Land Use Transition Risk Assessment And Transition Simulation Study

Land use transition risk research is an important direction of land use transition research,deepening the understanding of land use transition risks,exploring the construction of a scientific and reasonable transition risk assessment system,and strengthening the theoretical support role of transition risk research in policy formulation and improvement,which is of practical significance for promoting the optimal allocation of land resources in the future,optimizing the spatial pattern of land use,and highlighting the advantages of land use transformation.Taking the old revolutionary areas of Guangxi in 2005,2010,2015 and 2020 as the research areas,this paper uses spatial analysis technology to characterize the characteristics of land use transformation,identifies the differences between explicit and implicit forms of land use transformation,measures the risks of land use transition in Guangxi Left and Right River Revolutionary Old Area and its state evolution in spatial relations,and uses the CA-Markov model to simulate the development of land use transformation in the study area under multiple scenarios in the future.The conclusions of the study are as follows:(1)Research on the characteristics of land use transformation.In terms of the change of land use quantity,the area of arable land,water area,construction land and unused land showed an increasing trend,while forest land and grassland showed a negative growth trend.In terms of land use dynamics,the comprehensive dynamics of2005-2010,2010-2015,2015-2020 and 2005-2020 were 0.10%,0.23%,0.10% and0.42%,respectively.In terms of land use transformation map analysis,land use transfer mainly occurs between cultivated land,forest land,grassland and construction land,and water areas and unused land basically do not participate in the transfer.During the study period,the mutual transfer of woodland and arable land and the mutual transfer of woodland and grassland were spatially symmetrical,which occurred in the southern and northern areas of the study area,respectively.From the perspective of land use fluctuation map,in terms of rising trend,the largest new area in the change area is cultivated land,forest land and forest land;In terms of downward trend,the largest addition of new area in the change area is woodland.The characteristics of implicit transformation,the amount of change during the period of each indicator and the distribution of counties of the transformation range are basically the same;The high-and higher-value areas of change in the amount of change and magnitude of transformation during the period were mainly distributed in the region centered on the central part.(2)Land use transition risk assessment.From the perspective of the spatiotemporal heterogeneity of transition risks,the overall risk,economic risk and social risk of the old revolutionary area of Guangxi left and right rivers transitioned from large risk to general risk and small risk during the study period,but the overall risk,economic risk and social risk level of the northern region were still dominated by larger risk,and the ecological risk level was still dominated by large risk and the spatial distribution was basically consistent.From the perspective of the spatiotemporal dependence characteristics of transition risk,according to the Global Moran’s I statistics,the correlation of the overall risk in the study area continued to weaken,the correlation fluctuation of economic risk decreased,the correlation fluctuation of social risk increased,and the long-term positive correlation of ecological risk remained weak.According to the Local Moran’s I statistics,the overall risk is mainly in the high-high aggregation area and the low-low accumulation area,distributed in the northern and southern regions,the economic risk develops from the high-high accumulation area and the low-low accumulation area to the discrete situation,the social risk develops from the discrete situation to the high-high accumulation area and the low-low accumulation area,and the ecological risk is mainly distributed in the high-high accumulation area distributed in the northern region.(3)Identification of influencing factors of land use transition risks.In terms of single-factor detection,the risk factors at the social level in 2005 and 2010 were more likely to lead to the risk of land use transition,and in 2015 the transition to the economic level and ecological level,and in 2020 it was mainly the economic level.In2005 and 2010,the proportion of tertiary industry and the level of urbanization rate were the most decisive Q,in 2015,the level of urbanization rate and the average fixed asset investment in the prefecture had the largest q-value,and in 2020,the q-value of the determinant of the proportion of fixed assets and tertiary industry was the largest.In terms of two-factor detection,from the perspective of interaction intensity,the two-factor effect q-value of the land use transition risk influencing factor in the old revolutionary area of Guangxi was generally greater than the single-factor q-value,and the interaction of the influencing factors was mainly manifested as nonlinear enhancement or two-factor enhancement.(4)Multi-scenario simulation of land use transformation.The CA-Markov model was used to simulate the land use transformation in the old revolutionary areas of Guangxi in 2025 and 2030,and it was divided into natural development scenarios,ecological protection scenarios and overall development scenarios according to the conversion rules.The results are as follows: the land use transformation of the three scenarios mainly occurs between forest land,grassland and cultivated land,and according to the set rules,the area of forest land,grassland and water area converted into construction land under the ecological protection scenario is reduced,and the area of forest land,grassland and cultivated land converted into construction land under the overall development scenario is also correspondingly reduced,but it is more balanced than the ecological protection scenario.