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Application of UAVs in Geo-Engineering for Hazard Observation

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

Deadline for manuscript submissions: closed (30 November 2022) | Viewed by 12179

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Special Issue Editors

Dr. Margherita Fiani

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Guest Editor

Department of Civil Engineering, University of Salerno, 84084 Fisciano, SA, Italy
Interests: laser scanning; Lidar; UAV; SAR; HRSI; DEM/DTM; civil structures monitoring; landslide mapping and monitoring

Prof. Dr. Maurizio Barbarella

E-Mail Website
Guest Editor

Department of Civil, Chemical, Environmental and Materials Engineering DICAM, University of Bologna, Bologna, Italy
Interests: GNSS applications; landslide e-structures monitoring; UAV; laser scanning/Lidar; DEM/DTM
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Special Issue Information

Dear Colleagues,

The earth’s surface has been modeled by geological dynamics that have shaped it and by complex geomorphological processes that drive its transformation and evolution over time. Structures lay on the ground, and their stability and safety are determined by the interaction between their foundations and the ground itself. Therefore, any dynamics involving the ground have strong—sometimes disastrous—effects on the safety of the structure itself. Resilience of structures and infrastructures to geo-hazard events has, therefore, become a crucial feature.

Accurate geomatic surveying is a prerequisite for studying the processes and dynamics of both the earth’s surface and of the structures on it. Such surveying is widely used in geology and geomorphology for its ability to provide reliable multiscale and multi-temporal base maps and accurate digital elevation models (DEM).

For some applications, geological engineering requires very high precision. To achieve this goal, it is now possible to make use of new remote sensing techniques that allow large areas of ground to be covered while guaranteeing, at the same time, excellent quality of the acquired data.

Among the remote sensing techniques that can be used in engineering geology and geomorphology studies, unmanned aerial vehicle (UAV) photogrammetry is a powerful tool for periodic remote monitoring of both ground movement and the stability of structures and infrastructure. The availability of low-cost optical and multispectral sensors mounted on UAVs and the recent introduction of highly automated processing systems based on the structure from motion (SfM) approach make it possible to easily obtain three-dimensional information from images. A critical issue for UAV applications is the proper management and calibration of sensors, as well as the robustness of the data processing algorithms.

The purpose of this Special Issue is to present new research advances on the applications of UAV photogrammetry for the characterization and monitoring of ground and structure/infrastructures, with special reference to their digital modeling. All kinds of innovative applications of UAVs to geo-engineering issues are therefore welcome. We expect contributions focusing on different aspects in this field, both algorithmic and methodological. More specifically, studies and applications on the potential of UAV data to produce three-dimensional models useful for analysis (from morphometric studies to deformation analysis) and verification of complex structures/infrastructures will be especially welcome.

We invite you to submit articles about your recent researches on the application of UAVs, which include but are not limited to the following:

UAVs for hazard damage assessment
UAV image processing and applications
UAV algorithm development, automation, implementation, and validation
UAV for monitoring and modeling natural hazards
UAV for monitoring and modeling civil structures/infrastructures (dams, bridges, viaducts, tunnels, etc.)

Prof. Dr. Margherita Fiani
Prof. Dr. Maurizio Barbarella
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

unmanned aerial vehicles (UAVs)
geohazards
geological engineering
geomorphology
geomorphometry
analytical methods and algorithms
digital terrain modeling (DTM)
structure from motion (SfM)

Published Papers (4 papers)

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19 pages, 13904 KiB

Open AccessArticle

Monitoring Mining Surface Subsidence with Multi-Temporal Three-Dimensional Unmanned Aerial Vehicle Point Cloud

by Xiaoyu Liu, Wu Zhu, Xugang Lian and Xuanyu Xu

Remote Sens. 2023, 15(2), 374; https://0-doi-org.brum.beds.ac.uk/10.3390/rs15020374 - 07 Jan 2023

Cited by 8 | Viewed by 1988

Abstract

Long-term and high-intensity coal mining has led to the increasingly serious surface subsidence and environmental problems. Surface subsidence monitoring plays an important role in protecting the ecological environment of the mining area and the sustainable development of modern coal mines. The development of surveying technology has promoted the acquisition of high-resolution terrain data. The combination of an unmanned aerial vehicle (UAV) point cloud and the structure from motion (SfM) method has shown the potential of collecting multi-temporal high-resolution terrain data in complex or inaccessible environments. The difference of the DEM (DoD) is the main method to obtain the surface subsidence in mining areas. However, the obtained digital elevation model (DEM) needs to interpolate the point cloud into the grid, and this process may introduce errors in complex natural topographic environments. Therefore, a complete three-dimensional change analysis is required to quantify the surface change in complex natural terrain. In this study, we propose a quantitative analysis method of ground subsidence based on three-dimensional point cloud. Firstly, the Monte Carlo simulation statistical analysis was adopted to indirectly evaluate the performance of direct georeferencing photogrammetric products. After that, the operation of co-registration was carried out to register the multi-temporal UAV dense matching point cloud. Finally, the model-to-model cloud comparison (M3C2) algorithm was used to quantify the surface change and reveal the spatio-temporal characteristics of surface subsidence. In order to evaluate the proposed method, four periods of multi-temporal UAV photogrammetric data and a period of airborne LiDAR point cloud data were collected in the Yangquan mining area, China, from 2020 to 2022. The 3D precision map of a sparse point cloud generated by Monte Carlo simulation shows that the average precision in X, Y and Z directions is 44.80 mm, 45.22 and 63.60 mm, respectively. The standard deviation range of the M3C2 distance calculated by multi-temporal data in the stable area is 0.13–0.19, indicating the consistency of multi-temporal photogrammetric data of UAV. Compared with DoD, the dynamic moving basin obtained by the M3C2 algorithm based on the 3D point cloud obtained more real surface deformation distribution. This method has high potential in monitoring terrain change in remote areas, and can provide a reference for monitoring similar objects such as landslides. Full article

(This article belongs to the Special Issue Application of UAVs in Geo-Engineering for Hazard Observation)

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29 pages, 5382 KiB

Open AccessArticle

Heap Leach Pad Surface Moisture Monitoring Using Drone-Based Aerial Images and Convolutional Neural Networks: A Case Study at the El Gallo Mine, Mexico

by Mingliang Tang and Kamran Esmaeili

Remote Sens. 2021, 13(8), 1420; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13081420 - 07 Apr 2021

Cited by 6 | Viewed by 2761

Abstract

An efficient metal recovery in heap leach operations relies on uniform distribution of leaching reagent solution over the heap leach pad surface. However, the current practices for heap leach pad (HLP) surface moisture monitoring often rely on manual inspection, which is labor-intensive, time-consuming, discontinuous, and intermittent. In order to complement the manual monitoring process and reduce the frequency of exposing technical manpower to the hazardous leaching reagent (e.g., dilute cyanide solution in gold leaching), this manuscript describes a case study of implementing an HLP surface moisture monitoring method based on drone-based aerial images and convolutional neural networks (CNNs). Field data collection was conducted on a gold HLP at the El Gallo mine, Mexico. A commercially available hexa-copter drone was equipped with one visible-light (RGB) camera and one thermal infrared sensor to acquire RGB and thermal images from the HLP surface. The collected data had high spatial and temporal resolutions. The high-quality aerial images were used to generate surface moisture maps of the HLP based on two CNN approaches. The generated maps provide direct visualization of the different moisture zones across the HLP surface, and such information can be used to detect potential operational issues related to distribution of reagent solution and to facilitate timely decision making in heap leach operations. Full article

(This article belongs to the Special Issue Application of UAVs in Geo-Engineering for Hazard Observation)

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25 pages, 21330 KiB

Open AccessArticle

Reliability and Uncertainties of the Analysis of an Unstable Rock Slope Performed on RPAS Digital Outcrop Models: The Case of the Gallivaggio Landslide (Western Alps, Italy)

by Niccolò Menegoni, Daniele Giordan and Cesare Perotti

Remote Sens. 2020, 12(10), 1635; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12101635 - 20 May 2020

Cited by 27 | Viewed by 3449

Abstract

A stability investigation based on Digital Outcrop Models (DOMs) acquired in emergency conditions by photogrammetric surveys based on Remote Piloted Aerial System (RPAS) was conducted on an unstable rock slope near Gallivaggio (Western Alps, Italy). The predicted mechanism of failure and volume of the unstable portion of the slope were successively verified on the DOMs acquired after the rockfall that effectively collapsed the May 29th, 2018. The comparison of the pre- and post-landslide 3D models shows that the estimated mode of failure was substantially correct. At the same time, the predicted volume of rock involved in the landslide was overestimated by around 10%. To verify if this error was due to the limited accuracy of the models georeferenced in emergency considering only the Global Navigation Satellite System/Inertial Measurement Unit (GNSS/IMU)-information of RPAS, several Ground Control Points (GCPs) were acquired after the failure. The analyses indicate that the instrumental error in the volume calculation due to the direct-georeferencing method is only of the 1.7%. In contrast, the significant part is due to the geological uncertainty in the reconstruction of the real irregular geometry of the invisible part of the failure surface. The results, however, confirm the satisfying relative accuracy of the direct-georeferenced DOMs, compatible with most geological and geoengineering purposes. Full article

(This article belongs to the Special Issue Application of UAVs in Geo-Engineering for Hazard Observation)

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16 pages, 7044 KiB

Open AccessTechnical Note

GIS-Based Landslide Susceptibility Mapping of the Circum-Baikal Railway in Russia Using UAV Data

by Svetlana Gantimurova, Alexander Parshin and Vladimir Erofeev

Remote Sens. 2021, 13(18), 3629; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13183629 - 11 Sep 2021

Cited by 13 | Viewed by 2600

Abstract

The investigation of hard-to-reach areas that are prone to landslides is challenging. The research of landslide hazards can be significantly advanced by using remote sensing data obtained from an unmanned aerial vehicle (UAV). Operational acquisition and high detail are the advantages of UAV data. The development of appropriate automated algorithms and software solutions is necessary for quick decision-making based on the received heterogeneous spatial data characterising various aspects of the environment. This article introduces the first phase of a long-term study about landslide detection and prediction that aims to develop an automatic algorithm for detecting potentially hazardous landslide areas, using data obtained by UAV surveys. As a part of the project, the selection of appropriate techniques was implemented and a landslide susceptibility (LS) map of the study site was developed. This paper presents the outcomes of the applied indirect heuristic approach of landslide susceptibility assessment using an analytical hierarchy process (AHP) in a GIS environment, based on UAV data. The results obtained have been tested on a real-world entity. Full article

(This article belongs to the Special Issue Application of UAVs in Geo-Engineering for Hazard Observation)

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