Response Characteristics of Soil Erosion to Spatial Conflict in the Production-Living-Ecological Space and Their DrivingMechanism: A Case Study of Dongting Lake Basin in China

• Published in: Land (Basel) Vol. 11; no. 10; p. 1794
• Main Authors: Liu, Changchang, Deng, Chuxiong, Li, Zhongwu, Liu, Yaojun
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.10.2022
• Subjects: Accelerated erosion; Agricultural land; Agriculture; Bivariate analysis; Catalysis; Climate change; Dongting Lake Basin; driving mechanism; Environmental economics; Erosion rates; Forest management; Lake basins; Lakes; Land use; Optimization; Population density; production-living-ecological space; Socioeconomic factors; Soil erosion; spatial conflict; Sustainable development; Watersheds; Dongting Lake; China
• DOI: 10.3390/land11101794
• ISSN: 2073-445X

Land use conflicts induced by human activities cause accelerated soil erosion. The response of soil erosion to spatial conflict in production-living-ecological space (PLES) is not clearly understood. In this research, models such as PLES spatial conflict, revised universal soil loss equation, bivariate spatial autocorrelation, and an optimal parameter-based geographical detector were used to explore the characteristics and drivers of soil erosion in response to spatial conflict in the PLES of the Dongting Lake watershed. Results show that spatial changes of the PLES first increased and then decreased. Approximately 45% of the area was consistently in moderate or higher conflict levels throughout the study period. The average soil erosion rate showed a decreasing trend for each year except in the period 2000–2005, when moderate erosion increased. The spatial correlation between spatial conflict and soil erosion was found to be in the form of an inverted “U” for the high-high and low-high agglomeration patterns, and a decreasing trend for the high-low ones. Approximately 27% of the area must be traded off between the spatial conflict of the PLES and soil erosion. The influence of GDP and population density was significant. DEM interacted strongly with GDP, NDVI, precipitation, population density, and “return of farmland to forest” policy. Different patterns were formed among the factors through actions such as amplification, mitigation, catalysis, and dependence effects. We propose policy recommendations based on the differences in the driving mechanisms of the respective models.