Eco-environment Quality Evaluation of Rare Earth Ore Mining Area Based on Remote Sensing Techniques

Author(s):  
Yan Peng ◽  
GuoJin He ◽  
Wei Jiang
Geosciences ◽  
2020 ◽  
Vol 10 (5) ◽  
pp. 164
Author(s):  
Valentine Piroton ◽  
Romy Schlögel ◽  
Christian Barbier ◽  
Hans-Balder Havenith

Central Asian mountain regions are prone to multiple types of natural hazards, often causing damage due to the impact of mass movements. In spring 2017, Kyrgyzstan suffered significant losses from a massive landslide activation event, during which also two of the largest deep-seated mass movements of the former mining area of Mailuu-Suu—the Koytash and Tektonik landslides—were reactivated. This study consists of the use of optical and radar satellite data to highlight deformation zones and identify displacements prior to the collapse of Koytash and to the more superficial deformation on Tektonik. Especially for the first one, the comparison of Digital Elevation Models of 2011 and 2017 (respectively, satellite and unmanned aerial vehicle (UAV) imagery-based) highlights areas of depletion and accumulation, in the scarp and near the toe, respectively. The Differential Synthetic Aperture Radar Interferometry analysis identified slow displacements during the months preceding the reactivation in April 2017, indicating the long-term sliding activity of Koytash and Tektonik. This was confirmed by the computation of deformation time series, showing a positive velocity anomaly on the upper part of both landslides. Furthermore, the analysis of the Normalized Difference Vegetation Index revealed land cover changes associated with the sliding process between June 2016 and October 2017. In addition, in situ data from a local meteorological station highlighted the important contribution of precipitation as a trigger of the collapse. The multidirectional approach used in this study demonstrated the efficiency of applying multiple remote sensing techniques, combined with a meteorological analysis, to identify triggering factors and monitor the activity of landslides.


2016 ◽  
Vol 36 (6) ◽  
Author(s):  
彭燕 PENG Yan ◽  
何国金 HE Guojin ◽  
张兆明 ZHANG Zaoming ◽  
江威 JIANG Wei ◽  
欧阳志云 OUYANG Zhiyun ◽  
...  

2021 ◽  
Author(s):  
DENISSE Archundia ◽  
Victor Vidaña-Guillen ◽  
Juan Valenzuela-Munguia ◽  
Francisco elizandro Molina Freaner

Abstract Groundwater metal pollution is a major concern for societies, especially in areas where the mining industry is important. Index-based techniques, as the DRASTIC index, are often used to assess the intrinsic groundwater vulnerability and could be modified to evaluate the aquifer vulnerability to specific contaminants. Mines, mining wastes and related features are detectable with remote sensing techniques. In this work we evaluate the vulnerability of the Rio Sonora Aquifer to metallic pollution by the traditional DRASTIC method and by the addition of a land use (Lu) parameter in which possible sources of metals (detected by remote sensing) were considered (DRASTIC+Lu). The methodology allowed us to locate possible sources of metallic contamination. The Sonora River channel showed the higher vulnerability in both calculated vulnerability indices (DRASTIC and DRASTIC+Lu). Generally, the addition of the land use parameter caused a decrease in vulnerability but also a local increase where possible sources of metals were found. Thus, the modified method facilitated the identification of highly vulnerable areas which is relevant to better protect the studied aquifer.


2019 ◽  
Vol 11 (8) ◽  
pp. 987 ◽  
Author(s):  
Yan Peng ◽  
Zhaoming Zhang ◽  
Guojin He ◽  
Mingyue Wei

An improved GrabCut method based on a visual attention model is proposed to extract rare-earth ore mining area information using high-resolution remote sensing images. The proposed method makes use of advantages of both the visual attention model and GrabCut method, and the visual attention model was referenced to generate a saliency map as the initial of the GrabCut method instead of manual initialization. Normalized Difference Vegetation Index (NDVI) was designed as a bound term added into the Energy Function of GrabCut to further improve the accuracy of the segmentation result. The proposed approach was employed to extract rare-earth ore mining areas in Dingnan County and Xunwu County, China, using GF-1 (GaoFen No.1 satellite launched by China) and ALOS (Advanced Land Observation Satellite) high-resolution remotely-sensed satellite data, and experimental results showed that FPR (False Positive Rate) and FNR (False Negative Rate) were, respectively, lower than 12.5% and 6.5%, and PA (Pixel Accuracy), MPA (Mean Pixel Accuracy), MIoU (Mean Intersection over Union), and FWIoU (frequency weighted intersection over union) all reached up to 90% in four experiments. Comparison results with traditional classification methods (such as Object-oriented CART (Classification and Regression Tree) and Object-oriented SVM (Support Vector Machine)) indicated the proposed method performed better for object boundary identification. The proposed method could be useful for accurate and automatic information extraction for rare-earth ore mining areas.


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