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The Use of Remote Sensing Technology for Forest Fire
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The Use of Remote Sensing Technology for Forest Fire

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

Deadline for manuscript submissions: closed (15 October 2024) | Viewed by 8028

Special Issue Editors

Prof. Dr. Fuquan Zhang

E-Mail Website
Guest Editor
College of Information Science and Technology, Nanjing Forestry University, Nanjing 210037, China
Interests: intelligent forestry; forestry Internet of Things; wildland fire behavior; wildland fire management
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Ting Yun

E-Mail Website
Guest Editor
School of Information Science and Technology, Nanjing Forestry University, Nanjing 210037, China
Interests: artificial intelligence for forestry; forest digital twin; LiDAR data; remote sensing
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Luis A. Ruiz

E-Mail Website
Guest Editor
GeoEnvironmental Cartography and Remote Sensing Group (CGAT), Universitat Politècnica de València, Camino de Vera s/n, 46022 Valencia, Spain
Interests: lidar for forest structure analysis; 3D fire behavior models; object-based feature extraction and classification; land use/land cover change analysis
Special Issues, Collections and Topics in MDPI journals
Dr. António Bento-Gonçalves

E-Mail Website
Guest Editor
Communication and Society Research Centre, Department of Geography, Institute of Social Sciences, University of Minho, 4800-058 Guimarães, Portugal
Interests: physical geography; forest fires; soil erosion and land degradation; natural hazards
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

As an important ecological factor in ecosystems, wildland fires and forest fires play a crucial role in the global ecosystem. However, uncontrolled fires can become a major threat to the environment and human lives, causing significant economic and ecological losses. Therefore, it is necessary to strengthen the research on forest fire management systems.

With the extensive application of modern information technology in the fire and smoke alarms, fire risk evaluation, fire behavior assessment, fire spreading analysis, and the exploratory appraisal of forest degeneration after fire disasters have become the primary strategies for forestland fire management.

The use of remote sensing and machine learning technology for forest fire prediction, deep-learning-based forest fire monitoring, and UAV-based forest fire severity classification have been gaining increasing attention in the field of fire management. The development of smart fire management needs to further promote the research, development, and application of more accurate and efficient methods for forest fire prediction and management, which can help reduce the risk of forest fires and provide timely and effective responses to forest fire emergencies. These technologies have the potential to greatly improve forest fire management and prevention efforts.

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:

  • Wildland fire or forest fire spreading, monitoring, or prediction;
  • Wildland fire or forest fire detection;
  • UAV-based forest fire severity classification;
  • Deep learning models for analyzing forest succession in chronological sequence;
  • Pattern recognition techniques for forest parameter retrieval;
  • Visible light smoke and fire recognition processing and intellectualization;
  • Early fire detection;
  • The accuracy of a fire protection system's positioning;
  • UAV-based forest fire spreading, monitoring, or prediction;
  • Forest aviation patrol.

You may choose our Joint Special Issue in Fire.

Prof. Dr. Fuquan Zhang
Prof. Dr. Ting Yun
Prof. Dr. Luis A. Ruiz
Dr. António Bento-Gonçalves
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. 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.

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

Published Papers (3 papers)

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Research

21 pages, 6808 KiB  
Article
LUFFD-YOLO: A Lightweight Model for UAV Remote Sensing Forest Fire Detection Based on Attention Mechanism and Multi-Level Feature Fusion
by Yuhang Han, Bingchen Duan, Renxiang Guan, Guang Yang and Zhen Zhen
Remote Sens. 2024, 16(12), 2177; https://doi.org/10.3390/rs16122177 - 15 Jun 2024
Cited by 3 | Viewed by 1742
Abstract
The timely and precise detection of forest fires is critical for halting the spread of wildfires and minimizing ecological and economic damage. However, the large variation in target size and the complexity of the background in UAV remote sensing images increase the difficulty [...] Read more.
The timely and precise detection of forest fires is critical for halting the spread of wildfires and minimizing ecological and economic damage. However, the large variation in target size and the complexity of the background in UAV remote sensing images increase the difficulty of real-time forest fire detection. To address this challenge, this study proposes a lightweight YOLO model for UAV remote sensing forest fire detection (LUFFD-YOLO) based on attention mechanism and multi-level feature fusion techniques: (1) GhostNetV2 was employed to enhance the conventional convolution in YOLOv8n for decreasing the number of parameters in the model; (2) a plug-and-play enhanced small-object forest fire detection C2f (ESDC2f) structure was proposed to enhance the detection capability for small forest fires; (3) an innovative hierarchical feature-integrated C2f (HFIC2f) structure was proposed to improve the model’s ability to extract information from complex backgrounds and the capability of feature fusion. The LUFFD-YOLO model surpasses the YOLOv8n, achieving a 5.1% enhancement in mAP and a 13% reduction in parameter count and obtaining desirable generalization on different datasets, indicating a good balance between high accuracy and model efficiency. This work would provide significant technical support for real-time forest fire detection using UAV remote-sensing images. Full article
(This article belongs to the Special Issue The Use of Remote Sensing Technology for Forest Fire)
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32 pages, 9326 KiB  
Article
Deep Learning Approach to Improve Spatial Resolution of GOES-17 Wildfire Boundaries Using VIIRS Satellite Data
by Mukul Badhan, Kasra Shamsaei, Hamed Ebrahimian, George Bebis, Neil P. Lareau and Eric Rowell
Remote Sens. 2024, 16(4), 715; https://doi.org/10.3390/rs16040715 - 18 Feb 2024
Cited by 3 | Viewed by 3353
Abstract
The rising severity and frequency of wildfires in recent years in the United States have raised numerous concerns regarding the improvement in wildfire emergency response management and decision-making systems, which require operational high temporal and spatial resolution monitoring capabilities. Satellites are one of [...] Read more.
The rising severity and frequency of wildfires in recent years in the United States have raised numerous concerns regarding the improvement in wildfire emergency response management and decision-making systems, which require operational high temporal and spatial resolution monitoring capabilities. Satellites are one of the tools that can be used for wildfire monitoring. However, none of the currently available satellite systems provide both high temporal and spatial resolution. For example, GOES-17 geostationary satellite fire products have high temporal (1–5 min) but low spatial resolution (≥2 km), and VIIRS polar orbiter satellite fire products have low temporal (~12 h) but high spatial resolution (375 m). This work aims to leverage currently available satellite data sources, such as GOES and VIIRS, along with deep learning (DL) advances to achieve an operational high-resolution, both spatially and temporarily, wildfire monitoring tool. Specifically, this study considers the problem of increasing the spatial resolution of high temporal but low spatial resolution GOES-17 data products using low temporal but high spatial resolution VIIRS data products. The main idea is using an Autoencoder DL model to learn how to map GOES-17 geostationary low spatial resolution satellite images to VIIRS polar orbiter high spatial resolution satellite images. In this context, several loss functions and DL architectures are implemented and tested to predict both the fire area and the corresponding brightness temperature. These models are trained and tested on wildfire sites from 2019 to 2021 in the western U.S. The results indicate that DL models can improve the spatial resolution of GOES-17 images, leading to images that mimic the spatial resolution of VIIRS images. Combined with GOES-17 higher temporal resolution, the DL model can provide high-resolution near-real-time wildfire monitoring capability as well as semi-continuous wildfire progression maps. Full article
(This article belongs to the Special Issue The Use of Remote Sensing Technology for Forest Fire)
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24 pages, 7753 KiB  
Article
FuF-Det: An Early Forest Fire Detection Method under Fog
by Yaxuan Pang, Yiquan Wu and Yubin Yuan
Remote Sens. 2023, 15(23), 5435; https://doi.org/10.3390/rs15235435 - 21 Nov 2023
Cited by 2 | Viewed by 2036
Abstract
In recent years, frequent forest fires have seriously threatened the earth’s ecosystem and people’s lives and safety. With the development of machine vision and unmanned aerial vehicle (UAVs) technology, UAV monitoring combined with machine vision has become an important development trend in forest [...] Read more.
In recent years, frequent forest fires have seriously threatened the earth’s ecosystem and people’s lives and safety. With the development of machine vision and unmanned aerial vehicle (UAVs) technology, UAV monitoring combined with machine vision has become an important development trend in forest fire monitoring. In the early stages, fire shows the characteristics of a small fire target and obvious smoke. However, the presence of fog interference in the forest will reduce the accuracy of fire point location and smoke identification. Therefore, an anchor-free target detection algorithm called FuF-Det based on an encoder–decoder structure is proposed to accurately detect early fire points obscured by fog. The residual efficient channel attention block (RECAB) is designed as a decoder unit to improve the problem of the loss of fire point characteristics under fog caused by upsampling. Moreover, the attention-based adaptive fusion residual module (AAFRM) is used to self-enhance the encoder features, so that the features retain more fire point location information. Finally, coordinate attention (CA) is introduced to the detection head to make the image features correspond to the position information, and improve the accuracy of the algorithm to locate the fire point. The experimental results show that compared with eight mainstream target detection algorithms, FuF-Det has higher average precision and recall as an early forest fire detection method in fog and provides a new solution for the application of machine vision to early forest fire detection. Full article
(This article belongs to the Special Issue The Use of Remote Sensing Technology for Forest Fire)
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