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Remote Sensing
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Application of Advanced Remote Sensing Techniques in Mining Areas
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Application of Advanced Remote Sensing Techniques in Mining Areas

A special issue of Remote Sensing (ISSN 2072-4292).

Deadline for manuscript submissions: 31 December 2025 | Viewed by 1076

Special Issue Editors

Dr. Tingting He

E-Mail Website
Guest Editor
Department of Land Management, Zhejiang University, Hangzhou 310058, China
Interests: land use structure; spatial morphological change; population change; government management; regional planning
Special Issues, Collections and Topics in MDPI journals
Dr. He Ren

E-Mail Website
Guest Editor
Academy of Eco-Civilization Development for Jing-Jin-Ji Megalopolis, Tianjin Normal University, Tianjin 300387, China
Interests: coal mining; vegetation; soil; UAV; multispectral satellite imagery
Dr. Yongjun Yang

E-Mail Website
Guest Editor
School of Environment Science and Spatial Informatics, China University of Mining and Technology, Xuzhou, China
Interests: soil; cadmium; average daily intake

Special Issue Information

Dear Colleagues,

Mining contributes significantly to economic growth; however, its negative impact on the ecological environment has become increasingly evident. To address these environmental challenges, remote sensing technology has emerged as a vital tool, offering an effective means of monitoring the ecological impacts of mining activities and assessing the quality of reclamation efforts. The effective integration of advanced sensors, such as satellite-based multispectral, hyperspectral, synthetic aperture radar (SAR), and night-time light remote sensing, along with unmanned aerial vehicles (UAVs), cloud computing platforms like the Google Earth Engine (GEE), and deep learning algorithms has enabled the precise spatiotemporal analysis of mining and reclamation activities. This synergy facilitates timely decision making, enhances monitoring efficiency, and promotes sustainable resource management and environmental protection.

This Special Issue invites submissions discussing the latest remote sensing methods and applications in this field. Topics of interest include environmental change monitoring (e.g., mining disturbances and reclamation effectiveness), mining area safety assessments (e.g., spontaneous combustion of waste dumps and subsidence monitoring), and novel data processing techniques and evaluation models (e.g., multi-source data fusion and deep learning approaches).

We encourage authors to submit novel research papers and reviews on topics including but not limited to the following:

  • Advanced remote sensing in large-scale mining feature identification;
  • Monitoring and locating thermal anomalies in coal waste dumps/piles;
  • Application of multi-source remote sensing data in monitoring mining activities;
  • Mining area mapping and change detection (i.e., disturbances and reclamation).

Dr. Tingting He
Dr. He Ren
Dr. Yongjun Yang
Guest Editors

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

  • high-resolution remote sensing
  • optical data
  • SAR images
  • surface/open-pit mining
  • disturbances
  • coal waste dumps
  • mining subsidence area
  • land degradation
  • land reclamation and ecological restoration
  • active/abandoned mining
  • deep learning
  • change detection
  • Google Earth Engine

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (2 papers)

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Research

21 pages, 12849 KiB  
Article
Exploring the Effectiveness of Fusing Synchronous/Asynchronous Airborne Hyperspectral and LiDAR Data for Plant Species Classification in Semi-Arid Mining Areas
by Yu Tian, Zehao Feng, Lixiao Tu, Chuning Ji, Jiazheng Han, Yibo Zhao and You Zhou
Remote Sens. 2025, 17(9), 1530; https://doi.org/10.3390/rs17091530 - 25 Apr 2025
Viewed by 97
Abstract
Plant species classification in semi-arid mining areas is of great significance in assessing the environmental impacts and ecological restoration effects of coal mining. However, in semi-arid mining areas characterized by mixed arbor–shrub–herb vegetation, the complex vegetation distribution patterns and spectral features render single-sensor [...] Read more.
Plant species classification in semi-arid mining areas is of great significance in assessing the environmental impacts and ecological restoration effects of coal mining. However, in semi-arid mining areas characterized by mixed arbor–shrub–herb vegetation, the complex vegetation distribution patterns and spectral features render single-sensor approaches inadequate for achieving fine classification of plant species in such environments. How to effectively fuse hyperspectral images (HSI) data with light detection and ranging (LiDAR) to achieve better accuracy in classifying vegetation in semi-arid mining areas is worth exploring. There is a lack of precise evaluation regarding how these two data collection approaches impact the accuracy of fine-scale plant species classification in semi-arid mining environments. This study established two experimental scenarios involving the synchronous and asynchronous acquisition of HSI and LiDAR data. The results demonstrate that integrating LiDAR data, whether synchronously or asynchronously acquired, significantly enhances classification accuracy compared to using HSI data alone. The overall classification accuracy for target vegetation increased from 71.7% to 84.7% (synchronous) and 80.2% (asynchronous), respectively. In addition, the synchronous acquisition mode achieved a 4.5% higher overall accuracy than asynchronous acquisition, with particularly pronounced improvements observed in classifying vegetation with smaller canopies (Medicago sativa L.: 17.4%, Pinus sylvestris var. mongholica Litv.: 11.7%, and Artemisia ordosica Krasch.: 7.5%). This study can provide important references for ensuring classification accuracy and error analysis of land cover based on HSI-LiDAR fusion in similar scenarios. Full article
(This article belongs to the Special Issue Application of Advanced Remote Sensing Techniques in Mining Areas)
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22 pages, 27479 KiB  
Article
A Novel Approach to Automatically Identify Open-Pit Coal Mining Dynamics Based on Temporal Satellite Images
by Zhibin Li, Yanling Zhao, He Ren, Tingting He and Yueming Sun
Remote Sens. 2025, 17(6), 1029; https://doi.org/10.3390/rs17061029 - 15 Mar 2025
Viewed by 551
Abstract
Open-pit coal mining drives socioeconomic development but imposes significant environmental impacts. The timely monitoring of mining dynamics is essential for sustainable resource exploitation and ecological restoration. However, existing studies often rely on predefined mining boundaries, limiting their applicability in unknown regions. This study [...] Read more.
Open-pit coal mining drives socioeconomic development but imposes significant environmental impacts. The timely monitoring of mining dynamics is essential for sustainable resource exploitation and ecological restoration. However, existing studies often rely on predefined mining boundaries, limiting their applicability in unknown regions. This study proposes an innovative approach that leverages the intra-annual coal frequency index (ACFI) to identify potential open-pit mining areas, and integrates the Rays method to monitor their temporal changes. By applying a predefined discriminative rule, this approach effectively distinguishes open-pit coal mines from other disturbances and enables spatiotemporal monitoring without the need for prior knowledge of their locations. Applied to the Chenbarhu Banner coalfield, Inner Mongolia, the method achieved 92% accuracy and a kappa coefficient of 0.84 in identifying mining areas. It effectively distinguished active and closed mines, detecting key temporal features with 94% accuracy (kappa = 0.86). The study also identified mining directions and extents, such as 4–13° for the Baorixile mine and 69–141° for the Dongming mine, while excluding non-mining areas with high precision. A strong correlation (r = 0.929, p < 0.01) between annual mining area and coal production further validated the approach. This method provides accurate, scalable tools for monitoring mining dynamics and supports decision-making in regulatory and ecological management processes. Full article
(This article belongs to the Special Issue Application of Advanced Remote Sensing Techniques in Mining Areas)
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