scholarly journals REMOTE SENSING EXTRACTION OF STOPES AND TAILINGS PONDS IN AN ULTRA-LOW-GRADE IRON MINING AREA

2018 ◽  
Vol XLII-3 ◽  
pp. 1237-1239
Author(s):  
B. Ma ◽  
Y. Chen ◽  
X. Li ◽  
L. Wu
Keyword(s):  
Remote Sensing ◽  
Environmental Protection ◽  
Iron Ore ◽  
Mining Area ◽  
Open Pit ◽  
Open Pit Mining ◽  
Landsat 8 ◽  
Low Grade ◽  
Disaster Prevention ◽  
Tailings Ponds

With the development of economy, global demand for steel has accelerated since 2000, and thus mining activities of iron ore have become intensive accordingly. An ultra-low-grade iron has been extracted by open-pit mining and processed massively since 2001 in Kuancheng County, Hebei Province. There are large-scale stopes and tailings ponds in this area. It is important to extract their spatial distribution information for environmental protection and disaster prevention. A remote sensing method of extracting stopes and tailings ponds is studied based on spectral characteristics by use of Landsat 8 OLI imagery and ground spectral data. The overall accuracy of extraction is 95.06 %. In addition, tailings ponds are distinguished from stopes based on thermal characteristics by use of temperature image. The results could provide decision support for environmental protection, disaster prevention, and ecological restoration in the ultra-low-grade iron ore mining area.

scholarly journals Dust Dispersion and Its Effect on Vegetation Spectra at Canopy and Pixel Scales in an Open-Pit Mining Area

2020 ◽  
Vol 12 (22) ◽  
pp. 3759
Author(s):  
Baodong Ma ◽  
Xuexin Li ◽  
Ziwei Jiang ◽  
Ruiliang Pu ◽  
Aiman Liang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Mining Area ◽  
Open Pit ◽  
Open Pit Mining ◽  
Vegetation Canopy ◽  
Dust Pollution ◽  
Mining Areas ◽  
Dust Effect ◽  
Dust Dispersion ◽  
Pixel Scale

Dust pollution is severe in some mining areas in China due to rapid industrial development. Dust deposited on the vegetation canopy may change its spectra. However, a relationship between canopy spectra and dust amount has not been quantitatively studied, and a pixel-scale condition for remote sensing application has not been considered yet. In this study, the dust dispersion characteristics in an iron mining area were investigated using the American Meteorological Society (AMS) and the U.S. Environmental Protection Agency (EPA) regulatory model (AERMOD). Further, based on the three-dimensional discrete anisotropic radiative transfer (DART) model, the spectral characteristics of vegetation canopy under the dusty condition were simulated, and the influence of dustfall on vegetation canopy spectra was studied. Finally, the dust effect on vegetation spectra at the canopy scale was extended to a pixel scale, and the response of dust effect on vegetation spectra at the pixel scale was determined under different fractional vegetation covers (FVCs). The experimental results show that the dust pollution along a haul road was more severe and extensive than that in a stope. Taking dust dispersion along the road as an example, the variation of vegetation canopy spectra increased with the height of dust deposited on the vegetation canopy. At the pixel scale, a lower vegetation FVC would weaken the influence of dust on the spectra. The results derived from simulation spectral data were tested using satellite remote sensing images. The tested result indicates that the influence of dust retention on the pixel spectra with different FVCs was consistent with that created with the simulated data. The finding could be beneficial for those making decisions on monitoring vegetation under dusty conditions and reducing dust pollution in mining areas using remote sensing technology.

scholarly journals Fine Land Cover Classification in an Open Pit Mining Area Using Optimized Support Vector Machine and WorldView-3 Imagery

2019 ◽  
Vol 12 (1) ◽  
pp. 82 ◽  
Author(s):  
Weitao Chen ◽  
Xianju Li ◽  
Lizhe Wang
Keyword(s):  
Remote Sensing ◽  
Land Cover ◽  
Classification Accuracy ◽  
Optimization Methods ◽  
Land Cover Classification ◽  
Mining Area ◽  
Open Pit ◽  
Open Pit Mining ◽  
Support Vector ◽  
Carbonate Index

Fine land cover classification in an open pit mining area (LCCOM) is essential in analyzing the terrestrial environment. However, researchers have been focusing on obtaining coarse LCCOM while using high spatial resolution remote sensing data and machine learning algorithms. Although support vector machines (SVM) have been successfully used in the remote sensing community, achieving a high classification accuracy of fine LCCOM using SVM remains difficult because of two factors. One is the lack of significant features for efficiently describing unique terrestrial characteristics of open pit mining areas and another is the lack of an optimized strategy to obtain suitable SVM parameters. This study attempted to address these two issues. Firstly, a novel carbonate index that was based on WorldView-3 was proposed and introduced into the used feature set. Additionally, three optimization methods—genetic algorithm (GA), k-fold cross validation (CV), and particle swarm optimization (PSO)—were used for obtaining the optimization parameters of SVM. The results show that the carbonate index was effective for distinguishing the dumping ground from other open pit mining lands. Furthermore, the three optimization methods could significantly increase the overall classification accuracy (OA) of the fine LCCOM by 8.40%. CV significantly outperformed GA and PSO, and GA performed slightly better than PSO. CV was more suitable for most of the fine land cover types of crop land, and PSO for road and open pit mining lands. The results of an independent test set revealed that the optimized SVM models achieved significant improvements, with an average of 8.29%. Overall, the proposed strategy was effective for fine LCCOM.

Investigation of the Transformation of the Plant Ecosystem in the Vicinity of the Tsemesskaya Bay in the Black Sea Based on Remote Sensing Data

2021 ◽  
Vol 25 (6) ◽  
pp. 61-67
Author(s):  
I.V. Zen’kov ◽  
Trinh Le Hung ◽  
Yu.P. Yuronen ◽  
P.M. Kondrashov ◽  
A.A. Latyntsev ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Black Sea ◽  
Remote Sensing Data ◽  
Open Pit ◽  
Open Pit Mining ◽  
The Black Sea ◽  
Industrial Facilities ◽  
Logistics Center ◽  
Sensing Data ◽  
The City

A brief description of the industrial and logistics center operating in the city of Novorossiysk on the coast of the Tsemesskaya Bay in the Black Sea is presented. According to remote sensing data, the area of open pit mining of rock dumps dumped during the development of three marl deposits for use at four cement plants was determined. According to the results of satellite imagery and analytical calculations, downward trends in changes in the density of vegetation cover in territories with natural landscapes adjacent to the territory of industrial facilities located on the coast of the Tsemesskaya Bay were revealed.

Asbestos rehabilitation project as a model of landscape sustainability

2022 ◽  
Vol 13 (1) ◽  
pp. 0-0
Keyword(s):  
Mining Area ◽  
Open Pit ◽  
Open Pit Mining ◽  
Soil Protection ◽  
Rehabilitation Management ◽  
Landscape Sustainability ◽  
National Legislation ◽  
Health Measures ◽  
Safety And Health ◽  
The World

Greece was one of the biggest producers of asbestos in the world as well as a consumer. It took advantage of the asbestos rich Zidani mine, in the region of Western Macedonia in Greece. However, due to serious health problems caused by inhaling asbestos, it was banned in 1979 and the mine closed in March 2000. Rehabilitation management of the abandoned asbestos mining area, the depositions in the open - pit mining area and the tailings remnants was necessary in order to avoid health and environmental problems in the wider area The detailed soil protection and rehabilitation project of the degraded mining area was implemented taking all necessary and appropriate safety and health measures according to the requirements of the relevant E.U and National legislation, so that accidents would be prevented. Results show that the rehabilitation, soil protection and enhancement of the area help the ecosystems to be sustainable, ecologically and socially acceptable

Sign in / Sign up

Export Citation Format

Share Document