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Remote Sensing
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New Advances in Remote Sensing Techniques Applied in Surface and...
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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: 30 September 2024 | Viewed by 1454

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

Published Papers (1 paper)

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Research

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 - 07 Dec 2023
Viewed by 888
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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