion area in Changting has obvious spatial differences and topography, meteorological, and economic and social variables are the primary driving factors of ecological vulnerability. The analysis of spatial distribution of ecological vulnerability showed significant sets of contiguous locations of severe and mild ecological vulnerability. The total index of ecological vulnerability in the study area reduced by 9.49% from 2000 to 2020, yet it was still just mildly vulnerable. The proportion of severe and extremely vulnerable areas declined by 4.87% and 5.61%, respectively. The prediction results for the coming ten years showed that the ecological vulnerability of red soil erosion in Changting will tend to improve. In summary, it is found that after years of continuous ecological management in the red soil erosion area of Changting, the ecological restoration effect of the soil erosion area is obvious.

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APA

Wu, X., Zhu, C., Yu, J., Zhai, L., Zhang, H., Yang, K., & Hou, X. (2022). Ecological Vulnerability in the Red Soil Erosion Area of Changting under Continuous Ecological Restoration: Spatiotemporal Dynamic Evolution and Prediction. Forests, 13(12). https://doi.org/10.3390/f13122136

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