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Forests
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Wildfire Monitoring and Risk Management in Forests
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Wildfire Monitoring and Risk Management in Forests

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

Deadline for manuscript submissions: 25 July 2024 | Viewed by 6189

Special Issue Editor

Prof. Dr. Manuel Marey-Pérez

E-Mail Website
Guest Editor
Research Group—PROePLA, Department of Crop Production and Project Management of Campus Terra in Lugo, University Santiago de Compostela, 27002 Lugo, Spain
Interests: forest management; project management; wildfire
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Wildfires are a significant result of the interaction between different sources of pressure on forest ecosystems. Each year, forest fires destroy more than 1.3 million hectares in Europe, burn over 25 million acres in the U.S. and affect 98 million hectares of forest globally.

Scientific progress in the search for solutions to the problem of wildfires has been remarkable in recent years. Proof of this is the multitude of existing scientific publications. In my experience, there are two areas on which it is particularly important to focus the research effort. The first is the monitoring of wildfires, climate change and rising global temperatures, which have led to a surge in the intensity, frequency and duration of wildfires; scientific forecasts indicate that this trend will accelerate. On the other hand, it is becoming increasingly important to determine the risk of a forest burning, understanding risk as the aggregation of the probability and the environmental, social and economic impacts that wildfires can have.

I encourage researchers from all fields, including experimental studies, tools and models, to contribute to this Special Issue in order to promote knowledge and adaptation strategies for the management and resilience of forest ecosystems.

Prof. Dr. Manuel Marey-Pérez
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 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

  • wildfire mitigation strategy
  • wildfire risk
  • firefighting
  • fire index
  • wildfire prediction models
  • fire activity
  • fire behavior
  • fire management

Published Papers (6 papers)

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Research

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19 pages, 4713 KiB  
Article
Forest Fire Smoke Detection Based on Multiple Color Spaces Deep Feature Fusion
by Ziqi Han, Ye Tian, Change Zheng and Fengjun Zhao
Forests 2024, 15(4), 689; https://doi.org/10.3390/f15040689 - 11 Apr 2024
Viewed by 512
Abstract
The drastic increase of forest fire occurrence, which in recent years has posed severe threat and damage worldwide to the natural environment and human society, necessitates smoke detection of the early forest fire. First, a semantic segmentation method based on multiple color spaces [...] Read more.
The drastic increase of forest fire occurrence, which in recent years has posed severe threat and damage worldwide to the natural environment and human society, necessitates smoke detection of the early forest fire. First, a semantic segmentation method based on multiple color spaces feature fusion is put forward for forest fire smoke detection. Considering that smoke images in different color spaces may contain varied and distinctive smoke features which are beneficial for improving the detection ability of a model, the proposed model integrates the function of multi-scale and multi-type self-adaptive weighted feature fusion with attention augmentation to extract the enriched and complementary fused features of smoke, utilizing smoke images from multi-color spaces as inputs. Second, the model is trained and evaluated on part of the FIgLib dataset containing high-quality smoke images from watchtowers in the forests, incorporating various smoke types and complex background conditions, with a satisfactory smoke segmentation result for forest fire detection. Finally, the optimal color space combination and the fusion strategy for the model is determined through elaborate and extensive experiments with a superior segmentation result of 86.14 IoU of smoke obtained. Full article
(This article belongs to the Special Issue Wildfire Monitoring and Risk Management in Forests)
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24 pages, 22362 KiB  
Article
Global Wildfire Danger Predictions Based on Deep Learning Taking into Account Static and Dynamic Variables
by Yuheng Ji, Dan Wang, Qingliang Li, Taihui Liu and Yu Bai
Forests 2024, 15(1), 216; https://doi.org/10.3390/f15010216 - 22 Jan 2024
Viewed by 1090
Abstract
Climate change will intensify the danger of wildfires, significantly impacting human life. Deep Learning (DL) has been extensively applied in wildfire prediction research. In the realm of wildfire prediction, previous deep learning methods have overlooked the inherent differences between static positional information and [...] Read more.
Climate change will intensify the danger of wildfires, significantly impacting human life. Deep Learning (DL) has been extensively applied in wildfire prediction research. In the realm of wildfire prediction, previous deep learning methods have overlooked the inherent differences between static positional information and dynamic variables. Additionally, most existing deep learning models have not integrated the global system characteristics of the Earth’s features and teleconnection during the learning phase. Here, we propose a static location-aware ConvLSTM (SLA-ConvLSTM) model that is aware of static positional elements and interconnected with global information and teleconnection. The model we propose can discern the influence of dynamic variables across various geographical locations on predictive outcomes. Compared with other deep learning models, our SLA-ConvLSTM model has achieved commendable performance. The outcomes indicate that the collaborative interplay of spatiotemporal features and the extraction of static positional information present a promising technique for wildfire prediction. Moreover, the incorporation of climate indices and global feature variables enhances the predictive capability of the model in wildfire prediction. Full article
(This article belongs to the Special Issue Wildfire Monitoring and Risk Management in Forests)
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14 pages, 3356 KiB  
Article
Revealing Three-Dimensional Variations in Fuel Structures in Subtropical Forests through Backpack Laser Scanning
by Ping Kang, Shitao Lin, Chao Huang, Shun Li, Zhiwei Wu and Long Sun
Forests 2024, 15(1), 155; https://doi.org/10.3390/f15010155 - 11 Jan 2024
Cited by 1 | Viewed by 888
Abstract
Wildfire hazard is a prominent issue in subtropical forests as climate change and extreme drought events increase in frequency. Stand-level fuel load and forest structure are determinants of forest fire occurrence and spread. However, current fuel management often lacks detailed vertical fuel distribution, [...] Read more.
Wildfire hazard is a prominent issue in subtropical forests as climate change and extreme drought events increase in frequency. Stand-level fuel load and forest structure are determinants of forest fire occurrence and spread. However, current fuel management often lacks detailed vertical fuel distribution, limiting accurate fire risk assessment and effective fuel policy implementation. In this study, backpack laser scanning (BLS) is used to estimate several 3D structural parameters, including canopy height, crown base height, canopy volume, stand density, vegetation area index (VAI), and vegetation coverage, to characterize the fuel structure characteristics and vertical density distribution variation in different stands of subtropical forests in China. Through standard measurement using BLS point cloud data, we found that canopy height, crown base height, stand density, and VAI in the lower and middle-height strata differed significantly among stand types. Compared to vegetation coverage, the LiDAR-derived VAI can better show significant stratified changes in fuel density in the vertical direction among stand types. Among stand types, conifer-broadleaf mixed forest and C. lanceolata had a higher VAI in surface strata than other stand types, while P. massoniana and conifer-broadleaf mixed forests were particularly unique in having a higher VAI in the lower and middle-height strata, corresponding to the higher surface fuel and ladder fuel in the stand, respectively. To provide more informative support for forest fuel management, BLS LiDAR data combined with other remote sensing data were advocated to facilitate the visualization of fuel density distribution and the development of fire risk assessment. Full article
(This article belongs to the Special Issue Wildfire Monitoring and Risk Management in Forests)
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20 pages, 3200 KiB  
Article
Forest Fire Spread Hazard and Landscape Pattern Characteristics in the Mountainous District, Beijing
by Bo Wang, Weiwei Li, Guanghui Lai, Ning Chang, Feng Chen, Ye Bai and Xiaodong Liu
Forests 2023, 14(11), 2139; https://doi.org/10.3390/f14112139 - 27 Oct 2023
Viewed by 1085
Abstract
Objective: This study established an index system for assessing forest fire spread hazards and conducted a forest fire spread hazard assessment in the mountainous district of Beijing (including Fangshan, Mentougou, Changping, Yanqing, Huairou, Miyun, and Pinggu). The relationship between forested landscape spatial pattern [...] Read more.
Objective: This study established an index system for assessing forest fire spread hazards and conducted a forest fire spread hazard assessment in the mountainous district of Beijing (including Fangshan, Mentougou, Changping, Yanqing, Huairou, Miyun, and Pinggu). The relationship between forested landscape spatial pattern and forest fire spread hazard was explored; this method provided the basis for the establishment of a landscape forest fire security guarantee system. Methods: The forest fire spread hazard assessment index system was constructed from four aspects: forest fuel, meteorological factors, topographic factors, and fire behavior. The weighted comprehensive evaluation method and area-weighted average method were used to calculate the forest fire spread hazard indices at the subcompartment scale and township scale. Moran’s I index was selected as the spatial autocorrelation index to analyze the autocorrelation degree and spatial distribution of the forest fire spread hazard index. Eleven representative landscape pattern indices were selected to analyze the main landscape spatial pattern affecting forest fire spread hazard by correlation analysis and principal component analysis. Results: (1) The areas with high, medium–high, medium-low, and low forest fire spread hazard grades accounted for 39.87%, 33.10%, 11.37%, and 15.66% of the study area, respectively, at the subcompartment scale and for 52.36%, 22.58%, 18.39%, and 6.67% of the study area, respectively, at the township scale. (2) The forest fire spread hazard index results obtained at the subcompartment and township scales in the Mountainous District of Beijing showed a spatial agglomeration distribution law. (3) The forest fire spread hazard was influenced mainly by landscape diversity (SHDI and PRD), landscape aggregation (AI, CONTAG, and PD), and landscape area (TA). Conclusions: The overall forest fire spread hazard in the mountainous district of Beijing showed a gradual increase from plains to mountainous areas. The land types of the high-spread hazard subcompartment mainly included general shrubbery and coniferous forestlands, and the dominant species in the high-spread hazard arbor forest subcompartment were mainly Platycladus orientalis, Pinus tabuliformis, and Quercus mongolica. Full article
(This article belongs to the Special Issue Wildfire Monitoring and Risk Management in Forests)
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32 pages, 37209 KiB  
Article
Method of Wildfire Risk Assessment in Consideration of Land-Use Types: A Case Study in Central China
by Weiting Yue, Chao Ren, Yueji Liang, Xiaoqi Lin and Jieyu Liang
Forests 2023, 14(7), 1393; https://doi.org/10.3390/f14071393 - 07 Jul 2023
Cited by 2 | Viewed by 1283
Abstract
Research on wildfire risk can quantitatively assess the risk of wildfire damage to the population, economy, and natural ecology. However, existing research has primarily assessed the spatial risk of wildfires across an entire region, neglecting the impact of different land-use types on the [...] Read more.
Research on wildfire risk can quantitatively assess the risk of wildfire damage to the population, economy, and natural ecology. However, existing research has primarily assessed the spatial risk of wildfires across an entire region, neglecting the impact of different land-use types on the assessment outcomes. The purpose of the study is to construct a framework for assessing wildfire risk in different land-use types, aiming to comprehensively assess the risk of wildfire disasters in a region. We conducted a case study in Central China, collecting and classifying historical wildfire samples according to land-use types. The Light Gradient Boosting Machine (LGBM) was employed to construct wildfire susceptibility models for both overall and individual land-use types. Additionally, a subjective and objective combined weighting method using the Analytic Hierarchy Process (AHP) and Entropy Weight Method (EWM) was utilized to build the wildfire vulnerability model. By integrating susceptibility and vulnerability information, we comprehensively assessed the combined risk of wildfire disasters across land-use types. The results demonstrate the following: (1) Assessing wildfire susceptibility based on different land-use types compensated for limitations in analyzing overall wildfire susceptibility, with a higher prediction performance and more detailed susceptibility information. (2) Significant variations in wildfire susceptibility distribution existed among different land-use types, with varying contributions of factors. (3) Using the AHP-EWM combined weighting method effectively addressed limitations of a single method in determining vulnerability. (4) Land-use types exerted a significant impact on wildfire risk assessment in Central China. Assessing wildfire risk for both overall and individual land-use types enhances understanding of spatial risk distribution and specific land use risk. The experimental results validate the feasibility and effectiveness of the proposed evaluation framework, providing guidance for wildfire prevention and control. Full article
(This article belongs to the Special Issue Wildfire Monitoring and Risk Management in Forests)
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Other

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23 pages, 7556 KiB  
Brief Report
Research on a Real-Time Monitoring System for Campus Woodland Fires via Deep Learning
by Dengwei Xu, Jie Chen, Qi Wu and Zheng Wang
Forests 2024, 15(3), 483; https://doi.org/10.3390/f15030483 - 05 Mar 2024
Viewed by 724
Abstract
To solve the problems of low recognition accuracy and large amounts of computation required in forest fire detection algorithms, this paper, aiming to make improvements in these two aspects, proposes a G-YOLOv5n-CB forest fire detection algorithm based on the YOLOv5 algorithm and develops [...] Read more.
To solve the problems of low recognition accuracy and large amounts of computation required in forest fire detection algorithms, this paper, aiming to make improvements in these two aspects, proposes a G-YOLOv5n-CB forest fire detection algorithm based on the YOLOv5 algorithm and develops a set of real-time fire monitoring systems applicable to campus forest land with the aid of deep learning technology. The system employs an unmanned vehicle to navigate automatically and collect image information through a camera and deploys its algorithm on the unmanned vehicle’s Jetson Nano hardware platform. The results demonstrate that the proposed YOLOv5n-CB algorithm increased the mAP value index by 1.4% compared with the original algorithm on the self-made forest fire dataset. The improved G-YOLOv5n-CB model was deployed on the Jetson Nano platform for testing, and its detection speed reached 15 FPS. It can accurately detect and display real-time forest fires on campus and has, thus, a high application value. Full article
(This article belongs to the Special Issue Wildfire Monitoring and Risk Management in Forests)
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