Nisha Bao

Satellite remote sensing technology, with its ability to record spatial and temporal land surface conditions, has been extensively and effectively utilized in evaluating mining environments. Western China, characterized by arid ecosystems, is a significant mineral resource area, boasting at least ten super-large mineral bases, including coal, non-ferrous metal ores, and metal mines. Surface mining activities, marked by large spatial and temporal scales, can exacerbate the fragility and changes to the ecological environment in vulnerable areas. Consequently, it is crucial to assess and comprehend the spatial and temporal impacts of mining on arid ecological systems for green mine construction and mine reclamation. This study focuses on three typical open-pit mines in the arid regions of Xinjiang, China (site Ⅰ: Jinbao Iron open-pit mine, site Ⅱ: Heishan coal open-pit mine, site Ⅲ: Wulagen Lead Zinc open-pit mine). The primary objective was to develop a remote sensing index (Mined Land Ecological Status Index, MLESI) that considers biological factors such as dryness, bare soil flatness, land surface temperature, and slope to assess the ecological status in arid mining areas. Subsequently, Principal Component Analysis was employed to couple these four factors to construct MLESI. The efficacy of MLESI was compared with the Remote Sensing Ecological Index (RSEI) and the Land Surface Ecological Status Composition Index (LSESCI) in different landforms in arid mining areas characterized by bare soil and rock. The spatial and temporal changes in mining effects from 2005 to 2020 were analyzed using the Sen+Mann-Kendall method based on Landsat time series images. The results indicated that the average Pearson correlation coefficient (r) between MLESI and each factor exceeded 0.65. The heat factor had the highest correlation coefficient of 0.8 with MLESI for mine site Ⅰ, while the dryness factor had the highest correlation of 0.82 with MLESI for mine site Ⅱ. The slope factor had the highest correlation coefficient of 0.82 for mine site Ⅲ. For mine sites Ⅰ and Ⅱ, the LSESCI overestimated the areas of poor ecological status, identifying most of the natural land as poor. RSEI was unable to reveal the changes in ecological status correlating with landform variety. In general, MLESI was not only highly effective in characterizing the mining area from natural bare soil and rock lands but also in indicating ecological changes along the mining direction with landform changes. The ecological status in mine site I deteriorated by 54.84% since 2015 due to extensive surface mining activities. For mine site II, the ecological status gradually declined from an MLESI value of 0.68 in 2005 to 0.38 in 2020, with a total of 2.36 km2 area experiencing significant changes to poor ecological status over 15 years. For mine site Ⅲ, the ecological status improved due to land reclamation, reaching the highest MLESI value of 0.77 in 2017. An area of 0.95 km2 experienced significant changes to good ecological status from 2008 to 2020. Therefore, the proposed MLESI outperformed RSEI and LSEISCI in monitoring different mine ecological statuses in a typical arid ecosystem.

Iron tailings are crystalline powders predominantly composed of iron (Fe) and silicon dioxide (SiO2). Spatially characterizing of the physical and chemical properties of iron tailings is greatly important for optimal utilization and proper disposal of tailings. Visible–near infrared–shortwave infrared (VIS–NIR–SWIR; 350–2500 nm) spectroscopy offers a rapid, non-destructive, and cost-effective method for quantitatively analyzing tailings properties. The main objective of this study was to map the spatial distribution of total Fe (TFe) and SiO2 content in a tailings dam through the use of laboratory spectra and GF-5 hyperspectral imagery based a calibration transfer model approach. A total of 77 samples were collected from the surface of targeting field and scanned by a laboratory VIS–NIR–SWIR reflectance spectrometer. The competitive adaptive re-weighted sampling (CARS) algorithm was applied to select important spectral features. Subsequently, different spectral indices were calculated to enhance the prediction performance of the calibration models. Rulefit and random forest (RF) algorithms were used to calibrate spectral information with associated tailing properties. The results showed that the Rulefit algorithm with selected feature bands and calculated spectral indices yielded the highest estimation accuracy for TFe (R2 = 0.86, RMSE = 1.30%, LCCC = 0.87 and bias = -0.45) and SiO2 (R2 = 0.74, RMSE = 2.00%, LCCC = 0.84 and bias = 0.38). The direct standardization (DS) algorithm was applied to correct GF-5 hyperspectral images and enhance the efficiency of calibration model transfer process. Finally, the Rulefit models were transferred to corrected GF-5 hyperspectral images for mapping the spatial distribution of TFe and SiO2 contents. Our results demonstrated the possibility of successful transfer of laboratory spectral-based model to the GF-5 hyperspectral imagery for mapping spatial distribution of tailing compositions. This finding can be applied for efficiently recovering valuable metals and minimizing environmental risks.

The non‐Lambertian surface features varying particle size and discrete distribution, resulting in reflectance to be unevenly distributed in different directions. Mine soil with a high content of coarse particles and non‐uniform particle distribution exhibits significant non‐Lambertian properties on its surface. Consequently, not only vertical observation of the reflectance spectra but also multi‐angle reflectance spectra are related to the physical and chemical properties (e.g. soil organic carbon, moisture content and particle size) of mine soil. Understanding the bidirectional reflectance distribution of mine soil with various particle sizes is essential for accurately estimating soil properties using spectroscopy. Current estimations of soil properties using spectroscopy mainly focus on vertical observations, overlooking the bidirectional reflectance characteristics. This study reports the bidirectional reflectance distribution of mine soil with various particle sizes. Furthermore, the performance of different bidirectional reflectance distribution function (BRDF) models in simulating the bidirectional reflectance of mine soil with various particle sizes was evaluated. Soil samples from three typical mine areas were collected and sieved into seven particle sizes ranging from 25 to 3500 μm. The bidirectional reflectance in the Vis–NIR wavelength region was measured in a laboratory using the Northeastern University bidirectional reflectance measurement system. The performance of five BRDF models (isotropic multiple scattering approximation, anisotropic multiple scattering approximation, H2008, H2012 and SOILSPECT) in modelling the bidirectional reflectance distribution of mine soil with different particle sizes was compared. Sobol's sensitivity indices were used to quantify the contributions of the parameters in the BRDF models. The results showed that (1) small mine soil particles (25 μm) exhibited greater reflectance than large particles (3500 μm). Large particles (3500 μm) exhibited backward scattering, whereas small particles (25 μm) exhibited extremely forward scattering characteristics because of the high silicon dioxide content; (2) the SOILSPECT model outperformed the other BRDF models in simulating the bidirectional reflectance of mine soil and had the smallest root mean square error (0.004–0.04); (3) the single‐scattering albedo ( ɷ ) parameter had the greatest contribution in the SOILSPECT model. Four parameters in the phase function ( b , b ′, c and c ′) effectively indicated the scattering behaviour of mine soil with different particle sizes. These findings improve our understanding of the scattering characteristics of mine soil with various particle sizes and can be used to improve the accuracy of extracting particle size and other soil properties from mine soil.

Step-feature lines are one of the important geometrical elements for drawing the status quo maps of open-pit mines, and the efficient and accurate automatic extraction and updating of step-feature lines is of great significance for open-pit-mine stripping planning and analysis. In this study, an automatic extraction method of step-feature lines in an open-pit mine based on unmanned-aerial-vehicle (UAV) point-cloud data is proposed. The method is mainly used to solve the key problems, such as low accuracy, local-feature-line loss, and the discontinuity of the step-feature-line extraction method. The method first performs the regular raster resampling of the open-pit-mine cloud based on the MLS algorithm, then extracts the step-feature point set by detecting the elevation-gradient change in the resampled point cloud, further traces the step-feature control nodes by the seed-growth tracking algorithm, and finally generates smooth step-feature lines by fitting the space curve to the step-feature control nodes. The results show that the method effectively improves the accuracy of step-feature-line extraction and solves the problems of local-feature-line loss and discontinuity.

Iron ore tailings are mainly composed of SiO₂ and iron, whose content determines the potential reuse strategy of the tailings. Compared with the traditional wet chemistry approach, spectroscopy has proven its superior effectiveness in characterizing and predicting minerals, such as iron oxides, clay, and SiO₂. This study aims to estimate the content of SiO₂ and TFe in iron ore tailings based on visible–near infrared (VIS–NIR, 350–2500 nm) and thermal infrared (TIR, 8–14 μm) spectroscopy. The outer product analysis (OPA) method is used to combine VIS–NIR and TIR spectral domains, from which an outer product matrix of fusion data can be generated. The study area is the iron ore tailing dam from Waitoushan, which is one of the super-large iron deposits in the Anshan–Benxi iron cluster of northeastern China. The spectral analysis results demonstrated the following: (1) The reflectance feature at 1163–2499 nm in the VIS–NIR range correlates with TFe and the emissivity feature at 8–9.4 and 10.7–12 μm in the TIR range correlates with SiO₂. (2) Compared with the original absorbance spectra, the correlation coefficients of fusion spectra improve from 0.66 to 0.87 for TFe and from 0.64 to 0.84 for SiO₂. (3) The partial least squares regression, random forest (RF), and extreme learning machine exploiting particle swarm optimization modeling methods are established for SiO₂ and TFe estimation. The prediction accuracy results indicate that the prediction model with OPA-fused spectra performs significantly better than with individual VIS–NIR and TIR spectra. The RF model with input-fused spectra provides the highest accuracy with the coefficients of determination of 0.95 and 0.91, root mean square errors of 0.97% and 0.96%, and ratios of performance to interquartile distance of 6.49 and 2.31 for SiO₂ and TFe content estimation, respectively. These outcomes provide a theoretical basis and technical support for tailing composition estimation using spectroscopy.

To address the global phenomenon of the salinisation of large land areas, a quantitative inversion model of the salinity of saline soils and soil visible–near-infrared (NIR) spectral data was developed by considering saline soils in Zhenlai County, Jilin Province, China as the research object. The original spectral data were first subjected to Savitzky–Golay (SG) smoothing, multiplicative scattering correction (MSC) pre-processing, and a combined transformation technique. The pre-processed spectral data were then analysed to construct the difference index (DI), ratio index (RI), and normalised difference index (NDI), and the Spearman rank correlation coefficient (r) between these three spectral indices and the salt content in the samples was calculated, while a combined spectral index (r > 0.8) was eventually selected as a sensitive spectral index. Finally, a quantitative inversion model for the salinity of saline soils was developed, and the model’s accuracy was evaluated based on partial least squares regression (PLSR), the random forest (RF) algorithm, and the radial basis function (RBF) neural network algorithm. The results indicated that the inversion of soil salt content using the selected combination of spectral indices based on the RBF neural network algorithm was the most effective, with the prediction model yielding an R² value of 0.950, a root mean square error (RMSE) of 1.014, and a relative percentage deviation (RPD) of 4.479, which suggested a good prediction effect.

Soil nutrient is one of the most important properties to support farmland quality and product. Imaging spectrometry has the potential for rapid acquisition and real-time monitoring of soil characteristics. The goal of this study was to explore the preprocessing and modeling method of hyperspectral image acquired from UAV platform for soil organic matter (SOM) and soil total nitrogen (STN) content estimation in farmland. The results showed that: 1) Multiple Scattering Correction method performed better in reducing image scattering noise rather than Standard Normal Variate transformation or spectral derivatives with higher correlation and lower signal-to-noise ratio; 2) The proposed feature selection method, which was combined with Competitive Adaptive Reweighted Sampling algorithm (CARS) and Successive Projections Algorithm (SPA), could provide selective preference for hyperspectral bands with final 24 feature bands for SOM estimation and 22 feature bands for STN estimation; 3) The particle swarm optimization (PSO) algorithm was selected to optimize input weights and hidden biases of extreme learning machine (ELM)  model for SOM and STN prediction. The PSO-ELM model with input selective preference bands produced higher prediction accuracy with the R2 of 0.73, RPD of 1.91 for SOM and R2 of 0.63, RPD of 1.53 for STN respectively rather than ELM model. These outcomes provided a technical support for wider application of soil properties estimation using imaging spectrometry in agriculture precision monitoring and mapping.

The surface mining activities in grassland and rangeland zones directly affect the livestock production, forage quality, and regional grassland resources. Mine rehabilitation is necessary for accelerating the recovery of the grassland ecosystem. In this work, we investigate the integration of data obtained via a synthetic aperture radar (Sentinel-1 SAR) with data obtained by optical remote sensing (Worldview-3, WV-3) in order to monitor the conditions of a vegetation area rehabilitated after coal mining in North China. The above-ground biomass (AGB) is used as an indicator of the rehabilitated vegetation conditions and the success of mine rehabilitation. The wavelet principal component analysis is used for the fusion of the WV-3 and Sentinel-1 SAR images. Furthermore, a multiple linear regression model is applied based on the relationship between the remote sensing features and the AGB field measurements. Our results show that WV-3 enhanced vegetation indices (EVI), mean texture from band8 (near infrared band2, NIR2), the SAR vertical and horizon (VH) polarization, and band 8 (NIR2) from the fused image have higher correlation coefficient value with the field-measured AGB. The proposed AGB estimation model combining WV-3 and Sentinel 1A SAR imagery yields higher model accuracy (R² = 0.79 and RMSE = 22.82 g/m²) compared to that obtained with any of the two datasets only. Besides improving AGB estimation, the proposed model can also reduce the uncertainty range by 7 g m⁻² on average. These results demonstrate the potential of new multispectral high-resolution datasets, such as Sentinel-1 SAR and Worldview-3, in providing timely and accurate AGB estimation for mine rehabilitation planning and management.