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Fire
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Geospatial Data in Wildfire Management
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Geospatial Data in Wildfire Management

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

Deadline for manuscript submissions: closed (15 April 2024) | Viewed by 11420

Special Issue Editors

Dr. Guoxiong Zhou

E-Mail Website
Guest Editor
College of Computer & Information Engineering, Central South University of Forestry and Technology, Changsha 410004, China
Interests: artificial intelligence; computer vision; plant phenotyping; precision agriculture
Special Issues, Collections and Topics in MDPI journals
Dr. Johnny Li

E-Mail Website1 Website2
Guest Editor
Department of Soil and Water Systems, University of Idaho, Moscow, ID, USA
Interests: climate-smart agriculture; drone and robotics; precision agriculture; remote sensing; artificial intelligence; deep learning; crop modeling; plant phenotyping
Special Issues, Collections and Topics in MDPI journals
Dr. Weiwei Cai

E-Mail
Guest Editor
School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi 214122, China
Interests: forest fire prevention; computer neural networks
Special Issues, Collections and Topics in MDPI journals
Dr. Yanfeng Wang

E-Mail
Guest Editor
Department of System Engineering, National University of Defense Technology, Changsha, China
Interests: strategic planning evaluation; system simulation

Special Issue Information

Dear Colleagues,

We are honored to invite you to submit your research in this Special Issue. The forest is an important part of the earth's ecosystem, and protecting the forest is everyone's responsibility. Once a forest fire occurs, most of the time, we can only wait for it to end naturally, which can cause serious damage to forests and the ecological environment. Thus, research on forest fires acts as an important basis for protecting forest grassland resources and promoting the sustainable development of forestry. This Special Issue focuses on forest fire management, particularly at the level of monitoring and prevention. Research results cover both laboratory events and regional wildfire events, and papers from other related disciplines are also welcome.

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:

  • Through computer, UAV equipment, aircraft, and satellite sensors, the remote sensing of forest fires is used for algorithm development and monitoring;
  • Behavioral science, decision support tools, and risk analysis are related to fire management policies and operational event management;
  • Fire dynamic modeling (including fire site modeling and fire fighting measures);
  • Fire risk assessment and quantification (including risk acceptability);
  • The impact of fire on the development of forest ecosystem and even society (short-term, long-term, etc.).

We look forward to receiving your contributions.

Dr. Guoxiong Zhou
Dr. Liujun Li
Dr. Weiwei Cai
Dr. Yanfeng Wang
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

  • forest fire smoke detection
  • visualization
  • fire simulation
  • sustainable development
  • forest fire risk prevention

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

Further information on MDPI's Special Issue polices can be found here.

Published Papers (5 papers)

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Research

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15 pages, 3868 KiB  
Article
Forest Fire Object Detection Analysis Based on Knowledge Distillation
by Jinzhou Xie and Hongmin Zhao
Fire 2023, 6(12), 446; https://doi.org/10.3390/fire6120446 - 22 Nov 2023
Cited by 2 | Viewed by 2042
Abstract
This paper investigates the application of the YOLOv7 object detection model combined with knowledge distillation techniques in forest fire detection. As an advanced object detection model, YOLOv7 boasts efficient real-time detection capabilities. However, its performance may be constrained in resource-limited environments. To address [...] Read more.
This paper investigates the application of the YOLOv7 object detection model combined with knowledge distillation techniques in forest fire detection. As an advanced object detection model, YOLOv7 boasts efficient real-time detection capabilities. However, its performance may be constrained in resource-limited environments. To address this challenge, this research proposes a novel approach: considering that deep neural networks undergo multi-layer mapping from the input to the output space, we define the knowledge propagation between layers by evaluating the dot product of features extracted from two different layers. To this end, we utilize the Flow of Solution Procedure (FSP) matrix based on the Gram matrix and redesign the distillation loss using the Pearson correlation coefficient, presenting a new knowledge distillation method termed ILKDG (Intermediate Layer Knowledge Distillation with Gram Matrix-based Feature Flow). Compared with the classical knowledge distillation algorithm, KD, ILKDG achieved a significant performance improvement on a self-created forest fire detection dataset. Specifically, without altering the student network’s parameters or network layers, [email protected] improved by 2.9%, and [email protected]:0.95 increased by 2.7%. These results indicate that the proposed ILKDG method effectively enhances the accuracy and performance of forest fire detection without introducing additional parameters. The ILKDG method, based on the Gram matrix and Pearson correlation coefficient, presents a novel knowledge distillation approach, providing a fresh avenue for future research. Researchers can further optimize and refine this method to achieve superior results in fire detection. Full article
(This article belongs to the Special Issue Geospatial Data in Wildfire Management)
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11 pages, 27360 KiB  
Article
Unsupervised Flame Segmentation Method Based on GK-RGB in Complex Background
by Xuejie Shen, Zhihuan Liu and Zhuonong Xu
Fire 2023, 6(10), 384; https://doi.org/10.3390/fire6100384 - 7 Oct 2023
Viewed by 1421
Abstract
Fires are disastrous events with significant negative impacts on both people and the environment. Thus, timely and accurate fire detection and firefighting operations are crucial for social development and ecological protection. In order to segment the flame accurately, this paper proposes the GK-RGB [...] Read more.
Fires are disastrous events with significant negative impacts on both people and the environment. Thus, timely and accurate fire detection and firefighting operations are crucial for social development and ecological protection. In order to segment the flame accurately, this paper proposes the GK-RGB unsupervised flame segmentation method. In this method, RGB segmentation is used as the central algorithm to extract flame features. Additionally, a Gaussian filtering method is applied to remove noise interference from the image. Moreover, K-means mean clustering is employed to address incomplete flame segmentation caused by flame colours falling outside the fixed threshold. The experimental results show that the proposed method achieves excellent results on four flame images with different backgrounds at different time periods: Accuracy: 97.71%, IOU: 81.34%, and F1-score: 89.61%. Compared with other methods, GK-RGB has higher segmentation accuracy and is more suitable for the detection of fire. Therefore, the method proposed in this paper helps the application of firefighting and provides a new reference value for the detection and identification of fires. Full article
(This article belongs to the Special Issue Geospatial Data in Wildfire Management)
18 pages, 17299 KiB  
Article
Forest Fire Driving Factors and Fire Risk Zoning Based on an Optimal Parameter Logistic Regression Model: A Case Study of the Liangshan Yi Autonomous Prefecture, China
by Fuhuan Zhang, Bin Zhang, Jun Luo, Hui Liu, Qingchun Deng, Lei Wang and Ziquan Zuo
Fire 2023, 6(9), 336; https://doi.org/10.3390/fire6090336 - 26 Aug 2023
Cited by 6 | Viewed by 2178
Abstract
Planning the analyses of the spatial distribution and driving factors of forest fires and regionalizing fire risks is an important part of forest fire management. Based on the Landsat-8 active fire dataset of the Liangshan Yi Autonomous Prefecture from 2014 to 2021, this [...] Read more.
Planning the analyses of the spatial distribution and driving factors of forest fires and regionalizing fire risks is an important part of forest fire management. Based on the Landsat-8 active fire dataset of the Liangshan Yi Autonomous Prefecture from 2014 to 2021, this paper proposes an optimal parameter logistic regression (OPLR) model, conducts forest fire risk zoning research under the optimal spatial analysis scale and model parameters, and establishes a forest fire risk prediction model. The results showed that the spatial unit of the optimal spatial analysis scale in the study area was 5 km and that the prediction accuracy of the OPLR was about 81%. The climate was the main driving factor of forest fires, while temperature had the greatest influence on the probability of forest fires. According to the forest fire prediction model, mapping the fire risk zoning, in which the medium- and high-risk area was 6021.13 km2, accounted for 9.99% of the study area. The results contribute to a better understanding of forest fire management based on the local environmental characteristics of the Liangshan Yi Autonomous Prefecture and provide a reference for related forest fire prevention and control management. Full article
(This article belongs to the Special Issue Geospatial Data in Wildfire Management)
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17 pages, 18290 KiB  
Article
Forest Flame Detection in Unmanned Aerial Vehicle Imagery Based on YOLOv5
by Haiqing Liu, Heping Hu, Fang Zhou and Huaping Yuan
Fire 2023, 6(7), 279; https://doi.org/10.3390/fire6070279 - 19 Jul 2023
Cited by 10 | Viewed by 2500
Abstract
One of the major responsibilities for forest police is forest fire prevention and forecasting; therefore, accurate and timely fire detection is of great importance and significance. We compared several deep learning networks based on the You Only Look Once (YOLO) framework to detect [...] Read more.
One of the major responsibilities for forest police is forest fire prevention and forecasting; therefore, accurate and timely fire detection is of great importance and significance. We compared several deep learning networks based on the You Only Look Once (YOLO) framework to detect forest flames with the help of unmanned aerial vehicle (UAV) imagery. We used the open datasets of the Fire Luminosity Airborne-based Machine Learning Evaluation (FLAME) to train the YOLOv5 and its sub-versions, together with YOLOv3 and YOLOv4, under equal conditions. The results show that the YOLOv5n model can achieve a detection speed of 1.4 ms per frame, which is higher than that of all the other models. Furthermore, the algorithm achieves an average accuracy of 91.4%. Although this value is slightly lower than that of YOLOv5s, it achieves a trade-off between high accuracy and real-time. YOLOv5n achieved a good flame detection effect in the different forest scenes we set. It can detect small target flames on the ground, it can detect fires obscured by trees or disturbed by the environment (such as smoke), and it can also accurately distinguish targets that are similar to flames. Our future work will focus on improving the YOLOv5n model so that it can be deployed directly on UAV for truly real-time and high-precision forest flame detection. Our study provides a new solution to the early prevention of forest fires at small scales, helping forest police make timely and correct decisions. Full article
(This article belongs to the Special Issue Geospatial Data in Wildfire Management)
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Other

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7 pages, 823 KiB  
Brief Report
The Power Grid/Wildfire Nexus: Using GIS and Satellite Remote Sensing to Identify Vulnerabilities
by Alyssa Farnes, Keith Weber, Cassie Koerner, Kathy Araújo and Christopher Forsgren
Fire 2023, 6(5), 187; https://doi.org/10.3390/fire6050187 - 4 May 2023
Viewed by 1879
Abstract
The effects of wildfire on the power grid are a recurring concern for utility companies who need reliable information about where to prioritize infrastructure hardening. Though there are existing data layers that provide measures of burn probability, these models predominately consider long-term climate [...] Read more.
The effects of wildfire on the power grid are a recurring concern for utility companies who need reliable information about where to prioritize infrastructure hardening. Though there are existing data layers that provide measures of burn probability, these models predominately consider long-term climate variables, which are not helpful when analyzing current season trends. Utility companies need data that are temporally and locally relevant. To determine the primary drivers of burn probability relative to power grid vulnerability, this study assessed potential wildfire drivers that are both readily accessible and regularly updated. Two study areas in Idaho, USA with contrasting burn probabilities were compared. Wildfire drivers were obtained and differentiated between the study areas across the 2018–2021 growing seasons. This study determined that mean wind speed, cumulative growing season precipitation, and the mean Normalized Difference Vegetation Index (NDVI) for an area of interest may be reliable indicators of burn probability on a temporally relevant scale. This assessment demonstrates a method and variables that may be utilized by municipal electric utilities, electric cooperatives, and other power utilities to determine where to harden power grid infrastructure within wildfire-prone areas. Full article
(This article belongs to the Special Issue Geospatial Data in Wildfire Management)
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