XIAO Wu，REN He，ZHAO Yanling，et al. Monitoring and early warning the spontaneous combustion of coal waste dumps supported by unmanned aerial vehicle remote sensing[J]. Coal Science and Technology，2023，51（2）：412−421
. DOI: 10.13199/j.cnki.cst.2022-1901| Citation: |
XIAO Wu，REN He，ZHAO Yanling，et al. Monitoring and early warning the spontaneous combustion of coal waste dumps supported by unmanned aerial vehicle remote sensing[J]. Coal Science and Technology，2023，51（2）：412−421 . DOI: 10.13199/j.cnki.cst.2022-1901
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Spontaneous combustion of coal waste dumps is a huge challenge in land reclamation and ecological environment protection in mining areas. Advance and timely monitoring and early warning in spontaneous combustion process are crucial, and have always been a difficult issue in research and governance. Based on unmanned aerial vehicle (UAV) remote sensing technology, this study proposed a method for assessing the spontaneous combustion risk of coal waste dumps by using the reclaimed vegetation, alfalfa (Medicago sativa L.), and evaluated the feasibility of the method in potential spontaneous combustion monitoring and warning. Taking a coal waste dump after reclamation in Shanxi province, China, as an example, this study obtained the images of the coal waste dump by using an UAV equipped with visible and thermal infrared cameras. Then, the imagery features were extracted from the UAV images and used to estimate the alfalfa growth parameters, aboveground biomass (AGB), plant height (PH), and plant water content (PWC). On this basis, a spontaneous combustion risk assessment method was developed, and was applied to explore the feasibility in the study area A1 where spontaneous combustion had occurred. Then, the above method was used to assess the risk of study area A2, where the spontaneous combustion was unknown (or potential). The research results indicated that: ① UAV is an effective tool for vegetation monitoring in coal waste dumps, and alfalfa growth information can be accurately estimated based on the UAV remote sensing imagery features. The determination of coefficient (R2) of the alfalfa AGB and PH estimation model based on random forest (RF) was 0.92 and 0.78, respectively, and the root mean square error (RMSE) was 90.58 g/cm2 and 4.29%, respectively. The alfalfa PH estimation based on crop height model (CHM) resulted in anR2 of 0.92 and an RMSE of 7.58 cm. ② The three alfalfa growth parameters indicated the explanatory ability to the spontaneous combustion of coal waste dumps, which showed a certain negative correlation with the soil temperature at a depth of 25 cm (Ts,25) in spatial distribution (R2= −0.43−−0.51). Furthermore, alfalfa AGB showed the best performance (R2= −0.51). ③ The assessment result based on alfalfa AGB can grasp the scope, intensity and change direction of the underground spontaneous combustion process to some extent, so as to realize the monitoring and early warning of the potential spontaneous combustion risk of coal waste dump. Our research aimed at providing a new idea and the method support for the spontaneous combustion prevention of coal waste dumps after reclamation in mining areas.
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