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Drones
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Drones for Natural Hazards
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Drones for Natural Hazards

A special issue of Drones (ISSN 2504-446X). This special issue belongs to the section "Drones in Ecology".

Deadline for manuscript submissions: 16 November 2024 | Viewed by 13380

Special Issue Editors

Dr. Vasil Yordanov

E-Mail Website1 Website2
Guest Editor
Department of Civil and Environmental Engineering, Politecnico di Milano, P.zza Leonardo da Vinci, 32, Building 3, 20133 Milano, Italy
Interests: disaster; landslides; remote sensing; UAV; photogrammetry; disaster; monitoring; mapping; geospatial artificial intelligence; open science
Special Issues, Collections and Topics in MDPI journals
Dr. Luigi Barazzetti

E-Mail Website
Guest Editor
Department of Architecture, Built Environment and Construction Engineering (ABC), Politecnico di Milano, Via Ponzio 31, 20133 Milan, Italy
Interests: photogrammetry; laser scanning; automation; 3D modelling; monitoring; computer vision
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Maria Antonia Brovelli

E-Mail Website
Guest Editor
Department of Civil and Environmental Engineering, Politecnico di Milano, P.zza Leonardo da Vinci, 32, Building 3, 20133 Milano, Italy
Interests: geospatial web; geodata science; citizen science; open science; open data; open geospatial software; geospatial artificial intelligence
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In recent years, unmanned aerial vehicles (UAVs) have undergone incredible technological development and, at the same time, developed an even wider application range. Consecutively, systems that offer high-resolution/-quality data products acquired in a non-invasive and remote manner are very widely adopted in the disaster risk management cycle—preparedness, response, recovery, and mitigation. Scholars and professionals alike are implementing and continue to develop applications of UAVs and intelligent swarms for a variety of tasks ranging from hazard mapping and monitoring to more operational ones such as emergency response and search and rescue.

In addition, UAV derived high resolutions datasets from passive or active sensors, are great assets for improving and validating spaceborne applications in the disaster domain.

Furthermore, the data products from such aerial systems are easy to implement with geographic information systems and combined with geospatial artificial intelligence are further advancing the progress and develop the scientific research, and decision-making processes.

Finally, the availability of consumer grade UAVs at affordable price is a main driving factor for adopting citizen science contribution to the risk-related activities.

We are pleased to invite you to submit manuscripts to the MDPI journal Drones for our Special Issue “Drones for Natural Hazards”. Articles should be related to but not limited by the considered topics:

  • UAV photogrammetry and remote sensing
  • Change detection
  • Emergency response
  • RTK/PPK- and GCP- based orientation and processing methods
  • Novel cloud processing services
  • New approaches for hazard monitoring and mapping
  • UAV Citizen science through collaborative approaches and platforms
  • Integration and validation and of satellite-based outputs through UAV-based observations

You may choose our Joint Special Issue in Remote Sensing.

Dr. Vasil Yordanov
Dr. Luigi Barazzetti
Prof. Dr. Maria Antonia Brovelli
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. Drones is an international peer-reviewed open access monthly 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 2600 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

  • UAV
  • drones
  • hazards
  • mapping
  • geographic information science
  • remote sensing
  • landslides
  • floods
  • wildfires
  • volcanoes
  • disasters
  • relief

Published Papers (4 papers)

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Research

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18 pages, 40309 KiB  
Article
Research on Identification and Location of Mining Landslide in Mining Area Based on Improved YOLO Algorithm
by Xugang Lian, Yu Li, Xiaobing Wang, Lifan Shi and Changhao Xue
Drones 2024, 8(4), 150; https://0-doi-org.brum.beds.ac.uk/10.3390/drones8040150 - 14 Apr 2024
Viewed by 533
Abstract
The wide range and high intensity of landslides in the mining area pose a great threat to the safety of human life and property. It is particularly important to identify and monitor them. However, due to the serious surface damage, small landslide scale, [...] Read more.
The wide range and high intensity of landslides in the mining area pose a great threat to the safety of human life and property. It is particularly important to identify and monitor them. However, due to the serious surface damage, small landslide scale, complex background and other factors in the mining area, it is impossible to accurately identify and detect the landslide in the mining area. It is necessary to select an efficient detection model to detect it. In this paper, aiming at the problem of landslide identification in mining area, the remote sensing image of mining area is obtained by unmanned aerial vehicle (UAV), and the landslide data set of mining area is constructed by data enhancement method. An improved YOLOv8 algorithm is proposed. By adding a mixed attention mechanism in the channel and spatial dimensions, the detection accuracy of the model for mining landslide is improved, and the monitoring of landslide changes in the mining area is successfully completed. At the same time, an algorithm for locating the landslide position is proposed. Through this algorithm, the detected landslide pixel coordinates can be converted into geodetic coordinates. The results show that the improved YOLOv8 algorithm proposed in this paper has a recognition accuracy of 93.10% for mining area landslides. Compared with the [email protected] of the original YOLOv8 algorithm and YOLOv5 algorithm, the improved YOLOv8 algorithm has an increase of 4.2% and 5.1%. This study has realized the monitoring and positioning of the landslide in the mining area, which can provide the necessary data support for the ecological restoration on mining area. Full article
(This article belongs to the Special Issue Drones for Natural Hazards)
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24 pages, 57683 KiB  
Article
Estimating Landslide Surface Displacement by Combining Low-Cost UAV Setup, Topographic Visualization and Computer Vision Techniques
by Vasil Yordanov, Quang Xuan Truong and Maria Antonia Brovelli
Drones 2023, 7(2), 85; https://0-doi-org.brum.beds.ac.uk/10.3390/drones7020085 - 27 Jan 2023
Cited by 2 | Viewed by 1920
Abstract
Many techniques are available for estimating landslide surface displacements, whether from the ground, air- or spaceborne. In recent years, Unmanned Areal Vehicles have also been applied in the domain of landslide hazards, and have been able to provide high resolution and precise datasets [...] Read more.
Many techniques are available for estimating landslide surface displacements, whether from the ground, air- or spaceborne. In recent years, Unmanned Areal Vehicles have also been applied in the domain of landslide hazards, and have been able to provide high resolution and precise datasets for better understanding and predicting landslide movements and mitigating their impacts. In this study, we propose an approach for monitoring and detecting landslide surface movements using a low-cost lightweight consumer-grade UAV setup and a Red Relief Image Map (a topographic visualization technique) to normalize the input datasets and mitigate unfavourable illumination conditions that may affect the further implementation of Lucas–Kanade optical flow for the final displacement estimation. The effectiveness of the proposed approach in this study was demonstrated by applying it to the Ruinon landslide, Northern Italy, using the products of surveys carried out in the period 2019–2021. Our results show that the combination of different techniques can accurately and effectively estimate landslide movements over time and at different magnitudes, from a few centimetres to more than several tens of meters. The method applied is shown to be very computationally efficient while yielding precise outputs. At the same time, the use of only free and open-source software allows its straightforward adaptation and modification for other case studies. The approach can potentially be used for monitoring and studying landslide behaviour in areas where no permanent monitoring solutions are present. Full article
(This article belongs to the Special Issue Drones for Natural Hazards)
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Review

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27 pages, 16496 KiB  
Review
Unmanned Aerial Vehicles (UAVs) in Landslide Investigation and Monitoring: A Review
by Jianwei Sun, Guoqin Yuan, Laiyun Song and Hongwen Zhang
Drones 2024, 8(1), 30; https://0-doi-org.brum.beds.ac.uk/10.3390/drones8010030 - 22 Jan 2024
Cited by 1 | Viewed by 2261
Abstract
Over the past decade, Unmanned Aerial Vehicles (UAVs) have emerged as essential tools for landslide studies, particularly in on-site investigations. This paper reviews UAV applications in landslide studies, with a focus on static geological characteristics, monitoring temporal and spatial dynamics, and responses post-events. [...] Read more.
Over the past decade, Unmanned Aerial Vehicles (UAVs) have emerged as essential tools for landslide studies, particularly in on-site investigations. This paper reviews UAV applications in landslide studies, with a focus on static geological characteristics, monitoring temporal and spatial dynamics, and responses post-events. We discuss the functions and limitations of various types of UAVs and sensors (RGB cameras, multi-spectral cameras, thermal IR cameras, SAR, LiDAR), outlining their roles and data processing methods in landslide applications. This review focuses on the UAVs’ roles in landslide geology surveys, emphasizing landslide mapping, modeling and characterization. For change monitoring, it provides an overview of the temporal and spatial evolution through UAV-based monitoring, shedding light on dynamic landslide processes. Moreover, this paper underscores UAVs’ crucial role in emergent response scenarios, detailing strategies and automated detection using machine learning algorithms. The discussion on challenges and opportunities highlights the need for ongoing UAV technology advancements, addressing regulatory hurdles, hover time limitations, 3D reconstruction accuracy and potential integration with technologies like UAV swarms. Full article
(This article belongs to the Special Issue Drones for Natural Hazards)
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29 pages, 1617 KiB  
Review
Drones for Flood Monitoring, Mapping and Detection: A Bibliometric Review
by Umair Iqbal, Muhammad Zain Bin Riaz, Jiahong Zhao, Johan Barthelemy and Pascal Perez
Drones 2023, 7(1), 32; https://0-doi-org.brum.beds.ac.uk/10.3390/drones7010032 - 01 Jan 2023
Cited by 15 | Viewed by 6994
Abstract
Floods are one of the most often occurring and damaging natural hazards. They impact the society on a massive scale and result in significant damages. To reduce the impact of floods, society needs to keep benefiting from the latest technological innovations. Drones equipped [...] Read more.
Floods are one of the most often occurring and damaging natural hazards. They impact the society on a massive scale and result in significant damages. To reduce the impact of floods, society needs to keep benefiting from the latest technological innovations. Drones equipped with sensors and latest algorithms (e.g., computer vision and deep learning) have emerged as a potential platform which may be useful for flood monitoring, mapping and detection activities in a more efficient way than current practice. To better understand the scope and recent trends in the domain of drones for flood management, we performed a detailed bibliometric analysis. The intent of performing the bibliometric analysis waws to highlight the important research trends, co-occurrence relationships and patterns to inform the new researchers in this domain. The bibliometric analysis was performed in terms of performance analysis (i.e., publication statistics, citations statistics, top publishing countries, top publishing journals, top publishing institutions, top publishers and top Web of Science (WoS) categories) and science mapping (i.e., citations by country, citations by journals, keyword co-occurrences, co-authorship, co-citations and bibliographic coupling) for a total of 569 records extracted from WoS for the duration 2000–2022. The VOSviewer open source tool has been used for generating the bibliographic network maps. Subjective discussions of the results explain the obtained trends from the bibliometric analysis. In the end, a detailed review of top 28 most recent publications was performed and subjected to process-driven analysis in the context of flood management. The potential active areas of research were also identified for future research in regard to the use of drones for flood monitoring, mapping and detection activities. Full article
(This article belongs to the Special Issue Drones for Natural Hazards)
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