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Fire
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Innovative Applications of Remote Sensing and Machine Learning in Forest...
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Innovative Applications of Remote Sensing and Machine Learning in Forest Fire Detection and Prevention

A special issue of Fire (ISSN 2571-6255). This special issue belongs to the section "Fire Research at the Science–Policy–Practitioner Interface".

Deadline for manuscript submissions: 28 February 2026 | Viewed by 1886

Special Issue Editors

Prof. Dr. Fuquan Zhang

E-Mail Website
Guest Editor
College of Information Science and Technology, Nanjing Forestry University, Nanjing, China
Interests: intelligent forestry; forestry Internet of Things; wildland fire behavior; wildland fire management
Special Issues, Collections and Topics in MDPI journals
Dr. Yiqing Xu

E-Mail Website
Guest Editor
School of Computer and Software, Nanjing University of Industry Technology, Nanjing, China
Interests: intelligent forestry; forestry fire detection

Special Issue Information

Dear Colleagues,

Wildland and forest fires, as significant ecological factors within ecosystems, play a pivotal role in maintaining the balance of the global ecosystem. While they contribute to natural ecological processes, uncontrolled fires pose a substantial threat to both the environment and human lives, leading to severe economic and ecological consequences. Therefore, there is an urgent need to enhance research on forest fire management systems.​

With the widespread integration of modern information technology, various aspects such as fire and smoke alarms, fire risk evaluation, fire behavior assessment, fire spread analysis, and post-fire forest degradation assessment have emerged as key strategies in forest fire management. These technological applications enable more proactive and scientific approaches to dealing with forest fires.​

In recent years, the utilization of remote sensing and machine learning for forest fire prediction, deep learning-based forest fire monitoring, and UAV-assisted forest fire severity classification have received growing attention in the fire management domain. These advanced technologies represent a significant leap forward in our ability to anticipate, detect, and analyze forest fires. To further develop smart fire management, continuous research, development, and application of more precise and efficient forest fire prediction and management methods are essential. By doing so, we can effectively reduce the risk of forest fires and respond promptly and effectively to forest fire emergencies. These technological advancements hold great promise for significantly improving forest fire management and prevention efforts, paving the way for a more sustainable approach to safeguarding our forests.

This Special Issue aims to cover the full range of applications in forest fire prediction and management. Possible topics include, but are not limited to, the following:

  • Wildland and forest fire spreading analysis, monitoring, and prediction;
  • Wildland and forest fire detection;
  • UAV-based forest fire severity classification;
  • Deep learning models for chronological analysis of forest succession;
  • Pattern recognition techniques for forest parameter retrieval;
  • Visible light smoke and fire recognition processing and intelligentization;
  • Early fire detection;
  • Accuracy of fire protection system positioning;
  • UAV-based forest fire spreading, monitoring, and prediction;
  • Forest aviation patrol.

Prof. Dr. Fuquan Zhang
Dr. Yiqing Xu
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 250 words) can be sent to the Editorial Office for assessment.

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
  • fire detection
  • fire and smoke alarms
  • fire risk evaluation
  • fire behavior assessment
  • fire spread analysis
  • remote sensing
  • machine learning

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

Published Papers (2 papers)

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Research

23 pages, 11346 KB  
Article
Improved Multi-Objective Crested Porcupine Optimizer for UAV Forest Fire Cruising Strategy
by Yiqing Xu, Dejie Huang, Long Zhang and Fuquan Zhang
Fire 2026, 9(1), 40; https://doi.org/10.3390/fire9010040 - 16 Jan 2026
Viewed by 264
Abstract
When forest fires occur, timely detection and initial attack are critical for fire prevention. This study focuses on optimizing the cruise path of Unmanned Aerial Vehicles (UAVs) from the perspective of initial attack. It aims to maximize coverage of regions where initial attack [...] Read more.
When forest fires occur, timely detection and initial attack are critical for fire prevention. This study focuses on optimizing the cruise path of Unmanned Aerial Vehicles (UAVs) from the perspective of initial attack. It aims to maximize coverage of regions where initial attack success rates are low, shorten the time taken to detect fires, and, in turn, boost detection effectiveness and the initial attack success. In this paper, a path planning strategy, Improved Multi-Objective Crested Porcupine Optimizer (IMOCPO), is proposed. This strategy employs a weighted sum approach to formulate a composite objective function that balances global search and local optimization capabilities, considering practical requirements such as UAV endurance and uneven distribution of risk areas, thus enhancing adaptability in complex forest environments. The weight selection is justified through systematic grid search and validated by sensitivity analysis. The proposed strategy was compared and evaluated with a related strategy using four metrics: high-risk coverage rate, grid coverage rate, Average Distance Risk (ADR), and Average Grid Risk (AGR). Results show that the proposed path planning strategy performs better in these metrics. This study provides an effective solution for optimizing UAV cruise strategies in forest fire monitoring and has practical significance for improving the intelligence of forest fire prevention. Full article
(This article belongs to the Special Issue Innovative Applications of Remote Sensing and Machine Learning in Forest Fire Detection and Prevention)
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25 pages, 5766 KB  
Article
Early-Stage Wildfire Detection: A Weakly Supervised Transformer-Based Approach
by Tina Samavat, Amirhessam Yazdi, Feng Yan and Lei Yang
Fire 2025, 8(11), 413; https://doi.org/10.3390/fire8110413 - 25 Oct 2025
Viewed by 1255
Abstract
Smoke detection is a practical approach for early identification of wildfires and mitigating hazards that affect ecosystems, infrastructure, property, and the community. The existing deep learning (DL) object detection methods (e.g., Detection Transformer (DETR)) have demonstrated significant potential for early awareness of these [...] Read more.
Smoke detection is a practical approach for early identification of wildfires and mitigating hazards that affect ecosystems, infrastructure, property, and the community. The existing deep learning (DL) object detection methods (e.g., Detection Transformer (DETR)) have demonstrated significant potential for early awareness of these events. However, their precision is influenced by the low visual salience of smoke and the reliability of the annotation, and collecting real-world and reliable datasets with precise annotations is a labor-intensive and time-consuming process. To address this challenge, we propose a weakly supervised Transformer-based approach with a teacher–student architecture designed explicitly for smoke detection while reducing the need for extensive labeling efforts. In the proposed approach, an expert model serves as the teacher, guiding the student model to learn from a variety of data annotations, including bounding boxes, point labels, and unlabeled images. This adaptability reduces the dependency on exhaustive manual annotation. The proposed approach integrates a Deformable-DETR backbone with a modified loss function to enhance the detection pipeline by improving spatial reasoning, supporting multi-scale feature learning, and facilitating a deeper understanding of the global context. The experimental results demonstrate performance comparable to, and in some cases exceeding, that of fully supervised models, including DETR and YOLOv8. Moreover, this study expands the existing datasets to offer a more comprehensive resource for the research community. Full article
(This article belongs to the Special Issue Innovative Applications of Remote Sensing and Machine Learning in Forest Fire Detection and Prevention)
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