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New Advances in Remote Sensing Techniques Applied in Surface and Underground Mine Operations

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing in Geology, Geomorphology and Hydrology".

Deadline for manuscript submissions: 15 August 2025 | Viewed by 8393

Special Issue Editor

Prof. Dr. Kamran Esmaeili

E-Mail Website1 Website2
Guest Editor
Department of Civil and Mineral Engineering, University of Toronto, Toronto, ON M5S 1A4, Canada
Interests: mining; geomechanics; remote sensing; machine learning; rock mechanics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

For many years, collecting data in mine operations was a highly manual process, providing data with a low temporal and spatial resolution that hindered timely and efficient decision making. Discontinuous and intermittent mining process monitoring approaches and decision making based on partial information and missing facts are no longer suitable to address the complex mining challenges. Innovative solutions have been developed for the real-time acquisition of high-resolution mining data in order to make effective decisions and maximize the efficiency, safety and profitability of mining processes.

Remote sensing plays a crucial role in modern mine operations by providing valuable information regarding the Earth’s surface without requiring direct physical contact. Satellites, aircraft, radars, drones, and terrestrial instruments equipped with various sensors (e.g. LiDAR, Thermal, Hyperspectral) are employed to collect data from a distance.

This Special Issue welcomes the submission of papers that address state-of-the-art approaches and applications of remote sensing in mineral exploration and target identification; geological, geotechnical and geometallurgical mapping; environmental monitoring (e.g., the extent of disturbances caused by mining operations, land reclamation and rehabilitation); pit slope stability; underground space monitoring and mapping; mineral mapping and grade estimation; mine infrastructure planning and monitoring (haul roads, tailings dams, and waste disposal sites); and safety and security (e.g., identifying potential safety hazards and security breaches).

Prof. Dr. Kamran Esmaeili
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • open-pit mining
  • underground mining
  • mapping
  • mine process monitoring
  • operational efficiency, mine safety
  • mine environmental monitoring

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Published Papers (4 papers)

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Research

22 pages, 7865 KiB  
Article
Applying Knowledge-Based and Data-Driven Methods to Improve Ore Grade Control of Blast Hole Drill Cuttings Using Hyperspectral Imaging
by Somaieh Akbar, Mehdi Abdolmaleki, Saleh Ghadernejad and Kamran Esmaeili
Remote Sens. 2024, 16(15), 2823; https://doi.org/10.3390/rs16152823 - 1 Aug 2024
Cited by 3 | Viewed by 1561
Abstract
This study introduces a novel method utilizing hyperspectral imaging for instantaneous ore-waste analysis of drill cuttings. To implement this technique, we collected samples of drill cuttings at regular depth intervals from five blast holes in an open pit gold mine and subjected them [...] Read more.
This study introduces a novel method utilizing hyperspectral imaging for instantaneous ore-waste analysis of drill cuttings. To implement this technique, we collected samples of drill cuttings at regular depth intervals from five blast holes in an open pit gold mine and subjected them to scanning using a hyperspectral imaging system. Subsequently, we employed two distinct methods for processing the hyperspectral images. A knowledge-based method was used to estimate ore grade within each sampled interval, and a data-driven technique was employed to distinguish the ore and waste for each sample interval. Firstly, leveraging the mixed mineralogical composition of the samples, the Linear Spectral Unmixing (LSU) technique was utilized to predict ore grade for each sample. Additionally, the Gradient Boosting Classifier (GBC) was used as an efficient data-driven approach to classify ore-waste samples. Both methods rendered accurate results when they were compared with results obtained through laboratory X-ray diffraction (XRD) analysis and gold assay analysis for the same sample intervals. Adopting the proposed methodology in open pit mine operations can significantly enhance the process of grade control during blast hole drilling. This includes reducing costs, saving time, minimizing uncertainty in ore grade estimation, and establishing more precise ore-waste boundaries in resource block models. Full article
(This article belongs to the Special Issue New Advances in Remote Sensing Techniques Applied in Surface and Underground Mine Operations)
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21 pages, 17830 KiB  
Article
Identifying Factors Influencing Surface Deformations from Underground Mining Using SAR Data, Machine Learning, and the SHAP Method
by Konrad Cieślik, Wojciech Milczarek, Ewa Warchala, Paweł Kosydor and Robert Rożek
Remote Sens. 2024, 16(13), 2428; https://doi.org/10.3390/rs16132428 - 2 Jul 2024
Cited by 2 | Viewed by 1641
Abstract
The article presents the results of significance analyses of selected mining and geological variables for an area of underground mining activity. The study area was a region of an underground copper ore mine located in southwest Poland. The input data consisted of satellite [...] Read more.
The article presents the results of significance analyses of selected mining and geological variables for an area of underground mining activity. The study area was a region of an underground copper ore mine located in southwest Poland. The input data consisted of satellite radar data from the Sentinel 1 mission as well as mining and geological data. The line-of-sight subsidence, calculated with the use of the small baseline subset method and arranged in time series, was decomposed to extract the vertical component. The significance analysis of individual variables for the observed surface subsidence was performed using the SHapley Additive exPlanations method for the XGBoost machine learning model. The results of the analysis showed that the observed ground surface subsidence velocities were most influenced by the thickness of the PZ3 layer, which is located approximately 200 m above the roof of the mined seam, the thickness of the seam, and the timing of mining. It was also found that the proposed model was able to detect a nonlinear relationship between the analyzed excavations. The most significant influence on ground subsidence over mine excavations are mining parameters such as the spatially averaged thickness of the deposit and the time since liquidation of the deposit. The proposed approach can be successfully employed in planning both mining operations and mine closure in such a manner that the environmental impact is minimized. Full article
(This article belongs to the Special Issue New Advances in Remote Sensing Techniques Applied in Surface and Underground Mine Operations)
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28 pages, 38039 KiB  
Article
Is Endmember Extraction a Critical Step in the Analysis of Hyperspectral Images in Mining Environments?
by Jingping He, Dean N. Riley and Isabel Barton
Remote Sens. 2024, 16(12), 2137; https://doi.org/10.3390/rs16122137 - 13 Jun 2024
Cited by 1 | Viewed by 1524
Abstract
Hyperspectral imaging systems (HSIs) are becoming widespread in the mining industry for mineral classification. The spectral features detectable from near infrared to long-wave infrared make HSIs a potentially efficient tool for exploration, clay mapping, and leach pad modeling. However, the redundancy of hyperspectral [...] Read more.
Hyperspectral imaging systems (HSIs) are becoming widespread in the mining industry for mineral classification. The spectral features detectable from near infrared to long-wave infrared make HSIs a potentially efficient tool for exploration, clay mapping, and leach pad modeling. However, the redundancy of hyperspectral data makes the analysis of hyperspectral images complicated and slow. Many researchers have proposed different algorithms and strategies to speed up processing and increase accuracy. These procedures rely on endmember extraction as one of the critical steps. However, no one has tested whether endmember extraction actually improves accuracy under all circumstances. Eliminating endmember extraction, if possible, would speed up the analysis of hyperspectral data. This study tested whether endmember extraction improves the accuracy and efficiency of mapping materials at leach pads, which are among the most complicated situations in mining environments. We compared the accuracy of abundance maps produced with fully constrained least squares (FCLS) (a) with endmember extraction by N-FINDR and (b) without endmember extraction, using a spectral library instead. The results from endmember extraction showed lower accuracy than the results from using a spectral library, probably because the spectral data were noisy and the scanned materials were mixtures. The application of FCLS to hyperspectral images provides useful information for metallurgists. The abundance maps showed that kaolinite, muscovite, and precipitation (hexahydrite and pickeringite) were the dominant minerals on the leach pad. The abundance maps of pipes and precipitation can be used to monitor leaching conditions. Lixiviant ponds mapped out in the abundance map of water can indicate saturation. This technique can also detect organic leakage and agglomeration effectiveness, but it will need different wavelength ranges and more future study. This paper also suggests best practices for using hyperspectral imaging systems to map leach pads. Full article
(This article belongs to the Special Issue New Advances in Remote Sensing Techniques Applied in Surface and Underground Mine Operations)
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18 pages, 4629 KiB  
Article
Tracking the Vegetation Change Trajectory over Large-Surface Coal Mines in the Jungar Coalfield Using Landsat Time-Series Data
by Yanfang Wang, Shan Zhao, Hengtao Zuo, Xin Hu, Ying Guo, Ding Han and Yuejia Chang
Remote Sens. 2023, 15(24), 5667; https://doi.org/10.3390/rs15245667 - 7 Dec 2023
Cited by 11 | Viewed by 2120
Abstract
Coal mining and ecological restoration activities significantly affect land surfaces, particularly vegetation. Long-term quantitative analyses of vegetation disturbance and restoration are crucial for effective mining management and ecological environmental supervision. In this study, using the Google Earth Engine and all available Landsat images [...] Read more.
Coal mining and ecological restoration activities significantly affect land surfaces, particularly vegetation. Long-term quantitative analyses of vegetation disturbance and restoration are crucial for effective mining management and ecological environmental supervision. In this study, using the Google Earth Engine and all available Landsat images from 1987 to 2020, we employed the Landsat-based Detection of Trends in Disturbance and Recovery (LandTrendr) algorithm and Support Vector Machine (SVM) to conduct a comprehensive analysis of the year, intensity, duration, and pattern of vegetation disturbance and restoration in the Heidaigou and Haerwusu open-pit coal mines (H-HOCMs) in the Jungar Coalfield of China. Our findings indicate that the overall accuracy for extractions of disturbance and restoration events in the H-HOCMs area is 83% and 84.5%, respectively, with kappa coefficients of 0.82 for both. Mining in Heidaigou has continued since its beginning in the 1990s, advancing toward the south and then eastward directions, and mining in the Haerwusu has advanced from west to east since 2010. The disturbance magnitude of the vegetation greenness in the mining area is relatively low, with a duration of about 4–5 years, and the restoration magnitude and duration vary considerably. The trajectory types show that vegetation restoration (R, 44%) occupies the largest area, followed by disturbance (D, 31%), restoration–disturbance (RD, 16%), disturbance–restoration (DR, 8%), restoration–disturbance–restoration (RDR), and no change (NC). The LandTrendr algorithm effectively detected changes in vegetation disturbance and restoration in H-HOCMs. Vegetation disturbance and restoration occurred in the study area, with a cumulative disturbance-to-restoration ratio of 61.79% since 1988. Significant restoration occurred primarily in the external dumps and continued ecological recovery occurred in the surrounding area. Full article
(This article belongs to the Special Issue New Advances in Remote Sensing Techniques Applied in Surface and Underground Mine Operations)
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