Ecological environment quality evaluation of Zhangjiamao mining area based on DBO–PPC model

Abstract

Mining will destroy and occupy a large amount of land resources and cause lasting negative impact on ecological environment, so it is urgent to evaluate the change of ecological environment quality in mining area. In order to accurately monitor the ecological environment quality of the mining area, this study took 24 annual Landsat images of the Zhangjiamao Mining area from 2000 to 2023 as the basic data, and calculated four ecological indicators of NDVI (Normalized Difference Vegetation Index, NDVI), WET (Humidity Index, WET), LST (Land Surface Temperature, LST) and NDBSI (Normalized Differential Build-up and bare Soil Index, NDBSI). In addition, four population intelligent optimization algorithms including the Fruit Fly Optimization Algorithm-Projection Pursuit Clustering (FOA–PPC), the Particle Swarm Optimization-Projection Pursuit Clustering (PSO–PPC), the Grey Wolf Optimizer-Projection Pursuit Clustering (GWO–PPC) and the Dung Beetle Optimizer-Projection Pursuit Clustering (DBO–PPC) were used to derive the ecological environment quality evaluation method in the mining area, and the average correlation was used to verify the accuracy. The results showed that: ① The average correlation and intra class aggregation of the DBO–PPC model are higher than those of the PSO–PPC model, FOA–PPC model, and GWO–PPC model, and are closer to the EI index, indicating that the DBO–PPC model can better evaluate the ecological environment of the study area; ② Based on the DBO–PPC model, the average ecological and environmental quality of the Zhangjiamao mining area from 2000 to 2023 is about 0.4, and the ecological and environmental quality is mainly poor or relatively poor, accounting for about 55.94% of the total area. In terms of space, the ecological environment of the Changjiagou Reservoir is superior during the study period. The ecological environment in the northeastern and central areas of the mining area is better, with more vegetation coverage. ③ The proportion of subsidence area in the mining area is 81.28%, and the maximum subsidence is –0.15 m. Subsidence in the coal extraction area is significantly higher than that in the whole mining area, accounting for 89.56% of the coal extraction area, and the ecological environment quality decreases at a rate of –0.000 4, indicating that mining activities cause surface subsidence in the study area, which further affects the ecological environment. To sum up, the DBO–PPC model has a strong rationality in monitoring and evaluating the ecological environment quality in the mining areas, so as to provide technical means for the sustainable development of ecological environment in the mining areas.

Keywords

• swarm intelligent optimization algorithm
• projection pursuit
• dbo–ppc
• ecological environment evaluation