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Remote Sensing
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Forest Fire Monitoring Using Remotely Sensed Imagery
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Forest Fire Monitoring Using Remotely Sensed Imagery

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Forest Remote Sensing".

Deadline for manuscript submissions: 31 May 2026 | Viewed by 825

Special Issue Editor

Dr. Michael Wing

E-Mail Website
Guest Editor
Department of Forest Engineering, Resources, and Management, Oregon State University, Peavy Hall, 3100 SW Jefferson Way, Corvallis, OR 97333, USA
Interests: remote sensing; unoccupied aircraft systems; natural resources; geomatics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Forest fires pose a growing global threat, affecting forest productivity and endangering communities living in close proximity to forests. The fire impacts can be substantial in affecting forest productivity and the resources within, including water, fish and wildlife, biodiversity, recreation, timber resources, and others. The Landsat program has provided continuous remote sensing imagery from space for over 50 years. A number of other aerial- and space-based sensors have appeared during this time and have been applied for various wildfire observation studies.

This Special Issue focuses on the innovative use of remotely sensed imagery acquired by air- and space-based sensors and platforms for forest fire monitoring. Topics may include assessing fire fuels, severity, impacts on forest resources, operational response, and recovery at various scales. Topics may also involve sensor and platform functionality and comparison studies. Sensor fusion investigations that combine multiple sensor types are particularly encouraged.

Articles may address, but are not limited, to the following topics related to forest fire:

  • Water and hydrology impacts
  • Fish and wildlife influences
  • Fuel analysis
  • Real-time assistance to fire operations
  • Timber productivity
  • Restoration
  • Human threats
  • Sensor and platform selection
  • Sensor fusion
  • Sensor function comparisons
  • Machine learning techniques for image analysis

Dr. Michael Wing
Guest Editor

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. Remote Sensing is an international peer-reviewed open access semimonthly 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 2700 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
  • productivity
  • water
  • hydrology
  • timber resources
  • fire operations

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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

24 pages, 25203 KB  
Article
RLFNet: A Real-Time Lightweight Network for Forest Fire Detection on Edge Devices
by Zhengshen Huang, Weili Kou, Chen Zheng, Guangzhi Di, Qixing Zhang and Chenhao Ma
Remote Sens. 2026, 18(10), 1543; https://doi.org/10.3390/rs18101543 - 13 May 2026
Abstract
Forest fires cause severe ecological and economic losses, so timely and accurate detection becomes crucial for effective prevention and control. Edge devices with intelligent algorithms can detect forest fires in real time. Current deep learning algorithms can achieve high accuracy, but they are [...] Read more.
Forest fires cause severe ecological and economic losses, so timely and accurate detection becomes crucial for effective prevention and control. Edge devices with intelligent algorithms can detect forest fires in real time. Current deep learning algorithms can achieve high accuracy, but they are not suitable for edge devices because they require substantial computing resources. To address this issue, this study proposes a real-time lightweight forest fire detection network (RLFNet) improved from YOLOv11n, with three key enhancements to the backbone, neck, and head. (1) A Parallel Multi-Scale Extraction Block (PMEB) improves C3k2 with a dual-branch parallel strategy to enhance multi-scale feature extraction efficiency; (2) a Bidirectional Cross Fusion Module (BCFM) replaces simple Concat with a context-aware cross-gating mechanism to suppress background noise and reduce false alarms; and (3) a Faster Inference Detection Head (FIDH) leverages structural re-parameterization and group normalization to boost inference efficiency while reducing parameters. In addition, a Layer-Adaptive Magnitude-based Pruning (LAMP) strategy is applied to further improve model’s computational efficiency. Experimental results on the self-constructed Diverse Fire Scenario (DFS) dataset demonstrate that RLFNet reduces parameters and GFLOPs by 25.2% and 20.6%, boosts mAP50 by 5.3%, and achieves an inference speed of 225 FPS, attaining the best accuracy and speed among the compared models. Validation on a public remote sensing dataset further confirms its strong generalization. These results indicate that RLFNet provides a high efficiency and lightweight solution for edge devices to real-time detect forest fires. Full article
(This article belongs to the Special Issue Forest Fire Monitoring Using Remotely Sensed Imagery)
23 pages, 43629 KB  
Article
An Improved Framework for Forest Fire Severity Assessment in Mountainous Areas Based on the dNBR Index: A Case Study from Central Yunnan, China
by Li Han, Yun Liu, Qiuhua Wang, Tengteng Long, Ning Lu, Leiguang Wang and Weiheng Xu
Remote Sens. 2026, 18(8), 1118; https://doi.org/10.3390/rs18081118 - 9 Apr 2026
Viewed by 477
Abstract
Forest fires pose a considerable threat to the security of ecosystems and human society, rendering accurate assessments of fire severity critical for ecological recovery and effective fire management. The differenced Normalized Burn Ratio (dNBR) has been employed to evaluate forest fire severity; however, [...] Read more.
Forest fires pose a considerable threat to the security of ecosystems and human society, rendering accurate assessments of fire severity critical for ecological recovery and effective fire management. The differenced Normalized Burn Ratio (dNBR) has been employed to evaluate forest fire severity; however, it presents notable uncertainties owing to variations in data sources, temporal phases, and environmental factors. To address these challenges, this study analyzed 10 forest fires occurring between 2006 and 2023 in central Yunnan Province, China. First, a rapid sampling method utilizing very high-resolution imagery was developed to assess the performance of dNBR classification under varying conditions. Second, the study identified the optimal post-fire observation window and compared classification thresholds and accuracy between Landsat and Sentinel-2 imagery in assessing fire severity. Finally, the research explored the impacts of topographic correction and pre-fire vegetation differences on classification outcomes. The findings revealed the following: (1) Imagery captured in the spring of the fire year, characterized by minimal vegetation interference, demonstrated the highest classification stability and superior capability for identifying high-severity burns. (2) Landsat outperformed Sentinel-2 in regional accuracy (0.92 vs. 0.87), and direct threshold transfer between sensors resulted in a 39% underestimation of high-severity areas, underscoring the necessity for sensor-specific calibration. (3) Topographic correction provided limited practical benefits, merely yielding a marginal improvement in accuracy (+1.44%) with the SCS+C model in steep terrain, and was generally unnecessary. (4) The influence of pre-fire vegetation was discovered to be threshold-dependent: dNBR performed reliably in forests with pre-fire NDVI > 0.5, while adjusted approaches were solely recommended for sparse or heterogeneous vegetation. Overall, this study establishes a systematic framework for optimizing dNBR-based severity assessment, enhancing its accuracy and operational utility in forest fire management. Full article
(This article belongs to the Special Issue Forest Fire Monitoring Using Remotely Sensed Imagery)
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