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Fire
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Intelligent Forest Fire Prediction and Detection
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Intelligent Forest Fire Prediction and Detection

A special issue of Fire (ISSN 2571-6255).

Deadline for manuscript submissions: 30 September 2024 | Viewed by 6916

Special Issue Editors

Dr. Demin Gao

E-Mail Website
Guest Editor
College of Information Science and Technology, Nanjing Forestry University, Nanjing 210037, China
Interests: remote sensing; climate change; forest fires
Dr. Shuo Zhang

E-Mail
Guest Editor
School of Information Management, Nanjing University, Nanjing 210037, China
Interests: data analysis; machine learning; forest fires
Dr. Cheng He

E-Mail Website
Guest Editor
Investigation Academy, Nanjing Police University, Nanjing 210023, China
Interests: forest fires; UAV

Special Issue Information

Dear Colleagues,

In recent years, there has been a discernible escalation in the frequency and severity of global wildfires, which poses a significant peril to the preservation of biodiversity within forested regions. Concurrently with the loss of biodiversity in these areas, wildfires engender deleterious ramifications on the economic sector and contribute to an upsurge in human casualties. Given the recurring nature of forest fires, the mounting atmospheric temperatures, the heightened occurrence of severe weather phenomena, and the extensive human intervention in these domains, forests are experiencing a diminished capacity to withstand and recuperate from fires, thereby resulting in a profound reduction in their expanse.

Gaining a comprehensive understanding of the interconnections between meteorological elements, remote sensing methodologies, and statistical prediction models is pivotal for establishing correlations between the level of fire hazard and these specific regions. Such comprehension assumes paramount significance in comprehending the implications of climate change on these areas. Furthermore, it facilitates the formulation of strategic plans that foster sustainable growth and judicious exploitation of forest resources.

Submitted manuscripts must be original contributions, not previously published or submitted to other journals.

Dr. Demin Gao
Dr. Shuo Zhang
Dr. Cheng He
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. 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

  • novel techniques in wildfires (artificial intelligence, big data, cloud computing, machine learning, data mining, deep learning, and reinforcement learning)
  • remote sensing
  • Internet of Things
  • climate change
  • forest fires
  • fire models
  • fire monitoring
  • reviews on wildfire
  • prescribed burning
  • fire ecology
  • fire regime
  • fire behavior
  • fire Management
  • fuel characteristics and management
  • fire prediction and fighting techniques
  • fire Literature measurement and analysis of research trends

Published Papers (6 papers)

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Research

17 pages, 6150 KiB  
Article
Deep Learning-Based Forest Fire Risk Research on Monitoring and Early Warning Algorithms
by Dongfang Shang, Fan Zhang, Diping Yuan, Le Hong, Haoze Zheng and Fenghao Yang
Fire 2024, 7(4), 151; https://0-doi-org.brum.beds.ac.uk/10.3390/fire7040151 - 22 Apr 2024
Viewed by 370
Abstract
With the development of image processing technology and video analysis technology, forest fire monitoring technology based on video recognition is more and more important in the field of forest fire prevention and control. The objects currently applied to forest fire video image monitoring [...] Read more.
With the development of image processing technology and video analysis technology, forest fire monitoring technology based on video recognition is more and more important in the field of forest fire prevention and control. The objects currently applied to forest fire video image monitoring system monitoring are mainly flames and smoke. This paper proposes a forest fire risk monitoring and early warning algorithm, which integrates a deep learning model, infrared monitoring and early warning, and forest fire weather index. The algorithm first obtains the current visible image and infrared image of the same forest area, utilizing a smoke detection model based on deep learning to detect smoke in the visible image, and obtains the confidence level of the occurrence of fire in said visible image. Then, it determines whether the local temperature value of said infrared image exceeds a preset warning value, and obtains a judgment result based on the infrared image. It calculates again a current FWI based on environmental data, and determines a current fire danger level based on the current FWI. Finally, it determines whether or not to carry out a fire warning based on said fire danger level, said confidence level of the occurrence of fire in said visible image, and said judgment result based on the infrared image. The experimental results show that the accuracy of the algorithm in this paper reaches 94.12%, precision is 96.1%, recall is 93.67, and F1-score is 94.87. The algorithm in this paper can improve the accuracy of smoke identification at the early stage of forest fire danger occurrence, especially by excluding the interference caused by clouds, fog, dust, and so on, thus improving the fire danger warning accuracy. Full article
(This article belongs to the Special Issue Intelligent Forest Fire Prediction and Detection)
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19 pages, 6775 KiB  
Article
FFYOLO: A Lightweight Forest Fire Detection Model Based on YOLOv8
by Bensheng Yun, Yanan Zheng, Zhenyu Lin and Tao Li
Fire 2024, 7(3), 93; https://0-doi-org.brum.beds.ac.uk/10.3390/fire7030093 - 16 Mar 2024
Viewed by 1056
Abstract
Forest is an important resource for human survival, and forest fires are a serious threat to forest protection. Therefore, the early detection of fire and smoke is particularly important. Based on the manually set feature extraction method, the detection accuracy of the machine [...] Read more.
Forest is an important resource for human survival, and forest fires are a serious threat to forest protection. Therefore, the early detection of fire and smoke is particularly important. Based on the manually set feature extraction method, the detection accuracy of the machine learning forest fire detection method is limited, and it is unable to deal with complex scenes. Meanwhile, most deep learning methods are difficult to deploy due to high computational costs. To address these issues, this paper proposes a lightweight forest fire detection model based on YOLOv8 (FFYOLO). Firstly, in order to better extract the features of fire and smoke, a channel prior dilatation attention module (CPDA) is proposed. Secondly, the mixed-classification detection head (MCDH), a new detection head, is designed. Furthermore, MPDIoU is introduced to enhance the regression and classification accuracy of the model. Then, in the Neck section, a lightweight GSConv module is applied to reduce parameters while maintaining model accuracy. Finally, the knowledge distillation strategy is used during training stage to enhance the generalization ability of the model and reduce the false detection. Experimental outcomes demonstrate that, in comparison to the original model, FFYOLO realizes an mAP0.5 of 88.8% on a custom forest fire dataset, which is 3.4% better than the original model, with 25.3% lower parameters and 9.3% higher frames per second (FPS). Full article
(This article belongs to the Special Issue Intelligent Forest Fire Prediction and Detection)
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21 pages, 4842 KiB  
Article
Intelligent Methods for Forest Fire Detection Using Unmanned Aerial Vehicles
by Nikolay Abramov, Yulia Emelyanova, Vitaly Fralenko, Vyacheslav Khachumov, Mikhail Khachumov, Maria Shustova and Alexander Talalaev
Fire 2024, 7(3), 89; https://0-doi-org.brum.beds.ac.uk/10.3390/fire7030089 - 15 Mar 2024
Viewed by 1152
Abstract
This research addresses the problem of early detection of smoke and open fire on the observed territory by unmanned aerial vehicles. We solve the tasks of improving the quality of incoming video data by removing motion blur and stabilizing the video stream; detecting [...] Read more.
This research addresses the problem of early detection of smoke and open fire on the observed territory by unmanned aerial vehicles. We solve the tasks of improving the quality of incoming video data by removing motion blur and stabilizing the video stream; detecting the horizon line in the frame; and identifying fires using semantic segmentation with Euclidean–Mahalanobis distance and the modified convolutional neural network YOLO. The proposed horizon line detection algorithm allows for cutting off unnecessary information such as cloud-covered areas in the frame by calculating local contrast, which is equivalent to the pixel informativeness indicator of the image. Proposed preprocessing methods give a delay of no more than 0.03 s due to the use of a pipeline method for data processing. Experimental results show that the horizon clipping algorithm improves fire and smoke detection accuracy by approximately 11%. The best results with the neural network were achieved with YOLO 5m, which yielded an F1 score of 76.75% combined with a processing speed of 45 frames per second. The obtained results differ from existing analogs by utilizing a comprehensive approach to early fire detection, which includes image enhancement and alternative real-time video processing methods. Full article
(This article belongs to the Special Issue Intelligent Forest Fire Prediction and Detection)
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15 pages, 2257 KiB  
Article
Vegetation Classification and a Biomass Inversion Model for Wildfires in Chongli Based on Remote Sensing Data
by Feng Xu, Wenjing Chen, Rui Xie, Yihui Wu and Dongming Jiang
Fire 2024, 7(2), 58; https://0-doi-org.brum.beds.ac.uk/10.3390/fire7020058 - 17 Feb 2024
Viewed by 1085
Abstract
Vegetation classification, biomass assessment, and wildfire dynamics are interconnected wildfire-ecosystem components. The Chongli District, located in Zhangjiakou City, was the venue for skiing at the 2022 Winter Olympics. Its high mountains and dense forests create a unique environment. The establishment of alpine ski [...] Read more.
Vegetation classification, biomass assessment, and wildfire dynamics are interconnected wildfire-ecosystem components. The Chongli District, located in Zhangjiakou City, was the venue for skiing at the 2022 Winter Olympics. Its high mountains and dense forests create a unique environment. The establishment of alpine ski resorts highlighted the importance of comprehensive forest surveys. Understanding vegetation types and their biomass is critical to assessing the distribution of local forest resources and predicting the likelihood of forest fires. This study used satellite multispectral data from the Sentinel-2B satellite to classify the surface vegetation in the Chongli District through K-means clustering. By combining this classification with a biomass inversion model, the total biomass of the survey area can be calculated. The biomass inversion equation established based on multispectral remote sensing data and terrain information in the Chongli area have a strong correlation (shrub forest R2 = 0.811, broadleaf forest R2 = 0.356, coniferous forest R2 = 0.223). These correlation coefficients are key indicators for our understanding of the relationship between remote sensing data and actual vegetation biomass, reflecting the performance of the biomass inversion model. Taking shrubland as an example, a correlation coefficient as high as 0.811 shows the model’s ability to accurately predict the biomass of this type of vegetation. In addition, through multiple linear regression, the optimal shrub, broadleaf, and coniferous forest biomass models were obtained, with the overall accuracy reaching 93.58%, 89.56%, and 97.53%, respectively, meeting the strict requirements for survey accuracy. This study successfully conducted vegetation classification and biomass inversion in the Chongli District using remote sensing data. The research results have important implications for the prevention and control of forest fires. Full article
(This article belongs to the Special Issue Intelligent Forest Fire Prediction and Detection)
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15 pages, 9273 KiB  
Article
Influence of Terrain Slope on Sub-Surface Fire Behavior in Boreal Forests of China
by Yanlong Shan, Bo Gao, Sainan Yin, Diankun Shao, Lili Cao, Bo Yu, Chenxi Cui and Mingyu Wang
Fire 2024, 7(2), 55; https://0-doi-org.brum.beds.ac.uk/10.3390/fire7020055 - 14 Feb 2024
Viewed by 1033
Abstract
In recent years, the influence of extreme weather patterns has led to an alarming increase in the frequency and severity of sub-surface forest fires in boreal forests. The Ledum palustre-Larix gmelinii forests of the Daxing’an Mountains of China have emerged as a hotspot [...] Read more.
In recent years, the influence of extreme weather patterns has led to an alarming increase in the frequency and severity of sub-surface forest fires in boreal forests. The Ledum palustre-Larix gmelinii forests of the Daxing’an Mountains of China have emerged as a hotspot for sub-surface fires, and terrain slope has been recognized as a pivotal factor shaping forest fire behavior. The present study was conducted to (1) study the effect of terrain slope on the smoldering temperature and spread rate using simulated smoldering experiments and (2) establish occurrence probability prediction model of the sub-surface fires’ smoldering with different slopes based on the random forest model. The results showed that all the temperatures with different slopes were high, and the highest temperature was 947.91 °C. The spread rates in the horizontal direction were higher than those in the vertical direction, and the difference increased as the slope increased. The influence of slope on the peak temperature was greater than that of spread rate. The peak temperature was extremely positively correlated with the slope, horizontal distance and vertical depth. The spread rate was extremely positively correlated with the slope. The spread rate in the vertical direction was strongly positively correlated with the depth, but was strongly negatively correlated with the horizontal distance; the horizontal spread rate was opposite. The prediction equations for smoldering peak temperature and spread rate were established based on slope, horizontal distance, and vertical depth, and the model had a good fit (p < 0.01). Using random forest model, we established the occurrence prediction models for different slopes based on horizontal distance, vertical depth, and combustion time. The models had a good fit (AUC > 0.9) and high prediction accuracy (accuracy > 80%). The study proved the effect of slope on the characteristics of sub-surface fire smoldering, explained the variation in peak temperature and spread rate between different slopes, and established the occurrence prediction model based on the random forest model. The selected models had a good fit, and prediction accuracy met the requirement of the sub-surface fire prediction. Full article
(This article belongs to the Special Issue Intelligent Forest Fire Prediction and Detection)
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17 pages, 11471 KiB  
Article
CNTCB-YOLOv7: An Effective Forest Fire Detection Model Based on ConvNeXtV2 and CBAM
by Yiqing Xu, Jiaming Li, Long Zhang, Hongying Liu and Fuquan Zhang
Fire 2024, 7(2), 54; https://0-doi-org.brum.beds.ac.uk/10.3390/fire7020054 - 12 Feb 2024
Cited by 1 | Viewed by 1301
Abstract
In the context of large-scale fire areas and complex forest environments, the task of identifying the subtle features and aspects of fire can pose a significant challenge for the deep learning model. As a result, to enhance the model’s ability to represent features [...] Read more.
In the context of large-scale fire areas and complex forest environments, the task of identifying the subtle features and aspects of fire can pose a significant challenge for the deep learning model. As a result, to enhance the model’s ability to represent features and its precision in detection, this study initially introduces ConvNeXtV2 and Conv2Former to the You Only Look Once version 7 (YOLOv7) algorithm, separately, and then compares the results with the original YOLOv7 algorithm through experiments. After comprehensive comparison, the proposed ConvNeXtV2-YOLOv7 based on ConvNeXtV2 exhibits a superior performance in detecting forest fires. Additionally, in order to further focus the network on the crucial information in the task of detecting forest fires and minimize irrelevant background interference, the efficient layer aggregation network (ELAN) structure in the backbone network is enhanced by adding four attention mechanisms: the normalization-based attention module (NAM), simple attention mechanism (SimAM), global attention mechanism (GAM), and convolutional block attention module (CBAM). The experimental results, which demonstrate the suitability of ELAN combined with the CBAM module for forest fire detection, lead to the proposal of a new method for forest fire detection called CNTCB-YOLOv7. The CNTCB-YOLOv7 algorithm outperforms the YOLOv7 algorithm, with an increase in accuracy of 2.39%, recall rate of 0.73%, and average precision (AP) of 1.14%. Full article
(This article belongs to the Special Issue Intelligent Forest Fire Prediction and Detection)
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