Detecting long-term effects of mining-induced ground deformation on plant succession in semi-arid areas using a cellular automata model

Mining-induced ground deformation affects various plant communities in different ways. However, little is known about the effects of ground deformation on plant succession; no known quantitative approach currently exists to detecting the long-term effects. To address this issue, a plant community succession model based on cellular automata was developed. Plant succession over 30 years within a subsidence area in the Yungang mining area in China was simulated and compared under mining and non-mining scenarios, including under two sets of initial conditions (vegetated and bare). The normalized mean square error was applied to test the accuracy of the plant community succession model and to reveal the effects of ground deformation at the patch scale, which was 0.06–0.59 for simulated and observed plant patterns. Landscape indicators including contagion, patch cohesion, interspersion and juxtaposition, Shannon’s diversity, Shannon’s evenness, and aggregation indices were calculated and compared to reveal the effects of mine subsidence at the landscape scale. The results showed that the interspersion and juxtaposition, Shannon’s diversity, Shannon’s evenness, and aggregation indices were significantly influenced by ground deformation (P