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Drones
Special Issues
Drones for Wildfire and Prescribed Fire Science
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Drones for Wildfire and Prescribed Fire Science

A special issue of Drones (ISSN 2504-446X).

Deadline for manuscript submissions: 1 July 2024 | Viewed by 4290

Special Issue Editors

Dr. Brian K. Gullett

E-Mail Website
Guest Editor
Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, Durham, NC, USA
Interests: aerial emission sampling; UAS; forest fires; oil fires; detonations
Dr. Johanna Aurell

E-Mail Website
Guest Editor
Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, Durham, NC, USA
Interests: aerial emission sampling; UAS; forest fires; oil fires; detonations; stack sampling; gas chromatography
Dr. Pantelis Velanas

E-Mail Website
Guest Editor
Director of ACCELIGENCE Ltd., Nicosia, Cyprus
Interests: UAV design; computational fluid dynamics (CFD); embedded processing; wireless communication; firefighting; reforestation
Prof. Dr. Diego González-Aguilera

E-Mail Website
Guest Editor
Cartographic and Land Engineering Department, Higher Polytechnic School of Avila, University of Salamanca, Hornos Caleros, 50, 05003 Avila, Spain
Interests: photogrammetry; laser scanning; 3D modeling; topography; cartography
Special Issues, Collections and Topics in MDPI journals
Katerina Margariti

E-Mail Website
Guest Editor Assistant
Director of ACCELIGENCE HELLAS Ltd. (Manufacturing Branch), Argiroupoli, Greece
Interests: UAV design; computational fluid dynamics (CFD); embedded processing; wireless communication; firefighting; reforestation

Special Issue Information

Dear Colleagues,

The use of unmanned aircraft systems (UASs, drones) for research and operations on wildland fires has been rapidly accelerated by developments in aircraft systems, sensors, telemetry, circuitry, and computers. UASs can be used for safety reasons to spot potential flare ups or maintain positional the awareness of crew members, support fire-fighting efforts through release of fire suppressants, take meteorological measurements in support of plume dispersion calculations and downwind population hazards, characterize vegetation both pre- and post-burn, understand fire dynamics, engage in spot ignitions, and sample emissions to characterize smoke hazards. The 3D positional flexibility, system portability, night time capability, and real time telemetry of drones provide on-scene incident responders with valuable information to aid their decision making. Recent advances in instantaneous, in-flight analyses provided by edge computation using 5G/6G networks will be crucial for generating products that can support the response stage and fire management decisions.

The goal of this Special Issue is to collect original research articles and review papers that provide insights into the growing use of UASs as an essential tool in wildland fire management.

The articles and review papers submitted must clearly and directly address topics related to unmanned platforms, such as UASs, as well as related technologies and applications in wildland fire management, covering any of the main steps in wildfires: prevention, response, restoration. The papers and review papers submitted must contribute with new and innovative insights, methods, or technologies to the field of UAS in wildland fire management.

This Special Issue will welcome manuscripts that link the use of UAS to the following wildland fire themes:

  • Firefighter safety;
  • Fire management and control;
  • Characterization of fire dynamics;
  • Plume dispersion;
  • Detection of downwind population risks;
  • Characterization of fire emissions;
  • Meteorological measurements;
  • Detailed forest mapping;
  • Edge computing and the generation of cartographic products on the fly;
  • Detection and tracking of elements (e.g., people, animals, vehicles, etc.).

We look forward to receiving your original research articles and review papers.

Dr. Brian K. Gullett
Dr. Johanna Aurell
Dr. Pantelis Velanas
Prof. Dr. Diego González-Aguilera
Guest Editors

Katerina Margariti
Guest Editor Assistant

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

  • unmanned aircraft systems
  • drones
  • wildland fire
  • fire dynamics
  • sensors
  • firefighter safety
  • plume dispersion
  • meteorological measurements
  • fire emissions
  • exposure risk
  • fire prevention
  • fire response
  • fire restoration

Published Papers (3 papers)

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Research

16 pages, 2675 KiB  
Article
Superpixel-Based Graph Convolutional Network for UAV Forest Fire Image Segmentation
by Yunjie Mu, Liyuan Ou, Wenjing Chen, Tao Liu and Demin Gao
Drones 2024, 8(4), 142; https://0-doi-org.brum.beds.ac.uk/10.3390/drones8040142 - 03 Apr 2024
Viewed by 553
Abstract
Given the escalating frequency and severity of global forest fires, it is imperative to develop advanced detection and segmentation technologies to mitigate their impact. To address the challenges of these technologies, the development of deep learning-based forest fire surveillance has significantly accelerated. Nevertheless, [...] Read more.
Given the escalating frequency and severity of global forest fires, it is imperative to develop advanced detection and segmentation technologies to mitigate their impact. To address the challenges of these technologies, the development of deep learning-based forest fire surveillance has significantly accelerated. Nevertheless, the integration of graph convolutional networks (GCNs) in forest fire detection remains relatively underexplored. In this context, we introduce a novel superpixel-based graph convolutional network (SCGCN) for forest fire image segmentation. Our proposed method utilizes superpixels to transform images into a graph structure, thereby reinterpreting the image segmentation challenge as a node classification task. Additionally, we transition the spatial graph convolution operation to a GraphSAGE graph convolution mechanism, mitigating the class imbalance issue and enhancing the network’s versatility. We incorporate an innovative loss function to contend with the inconsistencies in pixel dimensions within superpixel clusters. The efficacy of our technique is validated on two different forest fire datasets, demonstrating superior performance compared to four alternative segmentation methodologies. Full article
(This article belongs to the Special Issue Drones for Wildfire and Prescribed Fire Science)
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15 pages, 6798 KiB  
Article
Effects of UAS Rotor Wash on Air Quality Measurements
by Johanna Aurell and Brian K. Gullett
Drones 2024, 8(3), 73; https://0-doi-org.brum.beds.ac.uk/10.3390/drones8030073 - 21 Feb 2024
Viewed by 1137
Abstract
Laboratory and field tests examined the potential for unmanned aircraft system (UAS) rotor wash effects on gas and particle measurements from a biomass combustion source. Tests compared simultaneous placement of two sets of CO and CO2 gas sensors and PM2.5 instruments [...] Read more.
Laboratory and field tests examined the potential for unmanned aircraft system (UAS) rotor wash effects on gas and particle measurements from a biomass combustion source. Tests compared simultaneous placement of two sets of CO and CO2 gas sensors and PM2.5 instruments on a UAS body and on a vertical or horizontal extension arm beyond the rotors. For 1 Hz temporal concentration comparisons, correlations of body versus arm placement for the PM2.5 particle sensors yielded R2 = 0.85, and for both gas sensor pairs, exceeded an R2 of 0.90. Increasing the timestep to 10 s average concentrations throughout the burns improved the R2 value for the PM2.5 to 0.95 from 0.85. Finally, comparison of the whole-test average concentrations further increased the correlations between body- and arm-mounted sensors, exceeding an R2 of 0.98 for both gases and particle measurements. Evaluation of PM2.5 emission factors with single-factor ANOVA analyses showed no significant differences between the values derived from the arm, either vertical or horizontal, and those from the body. These results suggest that rotor wash effects on body- and arm-mounted sensors are minimal in scenarios where short-duration, time-averaged concentrations are used to calculate emission factors and whole-area flux values. Full article
(This article belongs to the Special Issue Drones for Wildfire and Prescribed Fire Science)
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19 pages, 7435 KiB  
Article
Advancing Forest Fire Risk Evaluation: An Integrated Framework for Visualizing Area-Specific Forest Fire Risks Using UAV Imagery, Object Detection and Color Mapping Techniques
by Michal Aibin, Yuanxi Li, Rohan Sharma, Junyan Ling, Jiannan Ye, Jianming Lu, Jiesi Zhang, Lino Coria, Xingguo Huang, Zhiyuan Yang, Lili Ke and Panhaoqi Zou
Drones 2024, 8(2), 39; https://0-doi-org.brum.beds.ac.uk/10.3390/drones8020039 - 29 Jan 2024
Viewed by 1901
Abstract
Forest fires have significant implications for the Earth’s ecological balance, causing widespread devastation and posing formidable challenges for containment once they propagate. The development of computer vision methods holds promise in facilitating the timely identification of forest fire risks, thereby preventing potential economic [...] Read more.
Forest fires have significant implications for the Earth’s ecological balance, causing widespread devastation and posing formidable challenges for containment once they propagate. The development of computer vision methods holds promise in facilitating the timely identification of forest fire risks, thereby preventing potential economic losses. In our study conducted in various regions in British Columbia, we utilized image data captured by unmanned aerial vehicles (UAVs) and computer vision methods to detect various types of trees, including alive trees, debris (logs on the ground), beetle- and fire-impacted trees, and dead trees that pose a risk of a forest fire. We then designed and implemented a novel sliding window technique to process large forest areas as georeferenced orthogonal maps. The model demonstrates proficiency in identifying various tree types, excelling in detecting healthy trees with precision and recall scores of 0.904 and 0.848, respectively. Its effectiveness in recognizing trees killed by beetles is somewhat limited, likely due to the smaller number of examples available in the dataset. After the tree types are detected, we generate color maps, indicating different fire risks to provide a new tool for fire managers to assess and implement prevention strategies. This study stands out for its integration of UAV technology and computer vision in forest fire risk assessment, marking a significant step forward in ecological protection and sustainable forest management. Full article
(This article belongs to the Special Issue Drones for Wildfire and Prescribed Fire Science)
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