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27 pages, 6455 KiB

Open AccessArticle

Tackling the Wildfire Prediction Challenge: An Explainable Artificial Intelligence (XAI) Model Combining Extreme Gradient Boosting (XGBoost) with SHapley Additive exPlanations (SHAP) for Enhanced Interpretability and Accuracy

by Bin Liao, Tao Zhou, Yanping Liu, Min Li and Tao Zhang

Forests 2025, 16(4), 689; https://doi.org/10.3390/f16040689 - 16 Apr 2025

Viewed by 310

Abstract

The intensification of global climate change, combined with increasing human activities, has significantly increased wildfire frequency and severity, posing a major global environmental challenge. As an illustration, Guizhou Province in China encountered a total of 221 wildfires over a span of 12 days. [...] Read more.

The intensification of global climate change, combined with increasing human activities, has significantly increased wildfire frequency and severity, posing a major global environmental challenge. As an illustration, Guizhou Province in China encountered a total of 221 wildfires over a span of 12 days. Despite significant advancements in wildfire prediction models, challenges related to data imbalance and model interpretability persist, undermining their overall reliability. In response to these challenges, this study proposes an explainable wildfire risk prediction model (EWXS) leveraging Extreme Gradient Boosting (XGBoost), with a focus on Guizhou Province. The methodology involved converting raster and vector data into structured tabular formats, merging, normalizing, and encoding them using the Weight of Evidence (WOE) technique to enhance feature representation. Subsequently, the cleaned data were balanced to establish a robust foundation for the EWXS model. The performance of the EWXS model was evaluated in comparison to established models, such as CatBoost, using a range of performance metrics. The results indicated that the EWXS model achieved an accuracy of 99.22%, precision of 98.48%, recall of 96.82%, an F1 score of 97.64%, and an AUC of 0.983, thereby demonstrating its strong performance. Moreover, the SHAP framework was employed to enhance model interpretability, unveiling key factors influencing wildfire risk, including proximity to villages, meteorological conditions, air humidity, and variations in vegetation temperature. This analysis provides valuable support for decision-making bodies by offering clear, explanatory insights into the factors contributing to wildfire risk. Full article

(This article belongs to the Special Issue Forest Fires Prediction and Detection—2nd Edition)

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18 pages, 2532 KiB

Open AccessArticle

Exploring Thematic Evolution in Interdisciplinary Forest Fire Prediction Research: A Latent Dirichlet Allocation–Bidirectional Encoder Representations from Transformers Model Analysis

by Shuo Zhang

Forests 2025, 16(2), 346; https://doi.org/10.3390/f16020346 - 14 Feb 2025

Viewed by 468

Abstract

Facing the severe global wildfire challenge and the need for advanced prediction, this study analysed the evolving research in forest fire prediction using an LDA-BERT similarity model. Due to climate change, human activities, and natural factors, forest fires threaten ecosystems, society, and the [...] Read more.

Facing the severe global wildfire challenge and the need for advanced prediction, this study analysed the evolving research in forest fire prediction using an LDA-BERT similarity model. Due to climate change, human activities, and natural factors, forest fires threaten ecosystems, society, and the climate system. The vast existing literature on forest fire prediction makes it challenging to identify research themes manually. The proposed LDA-BERT model combines LDA and BERT. LDA was used for topic mining, determining the optimal number of topics by calculating the semantic consistency. BERT was employed in word vector training, using topic word probabilities as weights. The cosine similarity algorithm and normalisation were used to measure the topic similarity. Through empirical research on 13,552 publications from 1980–2023 retrieved from the Web of Science database, several key themes were identified, such as “wildfire risk management”, “vegetation and habitat changes”, and “climate change and forests”. Research trends show a shift from macro-level to micro-level studies, with modern technologies becoming a focus. Multidimensional scaling revealed a hierarchical theme distribution, with themes closely related to forest fires being dominant. This research offers valuable insights for the scientific community and policymakers, facilitating understanding these changes and contributing to wildfire mitigation. However, it has limitations like subjectivity in theme-representative word selection and needs further improvement in threshold setting and model performance evaluation. Future research can optimise these aspects and integrate emerging technologies to enhance forest fire prediction research. Full article

(This article belongs to the Special Issue Forest Fires Prediction and Detection—2nd Edition)

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23 pages, 2942 KiB

Open AccessArticle

Modeling of Forest Fire Risk Areas of Amazonas Department, Peru: Comparative Evaluation of Three Machine Learning Methods

by Alex J. Vergara, Sivmny V. Valqui-Reina, Dennis Cieza-Tarrillo, Ysabela Gómez-Santillán, Sandy Chapa-Gonza, Candy Lisbeth Ocaña-Zúñiga, Erick A. Auquiñivin-Silva, Ilse S. Cayo-Colca and Alexandre Rosa dos Santos

Forests 2025, 16(2), 273; https://doi.org/10.3390/f16020273 - 5 Feb 2025

Viewed by 1534

Abstract

Forest fires are the result of poor land management and climate change. Depending on the type of the affected eco-system, they can cause significant biodiversity losses. This study was conducted in the Amazonas department in Peru. Binary data obtained from the MODIS satellite [...] Read more.

Forest fires are the result of poor land management and climate change. Depending on the type of the affected eco-system, they can cause significant biodiversity losses. This study was conducted in the Amazonas department in Peru. Binary data obtained from the MODIS satellite on the occurrence of fires between 2010 and 2022 were used to build the risk models. To avoid multicollinearity, 12 variables that trigger fires were selected (Pearson ≤ 0.90) and grouped into four factors: (i) topographic, (ii) social, (iii) climatic, and (iv) biological. The program Rstudio and three types of machine learning were applied: MaxENT, Support Vector Machine (SVM), and Random Forest (RF). The results show that the RF model has the highest accuracy (AUC = 0.91), followed by MaxENT (AUC = 0.87) and SVM (AUC = 0.84). In the fire risk map elaborated with the RF model, 38.8% of the Amazonas region possesses a very low risk of fire occurrence, and 21.8% represents very high-risk level zones. This research will allow decision-makers to improve forest management in the Amazon region and to prioritize prospective management strategies such as the installation of water reservoirs in areas with a very high-risk level zone. In addition, it can support awareness-raising actions among inhabitants in the areas at greatest risk so that they will be prepared to mitigate and control risk and generate solutions in the event of forest fires occurring under different scenarios. Full article

(This article belongs to the Special Issue Forest Fires Prediction and Detection—2nd Edition)

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29 pages, 8852 KiB

Open AccessArticle

Assessment of Forest Fire Severity for a Management Conceptual Model: Case Study in Vilcabamba, Ecuador

by Fernando González, Fernando Morante-Carballo, Aníbal González, Lady Bravo-Montero, César Benavidez-Silva and Fantina Tedim

Forests 2024, 15(12), 2210; https://doi.org/10.3390/f15122210 - 16 Dec 2024

Cited by 3 | Viewed by 1566

Abstract

Wildfires are affecting natural ecosystems worldwide, causing economic and human losses and exacerbated by climate change. Models of fire severity and fire susceptibility are crucial tools for fire monitoring. This case study analyses a fire event on 3 September 2019 in Vilcabamba parish, [...] Read more.

Wildfires are affecting natural ecosystems worldwide, causing economic and human losses and exacerbated by climate change. Models of fire severity and fire susceptibility are crucial tools for fire monitoring. This case study analyses a fire event on 3 September 2019 in Vilcabamba parish, Loja province, Ecuador. This article aims to assess the severity and susceptibility of a fire through spectral indices and multi-criteria methods for establishing a fire action plan proposal. The methodology comprises the following: (i) the acquisition of Sentinel-2A products for the calculation of spectral indices; (ii) a fire severity model using differentiated indices (dNBR and dNDVI) and a fire susceptibility model using the Analytic Hierarchy Process (AHP) method; (iii) model validation using Logistic Regression (LR) and Non-metric Multidimensional Scaling (NMDS) algorithms; (iv) the proposal of an action plan for fire management. The Normalised Burn Ratio (NBR) index revealed that 10.98% of the fire perimeter has burned areas with moderate-high severity in post-fire scenes (2019) and decreased to 0.01% for post-fire scenes in 2021. The Normalised Difference Vegetation Index (NDVI) identified 67.28% of the fire perimeter with null photosynthetic activity in the post-fire scene (2019) and 5.88% in the post-fire scene (2021). The Normalised Difference Moisture Index (NDMI) applied in the pre-fire scene identified that 52.62% has low and dry vegetation (northeast), and 8.27% has high vegetation cover (southwest). The dNDVI identified 10.11% of unburned areas and 7.91% using the dNBR. The fire susceptibility model identified 11.44% of the fire perimeter with null fire susceptibility. These results evidence the vegetation recovery after two years of the fire event. The models demonstrated excellent performance for fire severity models and were a good fit for the AHP model. We used the Root Mean Square Error (RMSE) and area under the curve (AUC); dNBR and dNDVI have an RMSE of 0.006, and the AHP model has an RMSE of 0.032. The AUC = 1.0 for fire severity models and AUC = 0.6 for fire susceptibility. This study represents a holistic approach by combining Google Earth Engine (GEE), Geographic Information System (GIS), and remote sensing tools for proposing a fire action plan that supports decision making. This study provides escape routes that considered the most significant fire triggers, the AHP, and fire severity approaches for monitoring wildfires in Andean regions. Full article

(This article belongs to the Special Issue Forest Fires Prediction and Detection—2nd Edition)

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26 pages, 16930 KiB

Open AccessArticle

A Forest Fire Prediction Model Based on Meteorological Factors and the Multi-Model Ensemble Method

by Seungcheol Choi, Minwoo Son, Changgyun Kim and Byungsik Kim

Forests 2024, 15(11), 1981; https://doi.org/10.3390/f15111981 - 9 Nov 2024

Cited by 1 | Viewed by 1686

Abstract

More than half of South Korea’s land area is covered by forests, which significantly increases the potential for extensive damage in the event of a forest fire. The majority of forest fires in South Korea are caused by humans. Over the past decade, [...] Read more.

More than half of South Korea’s land area is covered by forests, which significantly increases the potential for extensive damage in the event of a forest fire. The majority of forest fires in South Korea are caused by humans. Over the past decade, more than half of these types of fires occurred during the spring season. Although human activities are the primary cause of forest fires, the fact that they are concentrated in the spring underscores the strong association between forest fires and meteorological factors. When meteorological conditions favor the occurrence of forest fires, certain triggering factors can lead to their ignition more easily. The purpose of this study is to analyze the meteorological factors influencing forest fires and to develop a machine learning-based prediction model for forest fire occurrence, focusing on meteorological data. The study focuses on four regions within Gangwon province in South Korea, which have experienced substantial damage from forest fires. To construct the model, historical meteorological data were collected, surrogate variables were calculated, and a variable selection process was applied to identify relevant meteorological factors. Five machine learning models were then used to predict forest fire occurrence and ensemble techniques were employed to enhance the model’s performance. The performance of the developed forest fire prediction model was evaluated using evaluation metrics. The results indicate that the ensemble model outperformed the individual models, with a higher F1-score and a notable reduction in false positives compared to the individual models. This suggests that the model developed in this study, when combined with meteorological forecast data, can potentially predict forest fire occurrence and provide insights into the expected severity of fires. This information could support decision-making for forest fire management, aiding in the development of more effective fire response plans. Full article

(This article belongs to the Special Issue Forest Fires Prediction and Detection—2nd Edition)

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24 pages, 19262 KiB

Open AccessArticle

Study on the Driving Factors of the Spatiotemporal Pattern in Forest Lightning Fires and 3D Fire Simulation Based on Cellular Automata

by Maolin Li, Yingda Wu, Yilin Liu, Yu Zhang and Qiang Yu

Forests 2024, 15(11), 1857; https://doi.org/10.3390/f15111857 - 23 Oct 2024

Viewed by 1241

Abstract

Lightning-induced forest fires frequently inflict substantial damage on forest ecosystems, with the Daxing’anling region in northern China recognized as a high-incidence region for such phenomena. To elucidate the occurrence patterns of forest fires caused by lightning and to prevent such fires, this study [...] Read more.

Lightning-induced forest fires frequently inflict substantial damage on forest ecosystems, with the Daxing’anling region in northern China recognized as a high-incidence region for such phenomena. To elucidate the occurrence patterns of forest fires caused by lightning and to prevent such fires, this study employs a multifaceted approach, including statistical analysis, kernel density estimation, and spatial autocorrelation analysis, to conduct a comprehensive examination of the spatiotemporal distribution patterns of lightning-induced forest fires in the Greater Khingan Mountains region from 2016–2020. Additionally, the geographical detector method is utilized to assess the explanatory power of three main factors: climate, topography, and fuel characteristics associated with these fires, encompassing both univariate and interaction detections. Furthermore, a mixed-methods approach is adopted, integrating the Zhengfei Wang model with a three-dimensional cellular automaton to simulate the spread of lightning-induced forest fire events, which is further validated through rigorous quantitative verification. The principal findings are as follows: (1) Spatiotemporal Distribution of Lightning-Induced Forest Fires: Interannual variability reveals pronounced fluctuations in the incidence of lightning-induced forest fires. The monthly concentration of incidents is most significant in May, July, and August, demonstrating an upward trajectory. In terms of temporal distribution, fire occurrences are predominantly concentrated between 1:00 PM and 5:00 PM, conforming to a normal distribution pattern. Spatially, higher incidences of fires are observed in the western and northwestern regions, while the eastern and southeastern areas exhibit reduced rates. At the township level, significant spatial autocorrelation indicates that Xing’an Town represents a prominent hotspot (p = 0.001), whereas Oupu Town is identified as a significant cold spot (p = 0.05). (2) Determinants of the Spatiotemporal Distribution of Lightning-Induced Forest Fires: The spatiotemporal distribution of lightning-induced forest fires is influenced by a multitude of factors. Univariate analysis reveals that the explanatory power of these factors varies significantly, with climatic factors exerting the most substantial influence, followed by topographic and fuel characteristics. Interaction factor analysis indicates that the interactive effects of climatic variables are notably more pronounced than those of fuel and topographical factors. (3) Three-Dimensional Cellular Automaton Fire Simulation Based on the Zhengfei Wang Model: This investigation integrates the fire spread principles from the Zhengfei Wang model into a three-dimensional cellular automaton framework to simulate the dynamic behavior of lightning-induced forest fires. Through quantitative validation against empirical fire events, the model demonstrates an accuracy rate of 83.54% in forecasting the affected fire zones. Full article

(This article belongs to the Special Issue Forest Fires Prediction and Detection—2nd Edition)

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25 pages, 12517 KiB

Open AccessArticle

Low Complexity Forest Fire Detection Based on Improved YOLOv8 Network

by Lin Lei, Ruifeng Duan, Feng Yang and Longhang Xu

Forests 2024, 15(9), 1652; https://doi.org/10.3390/f15091652 - 19 Sep 2024

Cited by 1 | Viewed by 1511

Abstract

Forest fires pose a significant threat to ecosystems and communities. This study introduces innovative enhancements to the YOLOv8n object detection algorithm, significantly improving its efficiency and accuracy for real-time forest fire monitoring. By employing Depthwise Separable Convolution and Ghost Convolution, the model’s computational [...] Read more.

Forest fires pose a significant threat to ecosystems and communities. This study introduces innovative enhancements to the YOLOv8n object detection algorithm, significantly improving its efficiency and accuracy for real-time forest fire monitoring. By employing Depthwise Separable Convolution and Ghost Convolution, the model’s computational complexity is significantly reduced, making it suitable for deployment on resource-constrained edge devices. Additionally, Dynamic UpSampling and Coordinate Attention mechanisms enhance the model’s ability to capture multi-scale features and focus on relevant regions, improving detection accuracy for small-scale fires. The Distance-Intersection over Union loss function further optimizes the model’s training process, leading to more accurate bounding box predictions. Experimental results on a comprehensive dataset demonstrate that our proposed model achieves a 41% reduction in parameters and a 54% reduction in GFLOPs, while maintaining a high mean Average Precision (mAP) of 99.0% at an Intersection over Union (IoU) threshold of 0.5. The proposed model offers a promising solution for real-time forest fire monitoring, enabling a timely detection of, and response to, wildfires. Full article

(This article belongs to the Special Issue Forest Fires Prediction and Detection—2nd Edition)

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29 pages, 9137 KiB

Open AccessArticle

A Multi-Scale Deep Learning Algorithm for Enhanced Forest Fire Danger Prediction Using Remote Sensing Images

by Jixiang Yang, Huiping Jiang, Sen Wang and Xuan Ma

Forests 2024, 15(9), 1581; https://doi.org/10.3390/f15091581 - 9 Sep 2024

Cited by 3 | Viewed by 1518

Abstract

Forest fire danger prediction models often face challenges due to spatial and temporal limitations, as well as a lack of universality caused by regional inconsistencies in fire danger features. To address these issues, we propose a novel algorithm, squeeze-excitation spatial multi-scale transformer learning [...] Read more.

Forest fire danger prediction models often face challenges due to spatial and temporal limitations, as well as a lack of universality caused by regional inconsistencies in fire danger features. To address these issues, we propose a novel algorithm, squeeze-excitation spatial multi-scale transformer learning (SESMTML), which is designed to extract multi-scale fire danger features from remote sensing images. SESMTML includes several key modules: the multi-scale deep feature extraction module (MSDFEM) captures global visual and multi-scale convolutional features, the multi-scale fire danger perception module (MFDPM) explores contextual relationships, the multi-scale information aggregation module (MIAM) aggregates correlations of multi-level fire danger features, and the fire danger level fusion module (FDLFM) integrates the contributions of global and multi-level features for predicting forest fire danger. Experimental results demonstrate the model’s significant superiority, achieving an accuracy of 83.18%, representing a 22.58% improvement over previous models and outperforming many widely used deep learning methods. Additionally, a detailed forest fire danger prediction map was generated using a test study area at the junction of the Miyun and Pinggu districts in Beijing, further confirming the model’s effectiveness. SESMTML shows strong potential for practical application in forest fire danger prediction and offers new insights for future research utilizing remote sensing images. Full article

(This article belongs to the Special Issue Forest Fires Prediction and Detection—2nd Edition)

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20 pages, 18626 KiB

Open AccessArticle

Forest Wildfire Risk Assessment of Anning River Valley in Sichuan Province Based on Driving Factors with Multi-Source Data

by Cuicui Ji, Hengcong Yang, Xiaosong Li, Xiangjun Pei, Min Li, Hao Yuan, Yiming Cao, Boyu Chen, Shiqian Qu, Na Zhang, Li Chun, Lingyi Shi and Fuyang Sun

Forests 2024, 15(9), 1523; https://doi.org/10.3390/f15091523 - 29 Aug 2024

Cited by 2 | Viewed by 1257

Abstract

Forest fires can lead to a decline in ecosystem functions, such as biodiversity, soil quality, and carbon cycling, causing economic losses and health threats to human societies. Therefore, it is imperative to map forest-fire risk to mitigate the likelihood of forest-fire occurrence. In [...] Read more.

Forest fires can lead to a decline in ecosystem functions, such as biodiversity, soil quality, and carbon cycling, causing economic losses and health threats to human societies. Therefore, it is imperative to map forest-fire risk to mitigate the likelihood of forest-fire occurrence. In this study, we utilized the hierarchical analysis process (AHP), a comprehensive weighting method (CWM), and random forest to map the forest-fire risk in the Anning River Valley of Sichuan Province. We selected non-photosynthetic vegetation (NPV), photosynthetic vegetation (PV), normalized difference vegetation index (NDVI), plant species, land use, soil type, temperature, humidity, rainfall, wind speed, elevation, slope, aspect, distance to road, and distance to residential as forest-fire predisposing factors. We derived the following conclusions. (1) Overlaying historical fire points with mapped forest-fire risk revealed an accuracy that exceeded 86%, indicating the reliability of the results. (2) Forest fires in the Anning River Valley primarily occur in February, March, and April, typically months characterized by very low rainfall and dry conditions. (3) Areas with high and medium forest-fire risk were mainly distributed in Dechang and Xide counties, while low-risk areas were most prevalent in Xichang city and Mianning country. (4) Rainfall, temperature, elevation, and NPV emerged as the main influencing factors, exerting a dominant role in the occurrence of forest fires. Specifically, a higher NPV coverage correlates with an increased risk of forest fire. In conclusion, this study represents a novel approach by incorporating NPV and PV as key factors in triggering forest fires. By mapping forest-fire risk, we have provided a robust scientific foundation and decision-making support for effective fire management strategies. This research significantly contributes to advancing ecological civilization and fostering sustainable development. Full article

(This article belongs to the Special Issue Forest Fires Prediction and Detection—2nd Edition)

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16 pages, 1772 KiB

Open AccessArticle

Forest Wildfire Detection from Images Captured by Drones Using Window Transformer without Shift

by Wei Yuan, Lei Qiao and Liu Tang

Forests 2024, 15(8), 1337; https://doi.org/10.3390/f15081337 - 1 Aug 2024

Cited by 1 | Viewed by 1541

Abstract

Cameras, especially those carried by drones, are the main tools used to detect wildfires in forests because cameras have much longer detection ranges than smoke sensors. Currently, deep learning is main method used for fire detection in images, and Transformer is the best [...] Read more.

Cameras, especially those carried by drones, are the main tools used to detect wildfires in forests because cameras have much longer detection ranges than smoke sensors. Currently, deep learning is main method used for fire detection in images, and Transformer is the best algorithm. Swin Transformer restricts the computation to a fixed-size window, which reduces the amount of computation to a certain extent, but to allow pixel communication between windows, it adopts a shift window approach. Therefore, Swin Transformer requires multiple shifts to extend the receptive field to the entire image. This somewhat limits the network’s ability to capture global features at different scales. To solve this problem, instead of using the shift window method to allow pixel communication between windows, we downsample the feature map to the window size after capturing global features through a single Transformer, and we upsample the feature map to the original size and add it to the previous feature map. This way, there is no need for multiple layers of stacked window Transformers; global features are captured after each window Transformer operation. We conducted experiments on the Corsican fire dataset captured by ground cameras and on the Flame dataset captured by drone cameras. The results show that our algorithm performs the best. On the Corsican fire dataset, the mIoU, F1 score, and OA reached 79.4%, 76.6%, and 96.9%, respectively. On the Flame dataset, the mIoU, F1 score, and OA reached 84.4%, 81.6%, and 99.9%, respectively. Full article

(This article belongs to the Special Issue Forest Fires Prediction and Detection—2nd Edition)

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19 pages, 4649 KiB

Open AccessArticle

SIMCB-Yolo: An Efficient Multi-Scale Network for Detecting Forest Fire Smoke

by Wanhong Yang, Zhenlin Yang, Meiyun Wu, Gui Zhang, Yinfang Zhu and Yurong Sun

Forests 2024, 15(7), 1137; https://doi.org/10.3390/f15071137 - 29 Jun 2024

Cited by 8 | Viewed by 1684

Abstract

Forest fire monitoring plays a crucial role in preventing and mitigating forest disasters. Early detection of forest fire smoke is essential for a timely response to forest fire emergencies. The key to effective forest fire monitoring lies in accounting for the various levels [...] Read more.

Forest fire monitoring plays a crucial role in preventing and mitigating forest disasters. Early detection of forest fire smoke is essential for a timely response to forest fire emergencies. The key to effective forest fire monitoring lies in accounting for the various levels of forest fire smoke targets in the monitoring images, enhancing the model’s anti-interference capabilities against mountain clouds and fog, and reducing false positives and missed detections. In this paper, we propose an improved multi-level forest fire smoke detection model based on You Only Look Once v5s (Yolov5s) called SIMCB-Yolo. This model aims to achieve high-precision detection of forest fire smoke at various levels. First, to address the issue of low precision in detecting small target smoke, a Swin transformer small target monitoring head is added to the neck of Yolov5s, enhancing the precision of small target smoke detection. Then, to address the issue of missed detections due to the decline in conventional target smoke detection accuracy after improving small target smoke detection accuracy, we introduced a cross stage partial network bottleneck with three convolutional layers (C3) and a channel block sequence (CBS) into the trunk. These additions help extract more surface features and enhance the detection accuracy of conventional target smoke. Finally, the SimAM attention mechanism is introduced to address the issue of complex background interference in forest fire smoke detection, further reducing false positives and missed detections. Experimental results demonstrate that, compared to the Yolov5s model, the SIMCB-Yolo model achieves an average recognition accuracy (mAP50) of 85.6%, an increase of 4.5%. Additionally, the mAP50-95 is 63.6%, an improvement of 6.9%, indicating good detection accuracy. The performance of the SIMCB-Yolo model on the self-built forest fire smoke dataset is also significantly better than that of current mainstream models, demonstrating high practical value. Full article

(This article belongs to the Special Issue Forest Fires Prediction and Detection—2nd Edition)

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19 pages, 6939 KiB

Open AccessArticle

A Forest Fire Prediction Method for Lightning Stroke Based on Remote Sensing Data

by Zhejia Zhang, Ye Tian, Guangyu Wang, Change Zheng and Fengjun Zhao

Forests 2024, 15(4), 647; https://doi.org/10.3390/f15040647 - 2 Apr 2024

Cited by 5 | Viewed by 2225 | Correction

Abstract

Forest fires ignited by lightning accounted for 68.28% of all forest fires in the Greater Khingan Mountains (GKM) region of northeast China. Forecasting the incidence of lightning-triggered forest fires in the region is imperative for mitigating deforestation, preserving biodiversity, and safeguarding distinctive natural [...] Read more.

Forest fires ignited by lightning accounted for 68.28% of all forest fires in the Greater Khingan Mountains (GKM) region of northeast China. Forecasting the incidence of lightning-triggered forest fires in the region is imperative for mitigating deforestation, preserving biodiversity, and safeguarding distinctive natural habitats and resources. Lightning monitoring data and vegetation moisture content have emerged as pivotal factors among the various influences on lightning-induced fires. This study employed innovative satellite remote sensing technology to swiftly acquire vegetation moisture content data across extensive forested regions. Firstly, the most suitable method to identify the lightning strikes that resulted in fires and two crucial lightning parameters correlated with fire occurrence are confirmed. Secondly, a logistic regression method is proposed for predicting the likelihood of fires triggered by lightning strikes. Finally, the method underwent verification using five years of fire data from the GKM area, resulting in an AUC value of 0.849 and identifying the primary factors contributing to lightning-induced fires in the region. Full article

(This article belongs to the Special Issue Forest Fires Prediction and Detection—2nd Edition)

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19 pages, 14298 KiB

Open AccessArticle

The Consequences of Climate Change in the Brazilian Western Amazon: A New Proposal for a Fire Risk Model in Rio Branco, Acre

by Kennedy da Silva Melo, Rafael Coll Delgado, Marcos Gervasio Pereira and Givanildo Pereira Ortega

Forests 2024, 15(1), 211; https://doi.org/10.3390/f15010211 - 21 Jan 2024

Cited by 4 | Viewed by 2513

Abstract

The objective of this study was to verify the link between climate change, changes in land use, and the increasing frequency of forest fires in the state of Acre. Recognizing the importance of an accurate assessment of fire risk, we also proposed a [...] Read more.

The objective of this study was to verify the link between climate change, changes in land use, and the increasing frequency of forest fires in the state of Acre. Recognizing the importance of an accurate assessment of fire risk, we also proposed a new fire risk index for the capital Rio Branco, using meteorological data. Validated reanalysis data from 1961 to 2020 extracted for Rio Branco and different land uses were used. Data on fire foci, deforestation, and agricultural crops were also obtained. The new model was based on the Fire Risk Atlantic Forest (FIAF) Index, developed for the Atlantic Forest biome, and was subjected to multiple regression analysis. To validate the new model, projections were calculated using different scenarios from the Intergovernmental Panel on Climate Change (IPCC). The new model, entitled Rio Branco Fire Risk (FIRERBR), revealed an increase in fire risk, especially associated with agriculture, in future scenarios (SSP2-4.5 and SSP5-8.5) from 2023 onward. Rainfall and relative air humidity also showed a reduction in projections, indicating a higher degree of fire danger for the region. Full article

(This article belongs to the Special Issue Forest Fires Prediction and Detection—2nd Edition)

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1 pages, 597 KiB

Open AccessCorrection

Correction: Zhang et al. A Forest Fire Prediction Method for Lightning Stroke Based on Remote Sensing Data. Forests 2024, 15, 647

by Zhejia Zhang, Ye Tian, Guangyu Wang, Change Zheng and Fengjun Zhao

Forests 2024, 15(5), 825; https://doi.org/10.3390/f15050825 - 8 May 2024

Viewed by 973

Abstract

There was an error in the original publication [...] Full article

(This article belongs to the Special Issue Forest Fires Prediction and Detection—2nd Edition)

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