Submit to Remote Sensing Review for Remote Sensing Propose a Special Issue

Journal Browser

Remote Sensing of Opencast Mining Land Use and Land Cover for Planning, Rehabilitation and Closure

Special Issue Editors
Special Issue Information
Keywords
Published Papers

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing in Agriculture and Vegetation".

Deadline for manuscript submissions: closed (4 April 2020) | Viewed by 34896

Share This Special Issue

Special Issue Editors

Assoc. Prof. Alex Lechner

E-Mail Website
Guest Editor

School of Environmental and Geographical SciencesUniversity of Nottingham Malaysia Jalan Broga, Semenyih, Selangor 43500, Malaysia
Interests: landscape ecology; remote sensing; mining; spatial planning; conservation biology

Assoc. Prof. Peter Erskine

E-Mail
Guest Editor

Sustainable Minerals Institute (SMI), Level 5, Sir James Foots Building (No. 47A), Corner of College Rd and Staff House Rd, The University of Queensland, Brisbane, QLD 4072, Australia
Interests: drone data; mine site rehabilitation, ecosystem functions and restoration ecology

Mr. Phillip McKenna

E-Mail Website
Assistant Guest Editor

Sustainable Minerals Institute (SMI), Level 5, Sir James Foots Building (No. 47A), Corner of College Rd and Staff House Rd, The University of Queensland, St Lucia, QLD 4072, Australia
Interests: Fire ecology; remote sensing; resilience; mine site rehabilitation and restoration ecology

Special Issue Information

Dear Colleagues,

Opencast mining is one of the most intensive forms of landscape change mediated by humans. Mining significantly alters both natural and anthropogenic systems and returning this land to desirable or historical states is often prevented due to the irreversible nature of mining activities. Additionally, communities living near or over resources are affected both directly and indirectly by mining operations. Although the income generated by mining can enrich people employed by the operations and supply chain, it can also directly affect traditional livelihoods, require resettlement of whole communities, create pollution and have adverse health consequences.

Disturbances associated with opencast mining, unlike underground mining operations, are predominantly found on the land surface and distinguishing the spatial and temporal changes is a task that is particularly well suited to remote sensing. Consequently, remote sensing is being increasingly used for mapping opencast mining land use and land cover to support planning, rehabilitation and closure. Common applications of remote sensing include the quantification of rehabilitation success using vegetation indices, characterizing mining land uses and land cover change for the assessment of land-use conflicts and mapping soil contamination. While the application of remote sensing in mining has increased in the past two decades, there are multiple challenges associated with the spatial, temporal and spectral characteristics of mining land covers that need to be addressed. Furthermore, there are certain areas where remote sensing has been underutilized, such as for assessing rehabilitation.

There are also multiple challenges associated with operationalizing remote sensing applications in the mining industry. From exploration to mine closure, remote sensing can provide spatially explicit products to inform the planning and monitoring of mining operations. Yet on-the-ground examples of how remote sensing has been integrated into operations are still rare and the literature is dominated by one-off research studies.

The focus of this Special Issue is on applications of remote sensing to opencast mining land use and land cover for planning, rehabilitation/reclamation, and closure. We are specifically interested in applications at a single-mine-site scale rather than regional scale.

The topics of interest include, but are not limited to, the application of remote sensing for the following:

Rehabilitation, restoration and revegetation of post-mining landscapes
Historical assessment of mining land use and land cover using time-series analysis
Socio-environmental assessments using land use and land cover mapping
Assessment of degraded mining landscapes, including historical or abandoned mines
Real world applied case studies where remote sensing has been used by the mining companies and/or government organizations
Stability and resilience of post-mining landforms
Mine closure and the success of post-mining landscapes

We are also interested in novel approaches to:

Acquiring remote sensing data using UAVs/drones and SAR
Machine learning and deep learning approaches
Integrating remote sensing data with GIS analysis

Assoc. Prof. Alex Lechner
Assoc. Prof. Peter Erskine
Mr. Phillip McKenna
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

Remote sensing
mine closure
mine rehabilitation
mining land cover
restoration
rehabilitation, reclamation.

Published Papers (7 papers)

Download All Papers

Research

Jump to: Review

15 pages, 4427 KiB

Open AccessArticle

An Improved Ground Control Point Configuration for Digital Surface Model Construction in a Coal Waste Dump Using an Unmanned Aerial Vehicle System

by He Ren, Yanling Zhao, Wu Xiao, Xin Wang and Tao Sui

Remote Sens. 2020, 12(10), 1623; https://doi.org/10.3390/rs12101623 - 19 May 2020

Cited by 11 | Viewed by 2683

Abstract

Coal production in opencast mining generates substantial waste materials, which are typically delivered to an on-site waste dump. As a large artificial loose pile, such dumps have a special multi-berm structure accompanied by some security issues due to wind and water erosion. Highly accurate digital surface models (DSMs) provide the basic information for detection and analysis of elevation change. Low-cost unmanned aerial vehicle systems (UAS) equipped with a digital camera have become a useful tool for DSM reconstruction. To achieve high-quality UAS products, consideration of the number and configuration of ground control points (GCPs) is required. Although increasing of GCPs will improve the accuracy of UAS products, the workload of placing GCPs is difficult and laborious, especially in a multi-berm structure such as a waste dump. Thus, the aim of this study is to propose an improved GCPs configuration to generate accurate DSMs of a waste dump to obtain accurate elevation information, with less time and fewer resources. The results of this study suggest that: (1) the vertical accuracy of DSMs is affected by the number of GCPs and their configuration. (2) Under a set number of GCPs, a difference of accuracy is obtained when the GCPs are located on different berms. (3) For the same number of GCPs, the type 4 (GCPs located on the 1st and 4th berms) in the study is the best configuration for higher vertical accuracy compared with other types. The principal objective of this study provides an effective GCP configuration for DSM construction of coal waste dumps with four berms, and also a reference for engineering piles using multiple berms. Full article

(This article belongs to the Special Issue Remote Sensing of Opencast Mining Land Use and Land Cover for Planning, Rehabilitation and Closure)

► Show Figures

Figure 1

17 pages, 3203 KiB

Open AccessArticle

Mapping Annual Land Disturbance and Reclamation in a Surface Coal Mining Region Using Google Earth Engine and the LandTrendr Algorithm: A Case Study of the Shengli Coalfield in Inner Mongolia, China

by Wu Xiao, Xinyu Deng, Tingting He and Wenqi Chen

Remote Sens. 2020, 12(10), 1612; https://doi.org/10.3390/rs12101612 - 18 May 2020

Cited by 55 | Viewed by 6743

Abstract

The development and utilization of mining resources are basic requirements for social and economic development. Both open-pit mining and underground mining have impacts on land, ecology, and the environment. Of these, open-pit mining is considered to have the greatest impact due to the drastic changes wrought on the original landform and the disturbance to vegetation. As awareness of environmental protection has grown, land reclamation has been included in the mining process. In this study, we used the Shengli Coalfield in the eastern steppe region of Inner Mongolia to demonstrate a mining and reclamation monitoring process. We combined the Google Earth Engine platform with time series Landsat images and the LandTrendr algorithm to identify and monitor mining disturbances to grassland and land reclamation in open-pit mining areas of the coalfield between 2003 and 2019. Pixel-based trajectories were used to reconstruct the temporal evolution of vegetation, and sequential Landsat archive data were used to achieve accurate measures of disturbances to vegetation. The results show that: (1) the proposed method can be used to determine the years in which vegetation disturbance and recovery occurred with accuracies of 86.53% and 78.57%, respectively; (2) mining in the Shengli mining area resulted in the conversion of 89.98 km² of land from grassland, water, etc., to barren earth, and only 23.54 km² was reclaimed, for a reclamation rate of 26.16%; and (3) the method proposed in this paper can achieve fast, efficient identification of surface mining land disturbances and reclamation, and has the potential to be applied to other similar areas. Full article

(This article belongs to the Special Issue Remote Sensing of Opencast Mining Land Use and Land Cover for Planning, Rehabilitation and Closure)

► Show Figures

Graphical abstract

20 pages, 6363 KiB

Open AccessArticle

Progress in the Reconstruction of Terrain Relief Before Extraction of Rock Materials—The Case of Liban Quarry, Poland

by Roksana Zarychta, Adrian Zarychta and Katarzyna Bzdęga

Remote Sens. 2020, 12(10), 1548; https://doi.org/10.3390/rs12101548 - 13 May 2020

Cited by 5 | Viewed by 2923

Abstract

Open pit mining leads to irreversible changes in topographical relief, which makes a return to the original morphology virtually impossible. This is important for quarries that were part of former mining areas. This research presents an innovative approach to the reconstruction of the relief of anthropogenically transformed land on the example of Liban Quarry in Cracow, where operations began before 1873 to 1986. The basis for the reconstructed area was a Topographic Map of Poland with a scale 1:10,000 from 1997, from which a set of data was obtained to perform spatial analyses. The estimation was conducted using the ordinary kriging method, enabling a reconstruction of the morphology of the studied area and presenting it in the form of a hypsometric map and a digital elevation model. The correctness of the modelling was verified by cross-validation and a kriging standard deviation map (SD_OK). These revealed low values of estimation errors in the places without contour lines on the base map. The comparison of the obtained maps and model with a Tactical Map of Poland with a scale 1:100,000 from 1934 indicated great similarities. The highest interpolation error value was recorded in the part of the pit where the difference between the actual and reconstructed elevation was about 30 m on average. In the exploited part, the SD_OK did not exceed 0.52 m, and in the entire studied area, it reached a maximum of 0.56 m. The proposed approach fulfilled the assumptions of reconstruction, as the analysis revealed elements matching the historic relief in both forms of presentation of the topography of the quarry, on the obtained hypsometric map and on the tactical map. Our study is among the very few in the world concerning the application of geostatistics in the restoration of the relief of land transformed by open pit mining activities. Full article

(This article belongs to the Special Issue Remote Sensing of Opencast Mining Land Use and Land Cover for Planning, Rehabilitation and Closure)

► Show Figures

Graphical abstract

21 pages, 24641 KiB

Open AccessArticle

Land Cover Changes in Open-Cast Mining Complexes Based on High-Resolution Remote Sensing Data

by Filipe Silveira Nascimento, Markus Gastauer, Pedro Walfir M. Souza-Filho, Wilson R. Nascimento, Jr., Diogo C. Santos and Marlene F. Costa

Remote Sens. 2020, 12(4), 611; https://doi.org/10.3390/rs12040611 - 12 Feb 2020

Cited by 27 | Viewed by 4555

Abstract

Remote sensing technologies can play a fundamental role in the environmental assessment of open-cast mining and the accurate quantification of mine land rehabilitation efforts. Here, we developed a systematic geographic object-based image analysis (GEOBIA) approach to map the amount of revegetated area and quantify the land use changes in open-cast mines in the Carajás region in the eastern Amazon, Brazil. Based on high-resolution satellite images from 2011 to 2015 from different sensors (GeoEye, WorldView-3 and IKONOS), we quantified forests, cangas (natural metalliferous savanna ecosystems), mine land, revegetated areas and water bodies. Based on the GEOBIA approach, threshold values were established to discriminate land cover classes using spectral bands, the normalized difference vegetation index (NDVI), normalized difference water index (NDWI) and a light detection and range sensor (LiDAR) digital terrain model and slope map. The overall accuracy was higher than 90%, and the kappa indices varied between 0.82 and 0.88. During the observation period, the mining complex expanded, which led to the conversion of canga and forest vegetation to mine land. At the same time, the amount of revegetated area increased. Thus, we conclude that our approach is capable of providing consistent information regarding land cover changes in mines, with a special focus on the amount of revegetation necessary to fulfill environmental liabilities. Full article

(This article belongs to the Special Issue Remote Sensing of Opencast Mining Land Use and Land Cover for Planning, Rehabilitation and Closure)

► Show Figures

Graphical abstract

20 pages, 6946 KiB

Open AccessArticle

Biomass Estimation for Semiarid Vegetation and Mine Rehabilitation Using Worldview-3 and Sentinel-1 SAR Imagery

by Nisha Bao, Wenwen Li, Xiaowei Gu and Yanhui Liu

Remote Sens. 2019, 11(23), 2855; https://doi.org/10.3390/rs11232855 - 1 Dec 2019

Cited by 24 | Viewed by 4524

Abstract

The surface mining activities in grassland and rangeland zones directly affect the livestock production, forage quality, and regional grassland resources. Mine rehabilitation is necessary for accelerating the recovery of the grassland ecosystem. In this work, we investigate the integration of data obtained via a synthetic aperture radar (Sentinel-1 SAR) with data obtained by optical remote sensing (Worldview-3, WV-3) in order to monitor the conditions of a vegetation area rehabilitated after coal mining in North China. The above-ground biomass (AGB) is used as an indicator of the rehabilitated vegetation conditions and the success of mine rehabilitation. The wavelet principal component analysis is used for the fusion of the WV-3 and Sentinel-1 SAR images. Furthermore, a multiple linear regression model is applied based on the relationship between the remote sensing features and the AGB field measurements. Our results show that WV-3 enhanced vegetation indices (EVI), mean texture from band8 (near infrared band2, NIR2), the SAR vertical and horizon (VH) polarization, and band 8 (NIR2) from the fused image have higher correlation coefficient value with the field-measured AGB. The proposed AGB estimation model combining WV-3 and Sentinel 1A SAR imagery yields higher model accuracy (R² = 0.79 and RMSE = 22.82 g/m²) compared to that obtained with any of the two datasets only. Besides improving AGB estimation, the proposed model can also reduce the uncertainty range by 7 g m⁻² on average. These results demonstrate the potential of new multispectral high-resolution datasets, such as Sentinel-1 SAR and Worldview-3, in providing timely and accurate AGB estimation for mine rehabilitation planning and management. Full article

(This article belongs to the Special Issue Remote Sensing of Opencast Mining Land Use and Land Cover for Planning, Rehabilitation and Closure)

► Show Figures

Graphical abstract

28 pages, 7938 KiB

Open AccessArticle

Multimodal and Multi-Model Deep Fusion for Fine Classification of Regional Complex Landscape Areas Using ZiYuan-3 Imagery

by Xianju Li, Zhuang Tang, Weitao Chen and Lizhe Wang

Remote Sens. 2019, 11(22), 2716; https://doi.org/10.3390/rs11222716 - 19 Nov 2019

Cited by 25 | Viewed by 4817

Abstract

Land cover classification (LCC) of complex landscapes is attractive to the remote sensing community but poses great challenges. In complex open pit mining and agricultural development landscapes (CMALs), the landscape-specific characteristics limit the accuracy of LCC. The combination of traditional feature engineering and machine learning algorithms (MLAs) is not sufficient for LCC in CMALs. Deep belief network (DBN) methods achieved success in some remote sensing applications because of their excellent unsupervised learning ability in feature extraction. The usability of DBN has not been investigated in terms of LCC of complex landscapes and integrating multimodal inputs. A novel multimodal and multi-model deep fusion strategy based on DBN was developed and tested for fine LCC (FLCC) of CMALs in a 109.4 km² area of Wuhan City, China. First, low-level and multimodal spectral–spatial and topographic features derived from ZiYuan-3 imagery were extracted and fused. The features were then input into a DBN for deep feature learning. The developed features were fed to random forest and support vector machine (SVM) algorithms for classification. Experiments were conducted that compared the deep features with the softmax function and low-level features with MLAs. Five groups of training, validation, and test sets were performed with some spatial auto-correlations. A spatially independent test set and generalized McNemar tests were also employed to assess the accuracy. The fused model of DBN-SVM achieved overall accuracies (OAs) of 94.74% ± 0.35% and 81.14% in FLCC and LCC, respectively, which significantly outperformed almost all other models. From this model, only three of the twenty land covers achieved OAs below 90%. In general, the developed model can contribute to FLCC and LCC in CMALs, and more deep learning algorithm-based models should be investigated in future for the application of FLCC and LCC in complex landscapes. Full article

(This article belongs to the Special Issue Remote Sensing of Opencast Mining Land Use and Land Cover for Planning, Rehabilitation and Closure)

► Show Figures

Graphical abstract

Review

Jump to: Research

34 pages, 5323 KiB

Open AccessReview

Remote Sensing of Mine Site Rehabilitation for Ecological Outcomes: A Global Systematic Review

by Phillip B. McKenna, Alex M. Lechner, Stuart Phinn and Peter D. Erskine

Remote Sens. 2020, 12(21), 3535; https://doi.org/10.3390/rs12213535 - 28 Oct 2020

Cited by 39 | Viewed by 7049

Abstract

The mining industry has been operating across the globe for millennia, but it is only in the last 50 years that remote sensing technology has enabled the visualization, mapping and assessment of mining impacts and landscape recovery. Our review of published literature (1970–2019) found that the number of ecologically focused remote sensing studies conducted on mine site rehabilitation increased gradually, with the greatest proportion of studies published in the 2010–2019 period. Early studies were driven exclusively by Landsat sensors at the regional and landscape scales while in the last decade, multiple earth observation and drone-based sensors across a diverse range of study locations contributed to our increased understanding of vegetation development post-mining. The Normalized Differenced Vegetation Index (NDVI) was the most common index, and was used in 45% of papers; while research that employed image classification techniques typically used supervised (48%) and manual interpretation methods (37%). Of the 37 publications that conducted error assessments, the average overall mapping accuracy was 84%. In the last decade, new classification methods such as Geographic Object-Based Image Analysis (GEOBIA) have emerged (10% of studies within the last ten years), along with new platforms and sensors such as drones (15% of studies within the last ten years) and high spatial and/or temporal resolution earth observation satellites. We used the monitoring standards recommended by the International Society for Ecological Restoration (SER) to determine the ecological attributes measured by each study. Most studies (63%) focused on land cover mapping (spatial mosaic); while comparatively fewer studies addressed complex topics such as ecosystem function and resilience, species composition, and absence of threats, which are commonly the focus of field-based rehabilitation monitoring. We propose a new research agenda based on identified knowledge gaps and the ecological monitoring tool recommended by SER, to ensure that future remote sensing approaches are conducted with a greater focus on ecological perspectives, i.e., in terms of final targets and end land-use goals. In particular, given the key rehabilitation requirement of self-sustainability, the demonstration of ecosystem resilience to disturbance and climate change should be a key area for future research. Full article

(This article belongs to the Special Issue Remote Sensing of Opencast Mining Land Use and Land Cover for Planning, Rehabilitation and Closure)

► Show Figures