Coupling Satellite Images and Unmanned Aerial Vehicle Data to Monitor the Exploitation of Open-Pit Mine

Discovering the variation of an open-pit mine in vertical, horizontal, and temporal dimensions as well to characterize the stages and the trends of the exploitation are indispensable tasks which provide information supporting decision making and planning for sustainable development of the mining industry. Remote sensing technique with the advantages of multi-spatial, multi-spectral, multi-temporal resolution is a promising solution to meet the information requirement. This study proposes an approach of coupling the high-resolution satellite images and Unmanned Aerial Vehicle (UAV) data to observe the variation of Tan An open rocky mine during its lifetime. Five satellite images with the resolution of 0.5 m acquired in 2006, 2012, 2014, 2016, 2018, and two ortho-images with 0.034 m resolution constructed from UAV photos captured in 2019, 2020 are used to make land cover maps. The analysis of land cover changes discovers 3 stages of open-pit mine exploitation consisting of unprompted exploitation, exploiting outbreak and stable exploitation corresponding to the changes in the mine. Besides, two Digital Surface Models (DSM) constructed by UAV photos are compared to calculate the elevation and volume changes. The assessment of the correlation between elevation change and land cover change indicates that the mineral exploitation is in the vertical range from 645 m to 660 m, and the exploitation trend is following the horizontal expansion rather than the deep excavation. Additionally, this experiment results in 79,422 m3 of mineral taken from the mine, and 34,022 m3 of soil used for the restoration within a year from June 2019 to June 2020.

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Using Video Acquired from an Unmanned Aerial Vehicle (UAV) to Measure Fracture Orientation in an Open-Pit Mine

GEOMATICA ◽

10.5623/cig2013-036 ◽

2013 ◽

Vol 67 (3) ◽

pp. 173-180 ◽

Cited By ~ 21

Author(s):

T. McLeod ◽

C. Samson ◽

M. Labrie ◽

K. Shehata ◽

J. Mah ◽

...

Keyword(s):

Unmanned Aerial Vehicle ◽

Three Dimensional ◽

Mining Industry ◽

Point Clouds ◽

Video Camera ◽

Open Pit ◽

Original Video ◽

3D Point Clouds ◽

Video Images ◽

Aerial Vehicle

This project explored the feasibility of using video images acquired with an unmanned aerial vehicle (UAV) to obtain three-dimensional (3D) point clouds using structure from motion (SfM) software. Missions were flown using an Aeryon Scout: a lightweight, vertical take-off and landing quadrotor micro UAV with a miniature video camera. The initial mission captured urban scene images that were used to assess system performance while the main mission focused on rock walls where 3D images were used to successfully measure fracture orientations. Point clouds generated from this combination of technologies were sparse, but in the future, improvements in the resolution of original video images would cascade through the processing and improve the overall results. Such a system could have a multitude of applications in the mining industry, contributing to both safety and financial considerations.

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SateLoc: A Virtual Fingerprinting Approach to Outdoor LoRa Localization Using Satellite Images

ACM Transactions on Sensor Networks ◽

10.1145/3461012 ◽

2021 ◽

Vol 17 (4) ◽

pp. 1-28

Author(s):

Yuxiang Lin ◽

Wei Dong ◽

Yi Gao ◽

Tao Gu

Keyword(s):

Land Cover ◽

Large Scale ◽

Satellite Images ◽

Low Cost ◽

Tracking Error ◽

Location Based Services ◽

Distance Constraint ◽

Combination Strategy ◽

Land Cover Types ◽

High Resolution Satellite Images

With the increasing relevance of the Internet of Things and large-scale location-based services, LoRa localization has been attractive due to its low-cost, low-power, and long-range properties. However, existing localization approaches based on received signal strength indicators are either easily affected by signal fading of different land-cover types or labor intensive. In this work, we propose SateLoc, a LoRa localization system that utilizes satellite images to generate virtual fingerprints. Specifically, SateLoc first uses high-resolution satellite images to identify land-cover types. With the path loss parameters of each land-cover type, SateLoc can automatically generate a virtual fingerprinting map for each gateway. We then propose a novel multi-gateway combination strategy, which is weighted by the environmental interference of each gateway, to produce a joint likelihood distribution for localization and tracking. We implement SateLoc with commercial LoRa devices without any hardware modification, and evaluate its performance in a 227,500-m urban area. Experimental results show that SateLoc achieves a median localization error of 43.5 m, improving more than 50% compared to state-of-the-art model-based approaches. Moreover, SateLoc can achieve a median tracking error of 37.9 m with the distance constraint of adjacent estimated locations. More importantly, compared to fingerprinting-based approaches, SateLoc does not require the labor-intensive fingerprint acquisition process.

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GRANULOMETRIC MAPS FROM HIGH RESOLUTION SATELLITE IMAGES

Image Analysis & Stereology ◽

10.5566/ias.v21.p19-24 ◽

2011 ◽

Vol 21 (1) ◽

pp. 19 ◽

Cited By ~ 6

Author(s):

Catherine Mering ◽

Franck Chopin

Keyword(s):

High Resolution ◽

Land Cover ◽

Arid Regions ◽

Satellite Images ◽

Distribution Analysis ◽

Granulometric Analysis ◽

High Resolution Satellite Images ◽

Thematic Mapping ◽

Urban Settlements

A new method of land cover mapping from satellite images using granulometric analysis is presented here. Discontinuous landscapes such as steppian bushes of semi arid regions and recently growing urban settlements are especially concerned by this study. Spatial organisations of the land cover are quantified by means of the size distribution analysis of the land cover units extracted from high resolution remotely sensed images. A granulometric map is built by automatic classification of every pixel of the image according to the granulometric density inside a sliding neighbourhood. Granulometric mapping brings some advantages over traditional thematic mapping by remote sensing by focusing on fine spatial events and small changes in one peculiar category of the landscape.

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Radiometric Normalization of Multi-temporal High Resolution Satellite Images with Quality Control for Land Cover Change Detection

10.4095/219814 ◽

2002 ◽

Author(s):

Y Du ◽

P M Teillet ◽

J Cihlar

Keyword(s):

Quality Control ◽

High Resolution ◽

Land Cover ◽

Change Detection ◽

Land Cover Change ◽

Satellite Images ◽

High Resolution Satellite Images ◽

Multi Temporal ◽

Land Cover Change Detection

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Activities of Musan Mine observed by Sentinel-1 Coherence Imagery

10.5194/egusphere-egu2020-12625 ◽

2020 ◽

Author(s):

Jihyun Moon ◽

Heejeong Seo ◽

Hoonyol Lee

Keyword(s):

Time Series ◽

Satellite Images ◽

European Space Agency ◽

Open Pit Mine ◽

Mining Activity ◽

Open Pit ◽

Open Pit Mining ◽

Series Data ◽

Mining Activities ◽

Mining Site

<p>Musan mine in North Korea is the largest open-pit iron mine in Asia with the proved reserves of about 2.06 billion tons and more than 9 square kilometers. Open-pit mining is one of the surface mining technique extracting minerals from the surface. Vegetation is rarely distributed at the mining site because the topsoil is removed and the ore is mined directly from the surface. Therefore, it is effective to observe surface displacement at the mining site using Interferometric Synthetic Aperture Radar (InSAR) technology. InSAR coherence detects random surface change that measures the activity or stability of the interferometric phase of InSAR data. High coherence will be maintained on the surface where there is no movement and only surface scattering. On the other hand, the surface where there is a lot of movement and volumetric scattering has low coherence value. Therefore, using 12-days InSAR coherence images from Sentinel-1 satellites, for example, it is possible to analyze how active the open-pit mine is during the 12 days. Sentinel-1A satellite images were acquired from June 11, 2015 to May 24, 2016, followed by Sentine-1B satellite images from September 27, 2016 to April 21, 2019. A total of 102 SAR images were downloaded from European Space Agency (ESA) portal. There is a gap between May 24 and September 27, 2016 due to the transition of the data acquisition plan. Over 100 12-days coherence data were obtained by applying InSAR. Stable spots and target spots were selected through average and standard deviation of the entire coherence time series data. Coherence values include not only the mining activity but also the effects of perpendicular baseline, temporal baseline, and weather. Therefore, NDAI (Normalized Difference Activity Index) was newly defined to remove the noise and only the coherence value due to the influence of the mining activity was extracted. The degree of activities can be observed by the time series coherence and NDAI images. This study needs other references related to mining activities in order to analyze the mining activities in more detail. This method can be applied to other open-pit mine.</p>

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THE ECOLOGICAL CONDITION OF LAND COVER AND ATMOSPHERIC AIR IN THE SANITARY-PROTECTIVE ZONE OF THE COAL MINING ENTERPRISE “CHERNOGORSKY” OPEN-PIT MINE “SUEK-KHAKASSIA” LLC

Ugol ◽

10.18796/0041-5790-2020-8-96-99 ◽

2020 ◽

pp. 96-99

Author(s):

A.T. Lavrinenko ◽

◽

N.A. Ostapova ◽

O.S. Safronova ◽

V.A. Azev ◽

...

Keyword(s):

Land Cover ◽

Coal Mining ◽

Ecological Condition ◽

Open Pit Mine ◽

Open Pit ◽

Atmospheric Air ◽

Mining Enterprise

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UAS Remote Sensing Products for Supporting Extraction Management and Restoration Monitoring in Open-Pit Mines

Proceedings ◽

10.3390/proceedings2019030004 ◽

2019 ◽

Vol 30 (1) ◽

pp. 4

Author(s):

Carabassa ◽

Montero ◽

Crespo ◽

Padró ◽

Balagué ◽

...

Keyword(s):

Remote Sensing ◽

Land Cover ◽

Cost Effective ◽

Open Pit Mine ◽

Open Pit ◽

Unmanned Aerial Systems ◽

Surface Model ◽

Waste Dumps ◽

Efficient Resource ◽

Restoration Monitoring

Accurate mapping of open-pit mine areas is a prerequisite for the efficient resource management of extractive companies, but also detailed mapping is a requirement for public administrations, especially regarding the monitoring of restored areas. In previous works, our team has contributed to a better knowledge of the use of Unmanned Aerial Systems (UAS) technologies for soil/vegetation restoration monitoring purposes, and in this work, we present a novel protocol to support combined interests of both private companies and governmental agencies. We introduce a case study in which we show the capability of multispectral sensors onboard of a low-weight multicopter to describe land cover typologies in restored areas (such as grass, scrubs, trees, topsoil and mine spoils) by applying remote sensing and GIS techniques. Moreover, we assess the capability of digital terrain models (Digital Elevation Model, Digital Surface Model, Digital Slope Model) derived from photogrammetric techniques, to provide useful and fast topographic information for the proper management of open-pit mine exploitation and restoration. By applying these techniques, we present a cost-effective workflow adequate to monitor land cover dynamics in restored areas, but also volumetric changes in stockpiles, waste dumps and extraction faces. This combined approach, supporting both environmental and industrial needs, aims to enhance the collaboration between sectors, establishing synergies, reducing costs by sharing knowledge, and adding transparency to their relation.

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