Scenario simulation of land use and land cover change in mining area

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 and land cover (LULC) change and the related 7 driving factors at a resolution of 60 m. Using the CLUE-S model, combined with Markov model, the simulation of LULC under three scenarios—namely, natural development scenario, ecological protection scenario and farmland protection scenario—were explored. Firstly, using LULC map in 2005 as input data, we predicted the land use spatial distribution pattern in 2016. By comparing the actual LULC map in 2016 with the simulated map in 2016, the prediction accuracy was evaluated based on the Kappa index. Then, after validation, the spatial distribution pattern of LULC in 2025 under the three scenarios was simulated. The results showed the following: (1) The driving factors had satisfactory explanatory power for LULC 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.

Analysis of carbon emissions from land cover change during 2000 to 2020 in Shandong Province, China

“…The conversion of water bodies and cultivated land is mainly concentrated in Jining City, which is also the region where changes in vegetation carbon storage are more pronounced. Jining City covers Weishan Lake, the largest freshwater lake in northern China, and the construction of water storage facilities such as dams and river embankments, as well as river ditches on agricultural land and aquaculture ponds, has led to large changes in the area of water bodies in Jining City 39 , 40 . In addition, there may be large variations in climate between the years chosen to generate GlobeLand30 data.…”

Forests and Peace in alternative pathways: scenarios of land use and land cover change for the Colombian Amazon

“…To facilitate the interpretation of changes and covers's transition, the classification categories of the maps were re-categorized into 7 types: "amazon forests", "floodplain forests", "fragmented forests and secondary vegetation", "grasslands and shrublands", "water bodies and wetlands", "pastures and crops" and " urban and artificialized cover ". The coverage maps were resampled at a resolution of 60m x 60m to facilitate the computational analyses of the explanatory model, the simulations of the scenarios, and to keep the detailed spatial resolution of the coverage and explanatory variables 4 .…”

“…Land use and land cover (LULC) change scenarios are a tool for territorial planning, especially in tropical regions with high biodiversity conserved in large forests [1][2][3] . LULC change scenarios are useful for visualizing and analyzing the effect of different development pathways, helping decision-makers to formulate scientific strategies and implement plausible policies that conserve natural ecosystems and provide ecosystem services [4][5][6] .…”

Modeling Land Use Change in Sana’a City of Yemen with MOLUSCE

“…Effective and repeatable simulation models can be used to explore the factors influencing past, present, and future projections and their significance in various situations [10]. Regionally distributed models for analysing and projecting LULC include Dinamica [11], Markov-FLUS [12], SLEUTH cellular automata [13], artificial neural network-Markov chain [14], CA-ANN [15], and CLUE-S [16]. The LULC concerns are addressed differently in each model [17].…”