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Forests
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Advanced Technologies for Forest Fire Detection and Monitoring
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Advanced Technologies for Forest Fire Detection and Monitoring

A special issue of Forests (ISSN 1999-4907). This special issue belongs to the section "Natural Hazards and Risk Management".

Deadline for manuscript submissions: 20 March 2026 | Viewed by 408

Special Issue Editors

Prof. Dr. Change Zheng

E-Mail Website
Guest Editor
School of Technology, Beijing Forestry University, Beijing 100083, China
Interests: forest fire detection; fire danger forecast; fire spread prediction
Dr. Fengjun Zhao

E-Mail Website
Guest Editor
Key Laboratory of Forest Protection of National Forestry and Grassland Administration, Ecology and Nature Conservation Institute, Chinese Academy of Forestry, National Forestry and Grassland Fire Monitoring, Early Warning and Prevention Engineering Technology Research Center, Beijing 100091, China
Interests: forest fire; forest fuel regulation; lightning fire; fire behavior; fire danger forecast
Special Issues, Collections and Topics in MDPI journals
Dr. Xingdong Li

E-Mail Website
Guest Editor
College of Mechanical and Electrical Engineering, Northeast Forestry University, Harbin 150040, China
Interests: forest fire; lstm; fire spread simulation; extreme learning machine; intelligent forest fire prevention and control technology
Prof. Dr. Yunfei Liu

E-Mail Website
Guest Editor
College of Information Science and Technology, Nanjing Forestry University, Nanjing 210037, China
Interests: forestry non-destructive detection; forestry Internet of things technology; microwave and optical technology
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Forests, as a crucial ecosystem, play a key role in maintaining global biodiversity and climate stability. However, the increasing frequency and intensity of forest fires pose a huge threat to these precious natural resources. With the rapid development of new technologies such as artificial intelligence and remote sensing, it has become particularly important to apply advanced technologies to forest fire monitoring. Comprehensively applying different new technologies in various development stages of forest fires for the danger forecast, early detection, and spread prediction of fires can provide effective support for curbing the occurrence and development of fires. This Special Issue aims to showcase the cutting-edge research results and innovative practices in the field of forest fire monitoring, aiming to build a platform for researchers, practitioners, and policymakers to exchange knowledge and ideas.  

Prof. Dr. Change Zheng
Dr. Fengjun Zhao
Dr. Xingdong Li
Prof. Dr. Yunfei Liu
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. Forests 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

  • forest fire
  • fire danger forecast
  • early detection
  • spread prediction
  • artificial intelligence
  • remote sensing
  • UAV
  • IoT
  • multi-source data

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Published Papers (2 papers)

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Article
Time-Series Correlation Optimization for Forest Fire Tracking
by Dongmei Yang, Guohao Nie, Xiaoyuan Xu, Debin Zhang and Xingmei Wang
Forests 2025, 16(7), 1101; https://doi.org/10.3390/f16071101 - 3 Jul 2025
Abstract
Accurate real-time tracking of forest fires using UAV platforms is crucial for timely early warning, reliable spread prediction, and effective autonomous suppression. Existing detection-based multi-object tracking methods face challenges in accurately associating targets and maintaining smooth tracking trajectories in complex forest environments. These [...] Read more.
Accurate real-time tracking of forest fires using UAV platforms is crucial for timely early warning, reliable spread prediction, and effective autonomous suppression. Existing detection-based multi-object tracking methods face challenges in accurately associating targets and maintaining smooth tracking trajectories in complex forest environments. These difficulties stem from the highly nonlinear movement of flames relative to the observing UAV and the lack of robust fire-specific feature modeling. To address these challenges, we introduce AO-OCSORT, an association-optimized observation-centric tracking framework designed to enhance robustness in dynamic fire scenarios. AO-OCSORT builds on the YOLOX detector. To associate detection results across frames and form smooth trajectories, we propose a temporal–physical similarity metric that utilizes temporal information from the short-term motion of targets and incorporates physical flame characteristics derived from optical flow and contours. Subsequently, scene classification and low-score filtering are employed to develop a hierarchical association strategy, reducing the impact of false detections and interfering objects. Additionally, a virtual trajectory generation module is proposed, employing a kinematic model to maintain trajectory continuity during flame occlusion. Locally evaluated on the 1080P-resolution FireMOT UAV wildfire dataset, AO-OCSORT achieves a 5.4% improvement in MOTA over advanced baselines at 28.1 FPS, meeting real-time requirements. This improvement enhances the reliability of fire front localization, which is crucial for forest fire management. Furthermore, AO-OCSORT demonstrates strong generalization, achieving 41.4% MOTA on VisDrone, 80.9% on MOT17, and 92.2% MOTA on DanceTrack. Full article
(This article belongs to the Special Issue Advanced Technologies for Forest Fire Detection and Monitoring)
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23 pages, 2463 KiB  
Article
MCDet: Target-Aware Fusion for RGB-T Fire Detection
by Yuezhu Xu, He Wang, Yuan Bi, Guohao Nie and Xingmei Wang
Forests 2025, 16(7), 1088; https://doi.org/10.3390/f16071088 - 30 Jun 2025
Abstract
Forest fire detection is vital for ecological conservation and disaster management. Existing visual detection methods exhibit instability in smoke-obscured or illumination-variable environments. Although multimodal fusion has demonstrated potential, effectively resolving inconsistencies in smoke features across diverse modalities remains a significant challenge. This issue [...] Read more.
Forest fire detection is vital for ecological conservation and disaster management. Existing visual detection methods exhibit instability in smoke-obscured or illumination-variable environments. Although multimodal fusion has demonstrated potential, effectively resolving inconsistencies in smoke features across diverse modalities remains a significant challenge. This issue stems from the inherent ambiguity between regions characterized by high temperatures in infrared imagery and those with elevated brightness levels in visible-light imaging systems. In this paper, we propose MCDet, an RGB-T forest fire detection framework incorporating target-aware fusion. To alleviate feature cross-modal ambiguity, we design a Multidimensional Representation Collaborative Fusion module (MRCF), which constructs global feature interactions via a state-space model and enhances local detail perception through deformable convolution. Then, a content-guided attention network (CGAN) is introduced to aggregate multidimensional features by dynamic gating mechanism. Building upon this foundation, the integration of WIoU further suppresses vegetation occlusion and illumination interference on a holistic level, thereby reducing the false detection rate. Evaluated on three forest fire datasets and one pedestrian dataset, MCDet achieves a mean detection accuracy of 77.5%, surpassing advanced methods. This performance makes MCDet a practical solution to enhance early warning system reliability. Full article
(This article belongs to the Special Issue Advanced Technologies for Forest Fire Detection and Monitoring)
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