Abstract
In this study, we selected 11 townships with severe ground subsidence located in Weishan County as the study area. Based on the interpretation data of Landsat images, the Binary logistic regression model was used to explore the relationship between land use change and the related 7 driving factors at a scale of 60m * 60m. Using the CLUE-S model, combined with Markov model, the simulation of land use under three scenarios–namely, natural development scenario, ecological protection scenario and farmland protection scenario–were explored. Firstly, using land use map in 2005 as input data, we predicted the land use spatial distribution pattern in 2016. By comparing the actual land use map in 2016 with the simulated map of land use pattern in 2016, the prediction accuracy was evaluated based on the Kappa index. Then, after validation, the distribution of land use pattern in 2025 under the three scenarios was simulated. The results showed the following: (1) The driving factors had satisfactory explanatory power for land use changes. The Kappa index was 0.82, which indicated good simulation accuracy of the CLUE-S model. (2) Under the three scenarios, the area of other agricultural land and water body showed an increasing trend; while the area of farmland, urban and rural construction land, subsided land with water accumulation, and tidal wetland showed a decreasing trend, and the area of urban and rural construction land and tidal wetland decreased the fastest. (3) Under the ecological protection scenario, the farmland decreased faster than the other two scenarios, and most of the farmland was converted to ecological land such as garden land and water body. Under the farmland protection scenario, the area of tidal wetland decreased the fastest, followed by urban and rural construction land. We anticipate that our study results will provide useful information for decision-makers and planners to take appropriate land management measures in the mining area.
Game Theory Analysis on the Generation Process of Transfer of Rural Construction Land Transfer
