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Frequent spontaneous combustion activities restrict ecological restoration of coal waste dumps after reclamation. Effective monitoring of vegetation restoration is important for ensuring land reclamation success and preserving the ecological environment in mining areas. Development of unmanned aerial vehicle (UAV) technology has enabled fine-scale vegetation monitoring. In this study, we focused on Medicago sativa L. (alfalfa), a representative herbaceous vegetation type, in a coal waste dump after reclamation in Shanxi province, China. The alfalfa aboveground biomass (AGB) was used as an indicator for assessing vegetation restoration. The objective of this study was to evaluate the capacity of UAV-based fusion of RGB, multispectral, and thermal infrared information for estimating alfalfa AGB using various regression models, including random forest regression (RFR), gradient boosting decision tree (GBDT), K-nearest neighbor (KNN), support vector regression (SVR), and stacking models. The main results are as follows: (i) UAV multi-source data fusion improved alfalfa AGB estimation accuracy, although the enhancement diminished with the increasing number of sensor types. (ii) The stacking model consistently outperformed RFR, GBDT, KNN, and SVR regression models across all feature fusion combinations. It achieved high accuracy with R2 of 0.86–0.88, RMSE of 80.06–86.87 g/m2, and MAE of 60.24–62.69 g/m2. Notably, the stacking model based on only RGB imagery features mitigated the accuracy loss from limited types of features, potentially reducing equipment costs. This study demonstrated the potential of UAV in improving vegetation restoration management of coal waste dumps after reclamation. [ABSTRACT FROM AUTHOR]

Spontaneous combustion of coal waste dumps is one of the main problems in mining areas. This risk still exists even after land reclamation and ecological restoration. Once spontaneous combustion of a coal waste dump occurs after reclamation, it will lead to repeated land degradation and vegetation damage again. Focusing on this issue, a monitoring method using unmanned aerial vehicle (UAV) RGB imagery based on alfalfa aboveground biomass (AGB) is proposed in this study. Two areas of a coal waste dump that suffered spontaneous combustion of coal waste after reclamation in Shanxi Province, China, were selected as the study area. Combined with the vegetation index and texture metrics extracted from UAV RGB imagery, the AGB was estimated using stepwise linear regression models. Finally, the intensity of spontaneous combustion was classified based on alfalfa AGB. In this study, our results show that: (i) the AGB estimation accuracy can be improved by combining the spectral and texture features [R2 = 0.86, root mean square error (RMSE) = 0.09 kg /m‐2, and mean absolute error (MAE) = 0.08 kg m‐2]; (ii) alfalfa AGB had a good ability to explain the underground thermal anomalies based on the deep soil temperature; (iii) Compared with the land surface temperature, alfalfa AGB was more suitable for monitoring the potential spontaneous combustion areas, especially in the initial stage, where spontaneous combustion has occurred but does not have a significant impact on the land surface. This study was intended to provide an improved method for detecting the spontaneous combustion monitoring of coal waste dumps, which may provide a reference for initial monitoring and prevention in mining areas. [ABSTRACT FROM AUTHOR]

Spontaneous combustion of coal waste dumps is a serious threat to the ecological environment and the safety of mining areas. Even after land reclamation and ecological restoration, such spontaneous combustion activities are still active. Achieving early warning of spontaneous combustion is necessary to protect the reclaimed ecosystem and reduce environmental pollution, but it has not yet been well studied. To this end, this study proposed a spatial analysis method to achieve early warning spontaneous combustion of coal waste dump after reclamation by integrating unmanned aerial vehicle (UAV) and vegetation (Medicago sativa/alfalfa) growth status. The experiment was implemented in two slope areas (Areas I and II) of a coal waste dump after reclamation in Shanxi province, China, which were under threat of spontaneous combustion. Three alfalfa growth parameters, aboveground biomass (AGB), plant water content (PWC), and plant height (PH) of the study area, were estimated from UAV imagery features and used to assess the spontaneous combustion risk. Then, soil deep temperature points (25 cm depth) distributed evenly in the study area were collected to determine the underground temperature situation. It was found that the UAV-derived rededge Chlorophyll index (CIrededge), canopy temperature depression (CTD), and canopy height model (CHM) achieved a better estimation of alfalfa AGB (R2 = 0.81, RMSE = 99.2 g/m2, and MAE = 74.9 g/m2), PWC (R2 = 0.68, RMSE = 3.9%, and MAE = 3.2%), and PH (R2 = 0.77, RMSE = 9.79 cm, and MAE = 7.68 cm) of the study area, respectively. We also observed that three alfalfa parameters were highly correlated with the soil deep temperature, but differed in degree (R2 = 0.46–0.81). Furthermore, they were consistent with the soil deep temperature in spatial distribution and could reveal the change direction of underground temperature, which will help us to detect those potential spontaneous combustion areas. These results indicated that vegetation is a prior indicator to the changes in underground temperature of coal waste dump. We believed that UAV can be an effective environmental management tool for the initial assessment of spontaneous combustion risk of coal waste dump after reclamation. • A UAV remote sensing approach to assess the spontaneous combustion risk of coal waste dump after reclamation. • Spontaneous combustion process caused damage and degradation to vegetation. • Vegetation growth parameters can be used to assess the severity and change direction of spontaneous combustion risk. • Surface temperature does not play a role in the detection of potential spontaneous combustion areas. • UAV is an effective management tool in spontaneous combustion monitoring of coal waste dump. [ABSTRACT FROM AUTHOR]