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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Engineering Remote Sensing".

Deadline for manuscript submissions: closed (31 December 2023) | Viewed by 14887

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Prof. Dr. Radosław Juszczak

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Department of Ecology and Environment Protection, Poznan University of Life Sciences, 60-637 Poznan, Poland
Interests: peatlands; GHG fluxes; remote sensing of peatlands; linking remote sensing and GHG fluxes; Sun Induced Fluorescence (SIF); ecosystem responses to climate change; climate change manipulation experiments
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Krzysztof Tajduś

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Strata Mechanics Research Institute, Polish Acadamy of Sciences, Cracow, Poland
Interests: rock mechanics; mining deformation modelling; geotechnics; FEM modelling

Prof. Dr. Paweł Ocłoń

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Energy Department, Faculty of Environmental and Energy Engineering, Krakow University of Technology, Al. Jana Pawła II, 31-864 Krakow, Poland
Interests: computational fluid dynamics; engineering thermodynamics; modeling and simulation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We are pleased to announce that we are now accepting submissions for the upcoming Special Issue focused on the application of Remote Sensing in mining, energy, and environmental engineering.

Mining, power, and environmental engineering activities are crucial for human existence and development worldwide. They are often, however, a source of major environment degradation, and their environmental impact and development can be assessed and monitored remotely.

Remote sensing (RS) is a powerful tool that can be used to monitor the tailings storage facility; stockpile; mining and post-mining inducted ground deformations; open-pit mines; slope design; hydro-, wind-, and solar-power installations; surface deformations around geothermal power plants; and other different technical infrastructures, as well as their impact on the environment. Ground-, UAV-, airborne-, or spaceborne-based RS approaches and platforms can be integrated with modelling in order to increase the efficiency and complementarity of monitoring activities at different temporal and spatial scales.

We are interested in high-quality submissions that use remote sensing to study any aspects of the environmental impact of mining, as well as power and environmental engineering infrastructures and activities. Special focus should be given to the innovative application of novel RS platforms, sensors, and models. For energy engineering applications, we are highly interested in applications of remote sensing for photovoltaics and wind energy. Studies integrating remote sensing with modelling are particularly welcome.

We look forward to receiving your manuscript.

Sincerely,

Prof. Dr. Radosław Juszczak
Prof. Dr. Krzysztof Tajduś
Prof. Dr. Paweł Ocłoń
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 of mines
remote sensing of power installations
remote sensing of engineering infrastructures
mining
power engineering
environmental engineering

Published Papers (9 papers)

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Research

27 pages, 9008 KiB

Open AccessArticle

Open-Pit Granite Mining Area Extraction Using UAV Aerial Images and the Novel GIPNet

by Xiaoliang Meng, Ding Zhang, Sijun Dong and Chunjing Yao

Remote Sens. 2024, 16(5), 789; https://0-doi-org.brum.beds.ac.uk/10.3390/rs16050789 - 24 Feb 2024

Viewed by 487

Abstract

The ability to rapidly and accurately delineate open-pit granite mining areas is pivotal for effective production planning and environmental impact assessment. Over the years, advancements in remote sensing techniques, including the utilization of satellite imagery, LiDAR technology and unmanned aerial vehicles, have revolutionized the way mining areas are monitored and managed. Simultaneously, in the context of the open-pit mining area extraction task, deep learning-based automatic recognition is gradually replacing manual visual interpretation. Leveraging the potential of unmanned aerial vehicles (UAVs) for real-time, low-risk remote sensing, this study employs UAV-derived orthophotos for mining area extraction. Central to the proposed approach is the novel Gather–Injection–Perception (GIP) module, designed to overcome the information loss typically associated with conventional feature pyramid modules during feature fusion. The GIP module effectively enriches semantic features, addressing a crucial information limitation in existing methodologies. Furthermore, the network introduces the Boundary Perception (BP) module, uniquely tailored to tackle the challenges of blurred boundaries and imprecise localization in mining areas. This module capitalizes on attention mechanisms to accentuate critical high-frequency boundary details in the feature map and synergistically utilizes both high- and low-dimensional feature map data for deep supervised learning. The suggested method demonstrates its superiority in a series of comparative experiments on a specially assembled dataset of research area images. The results are compelling, with the proposed approach achieving 90.67% precision, 92.00% recall, 91.33% F1-score, and 84.04% IoU. These figures not only underscore the effectiveness of suggested model in enhancing the extraction of open-pit granite mining areas but also provides a new idea for the subsequent application of UAV data in the mining scene. Full article

(This article belongs to the Special Issue Application of Remote Sensing for Mining, Energy and Environmental Engineering)

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17 pages, 3987 KiB

Open AccessArticle

Power-Type Structural Self-Constrained Inversion Methods of Gravity and Magnetic Data

by Yanbo Ming, Guoqing Ma, Taihan Wang, Bingzhen Ma, Qingfa Meng and Zongrui Li

Remote Sens. 2024, 16(4), 681; https://0-doi-org.brum.beds.ac.uk/10.3390/rs16040681 - 14 Feb 2024

Viewed by 578

Abstract

The inversion of gravity and magnetic data can obtain the density and magnetic structure of underground space, which provide important information for resource exploration and geological structure division. The most commonly used inversion method is smooth inversion in which the objective function is built with L2-norm, which has good stability, but it produces non-focused results that make subsequent interpretation difficult. The power-type structural self-constrained inversion (PTSS) method with L2-norm is proposed to improve the resolution of smooth inversion. A self-constraint term based on the power gradient of the results is introduced, which takes advantage of the structural feature that the power gradient can better focus on the model boundary to improve the resolution. For the joint inversion of gravity and magnetic data, the power-type mutual-constrained term between different physical structures and the self-constrained term can be simultaneously used to obtain higher-resolution results. The modeling tests demonstrated that the PTSS method can produce converged high-resolution results with good noise immunity in both the respective inversions and the joint inversion. Then, the PTSS joint inversion was applied to the airborne gravity and magnetic data of the iron ore district in Shandong, revealing the shape and location of the mineralized rock mass, which are crucial information for subsequent detailed exploration. Full article

(This article belongs to the Special Issue Application of Remote Sensing for Mining, Energy and Environmental Engineering)

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22 pages, 19719 KiB

Open AccessArticle

Study on the Impact of Offshore Wind Farms on Surrounding Water Environment in the Yangtze Estuary Based on Remote Sensing

by Lina Cai, Qunfei Hu, Zhongfeng Qiu, Jie Yin, Yuanzhi Zhang and Xinkai Zhang

Remote Sens. 2023, 15(22), 5347; https://0-doi-org.brum.beds.ac.uk/10.3390/rs15225347 - 13 Nov 2023

Cited by 1 | Viewed by 1049

Abstract

Offshore wind farms (OWFs), built extensively in recent years, induce changes in the surrounding water environment. The changes in the suspended sediment concentration (SSC) and chlorophyll-a concentration (Chl-aC) induced by an OWF in the Yangtze River Estuary were analyzed based on Chinese Gaofen (GF) satellite data. The results show the following: (1) The flow near the wind turbines makes the bottom water surge, driving the sediment to “re-suspend” and be lost, deepening the scour pit around the bottom of the wind turbines, which is known as “self-digging”. The interaction between the pillar of a wind turbine and tidal currents makes hydrodynamic factors more complicated. Blocking by wind turbines promoting the scour of the bottom seabed of the OWF results in speeding up the circulation rate of sediment loss and “re-suspension”, which contributes to the change in the SSC and Chl-aC. This kind of change in sediment transport in estuarine areas due to human construction affects the balance of the ecological environment. Long-term sediment loss around wind turbines also influences the safety of wind turbines. (2) The SSC and Chl-aC are mainly in the range of 200–600 mg/L and 3–7 μg/L, respectively, in the OWF area, higher than the values obtained in surrounding waters. The SSC and Chl-aC downstream of the OWF are higher than those upstream, with differences of 100–300 mg/L and 0.5–2 μg/L. High SSC and Chl-aC “tails” appear downstream of wind turbines, consistent with the direction of local tidal currents, with lengths in the range of 2–4 km. In addition, the water environment in the vicinity of a wind turbine array, with a roughly 2–5 km scope (within 4 km during flooding and around 2.5 km during ebbing approximately) downstream of the wind turbine array, is impacted by the OWF. (3) In order to solve the problem of “self-digging” induced by OWFs, it is suggested that the distance between two wind turbines should be controlled within 2–3.5 km in the main flow direction, promising that the second row of wind turbines will be placed on the suspended sediment deposition belt induced by the first row. In this way, the problems of ecosystem imbalance and tidal current structure change caused by sediment loss because of local scouring can be reduced. Furthermore, mutual compensation between wind turbines can solve the “self-digging” problem to a certain extent and ensure the safety of OWFs. Full article

(This article belongs to the Special Issue Application of Remote Sensing for Mining, Energy and Environmental Engineering)

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22 pages, 31972 KiB

Open AccessArticle

Retrieving Surface Deformation of Mining Areas Using ZY-3 Stereo Imagery and DSMs

by Wenmin Hu, Jiaxing Xu, Wei Zhang, Jiatao Zhao and Haokun Zhou

Remote Sens. 2023, 15(17), 4315; https://0-doi-org.brum.beds.ac.uk/10.3390/rs15174315 - 01 Sep 2023

Viewed by 812

Abstract

Measuring surface deformation is crucial for a better understanding of spatial-temporal evolution and the mechanism of mining-induced deformation, thus effectively assessing the mining-related geohazards, such as landslides or damage to surface infrastructures. This study proposes a method of retrieving surface deformation by combining multi-temporal digital surface models (DSMs) with image homonymous features using China’s ZY-3 satellite stereo imagery. DSM is generated from three-line-array images of ZY-3 satellite using a rational function model (RFM) as the imaging geometric model. Then, elevation changes in deformation are extracted using the difference of DSMs acquired at different times, while planar displacements of deformation are calculated using image homonymous features extracted from multi-temporal digital orthographic maps (DOMs). Scale invariant feature transform (SIFT) points and line band descriptor (LBD) lines are selected as two kinds of salient features for image homonymous features generation. Cross profiles are also extracted for deformation in typical regions. Four sets of stereo imagery acquired in 2012 to 2022 are used for deformation extraction and analysis in the Fushun coalfield of China, where surface deformation is quite distinct and coupled with rising and descending elevation together. The results show that 21.60% of the surface in the study area was deformed from 2012 to 2017, while a decline from 2017 to 2022 meant that 17.19% of the surface was deformed with a 95% confidence interval. Moreover, the ratio of descending area was reduced to 6.44% between 2017 and 2022, which is lower than the ratios in other years. The slip deformation area in the west open pit mine is about 1.22 km² and the displacement on the south slope is large, reaching an average of 26.89 m and sliding from south to north to the bottom of the pit between 2012 and 2017, but elevations are increased by an average of about 16.35 m, involving an area of about 0.86 km² between 2017 and 2022 due to the restoration of the open pit. The results demonstrate that more quantitative features and specific surface deformation can be retrieved in mining areas by combining image features with DSMs derived from ZY-3 satellite stereo imagery. Full article

(This article belongs to the Special Issue Application of Remote Sensing for Mining, Energy and Environmental Engineering)

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28 pages, 13496 KiB

Open AccessArticle

Integrated Spatiotemporal Analysis of Vegetation Condition in a Complex Post-Mining Area: Lignite Mine Case Study

by Jan Blachowski, Aleksandra Dynowski, Anna Buczyńska, Steinar L. Ellefmo and Natalia Walerysiak

Remote Sens. 2023, 15(12), 3067; https://0-doi-org.brum.beds.ac.uk/10.3390/rs15123067 - 12 Jun 2023

Cited by 1 | Viewed by 1396

Abstract

The motivation for this study arises from the need to monitor the condition of a rehabilitated post-mining areas even decades after the end of the recovery phase. This can be facilitated with satellite derived spectral vegetation indices and Geographic Information System (GIS) based spatiotemporal analysis. The study area described in this work is located in Western Poland and has unique characteristics, as it was subjected to the combined underground and open pit mining of lignite deposits that had been shaped by glaciotectonic processes. The mining ended in early 1970’ties and the area was subjected to reclamation procedures that ended in the 1980’ties. We used the Normalized Difference Vegetation Index (NDVI) and the Enhanced Vegetation Index (EVI) spectral indices derived from Sentinel-2 data for the 2015–2022. period. Then, we applied a combination of GIS-based map algebra statistics (local, zonal and combinatorial) and GI* spatial statistics (hot spot and temporal hot spot) for a complex analysis and assessment of the vegetation cover condition in a post-mining area thought to be in the rehabilitated phase. The mean values of NDVI and EVI for the post-mining study area range from 0.48 to 0.64 and 0.24 to 0.31 and are stable in the analyzed 8 year period. This indicates general good condition of the vegetation and post-recovery phase of the area of interest. However, the combination of spatiotemporal analysis allowed us to identify statistically significant clusters of higher and lower values of the vegetation indices and change of vegetation cover classes on 3% of the study area. These clusters signify the occurrence of local processes such as, the encroachment of aquatic vegetation in waterlogged subsidence basins, and growth of low vegetation in old pits filled with waste material, barren earth zones on external waste dumps, as well as present-day forest management activities. We have confirmed that significant vegetation changes related to former mining occur even five decades later. Furthermore, we identified clusters of the highest values that are associated with zones of older, healthy forest and deciduous tree species. The results confirmed applicability of Sentinel-2 derived vegetation indices for studies of post-mining environment and for the detection of local phenomena related to natural landscaping processes still taking place in the study area. The methodology adopted for this study consisting of a combination of GIS-based data mining methods can be used in combination or separately in other areas of interest, as well as aid their sustainable management. Full article

(This article belongs to the Special Issue Application of Remote Sensing for Mining, Energy and Environmental Engineering)

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17 pages, 6796 KiB

Open AccessArticle

Stability Analysis of Rocky Slopes on the Cuenca–Girón–Pasaje Road, Combining Limit Equilibrium Methods, Kinematics, Empirical Methods, and Photogrammetry

by Xavier Delgado-Reivan, Cristhian Paredes-Miranda, Silvia Loaiza, Michelle Del Pilar Villalta Echeverria, Maurizio Mulas and Luis Jordá-Bordehore

Remote Sens. 2023, 15(3), 862; https://0-doi-org.brum.beds.ac.uk/10.3390/rs15030862 - 03 Feb 2023

Cited by 4 | Viewed by 2004

Abstract

The 3D point clouds obtained from the low-cost, remote, and precise SfM (Structure from Motion) technique allow the extraction and acquisition of discontinuities and their characteristics both manually, with the compass and virtual ruler of the Cloud Compare software, and automatically with the DSE (Discontinuity Set Extractor) program, which is faster, more accurate, and safe. Some control plans have been used, which basically consist of identifying one or several fractures and taking measurements on them manually and remotely. The difference between both types of measurements is around 5°, which we believe is reasonable since it is within the precision and repeatability of measurements with a geologist’s compass. This work analyzes the stability of six slopes (five excavated and one natural) by applying five different analysis methodologies based on the rock mass classification system (SMR, RHRSmod, and Qslope), kinematic analysis, and analytical analysis (limit equilibrium). Their results were compared with what was observed in the field to identify the most appropriate analysis methodologies adjusted to reality. The necessary parameters for analyzing each of the slopes, such as orientation, quantity, spacing, and persistence of the discontinuities, were obtained from the automatic analysis. This type of analysis eliminates the subjectivity of the authors, although the findings are related and resemble those obtained manually. The main contribution of the article consists of the application of fast and low-cost techniques to the evaluation of slopes. It is a type of analysis that is in high demand today in many Andean countries, and this work aims to provide an answer. These methodologies suggested by scientific articles such as this one will later be integrated into some procedures and will be taken into account by technical reports. The results show that with the available information and by applying low-cost techniques, the SMR system is the methodology that presents the best results and adjusts better to the reality of the study area. Therefore, SMR is a necessary parameter to determine rockfall hazards through modified RHRS. Full article

(This article belongs to the Special Issue Application of Remote Sensing for Mining, Energy and Environmental Engineering)

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17 pages, 17640 KiB

Open AccessArticle

Fast Tailings Pond Mapping Exploiting Large Scene Remote Sensing Images by Coupling Scene Classification and Sematic Segmentation Models

by Pan Wang, Hengqian Zhao, Zihan Yang, Qian Jin, Yanhua Wu, Pengjiu Xia and Lingxuan Meng

Remote Sens. 2023, 15(2), 327; https://0-doi-org.brum.beds.ac.uk/10.3390/rs15020327 - 05 Jan 2023

Cited by 3 | Viewed by 1595

Abstract

In the process of extracting tailings ponds from large scene remote sensing images, semantic segmentation models usually perform calculations on all small-size remote sensing images segmented by the sliding window method. However, some of these small-size remote sensing images do not have tailings ponds, and their calculations not only affect the model accuracy, but also affect the model speed. For this problem, we proposed a fast tailings pond extraction method (Scene-Classification-Sematic-Segmentation, SC-SS) that couples scene classification and semantic segmentation models. The method can map tailings ponds rapidly and accurately in large scene remote sensing images. There were two parts in the method: a scene classification model, and a semantic segmentation model. Among them, the scene classification model adopted the lightweight network MobileNetv2. With the help of this network, the scenes containing tailings ponds can be quickly screened out from the large scene remote sensing images, and the interference of scenes without tailings ponds can be reduced. The semantic segmentation model used the U-Net model to finely segment objects from the tailings pond scenes. In addition, the encoder of the U-Net model was replaced by the VGG16 network with stronger feature extraction ability, which improves the model’s accuracy. In this paper, the Google Earth images of Luanping County were used to create the tailings pond scene classification dataset and tailings pond semantic segmentation dataset, and based on these datasets, the training and testing of models were completed. According to the experimental results, the extraction accuracy (Intersection Over Union, IOU) of the SC-SS model was 93.48%. The extraction accuracy of IOU was 15.12% higher than the U-Net model, while the extraction time was shortened by 35.72%. This research is of great importance to the remote sensing dynamic observation of tailings ponds on a large scale. Full article

(This article belongs to the Special Issue Application of Remote Sensing for Mining, Energy and Environmental Engineering)

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24 pages, 20468 KiB

Open AccessArticle

LiDAR-Based Local Path Planning Method for Reactive Navigation in Underground Mines

by Yuanjian Jiang, Pingan Peng, Liguan Wang, Jiaheng Wang, Jiaxi Wu and Yongchun Liu

Remote Sens. 2023, 15(2), 309; https://0-doi-org.brum.beds.ac.uk/10.3390/rs15020309 - 04 Jan 2023

Cited by 7 | Viewed by 1988

Abstract

Reactive navigation is the most researched navigation technique for underground vehicles. Local path planning is one of the main research difficulties in reactive navigation. At present, no technique can perfectly solve the problem of local path planning for the reactive navigation of underground vehicles. Aiming to address this problem, this paper proposes a new method for local path planning based on 2D LiDAR. First, we convert the LiDAR data into a binary image, and we then extract the skeleton of the binary image through a thinning algorithm. Finally, we extract the centerline of the current laneway from these skeletons and smooth the obtained roadway centerline as the current planned local path. Experiments show that the proposed method has high robustness and good performance. Additionally, the method can also be used for the global path planning of underground maps. Full article

(This article belongs to the Special Issue Application of Remote Sensing for Mining, Energy and Environmental Engineering)

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19 pages, 7361 KiB

Open AccessArticle

Multispectral LiDAR Point Cloud Segmentation for Land Cover Leveraging Semantic Fusion in Deep Learning Network

by Kai Xiao, Jia Qian, Teng Li and Yuanxi Peng

Remote Sens. 2023, 15(1), 243; https://0-doi-org.brum.beds.ac.uk/10.3390/rs15010243 - 31 Dec 2022

Cited by 3 | Viewed by 2300

Abstract

Multispectral LiDAR technology can simultaneously acquire spatial geometric data and multispectral wavelength intensity information, which can provide richer attribute features for semantic segmentation of point cloud scenes. However, due to the disordered distribution and huge number of point clouds, it is still a challenging task to accomplish fine-grained semantic segmentation of point clouds from large-scale multispectral LiDAR data. To deal with this situation, we propose a deep learning network that can leverage contextual semantic information to complete the semantic segmentation of large-scale point clouds. In our network, we work on fusing local geometry and feature content based on 3D spatial geometric associativity and embed it into a backbone network. In addition, to cope with the problem of redundant point cloud feature distribution found in the experiment, we designed a data preprocessing with principal component extraction to improve the processing capability of the proposed network on the applied multispectral LiDAR data. Finally, we conduct a series of comparative experiments using multispectral LiDAR point clouds of real land cover in order to objectively evaluate the performance of the proposed method compared with other advanced methods. With the obtained results, we confirm that the proposed method achieves satisfactory results in real point cloud semantic segmentation. Moreover, the quantitative evaluation metrics show that it reaches state-of-the-art. Full article

(This article belongs to the Special Issue Application of Remote Sensing for Mining, Energy and Environmental Engineering)

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