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Fire
Special Issues
Monitoring Wildfire Dynamics with Remote Sensing
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Monitoring Wildfire Dynamics with Remote Sensing

A special issue of Fire (ISSN 2571-6255).

Deadline for manuscript submissions: 31 December 2024 | Viewed by 3747

Special Issue Editors

Dr. José M. P. do Nascimento

E-Mail Website
Guest Editor
Instituto Superior de Engenharia de Lisboa, ISEL - Lisbon School of Engineering and Instituto de Telecomunicações, Lisbon, Portugal
Interests: electronic engineering and telecommunications and computers; fire; remote sensing
Dr. Houda Harkat

E-Mail Website
Guest Editor
School of Science and Technology, UNINOVA-CTS and LASI, NOVA University of Lisboa, 2829-516 Monte Caparica, Portugal
Interests: airborne fire detection; firefront forecast; decision support information systems; wildfire
Dr. Saad Dosse Bennani

E-Mail Website
Guest Editor
National School of Applied Sciences of Fez, Sidi Mohamed Ben Abdellah University, Fes, Morocco
Interests: energy; transmission lines; antennas; signal processing
Dr. Hasmath Farhana Thariq Ahmed

E-Mail Website
Guest Editor
Vellore Institute of Technology, Chennai, India
Interests: fire image detection; gesture recognition; machine learning; deep learning; sign language recognition

Special Issue Information

Dear Colleagues,

Forest fires are one of the most devastating factors in most vegetation zones worldwide, including forests and grasslands. Forest fires pose a challenge to ecosystem management since they can be both useful and detrimental. Drones, unmanned aerial vehicle (UAV), and remote sensing technology can be extremely useful in estimating the risk of forest fires across wide areas. Drones and UAVs are low-cost solutions with limited battery options. Thus, it does not provide round-the-clock monitoring. Airborne light detection and ranging (LiDAR) and digital aerial photogrammetry (DAP) have emerged as vital technologies for mapping forest structure and providing critical fundamental information to enhance predictions of forest disasters. Coarse spatial detail optical satellites, some of which operate in constellations of many micro-satellites, give spatially comprehensive data with a substantial temporal revisit rate, which can increasingly fit the knowledge demands linked to rescue efforts or disturbance occurrences.

This Special Issue invites submissions for papers that cover all elements of aerial image/video capture and processing (RGB/Infra-red, hyperspectral/multispectral, LiDAR/Radar data), as well as advanced artificial intelligence-based fire detection systems. The following topics are included, but are not limited to:

  • Building 3D models of forest environments using exploiting acquired aircraft images/videos and LiDAR/radar data.
  • Building mathematical models for fire propagation in forest environment relying on sensors data.
  • Automatic detection and localization of flames based on machine learning algorithms over RGB and hyperspectral images/videos
  • Real-time wildfire monitoring and forecasting frameworks.
  • Measure of Wildfire risk to support decision-making.
  • Mapping of wildfire based on Multitemporal Multispectral satellite data and probabilistic mathematical models.
  • Classification of vegetations changes based on satellites data.
  • Data treatment of internet of things (IoT) Sensor Networks for Decision Support in wildfire management.

We look forward to receiving your contributions.

Dr. José M. P. do Nascimento
Dr. Houda Harkat
Dr. Saad Dosse Bennani
Dr. Hasmath Farhana Thariq Ahmed
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. Fire 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 2400 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

  • fire detection
  • flames
  • machine learning
  • vegetation
  • satellites
  • LiDAR
  • radar
  • images
  • Unmanned Aerial Vehicle (UAV)
  • remote sensing

Published Papers (3 papers)

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Research

14 pages, 4779 KiB  
Article
Fire and Smoke Detection Using Fine-Tuned YOLOv8 and YOLOv7 Deep Models
by Mohamed Chetoui and Moulay A. Akhloufi
Fire 2024, 7(4), 135; https://doi.org/10.3390/fire7040135 - 12 Apr 2024
Viewed by 803
Abstract
Viewed as a significant natural disaster, wildfires present a serious threat to human communities, wildlife, and forest ecosystems. The frequency of wildfire occurrences has increased recently, with the impacts of global warming and human interaction with the environment playing pivotal roles. Addressing this [...] Read more.
Viewed as a significant natural disaster, wildfires present a serious threat to human communities, wildlife, and forest ecosystems. The frequency of wildfire occurrences has increased recently, with the impacts of global warming and human interaction with the environment playing pivotal roles. Addressing this challenge necessitates the ability of firefighters to promptly identify fires based on early signs of smoke, allowing them to intervene and prevent further spread. In this work, we adapted and optimized recent deep learning object detection, namely YOLOv8 and YOLOv7 models, for the detection of smoke and fire. Our approach involved utilizing a dataset comprising over 11,000 images for smoke and fires. The YOLOv8 models successfully identified fire and smoke, achieving a mAP:50 of 92.6%, a precision score of 83.7%, and a recall of 95.2%. The results were compared with a YOLOv6 with large model, Faster-RCNN, and DEtection TRansformer. The obtained scores confirm the potential of the proposed models for wide application and promotion in the fire safety industry. Full article
(This article belongs to the Special Issue Monitoring Wildfire Dynamics with Remote Sensing)
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20 pages, 8481 KiB  
Article
Relationships of Fire Rate of Spread with Spectral and Geometric Features Derived from UAV-Based Photogrammetric Point Clouds
by Juan Pedro Carbonell-Rivera, Christopher J. Moran, Carl A. Seielstad, Russell A. Parsons, Valentijn Hoff, Luis Á. Ruiz, Jesús Torralba and Javier Estornell
Fire 2024, 7(4), 132; https://doi.org/10.3390/fire7040132 - 11 Apr 2024
Viewed by 639
Abstract
Unmanned aerial vehicles (UAVs) equipped with RGB, multispectral, or thermal cameras have demonstrated their potential to provide high-resolution data before, during, and after wildfires and prescribed burns. Pre-burn point clouds generated through the photogrammetric processing of UAV images contain geometrical and spectral information [...] Read more.
Unmanned aerial vehicles (UAVs) equipped with RGB, multispectral, or thermal cameras have demonstrated their potential to provide high-resolution data before, during, and after wildfires and prescribed burns. Pre-burn point clouds generated through the photogrammetric processing of UAV images contain geometrical and spectral information of vegetation, while active fire imagery allows for deriving fire behavior metrics. This paper focuses on characterizing the relationship between the fire rate of spread (RoS) in prescribed burns and a set of independent geometrical, spectral, and neighborhood variables extracted from UAV-derived point clouds. For this purpose, different flights were performed before and during the prescribed burning in seven grasslands and open forest plots. Variables extracted from the point cloud were interpolated to a grid, which was sized according to the RoS semivariogram. Random Forest regressions were applied, obtaining up to 0.56 of R2 in the different plots studied. Geometric variables from the point clouds, such as planarity and the spectral normalized blue–red difference index (NBRDI), are related to fire RoS. In analyzing the results, the minimum value of the eigenentropy (Eigenentropy_MIN), the mean value of the planarity (Planarity_MEAN), and percentile 75 of the NBRDI (NBRDI_P75) obtained the highest feature importance. Plot-specific analyses unveiled distinct combinations of geometric and spectral features, although certain features, such as Planarity_MEAN and the mean value of the grid obtained from the standard deviation of the distance between points (Dist_std_MEAN), consistently held high importance across all plots. The relationships between pre-burning UAV data and fire RoS can complement meteorological and topographic variables, enhancing wildfire and prescribed burn models. Full article
(This article belongs to the Special Issue Monitoring Wildfire Dynamics with Remote Sensing)
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21 pages, 16516 KiB  
Article
Identifying Influential Spatial Drivers of Forest Fires through Geographically and Temporally Weighted Regression Coupled with a Continuous Invasive Weed Optimization Algorithm
by Parham Pahlavani, Amin Raei, Behnaz Bigdeli and Omid Ghorbanzadeh
Fire 2024, 7(1), 33; https://doi.org/10.3390/fire7010033 - 18 Jan 2024
Cited by 1 | Viewed by 1463
Abstract
Identifying the underlying factors derived from geospatial and remote sensing data that contribute to forest fires is of paramount importance. It aids experts in pinpointing areas and periods most susceptible to these incidents. In this study, we employ the geographically and temporally weighted [...] Read more.
Identifying the underlying factors derived from geospatial and remote sensing data that contribute to forest fires is of paramount importance. It aids experts in pinpointing areas and periods most susceptible to these incidents. In this study, we employ the geographically and temporally weighted regression (GTWR) method in conjunction with a refined continuous invasive weed optimization (CIWO) algorithm to assess certain spatially relevant drivers of forest fires, encompassing both biophysical and anthropogenic influences. Our proposed approach demonstrates theoretical utility in addressing the spatial regression problem by meticulously accounting for the autocorrelation and non-stationarity inherent in spatial data. We leverage tricube and Gaussian kernels to weight the GTWR for two distinct temporal datasets, yielding coefficients of determination (R2) amounting to 0.99 and 0.97, respectively. In contrast, traditional geographically weighted regression (GWR) using the tricube kernel achieved R2 values of 0.87 and 0.88, while the Gaussian kernel yielded R2 values of 0.8138 and 0.82 for the same datasets. This investigation underscores the substantial impact of both biophysical and anthropogenic factors on forest fires within the study areas. Full article
(This article belongs to the Special Issue Monitoring Wildfire Dynamics with Remote Sensing)
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