Land damage assessment using maize aboveground biomass estimated from unmanned aerial vehicle in high groundwater level regions affected by underground coal mining

Underground coal mining inevitably causes land subsidence, while negatively impacting land and ecological environments. This is particularly severe in coal-grain overlap areas (CGOA) in eastern China, which have high groundwater levels. Mining subsidence has substantially altered the original topography, and raised the groundwater level, which threatens grain security in the region. Therefore, it is necessary to determine the damaged farmland area in the CGOA. The traditional method to define the range of coal mining disturbance is usually based on surface subsidence. However, this fails to consider the multidimensional impacts of coal mining on the ecology, which is considered unreasonable. Therefore, this paper introduces a low-cost, fast, and non-destructive method for land damage assessment in a typical CGOA in eastern China, using maize aboveground biomass (AGB) as estimated from an unmanned aerial vehicle (UAV). There were three key results from the survey. (1) underground coal mining caused significant ecological problems in the study area, including subsidence (approximately 6 m) and the degradation of vegetation (maize AGB in a range of 192.73–1338.06 g/m²). In addition, the degradation of maize was affected by subsidence (0.61** Pearson coefficient found between the AGB and surface elevation). (2) An UAV combined with multispectral and digital cameras, allowed precise estimation of the AGB and the red-edge chlorophyII index (CIᵣₑdₑdgₑ) combined with the elevation factor had the best explanatory power using the random forest (RF) method (R² = 0.96, RMSE = 65.03 g/cm²). (3) The maize AGB could be used to assess land damage affected by underground coal mining, which accounted for 82.12% of the study area. The results of the study could provide a reference for land damage assessments in the CGOA, while also providing a guide for land reclamation and agricultural management decisions in the region.