From exploration to mine closure: Mineral resource lifecycle management supported by remote sensing technology

Remote sensing technology, with its advantages of being non-contact, wide-area, multi-temporal, and high-resolution, has become a core supporting means for the integrated "space-air-ground-well" management of mineral resources throughout their entire life cycle, spanning major stages such as geological exploration, mine construction, mining production, and mine closure and ecological restoration. This paper, based on current national standards such as the "General Rules for Geological Exploration of Solid Minerals" (GB/T 13908-2020), "Classification of Solid Mineral Resources Reserves" (GB/T 17766-2020), and "Technical Specifications for Monitoring and Evaluation of Mine Land Reclamation and Ecological Restoration" (GB/T 43935-2024), systematically elaborates on the specific application paths and typical cases of multi/hyperspectral remote sensing, InSAR, and UAV remote sensing technologies in stages. These include alteration information extraction in the exploration stage, environmental baseline surveys in the construction stage, dynamic monitoring and geological disaster early warning in the mining stage, and vegetation restoration and ecological restoration effect evaluation in the mine closure stage. Furthermore, it analyzes the advantages, limitations, and intelligent integration trends of remote sensing technology in conjunction with literature review, providing a reference for mineral exploration and sustainable management.

According to Article 4.2 of GB/T 13908-2020, geological exploration is divided into general survey, detailed survey, and exploration. The core task of the general survey stage is to delineate prospective areas and estimate inferred resource quantities. The detailed survey stage focuses on anomaly verification and control of resource quantity estimation. The exploration stage is dedicated to proving resource quantities and submitting a feasibility study report. Remote sensing technology plays a leading and planning role in this process. Its technical approach mainly relies on macroscopic structural interpretation, lithological identification, and mineralization alteration information extraction to achieve rapid large-scale screening. Hyperspectral/multispectral remote sensing (WorldView-3, GF-5, ASTER) can accurately extract hydrothermal alteration minerals such as iron staining, hydroxyl groups (Fe-OH, Mg-OH), and carbonates (e.g., kaolinite, sericite), thereby delineating alteration halos as indirect mineral exploration indicators. Combined with DEM data for fault, fold, and ring structure interpretation, favorable mineralization areas can be quickly identified (Chen et al., 2023). During the detailed survey and exploration phase, high-resolution three-dimensional geological models are constructed using UAV hyperspectral/lidar technology to optimize the layout of boreholes, trenches, and pits, and to assist in the preliminary estimation of resource quantities. Chen et al. (2023) conducted a study on hydrothermal alteration information extraction in the Beishan-Tongshan area of ​​Gansu Province using WorldView-3 data. They successfully identified and classified alteration anomalies of different intensities using principal component analysis and spectral feature fitting. This study confirmed the high degree of agreement between remote sensing anomalies and field sampling results, providing high-precision data support for target area selection and ground verification during the general survey phase.

Figure 1. Results of WorldView-3 multi-band alteration anomaly extraction

(Chen et al., 2023)

A: Mg-OH abnormality; B: Fe-OH abnormality;

C: Iron staining abnormality; D: Carbonate abnormality

Figure 1 shows the detailed zoning of each alteration type through spatial distribution comparison, providing an important reference for the layout of the reconnaissance route and the mapping and engineering verification in the general survey stage.

Figure 2. Distribution map of hydrothermal alteration anomalies extracted from WorldView-3 data in the Tongshan area (Chen et al., 2023)

Figure 2 shows the different colors used to indicate the intensity of Fe-OH, Mg-OH, iron staining, and carbonate anomalies. The superimposed field verification points (green dots) and mining area locations (purple pentagrams) clearly demonstrate the superposition relationship between alteration and tectonics, directly guiding the delineation of target areas.

Feasibility studies, mine design, and infrastructure construction are conducted in accordance with GB/T 17766-2020. The application of remote sensing technology at this stage focuses on environmental baseline surveys—based on multi-temporal satellite imagery, acquiring pre-construction land use, vegetation cover, topography, and ecological baseline data to provide quantitative support for the Environmental Impact Assessment (EIA); and utilizing high-resolution UAV remote sensing to generate three-dimensional terrain models (DTM/DSM) to assist in the site selection of plants, roads, tailings ponds, and spoil heaps, avoiding potential geological hazards (GB/T 43935-2024, Clause 6.1), thereby achieving early environmental risk prevention and green construction compliance goals.

During the large-scale mining phase, it is necessary to monitor production dynamics, safety hazards, and environmental impacts in real time (in accordance with the requirements of DZ/T 0392-2022 "Technical Specification for Remote Sensing Monitoring of Mine Environment"). Remote sensing technology can achieve high-frequency dynamic monitoring.

1. Monitoring of Mining Progress and Land Use – Based on multi-source satellite/UAV imagery, the expansion process of open-pit mines, the dynamics of overburden accumulation, tailings dam deformation, and land cover type transformation can be accurately tracked. Mi et al. (2019) used continuous Landsat imagery data and a random forest classifier in the coal mining area in the southern suburbs of Shanxi Province to quantitatively reveal the land use and cover changes caused by underground mining and land reclamation from 1987 to 2017, and accurately depicted the spatial pattern characteristics of mining area expansion and vegetation restoration.

Figure 3. Continuous land use/land cover classification from 1987 to 2017(Mi et al., 2019)

2. Geological Disaster Early Warning and Deformation Monitoring—InSAR technology is a core tool for monitoring slope slippage, goaf settlement, and tailings dam stability, enabling long-term continuous deformation observation with millimeter-level accuracy. Fadhillah et al. (2024) used an improved combined scatterer InSAR (ICOPS) technique in the Moksan open-pit iron ore mine in North Korea, combined with a deep learning optimization algorithm to process Sentinel-1 time series data. The monitoring results showed that the cumulative settlement of the eastern spoil heap reached 170 cm, and that of the western spoil heap reached 70 cm, with a maximum annual settlement rate exceeding 15 cm/a. This study, through spatiotemporal deformation rate maps and profile analysis, revealed the spatiotemporal evolution law of mining-induced settlement, providing quantitative evidence for slope stability early warning and disaster prevention.

Figure 4. InSAR cumulative deformation rate distribution map of the Mushan open-pit iron ore mine (Fadhillah et al., 2024)

The color gradient in the diagram shows the average annual deformation rate, which, when superimposed on the mining area boundary, directly supports safety management during the production phase.

Figure 5. InSAR cumulative deformation profile analysis of the Mushan open-pit iron ore area from 2016 to 2022 (Fadhillah et al., 2024).

(a) and the profile deformation results of the eastern dumping site sections A–Aʹ; (b) and B–Bʹ; (c) and the western dumping site sections C–Cʹ; (d) and D–Dʹ I.

The multiple profile lines in the figure show the cumulative subsidence and elevation changes at different times, quantifying the spatiotemporal characteristics of mining-induced deformation.

3. Ecological Quality Assessment – ​​By constructing a dedicated remote sensing ecological index, quantitative characterization of vegetation stress, soil and water pollution, and landscape fragmentation can be achieved. Song et al. (2023) constructed the Iron Ore Remote Sensing Ecological Index (IM-RSEI) based on Landsat data for the densely populated iron ore area of ​​Qian'an-Qianxi, integrating seven indicators: vegetation cover, bare soil greenness, humidity, black carbon particles, surface temperature, iron oxides, and landscape fragmentation. This study revealed that the ecological quality showed a trend of "deterioration followed by improvement" between 1992 and 2018, with the mean IM-RSEI value of the mining area (0.5412) being significantly lower than that of the surrounding buffer zone, confirming the negative impact of mining activities on the surrounding ecology.

Figure 6. Spatial distribution of IM-RSEI in QR from 1992 to 2018 (Song et al., 2023)

After the mine is closed, reclamation acceptance and long-term management must be carried out in accordance with the provisions of Chapter 8 of GB/T 43935-2024. At this stage, remote sensing technology becomes a tool for effect evaluation and long-term protection. Its applications mainly include: monitoring vegetation restoration and soil remodeling processes based on multi-temporal NDVI and EVI; tracking the dynamics of residual subsidence in the goaf using InSAR technology; and quantifying the effects of topographic reshaping, land reclamation, soil and water chemistry, and ecosystem restoration through RSEI/IM-RSEI indices, providing data support for reclamation acceptance (Article 8.4 of GB/T 43935-2024). Hu et al. (2022) monitored the vegetation restoration status of the reclamation area of ​​the Antaibao open-pit coal mine spoil heap based on multi-temporal Landsat data, and quantified the vegetation cover and biomass growth trend after reclamation using NDVI spatiotemporal distribution maps. This study confirmed a significant increase in vegetation cover after reclamation by comparing NDVI maps before and after the reclamation.

Figure 7. Time series analysis of NDVI at spatiotemporal scales at the southern landfill (Hu et al., 2022)(a) Results of univariate regression analysis; (b) Results of Sen+MK trend analysis.

SI, NSI, NSD, and SD represent significant increase, no significant increase, no significant decrease, and significant decrease, respectively.

The advantages of remote sensing technology throughout the entire lifecycle of mineral deposits lie in its non-contact, macroscopically efficient, and environmentally friendly, low-disturbance nature. It can form a closed-loop technology system with field verification, geophysical and geochemical exploration, and engineering control (Chapter 5 of GB/T 13908-2020; Clause 4.2 of GB/T 43935-2024). The integrated application of artificial intelligence and multi-source data fusion (satellite + UAV + InSAR) technology further improves the accuracy and reliability of remote sensing monitoring (Chen et al., 2023; Song et al., 2023). However, this technology also has limitations—it only reflects surface and shallow subsurface information and requires field sampling verification; the coherence loss problem in complex geomorphological areas still needs optimization. In the future, the integration of "remote sensing + industrial control terminal + Internet of Things + artificial intelligence" technologies will drive the intelligent transformation of mineral deposit management throughout its entire lifecycle.

References

[1] GB/T 13908-2020. General Rules for Geological Exploration of Solid Mineral Deposits [S]. Beijing: China Standards Press, 2020.

[2] GB/T 17766-2020. Classification of Solid Mineral Resources Reserves [S]. Beijing: China Standards Press, 2020.

[3] GB/T 43935-2024. Technical Specification for Monitoring and Evaluation of Mine Land Reclamation and Ecological Restoration [S]. Beijing: China Standards Press, 2024.

[4] DZ/T 0392-2022. Technical Specification for Remote Sensing Monitoring of Mine Environment [S]. Beijing: Ministry of Natural Resources, 2022.

[5] Chen C, Gao L, Xie F, Xia F, Li S. Extracting hydrothermally altered information using WorldView-3 data: a case study of Huitongshan, NW Gansu, China[J]. Frontiers in Earth Science, 2023, 11: 1250591.

[6] Song W, Gu H H, Song W, et al. Environmental assessments in dense mining areas using remote sensing information over Qian'an and Qianxi regions, China[J]. Ecological Indicators, 2023, 146: 109814.

[7] Fadhillah M F, Hakim W L, Lee S K, et al. Multitemporal analysis of land subsidence induced by open-pit mining activity using improved combined scatterer interferometry with deep learning algorithm optimization[J]. Scientific Reports, 2024, 14: 6311.

[8] Hu J M, Ye B Y, Bai Z K, et al. Remote sensing monitoring of vegetation reclamation in the Antaibao open-pit mine[J]. Remote Sensing, 2022, 14(22): 5634.

[9] Mi J, Yang Y, Zhang S, et al. Tracking the land use/land cover change in an area with underground mining and reforestation via continuous Landsat classification[J]. Remote Sensing, 2019, 11(14): 1719.