The intensive urban expansion in Mianzhu city and the mining of coal and phosphorite in the upstream areas resulted in a significant accumulation of heavy metals in local soils. The experimental site was selected at the Mianzhu city of Sichuan, China, major cropland for rapeseed, spanning from 103°54′E 30°09′N to 104°20′E, 30°09′-31 ...
The mining and processing of mineral resources have shown no shortage of economic benefits. However, coal mining will disturb the soil layer, destroy vegetation and cause the soil to lose its utilization …
The mining industry plays a significant role in economic growth and development. Coal is a viable renewable energy source with 185.175 billion deposits in Thar, which has not been deeply explored.
Coal mining has environmental impacts on surrounding areas, including heavy metal contamination of soil. This study explores the feasibility of using hyperspectral remote sensing to determine the heavy metal (Cr, Ni, Cu, Zn, Cd, Pb) content of soils in a coal-mining area in the city of Zoucheng, Shandong Province, China.
Retrieving soil heavy metals concentrations based on GaoFen-5 hyperspectral satellite image at an opencast coal mine, Inner Mongolia, China Environ Pollut. 2022 May 1; 300:118981. ... The present study collected 110 topsoil samples from an Opencast Coal Mine of Ordos, Inner Mongolia, China. Then, the spectra and soil heavy …
Shao et al. (Shao et al., 2020) designe d a 91-channel hyperspectral lidar with an acousto-optic tunable filter (AOTF) as the spectral device. After collecting the spectra of four coal/rock ...
Hyperspectral PLSR modeling can effectively predict heavy metal content of soils in coal‐mining areas, and preprocessing spectral data is crucial for achieving high prediction accuracy. Core ...
Hyperspectral estimation of soil organic matter (SOM) in coal mining regions is an important tool for enhancing fertilization in soil restoration programs. The correlation—partial least squares regression (PLSR) method effectively solves the information loss problem of correlation—multiple linear … See more
Request PDF | Use of hyperspectral imagery to detect affected vegetation and heavy metal polluted areas: a coal mining area, China | Some indicators for evaluating heavy metal pollution have been ...
To examine the influence of coal dust from mining on vegetative growth, three typical plants from near an open-pit coalmine in an arid region were selected, and their spectral signals were determined. ... Using hyperspectral indices to measure the effect of mine dust on the growth of three typical desert plants Guang Pu Xue Yu Guang Pu Fen Xi ...
To address this challenge, this study proposes a hybrid approach, namely, Light Gradient Boosting Machine plus Ordinary Kriging (LGBK), for predicting the spatial distribution of soil heavy metals. A total of 137 soil samples were collected from the Shengli Coal-mine Base in Inner Mongolia, China, and their heavy metal concentrations were …
Coal mining has led to increasingly serious land subsidence, and the reclamation of the subsided land has become a hot topic of concern for governments and scholars. Soil quality of reclaimed land is the key indicator to the evaluation of the reclamation effect; hence, rapid monitoring and evaluation of reclaimed land is of great …
By using hyperspectral technology and Enter-PLSR method, the study blank of soil available nitrogen in National Mine Park was filled. At the same time, the computation efficiency problem of PLSR ...
Hyperspectral estimation of soil organic matter (SOM) in coal mining regions is an important tool for enhancing fertilization in soil restoration programs. The …
Hyperspectral remote sensing technology has good characteristics, e.g., high speed, macro, and high resolution, etc., and has gradually become a focus of research to determine heavy metal content in soil. ... Taking a mining area in Hunan, China as a test area, this study retrieved the chromium content in the soil using 12 combination models …
During the mining operation, it is a critical task in coal mines to significantly improve the safety by precision coal mining sorting and rock classification from different layers. It implies that a technique for rapidly and accurately classifying coal/rock in-site needs to be investigated and established, which is of significance for improving the coal …
Aiming at the problem of coal gangue identification in the current fully mechanized mining face and coal washing, this article proposed a convolution neural network (CNN) coal and rock identification method based on hyperspectral data. First, coal and rock spectrum data were collected by a near-infrared spectrometer, and then …
Hyperspectral estimation of soil organic matter (SOM) in coal mining regions is an important tool for enhancing fertilization in soil restoration programs. The correlation—partial least squares regression (PLSR) method effectively solves the information loss problem of correlation—multiple linear stepwise regression, but results …
Hyperspectral technology has a striking ability to detect the quality of materials, and consequently, research on coal quality using this technique has increased many folds in recent years.
In the present study, GF-5 hyperspectral satellite images, as well as in-situ sampling data, were carried out to estimate the concentrations of Zn, Ni, and Cu at an open cast coal mine, China. The entire workflow and methods concerning spectra processing, characteristic bands selecting, calibration model construction used in the current study ...
limited research into hyperspectral monitoring of SOM in coal mining regions, where serious soil deg-radation is typical. Among the most efficient methods in constructing …
the feasibility of using hyperspectral remote sensing to determine the heavy metal (Cr, Ni, Cu, Zn, Cd, Pb) content of soils in a coal-mining area in the city of Zoucheng, Shandong Province, China. We used a plasma mass spectrometer to measure the heavy metal contents of soils and an ASD Field Spec4 spectrometer to measure soil hyperspectral …
@article{Zhang2022RetrievingSH, title={Retrieving soil heavy metals concentrations based on GaoFen-5 hyperspectral satellite image at an opencast coal mine, Inner Mongolia, China.}, author={Bo Zhang and Bin Guo and Bin Zou and Wei Wei and Yongzhi Lei and Tianqi Li}, journal={Environmental pollution}, year={2022}, pages={ 118981 }, url={https ...
Hyperspectral imaging can map clays, talc, and other deleterious rock phases and produce valuable information for building predictive models of mining and geometallurgical parameters. For this purpose, and in …
Hyperspectral PLSR modeling can effectively predict heavy metal content of soils in coal-mining areas, and preprocessing spectral data is crucial for achieving high prediction accuracy. Coal mining has environmental impacts on surrounding areas, including heavy metal contamination of soil. This study explores the feasibility of using …
The new system has been designed to leverage OSK's previous experience collecting and analyzing hyperspectral data to support operations in the energy, mining, and defence sectors. In the ...
Hyperspectral remote sensing has advantages in monitoring soil heavy metal concentration. In this study, AHSI remote sensing imagery was used to predict and map soil Cu concentration in a mining area. Considering different spectral responses of hyperspectral bands, a variable weighting method was proposed to weight the …
Spectral mapping. The ultimate aim of the utilization of hyperspectral data in mineral exploration is to map surface mineralogy proxy of mineral deposits and also to …
This paper primarily focuses on identifying and mapping vegetation stress caused by the impact of coal mining using airborne hyperspectral image (AVIRIS-NG) at a fine scale level and is validated using spectroscopic field data. In this work, we have calculated and tested vegetation stress-affected narrow banded vegetation indices ...
limited research into hyperspectral monitoring of SOM in coal mining regions, where serious soil deg-radation is typical. Among the most efficient methods in constructing reliable models in the hyperspectral remote sensing field, partial least squares regression (PLSR) has been the most frequently used for estimating SOM content. Nocita et al.