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Remote Sensing
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Monitoring and Restoration of Mining-Impacted Ecosystems Using Remote Sensing Technology
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Monitoring and Restoration of Mining-Impacted Ecosystems Using Remote Sensing Technology

A Special Issue of Remote Sensing (ISSN 2072-4292) belonging to the section "Ecological Remote Sensing".

Deadline for manuscript submissions: 31 January 2027 | Viewed by 4932

Editors

Dr. Qiang Yu

E-Mail Website
Guest Editor
The College of Forestry, Beijing Forestry University, Beijing 100083, China
Interests: complexity theory of spatial network; application of quantitative remote sensing in forestry; carbon use efficiency of forest ecosystem
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Huaguo Huang

E-Mail Website
Guest Editor
Forestry College, Beijing Forestry University, No. 35 Qinghua East Road, Beijing 100083, China
Interests: quantitative forest remote sensing; forestry information technology
Special Issues, Collections and Topics in MDPI journals
Dr. Jianbo Qi

E-Mail Website
Guest Editor
School of Geography, Beijing Normal University, Beijing 100875, China
Interests: earth observation; vegetation modeling; lidar; remote sensing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Under the background of global climate change and sustainable development, the contradiction between mineral resources exploitation and ecological protection is becoming increasingly prominent, and the traditional mining activities lack corresponding thinking about the problems in the field of ecological environment. The degree of destruction of mine ecological environment is closely related to the intensity of mining activities. Large-scale mining leads to irreversible changes in landform, soil structure, landscape heterogeneity, biodiversity, and so on. It is particularly urgent to combine the concept promotion and technological innovation of mine management and ecological restoration to enhance the function of mine ecosystem.

At present, the development of remote sensing science and technology provides new research ideas for ecological restoration and ecosystem function improvement in mining areas. MODIS, Landsat, Sentinel, GF, and other satellite images, LiDAR and other LIDAR data, and UAV data enable us to monitor and analyze the changes in the structure and function of mining ecosystem from different scales and levels, providing important data support for ecological restoration.

This Special Issue focuses on the functional changes in various ecosystems in mining areas in the process of ecological restoration supported by multi-source remote sensing data. The main research areas include high-precision inversion and mapping of mining ecosystems based on multi-source remote sensing image data; the interaction mechanism of structural characteristics and functions of mining ecosystems in the process of ecological restoration; the evolution law and driving mechanism of mining ecosystem structure and function based on long time series remote sensing data; the application of remote sensing technology such as machine learning and GEE in the research of ecological restoration in mining area.

Articles may explore, but are not limited to, the following topics:

  1. High-precision remote sensing identification of mining area ecological restoration based on machine learning and GEE;
  2. Multi-scale high time resolution mapping of mining ecosystem based on multi-source remote sensing data;
  3. Analysis of the evolution law and driving mechanism of mining ecosystem structure and function based on long time series remote sensing data;
  4. Interaction mechanism of structural characteristics and functions of mining ecosystem in the process of ecological restoration;
  5. Nbs (Nature-based Solution) ecosystem structure and function analysis based on multi-source and multi-scale remote sensing mining area.
  6. Dynamic monitoring and benefit assessment analysis of ecological restoration in mining areas based on multi-source remote sensing data.

Dr. Qiang Yu
Prof. Dr. Huaguo Huang
Dr. Jianbo Qi
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 250 words) can be sent to the Editorial Office for assessment.

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-anonymized 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

  • multi-source remote sensing data
  • remote sensing technology
  • mining ecosystem
  • ecological restoration
  • ecosystem structure and function

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Published Papers (5 papers)

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Research

24 pages, 30690 KB  
Article
Assessing the Potential of High-Resolution Multispectral and Structural Imagery for Plant Species Mapping in Mine Rehabilitation
by Phillip B. McKenna, Lorna Hernandez-Santin, Trevor Spedding, Ken Cross and Peter D. Erskine
Remote Sens. 2026, 18(17), 3029; https://doi.org/10.3390/rs18173029 - 4 Sep 2026
Viewed by 259
Abstract
Biodiversity monitoring is essential for evaluating mine rehabilitation success. Traditionally, assessments have relied on ground-based plot measurements, but advances in remote sensing offer opportunities to complement or replace plot-based surveys with spatially continuous monitoring approaches. We evaluated drone-derived multispectral imagery, the Soil Adjusted [...] Read more.
Biodiversity monitoring is essential for evaluating mine rehabilitation success. Traditionally, assessments have relied on ground-based plot measurements, but advances in remote sensing offer opportunities to complement or replace plot-based surveys with spatially continuous monitoring approaches. We evaluated drone-derived multispectral imagery, the Soil Adjusted Vegetation Index (SAVI), and canopy height models (CHM) for mapping plant species used in mine rehabilitation in central Queensland, Australia. Ten classification models tested four combinations of spectral and structural data. Incorporating CHM improved overall accuracy by up to 10%, with notable gains for vegetation classes containing Eucalyptus and Acacia species. Species-level accuracy ranged from 79% to 100% for Eucalyptus and 74% to 100% for Acacia species. Misclassification was greatest among closely related red gums (Eucalyptus tereticornis Sm. and Eucalyptus camaldulensis Dehnh.) and Corymbia citriodora (Hook.) K.D.Hill & L.A.S.Johnson. These results demonstrate that structural information substantially improves species discrimination and has the potential to enhance biodiversity monitoring across plot (500 m2), block (1–100 ha), and landscape (100–1000 ha) scales. Full article
(This article belongs to the Special Issue Monitoring and Restoration of Mining-Impacted Ecosystems Using Remote Sensing Technology)
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18 pages, 6750 KB  
Article
Preserving Spatial Fidelity in Heterogeneous Landscapes: A Biophysical Index-Guided 1 × 1 CNN for Multi-Source Remote Sensing Fusion
by Yanru Pei and Pengchong Wang
Remote Sens. 2026, 18(14), 2395; https://doi.org/10.3390/rs18142395 - 18 Jul 2026
Viewed by 511
Abstract
Continuous spatial reconstruction in highly fragmented landscapes remains a persistent challenge in remote sensing and geographic information systems. Standardized products such as MODIS Net Primary Productivity (NPP) provide temporally consistent ecological baselines, but their moderate spatial resolution can obscure abrupt transitions in disturbed [...] Read more.
Continuous spatial reconstruction in highly fragmented landscapes remains a persistent challenge in remote sensing and geographic information systems. Standardized products such as MODIS Net Primary Productivity (NPP) provide temporally consistent ecological baselines, but their moderate spatial resolution can obscure abrupt transitions in disturbed environments. This study develops a biophysical index-guided 1 × 1 Convolutional Neural Network (CNN) framework for NPP reconstruction over a mega-scale open-pit mining landscape. The framework uses five Landsat-derived biophysical tensors—Vegetation Moisture Stress Index (VMSI), Anti-Vegetation Environment Index (AVEI), Soil Adjusted Vegetation Environment Index (SAVEI), AAI-VHI, and Tasseled Cap Wetness (WET)—as point-wise predictors aligned to the MODIS NPP baseline grid. By restricting convolutional kernels to 1 × 1, the model performs nonlinear channel-wise mapping without incorporating neighboring grid cells, thereby reducing boundary mixing that can occur in conventional multi-pixel CNNs. Benchmark comparisons with Random Forest and a standard 3 × 3 CNN showed that the 3 × 3 CNN achieved slightly higher global accuracy, whereas the 1 × 1 CNN provided stronger gradient correspondence and lower full-domain spatial error in the 2020 spatial fidelity assessment. The results indicate that point-wise convolution guided by physically interpretable indices provides a conservative and interpretable option for standardized ecological reconstruction at the MODIS baseline grid scale. Full article
(This article belongs to the Special Issue Monitoring and Restoration of Mining-Impacted Ecosystems Using Remote Sensing Technology)
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21 pages, 19198 KB  
Article
Long-Term Assessment of Post-Mining Spectral Recovery Patterns: Integrating Disturbance Timing, Land-Surface Transitions, and Benchmark-Relative Spectral Closure
by Jianguang Wang, Jinping Liu, Yanqun Ren, Huiran Gao and Yaning Yi
Remote Sens. 2026, 18(12), 1945; https://doi.org/10.3390/rs18121945 - 12 Jun 2026
Cited by 1 | Viewed by 482
Abstract
Single-index greening trends can misrepresent post-mining recovery because they do not show whether disturbed surfaces are converging toward the spectral conditions of nearby stable vegetation. Here, we present a 22-year (2003–2024) Landsat-based assessment of the Nannihu molybdenum mine (Henan, China) by combining LandTrendr-based [...] Read more.
Single-index greening trends can misrepresent post-mining recovery because they do not show whether disturbed surfaces are converging toward the spectral conditions of nearby stable vegetation. Here, we present a 22-year (2003–2024) Landsat-based assessment of the Nannihu molybdenum mine (Henan, China) by combining LandTrendr-based disturbance and recovery timing from annual NBR series with a benchmark-relative spectral recovery index (RSRI) and five-epoch random forest land-surface classification used as contextual support. The classifier was trained on 2024 samples and transferred to earlier epochs without independent validation at each epoch. Historical class labels should therefore be treated as approximate contextual support. A five-type recovery pathway typology showed that only 41.8% of mine-affected pixels followed vegetated recovery pathways, while 28.2% stabilized as non-vegetated surfaces and 25.0% remained under persistent disturbance. Even the combined vegetation recovery type had a mean RSRI of only 0.309 (SD = 0.143), suggesting that greening alone does not imply close benchmark-relative spectral proximity to the local stable-vegetation reference. Disturbance magnitude was the feature most strongly associated with RSRI variation (XGBoost SHAP mean, |SHAP| = 0.075). The RSRI quantifies benchmark-relative spectral proximity using local stable-vegetation benchmarks, and it does not measure species composition, biomass, or ecosystem function. This site-specific case study indicates that benchmark-relative spectral assessment can complement conventional greening metrics in retrospective mine monitoring using open-access Landsat archives, with field validation the natural next step toward linking these spectral findings to ecological or functional recovery. Full article
(This article belongs to the Special Issue Monitoring and Restoration of Mining-Impacted Ecosystems Using Remote Sensing Technology)
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19 pages, 10588 KB  
Article
Impact of Climatic Variability and Mining Activities on Net Primary Productivity in the High-Intensity Open-Pit Mining Area
by Xuliang Guo, Huifeng Gao, Mingyue Liu, Jingjing Zhao, Fuping Li, Yongbin Zhang, Mengqi Chen, Xiaoguang Li, Guie Tian, Xiaojie Chi and Weidong Man
Remote Sens. 2026, 18(8), 1204; https://doi.org/10.3390/rs18081204 - 16 Apr 2026
Viewed by 615
Abstract
Evaluating Net Primary Productivity (NPP) variations driven by climatic variability and mining activities is fundamental for understanding ecological dynamics in high-intensity open-pit mining areas. Focusing on high-intensity open-pit mining areas of Qian’an, China, from 2016 to 2022, by integrating Sentinel-2, ERA-5 Land reanalysis [...] Read more.
Evaluating Net Primary Productivity (NPP) variations driven by climatic variability and mining activities is fundamental for understanding ecological dynamics in high-intensity open-pit mining areas. Focusing on high-intensity open-pit mining areas of Qian’an, China, from 2016 to 2022, by integrating Sentinel-2, ERA-5 Land reanalysis dataset and Dynamic World V1, we employed an improved Carnegie–Ames–Stanford Approach (CASA) framework alongside the Thornthwaite Memorial algorithm to quantify actual NPP (ANPP) and potential NPP (PNPP). Additionally, the Relative Contribution Index (RCI) was utilized to explicitly isolate mining-driven NPP (MNPP) variations. The results revealed a significant downward trajectory in ANPP within the high-intensity open-pit mining area, with a cumulative reduction of 5.3 × 108 gC a−1. This productivity loss exhibited significant spatial heterogeneity, with the most severe degradation concentrated in core mining districts, including Malanzhuang, Caiyuan, Yangdianzi, and Muchangkou. ANPP, MNPP, and PNPP maintained relative stability overall but displayed significant interannual fluctuations during 2019–2022. RCI analysis indicated MNPP dominated ANPP in 62.67% of the study area, with mining impacts intensifying in 62.83% of the region. Driver mechanisms identified precipitation as the dominant climatic factor enhancing ANPP, whereas mining activities constituted the primary driver of ANPP reduction. Mining accounted for 61.33% of ANPP changes, significantly exceeding climatic variability’s 38.67% contribution. In conclusion, these findings provide a scientific foundation for developing ecological carbon sink systems and optimizing ecological restoration strategies. Full article
(This article belongs to the Special Issue Monitoring and Restoration of Mining-Impacted Ecosystems Using Remote Sensing Technology)
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24 pages, 14623 KB  
Article
Vegetation Growth Changes and Their Constraining Effects on Ecosystem Services Under Ecological Restoration in the Shendong Mining Area
by Xufei Zhang, Zhichao Chen, Yiheng Jiao, Yiqiang Cheng, Zhenyao Zhu, Shidong Wang and Hebing Zhang
Remote Sens. 2025, 17(10), 1674; https://doi.org/10.3390/rs17101674 - 9 May 2025
Cited by 4 | Viewed by 1831
Abstract
Under the ecological restoration project, the vegetation in the mining area shows a significant improvement trend. Exploring the causal relationship among the implementation of ecological restoration projects in mining areas, vegetation restoration, and the improvement of ecosystem service functions is of great significance [...] Read more.
Under the ecological restoration project, the vegetation in the mining area shows a significant improvement trend. Exploring the causal relationship among the implementation of ecological restoration projects in mining areas, vegetation restoration, and the improvement of ecosystem service functions is of great significance for the current green development of coal mines. Therefore, in this study, we used the kernel Normalized Vegetation Index (kNDVI) to measure how vegetation growth has changed since ecological restoration projects began. Changes in four major ecosystem service functions, including soil conservation, net primary productivity (NPP), water yield, and habitat quality, were assessed before and after the restoration projects. The relationship between kNDVI and ecosystem services was further discussed by using the constraint line method. The results show the following: (1) Under the implementation of ecological restoration projects from 1994 to 2022, the annual vegetation growth rate in the mining area has progressively risen each year at a rate of 0.0046/a. Spatially speaking, 90.44% of the mining area had a substantial upward trend, indicating clear evidence of vegetation restoration. (2) Under the scientific ecological restoration of the mining areas, the total ecosystem service index increased from 0.41 in 1994 to 0.49 in 2022. The functions of ecosystem services have been enhanced to differing extents. (3) KNDVI’s constraint effect on the four ecosystem services changed dramatically before and after the ecological restoration effort. After the ecological restoration project, kNDVI’s constraint on ecosystem services decreased. (4) After restoration, the threshold value of kNDVI for maximizing the benefits of the four ecosystem services ranges from 0.1 to 0.2, and the constraint on the total ecosystem services reaches the threshold value of 0.225. This study employs more comprehensive data to examine the intricate relationship between environmental change and service function, which is crucial for the scientific management of ecological processes and facilitates the sustainable green development of mining areas. Full article
(This article belongs to the Special Issue Monitoring and Restoration of Mining-Impacted Ecosystems Using Remote Sensing Technology)
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