Retrieving zinc concentrations in topsoil with reflectance spectroscopy at Opencast Coal Mine sites

Heavy metals contaminations in mining areas aroused wide concerns globally. Efficient evaluation of its pollution status is a basis for further soil reclamation. Visible and near–infrared reflectance (Vis–NIR) spectroscopy has been diffusely used for retrieving heavy metals concentrations. However, the reliability and feasibility of calibrated models were still doubtful. The present study estimated zinc (Zn) concentrations via the random forest (RF) and partial least squares regression (PLSR) using ground in-situ Zn concentrations as well as soil spectral reflectance at an Opencast Coal Mine of Ordos, China in February 2020. The coefficient of determination (R2), root mean square error (RMSE), mean absolute error (MAE), and the ratio of performance to deviation (RPD) were selected to assess the robustness of the methods in estimating Zn contents. Moreover, the characteristic bands were chosen by Pearson correlation analysis and Boruta Algorithm. Finally, the comparison between RF and PLSR combined with eight spectral reflectance transformation methods was conducted for four concentration groups to determine the optimal model. The results indicated that: (1) Zn contents represented a skewed distribution (coefficient of variation (CV) = 33%); (2) the spectral reflectance tended to decrease with the increase of Zn contents during 580–1850 nm based on Savitzky–Golay smoothing (SG); (3) the continuous wavelet transform (CWT) demonstrated higher effectiveness than other spectral reflectance transformation methods in enhancing spectral responses, the R2 between Zn contents and the soil spectral reflectance achieved the highest (R2 = 0.71) by using CWT; (4) the RF combined with CWT exhibited the best performance than other methods in the current study (R2 = 0.97, RPD = 3.39, RMSE = 1.05 mg kg−1, MAE = 0.79 mg kg−1). The current study supplied a scientific scheme and theoretical support for predicting heavy metals concentrations via the Vis–NIR spectral method in possible contaminated areas such as coal mines and metallic mineral deposit areas.

A UAV- and field-based investigation of the land degradation and soil erosion at opencast coal mine dumps after 5 years’ evolution of natural processes

Open-pit coal mining has a large impact on land surface, both at the mining pits themselves and at waste sites. After artificial management is stopped, a reclaimed opencast coal mine dump is affected by wind and water erosion from natural processes, resulting in land degradation and even safety incidents. In this paper, the soil erosion and land degradation after 5 years of such natural processes, at the Xilinhot open pit coal mine dump in Inner Mongolia, were investigated. A multi-source data acquisition method was applied: the vegetation coverage index was extracted from GF-1 satellite imagery, high-precision terrain characteristics and the location and degree of soil erosion were obtained using an Unmanned Aerial Vehicle (UAV), and the physical properties of the topsoil were obtained by field sampling. On this basis, the degree and spatial distribution of erosion cracks were identified, and the causes of soil erosion and land degradation were analyzed using a geographical detector. The results show that: 1) The multi-source data acquisition method can provide effective basic data for the quantitative evaluation of the ecological environment at dumps; 2) slope aspect and vegetation fractional coverage are the main factors affecting the degree of degradation and soil erosion. Based on this analysis, several countermeasures are proposed to mitigate land degradation: 1) The windward slope be designed to imitate the natural landform; 2) engineering measures should be applied at the slope to restrain soil erosion; 3) pioneer plants should be widely planted on the platform at the early stage of reclamation.