R. Guardo, G. Bilotta, G. Ganci, Francesco Zuccarello, D. Andronico, A. Cappello
We hereby present VolcFire, a new cellular automaton model for fire propagation aimed at the creation of fire hazard maps for fires of volcanic origin. The new model relies on satellite-derived input data for the topography, land-use, fuel, and humidity information, and produces probabilistic maps of fire propagation simulating fire spread. The model contains several simplifications compared to the current state-of-the-art, limiting its usability to plan fire-fighting interventions during an event in favour of a reduced computational load. The accuracy and reliability of the model are also discussed by presenting its ability to reproduce two recent fires on Stromboli island, with good spatial fit (Brier score of 0.146±0.002 for the 3 July 2019 volcanic fire, and of 0.073±0.001 for the 25 May 2022 anthropogenic fire) and less than 1.5% variation across multiple simulations for the same event.
{"title":"Modeling Fire Hazards Induced by Volcanic Eruptions: The Case of Stromboli (Italy)","authors":"R. Guardo, G. Bilotta, G. Ganci, Francesco Zuccarello, D. Andronico, A. Cappello","doi":"10.3390/fire7030070","DOIUrl":"https://doi.org/10.3390/fire7030070","url":null,"abstract":"We hereby present VolcFire, a new cellular automaton model for fire propagation aimed at the creation of fire hazard maps for fires of volcanic origin. The new model relies on satellite-derived input data for the topography, land-use, fuel, and humidity information, and produces probabilistic maps of fire propagation simulating fire spread. The model contains several simplifications compared to the current state-of-the-art, limiting its usability to plan fire-fighting interventions during an event in favour of a reduced computational load. The accuracy and reliability of the model are also discussed by presenting its ability to reproduce two recent fires on Stromboli island, with good spatial fit (Brier score of 0.146±0.002 for the 3 July 2019 volcanic fire, and of 0.073±0.001 for the 25 May 2022 anthropogenic fire) and less than 1.5% variation across multiple simulations for the same event.","PeriodicalId":12279,"journal":{"name":"Fire","volume":"23 27","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140430009","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Z. Rehman, Laila Khan, Lee Hwain, Yun Chiho, B. Koo
In this study, process control factors such as dipping time, heat treatment time and curing conditions were optimized to prepare N-Si network sol–gel-based coatings on a cotton fabric. The dipping time was varied from 14 h to 30 min, the heat treatment time at ~90 °C was varied between no heating conditions to 15 h and the curing was performed at 165 °C. The microstructure of the coating was analyzed using low electron scanning microscopy (LV-SEM), while a compositional study of the coated substrate was carried out using FTIR and EDS techniques. From the thermal and combustion analysis of the coated samples using thermogravimetric and vertical flame test techniques, significant resistance to the degradation process was observed, particularly in the initial stages, in addition to the highest char residue for DI-0.5 h-15~32.93%. Similarly, for DI–5 h–RT, the peak degradation temperature was around ~372 °C, accompanied by a notable char residue of approximately 31.12%. The flame spread and burning rate profile further supported the findings; DI-0.5 h-15 and DI-5 h-RT had the lowest flame spread.
{"title":"Si-N Matrix as an Effective Fire Retardant Source for Cotton Fabric, Prepared through Sol–Gel Process","authors":"Z. Rehman, Laila Khan, Lee Hwain, Yun Chiho, B. Koo","doi":"10.3390/fire7030069","DOIUrl":"https://doi.org/10.3390/fire7030069","url":null,"abstract":"In this study, process control factors such as dipping time, heat treatment time and curing conditions were optimized to prepare N-Si network sol–gel-based coatings on a cotton fabric. The dipping time was varied from 14 h to 30 min, the heat treatment time at ~90 °C was varied between no heating conditions to 15 h and the curing was performed at 165 °C. The microstructure of the coating was analyzed using low electron scanning microscopy (LV-SEM), while a compositional study of the coated substrate was carried out using FTIR and EDS techniques. From the thermal and combustion analysis of the coated samples using thermogravimetric and vertical flame test techniques, significant resistance to the degradation process was observed, particularly in the initial stages, in addition to the highest char residue for DI-0.5 h-15~32.93%. Similarly, for DI–5 h–RT, the peak degradation temperature was around ~372 °C, accompanied by a notable char residue of approximately 31.12%. The flame spread and burning rate profile further supported the findings; DI-0.5 h-15 and DI-5 h-RT had the lowest flame spread.","PeriodicalId":12279,"journal":{"name":"Fire","volume":"16 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140430746","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Smoke, a byproduct of forest and grassland combustion, holds the key to precise and rapid identification—an essential breakthrough in early wildfire detection, critical for forest and grassland fire monitoring and early warning. To address the scarcity of middle–high-resolution satellite datasets for forest and grassland fire smoke, and the associated challenges in identifying smoke, the CAF_SmokeSEG dataset was constructed for smoke segmentation. The dataset was created based on GF-6 WFV smoke images of forest and grassland fire globally from 2019 to 2022. Then, an optimized segmentation algorithm, GFUNet, was proposed based on the UNet framework. Through comprehensive analysis, including method comparison, module ablation, band combination, and data transferability experiments, this study revealed that GF-6 WFV data effectively represent information related to forest and grassland fire smoke. The CAF_SmokeSEG dataset was found to be valuable for pixel-level smoke segmentation tasks. GFUNet exhibited robust smoke feature learning capability and segmentation stability. It demonstrated clear smoke area delineation, significantly outperforming UNet and other optimized methods, with an F1-Score and Jaccard coefficient of 85.50% and 75.76%, respectively. Additionally, augmenting the common spectral bands with additional bands improved the smoke segmentation accuracy, particularly shorter-wavelength bands like the coastal blue band, outperforming longer-wavelength bands such as the red-edge band. GFUNet was trained on the combination of red, green, blue, and NIR bands from common multispectral sensors. The method showed promising transferability and enabled the segmentation of smoke areas in GF-1 WFV and HJ-2A/B CCD images with comparable spatial resolution and similar bands. The integration of high spatiotemporal multispectral data like GF-6 WFV with the advanced information extraction capabilities of deep learning algorithms effectively meets the practical needs for pixel-level identification of smoke areas in forest and grassland fire scenarios. It shows promise in improving and optimizing existing forest and grassland fire monitoring systems, providing valuable decision-making support for fire monitoring and early warning systems.
{"title":"An Optimized Smoke Segmentation Method for Forest and Grassland Fire Based on the UNet Framework","authors":"Xinyu Hu, Feng Jiang, Xianlin Qin, Shuisheng Huang, Xinyuan Yang, Fangxin Meng","doi":"10.3390/fire7030068","DOIUrl":"https://doi.org/10.3390/fire7030068","url":null,"abstract":"Smoke, a byproduct of forest and grassland combustion, holds the key to precise and rapid identification—an essential breakthrough in early wildfire detection, critical for forest and grassland fire monitoring and early warning. To address the scarcity of middle–high-resolution satellite datasets for forest and grassland fire smoke, and the associated challenges in identifying smoke, the CAF_SmokeSEG dataset was constructed for smoke segmentation. The dataset was created based on GF-6 WFV smoke images of forest and grassland fire globally from 2019 to 2022. Then, an optimized segmentation algorithm, GFUNet, was proposed based on the UNet framework. Through comprehensive analysis, including method comparison, module ablation, band combination, and data transferability experiments, this study revealed that GF-6 WFV data effectively represent information related to forest and grassland fire smoke. The CAF_SmokeSEG dataset was found to be valuable for pixel-level smoke segmentation tasks. GFUNet exhibited robust smoke feature learning capability and segmentation stability. It demonstrated clear smoke area delineation, significantly outperforming UNet and other optimized methods, with an F1-Score and Jaccard coefficient of 85.50% and 75.76%, respectively. Additionally, augmenting the common spectral bands with additional bands improved the smoke segmentation accuracy, particularly shorter-wavelength bands like the coastal blue band, outperforming longer-wavelength bands such as the red-edge band. GFUNet was trained on the combination of red, green, blue, and NIR bands from common multispectral sensors. The method showed promising transferability and enabled the segmentation of smoke areas in GF-1 WFV and HJ-2A/B CCD images with comparable spatial resolution and similar bands. The integration of high spatiotemporal multispectral data like GF-6 WFV with the advanced information extraction capabilities of deep learning algorithms effectively meets the practical needs for pixel-level identification of smoke areas in forest and grassland fire scenarios. It shows promise in improving and optimizing existing forest and grassland fire monitoring systems, providing valuable decision-making support for fire monitoring and early warning systems.","PeriodicalId":12279,"journal":{"name":"Fire","volume":"15 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140431153","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A numerical investigation has been conducted to analyse the effect of wind on the vertical spread of fire through a front opening in a building’s external walls. The study utilises a building geometry established from previous experimental work conducted by the National Research Council of Canada (NRCC). A horizontal projection or a vertical spandrel is introduced above the opening of the compartment of fire origin. The purpose of the projection or spandrel is to inhibit the vertical spread of the fire, following the relevant requirements in the Australian National Construction Code (NCC). A computational fluid dynamics (CFD) package for fire-driven fluid flow, namely the Fire Dynamics Simulator (FDS), is employed to simulate the fire behaviour. The FDS model is validated against the NRCC’s experimental results, and a good agreement is achieved. Winds from three horizontal directions (front wind is normal to the opening, side wind is parallel to the opening, and back wind is from behind the building) have been investigated, with speeds ranging up to 10 m/s for each wind direction. Front wind speeds below 1 m/s are found to slightly enhance the vertical spread of the fire, while speeds exceeding 1 m/s are inclined to promote horizontal spread. The impact of side wind on the vertical fire spread was also found to vary with wind speed. The increase in the speed of back wind influences flame buoyancy, resulting in an augmented vertical fire spread. Furthermore, the numerical results reveal that a vertical spandrel of 1100 mm height is less effective in preventing vertical fire spread through openings, compared to a 1100 mm deep horizontal projection. The study suggests that the fire safety design in reducing the hazard of vertical fire spread through openings in buildings’ external walls could be further improved if the effect of wind is considered.
{"title":"Wind Effect on External Fire Spread through Openings under the Protection of Horizontal Projections or Vertical Spandrels—A Numerical Study","authors":"Yining Tang, Zhaofeng Tian, Xiao Chen, Brigitta Suendermann, Grant Gamble, Zefeng Huang","doi":"10.3390/fire7030066","DOIUrl":"https://doi.org/10.3390/fire7030066","url":null,"abstract":"A numerical investigation has been conducted to analyse the effect of wind on the vertical spread of fire through a front opening in a building’s external walls. The study utilises a building geometry established from previous experimental work conducted by the National Research Council of Canada (NRCC). A horizontal projection or a vertical spandrel is introduced above the opening of the compartment of fire origin. The purpose of the projection or spandrel is to inhibit the vertical spread of the fire, following the relevant requirements in the Australian National Construction Code (NCC). A computational fluid dynamics (CFD) package for fire-driven fluid flow, namely the Fire Dynamics Simulator (FDS), is employed to simulate the fire behaviour. The FDS model is validated against the NRCC’s experimental results, and a good agreement is achieved. Winds from three horizontal directions (front wind is normal to the opening, side wind is parallel to the opening, and back wind is from behind the building) have been investigated, with speeds ranging up to 10 m/s for each wind direction. Front wind speeds below 1 m/s are found to slightly enhance the vertical spread of the fire, while speeds exceeding 1 m/s are inclined to promote horizontal spread. The impact of side wind on the vertical fire spread was also found to vary with wind speed. The increase in the speed of back wind influences flame buoyancy, resulting in an augmented vertical fire spread. Furthermore, the numerical results reveal that a vertical spandrel of 1100 mm height is less effective in preventing vertical fire spread through openings, compared to a 1100 mm deep horizontal projection. The study suggests that the fire safety design in reducing the hazard of vertical fire spread through openings in buildings’ external walls could be further improved if the effect of wind is considered.","PeriodicalId":12279,"journal":{"name":"Fire","volume":"20 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140433830","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Poliana Domingos Ferro, G. Mataveli, Jeferson de Souza Arcanjo, D. J. Dutra, Thaís Pereira de Medeiros, Y. Shimabukuro, Ana Carolina Moreira Pessôa, G. de Oliveira, L. O. Anderson
Fires are one of the main sources of disturbance in fire-sensitive ecosystems such as the Amazon. Any attempt to characterize their impacts and establish actions aimed at combating these events presupposes the correct identification of the affected areas. However, accurate mapping of burned areas in humid tropical forest regions remains a challenging task. In this paper, we evaluate the performance of four operational BA products (MCD64A1, Fire_cci, GABAM and MapBiomas Fogo) on a regional scale in the southwestern Amazon and propose a new approach to BA mapping using fraction images extracted from data cubes of the Brazilian orbital sensors CBERS-4/WFI and CBERS-4A/WFI. The methodology for detecting burned areas consisted of applying the Linear Spectral Mixture Model to the images from the CBERS-4/WFI and CBERS-4A/WFI data cubes to generate shadow fraction images, which were then segmented and classified using the ISOSEG non-supervised algorithm. Regression and similarity analyses based on regular grid cells were carried out to compare the BA mappings. The results showed large discrepancies between the mappings in terms of total area burned, land use and land cover affected (forest and non-forest) and spatial location of the burned area. The global products MCD64A1, GABAM and Fire_cci tended to underestimate the area burned in the region, with Fire_cci underestimating BA by 88%, while the regional product MapBiomas Fogo was the closest to the reference, underestimating by only 7%. The burned area estimated by the method proposed in this work (337.5 km2) was 12% higher than the reference and showed a small difference in relation to the MapBiomas Fogo product (18% more BA). These differences can be explained by the different datasets and methods used to detect burned areas. The adoption of global products in regional studies can be critical in underestimating the total area burned in sensitive regions. Our study highlights the need to develop approaches aimed at improving the accuracy of current global products, and the development of regional burned area products may be more suitable for this purpose. Our proposed approach based on WFI data cubes has shown high potential for generating more accurate regional burned area maps, which can refine BA estimates in the Amazon.
火灾是亚马逊等对火敏感的生态系统的主要干扰源之一。要确定火灾影响的特征并制定旨在应对这些事件的行动，前提是正确识别受影响的区域。然而，在潮湿的热带森林地区准确绘制烧毁区域的地图仍然是一项具有挑战性的任务。在本文中，我们评估了亚马逊西南部区域范围内四种运行 BA 产品（MCD64A1、Fire_cci、GABAM 和 MapBiomas Fogo）的性能，并提出了一种使用从巴西轨道传感器 CBERS-4/WFI 和 CBERS-4A/WFI 数据立方体中提取的分数图像绘制 BA 地图的新方法。检测烧毁区域的方法包括对来自 CBERS-4/WFI 和 CBERS-4A/WFI 数据立方体的图像应用线性光谱混合模型生成阴影分数图像，然后使用 ISOSEG 非监督算法对其进行分割和分类。在常规网格单元的基础上进行回归和相似性分析，以比较 BA 映射。结果表明，在焚烧总面积、受影响的土地利用和土地覆盖（森林和非森林）以及焚烧区的空间位置方面，绘图之间存在很大差异。全球产品 MCD64A1、GABAM 和 Fire_cci 有低估该地区烧毁面积的趋势，其中 Fire_cci 低估了 BA 面积的 88%，而地区产品 MapBiomas Fogo 最接近参考值，仅低估了 7%。本研究提出的方法估算出的烧毁面积（337.5 平方公里）比参考值高出 12%，与 MapBiomas Fogo 产品相比差异较小（BA 高出 18%）。这些差异可以解释为用于检测烧毁区域的数据集和方法不同。在区域研究中采用全球产品可能会低估敏感地区的总烧毁面积。我们的研究强调了开发旨在提高当前全球产品准确性的方法的必要性，而开发区域烧毁面积产品可能更适合这一目的。我们提出的基于 WFI 数据立方体的方法在生成更准确的区域烧毁面积地图方面具有很大的潜力，可以完善亚马逊地区的 BA 估计值。
{"title":"Regional-Scale Assessment of Burn Scar Mapping in Southwestern Amazonia Using Burned Area Products and CBERS/WFI Data Cubes","authors":"Poliana Domingos Ferro, G. Mataveli, Jeferson de Souza Arcanjo, D. J. Dutra, Thaís Pereira de Medeiros, Y. Shimabukuro, Ana Carolina Moreira Pessôa, G. de Oliveira, L. O. Anderson","doi":"10.3390/fire7030067","DOIUrl":"https://doi.org/10.3390/fire7030067","url":null,"abstract":"Fires are one of the main sources of disturbance in fire-sensitive ecosystems such as the Amazon. Any attempt to characterize their impacts and establish actions aimed at combating these events presupposes the correct identification of the affected areas. However, accurate mapping of burned areas in humid tropical forest regions remains a challenging task. In this paper, we evaluate the performance of four operational BA products (MCD64A1, Fire_cci, GABAM and MapBiomas Fogo) on a regional scale in the southwestern Amazon and propose a new approach to BA mapping using fraction images extracted from data cubes of the Brazilian orbital sensors CBERS-4/WFI and CBERS-4A/WFI. The methodology for detecting burned areas consisted of applying the Linear Spectral Mixture Model to the images from the CBERS-4/WFI and CBERS-4A/WFI data cubes to generate shadow fraction images, which were then segmented and classified using the ISOSEG non-supervised algorithm. Regression and similarity analyses based on regular grid cells were carried out to compare the BA mappings. The results showed large discrepancies between the mappings in terms of total area burned, land use and land cover affected (forest and non-forest) and spatial location of the burned area. The global products MCD64A1, GABAM and Fire_cci tended to underestimate the area burned in the region, with Fire_cci underestimating BA by 88%, while the regional product MapBiomas Fogo was the closest to the reference, underestimating by only 7%. The burned area estimated by the method proposed in this work (337.5 km2) was 12% higher than the reference and showed a small difference in relation to the MapBiomas Fogo product (18% more BA). These differences can be explained by the different datasets and methods used to detect burned areas. The adoption of global products in regional studies can be critical in underestimating the total area burned in sensitive regions. Our study highlights the need to develop approaches aimed at improving the accuracy of current global products, and the development of regional burned area products may be more suitable for this purpose. Our proposed approach based on WFI data cubes has shown high potential for generating more accurate regional burned area maps, which can refine BA estimates in the Amazon.","PeriodicalId":12279,"journal":{"name":"Fire","volume":"41 23","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140432592","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
As a cigarette butt falls onto the forest surface fuel, it first smolders the fuel, then ignites into flames, and spreads as forest fire under certain conditions. In this study, the needles under a typical stand of P. massoniana were used as the research object. Needle beds with different moisture content and packing ratios were constructed indoors. Cigarette butt-ignition experiments were conducted under different wind velocities, and 30 experiment cycles were conducted under different conditions. There was a total of 5 (packing ratio) × 4 (moisture content) × 6 (wind velocity) = 120 sets of conditions, and a total of 3600 ignition experiments were conducted. The results showed that (1) the total ignition probability of the cigarette butts was 2.36%, which only occurred when the fuelbed moisture content was <10% and the wind velocity was >1 m/s. The ignition time of cigarette butts ranged from 2.73 to 7.25 min. (2) The fuelbed moisture content and wind velocity significantly influenced the ignition probability and time. With an increase in moisture content, the ignition probability of cigarette butts decreased, while the time required for ignition showed an increasing trend. Wind velocity had a dual effect on ignition. The ignition effect was optimal at a wind velocity of 4 m/s. With an increase in wind velocity, the ignition probability first increased and then decreased, and the ignition time first decreased and then increased. (3) The packing ratio had no significant effect on the ignition probability; however, the ignition time significantly decreased as the packing ratio increased. (4) The logistic regression method (LRM), general linear method (GLM), and nonlinear regression method (NLM) were used to establish a prediction model of ignition probability. The prediction effect of GLM was the worst, followed by LRM, and the NLM had the best prediction effect. The GLM was selected to establish the ignition time model, and the error was also within the allowance range. This study elucidated the underlying mechanism of factors affecting cigarette butt-based fuel ignition. In addition, the established prediction model provides a reference for human-caused forest fires and is highly significant for forest fire prevention.
{"title":"Ignition of Forest Fires by Cigarette Butts: Using Pinus massoniana Needles as an Example","authors":"Yunlin Zhang, Lingling Tian","doi":"10.3390/fire7030065","DOIUrl":"https://doi.org/10.3390/fire7030065","url":null,"abstract":"As a cigarette butt falls onto the forest surface fuel, it first smolders the fuel, then ignites into flames, and spreads as forest fire under certain conditions. In this study, the needles under a typical stand of P. massoniana were used as the research object. Needle beds with different moisture content and packing ratios were constructed indoors. Cigarette butt-ignition experiments were conducted under different wind velocities, and 30 experiment cycles were conducted under different conditions. There was a total of 5 (packing ratio) × 4 (moisture content) × 6 (wind velocity) = 120 sets of conditions, and a total of 3600 ignition experiments were conducted. The results showed that (1) the total ignition probability of the cigarette butts was 2.36%, which only occurred when the fuelbed moisture content was <10% and the wind velocity was >1 m/s. The ignition time of cigarette butts ranged from 2.73 to 7.25 min. (2) The fuelbed moisture content and wind velocity significantly influenced the ignition probability and time. With an increase in moisture content, the ignition probability of cigarette butts decreased, while the time required for ignition showed an increasing trend. Wind velocity had a dual effect on ignition. The ignition effect was optimal at a wind velocity of 4 m/s. With an increase in wind velocity, the ignition probability first increased and then decreased, and the ignition time first decreased and then increased. (3) The packing ratio had no significant effect on the ignition probability; however, the ignition time significantly decreased as the packing ratio increased. (4) The logistic regression method (LRM), general linear method (GLM), and nonlinear regression method (NLM) were used to establish a prediction model of ignition probability. The prediction effect of GLM was the worst, followed by LRM, and the NLM had the best prediction effect. The GLM was selected to establish the ignition time model, and the error was also within the allowance range. This study elucidated the underlying mechanism of factors affecting cigarette butt-based fuel ignition. In addition, the established prediction model provides a reference for human-caused forest fires and is highly significant for forest fire prevention.","PeriodicalId":12279,"journal":{"name":"Fire","volume":"32 12","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140434234","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Fire injury stresses Douglas-fir trees (Pseudotsuga menziesii) that survive a wildfire event, allowing subsequent Douglas-fir beetle (Dendroctonus pseudotsugae) infection to kill trees that may have otherwise survived. This study aimed to determine how fire injury, stand, and tree characteristics drive Douglas-fir beetle host tree selection five years post-fire. We paired 28 adjacent beetle-infected and uninfected stands (infected N = 14) and 140 Douglas-fir trees (infected N = 70) within the 416 Fire burn area in Southwest Colorado. We found no statistically significant differences between infected and uninfected stand characteristics. Individual tree height, DBH, and bark char severity index were significantly higher in infected versus uninfected trees. We created a regression decision tree model to determine the influence of fire injury and tree characteristics on the probability of infection. Trees with a height ≥ 27 m, bark char height < 2.3 m, and DBH < 80 cm had the greatest probability of attack (100%). Trees with a height < 27 m, bark char severity index < 5.5, and DBH < 49 cm had the lowest probability of attack (3.7%). Understanding the influence of fire on Douglas-fir beetle host selection allows land managers to model potential epidemic outbreaks and guide proactive management actions that may reduce beetle outbreak severity or preserve high-value trees not killed by fire.
{"title":"The Interacting Influence of Fire and Tree Characteristics on Douglas-Fir Beetle Host-Tree Selection Five Years Post-Fire","authors":"Matt Young, M. Remke, Julie E. Korb","doi":"10.3390/fire7030064","DOIUrl":"https://doi.org/10.3390/fire7030064","url":null,"abstract":"Fire injury stresses Douglas-fir trees (Pseudotsuga menziesii) that survive a wildfire event, allowing subsequent Douglas-fir beetle (Dendroctonus pseudotsugae) infection to kill trees that may have otherwise survived. This study aimed to determine how fire injury, stand, and tree characteristics drive Douglas-fir beetle host tree selection five years post-fire. We paired 28 adjacent beetle-infected and uninfected stands (infected N = 14) and 140 Douglas-fir trees (infected N = 70) within the 416 Fire burn area in Southwest Colorado. We found no statistically significant differences between infected and uninfected stand characteristics. Individual tree height, DBH, and bark char severity index were significantly higher in infected versus uninfected trees. We created a regression decision tree model to determine the influence of fire injury and tree characteristics on the probability of infection. Trees with a height ≥ 27 m, bark char height < 2.3 m, and DBH < 80 cm had the greatest probability of attack (100%). Trees with a height < 27 m, bark char severity index < 5.5, and DBH < 49 cm had the lowest probability of attack (3.7%). Understanding the influence of fire on Douglas-fir beetle host selection allows land managers to model potential epidemic outbreaks and guide proactive management actions that may reduce beetle outbreak severity or preserve high-value trees not killed by fire.","PeriodicalId":12279,"journal":{"name":"Fire","volume":"28 13","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140437677","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Open-field crop residue burning (OCRB) is a widespread agricultural practice with significant impacts on regional environments and public health. The effective management of OCRB remains a challenging task that requires timely access to various forms of monitored and forecasted information. Addressing this worldwide need, an open-source platform named AgriFireInfo v1.0, which is specifically tailored to monitoring and regulating regional OCRB activities, was developed. This technical note thoroughly illustrates the platform’s architecture, major modules, and visualization processes. Through AgriFireInfo v1.0, government agencies can access timely information about the spatial distribution of fire spots and emissions as well as meteorological conditions and air quality status. AgriFireInfo v1.0 also introduces an innovative Prevention Alarming Index, designed to identify regions prone to OCRB and promote comprehensive crop residue utilization. Furthermore, it offers the burning window and crop residue yields for controlled OCRB activities and can be used to analyze shifts in farmers’ burning behaviors and intensities. Future enhancements will focus on supplying holistic information on the burning windows and burning amounts of crop residues to further facilitate refined controlled burning activities and optimize decision-making processes. The flexibility and scalability of this platform can potentially allow users to easily customize and apply it to other regions or countries.
{"title":"AgriFireInfo v1.0: An Open-Source Platform for the Monitoring and Management of Open-Field Crop Residue Burning","authors":"Guangyi Yang, Xuelei Zhang, A. Xiu, Chao Gao, Mengduo Zhang, Qingqing Tong, Wei Liu, Yang Yu, Hongmei Zhao, Shichun Zhang, Shengjin Xie","doi":"10.3390/fire7030063","DOIUrl":"https://doi.org/10.3390/fire7030063","url":null,"abstract":"Open-field crop residue burning (OCRB) is a widespread agricultural practice with significant impacts on regional environments and public health. The effective management of OCRB remains a challenging task that requires timely access to various forms of monitored and forecasted information. Addressing this worldwide need, an open-source platform named AgriFireInfo v1.0, which is specifically tailored to monitoring and regulating regional OCRB activities, was developed. This technical note thoroughly illustrates the platform’s architecture, major modules, and visualization processes. Through AgriFireInfo v1.0, government agencies can access timely information about the spatial distribution of fire spots and emissions as well as meteorological conditions and air quality status. AgriFireInfo v1.0 also introduces an innovative Prevention Alarming Index, designed to identify regions prone to OCRB and promote comprehensive crop residue utilization. Furthermore, it offers the burning window and crop residue yields for controlled OCRB activities and can be used to analyze shifts in farmers’ burning behaviors and intensities. Future enhancements will focus on supplying holistic information on the burning windows and burning amounts of crop residues to further facilitate refined controlled burning activities and optimize decision-making processes. The flexibility and scalability of this platform can potentially allow users to easily customize and apply it to other regions or countries.","PeriodicalId":12279,"journal":{"name":"Fire","volume":"31 19","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140441924","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Birds contribute to the trophic interactions within mixed conifer ecosystems and provide a suite of services, such as nutrient transport, seed dispersal, habitat creation, and insect regulation. Avian communities vary in response to the structure and composition of their habitat, which may be drastically altered by fire, the predominant disturbance of western mixed conifer forests. We conducted avian point count surveys during the peak breeding season, five years post-fire, across four burn severities (unburned, low, moderate, and high) within the 416 Fire perimeter, a 55,000-acre mixed-severity fire that burned near Durango, Colorado in 2018. Avian communities in each burn severity were evaluated for richness, diversity, differentiation, indicator species, and functional guild composition. Species assemblages were significantly different across all burn severities, excluding the low to moderate areas comparison, with differentiation driven by live tree and snag density. Avian species’ richness and diversity were not significantly different across burn severities, highlighting the importance of utilizing multivariate community analysis. Unburned and high-burn areas had significant variation in functional guilds and numerous indicator species. This study provides evidence of avian community differentiation by burn severity, suggesting that management practices promoting heterogenous stand structure in warm–dry mixed conifer will positively influence avian biodiversity.
{"title":"Birds of the Burn: Avian Community and Functional Guild Variation Five Years Post-Fire in Warm–Dry Mixed Conifer, Southwest Colorado","authors":"Luke A. Scott, Julie E. Korb","doi":"10.3390/fire7030062","DOIUrl":"https://doi.org/10.3390/fire7030062","url":null,"abstract":"Birds contribute to the trophic interactions within mixed conifer ecosystems and provide a suite of services, such as nutrient transport, seed dispersal, habitat creation, and insect regulation. Avian communities vary in response to the structure and composition of their habitat, which may be drastically altered by fire, the predominant disturbance of western mixed conifer forests. We conducted avian point count surveys during the peak breeding season, five years post-fire, across four burn severities (unburned, low, moderate, and high) within the 416 Fire perimeter, a 55,000-acre mixed-severity fire that burned near Durango, Colorado in 2018. Avian communities in each burn severity were evaluated for richness, diversity, differentiation, indicator species, and functional guild composition. Species assemblages were significantly different across all burn severities, excluding the low to moderate areas comparison, with differentiation driven by live tree and snag density. Avian species’ richness and diversity were not significantly different across burn severities, highlighting the importance of utilizing multivariate community analysis. Unburned and high-burn areas had significant variation in functional guilds and numerous indicator species. This study provides evidence of avian community differentiation by burn severity, suggesting that management practices promoting heterogenous stand structure in warm–dry mixed conifer will positively influence avian biodiversity.","PeriodicalId":12279,"journal":{"name":"Fire","volume":"4 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140443175","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Grasslands are key to the Earth’s system and provide crucial ecosystem services. The degradation of the grassland ecosystem in South Africa is increasing alarmingly, and fire is regarded as one of the major culprits. Globally, anthropogenic climate changes have altered fire regimes in the grassland biome. Integrated fire-risk assessment systems provide an integral approach to fire prevention and mitigate the negative impacts of fire. However, fire risk-assessment is extremely challenging, owing to the myriad of factors that influence fire ignition and behaviour. Most fire danger systems do not consider fire causes; therefore, they are inadequate in validating the estimation of fire danger. Thus, fire danger assessment models should comprise the potential causes of fire. Understanding the key drivers of fire occurrence is key to the sustainable management of South Africa’s grassland ecosystems. Therefore, this study explored six statistical and machine learning models—the frequency ratio (FR), weight of evidence (WoE), logistic regression (LR), decision tree (DT), random forest (RF), and support vector machine (SVM) in Google Earth Engine (GEE) to assess fire danger in an Afromontane grassland protected area (PA). The area under the receiver operating characteristic curve results (ROC/AUC) revealed that DT showed the highest precision on model fit and success rate, while the WoE was used to record the highest prediction rate (AUC = 0.74). The WoE model showed that 53% of the study area is susceptible to fire. The land surface temperature (LST) and vegetation condition index (VCI) were the most influential factors. Corresponding analysis suggested that the fire regime of the study area is fuel-dominated. Thus, fire danger management strategies within the Golden Gate Highlands National Park (GGHNP) should include fuel management aiming at correctly weighing the effects of fuel in fire ignition and spread.
{"title":"An Integrated Grassland Fire-Danger-Assessment System for a Mountainous National Park Using Geospatial Modelling Techniques","authors":"Olga D. Mofokeng, S. Adelabu, Colbert M. Jackson","doi":"10.3390/fire7020061","DOIUrl":"https://doi.org/10.3390/fire7020061","url":null,"abstract":"Grasslands are key to the Earth’s system and provide crucial ecosystem services. The degradation of the grassland ecosystem in South Africa is increasing alarmingly, and fire is regarded as one of the major culprits. Globally, anthropogenic climate changes have altered fire regimes in the grassland biome. Integrated fire-risk assessment systems provide an integral approach to fire prevention and mitigate the negative impacts of fire. However, fire risk-assessment is extremely challenging, owing to the myriad of factors that influence fire ignition and behaviour. Most fire danger systems do not consider fire causes; therefore, they are inadequate in validating the estimation of fire danger. Thus, fire danger assessment models should comprise the potential causes of fire. Understanding the key drivers of fire occurrence is key to the sustainable management of South Africa’s grassland ecosystems. Therefore, this study explored six statistical and machine learning models—the frequency ratio (FR), weight of evidence (WoE), logistic regression (LR), decision tree (DT), random forest (RF), and support vector machine (SVM) in Google Earth Engine (GEE) to assess fire danger in an Afromontane grassland protected area (PA). The area under the receiver operating characteristic curve results (ROC/AUC) revealed that DT showed the highest precision on model fit and success rate, while the WoE was used to record the highest prediction rate (AUC = 0.74). The WoE model showed that 53% of the study area is susceptible to fire. The land surface temperature (LST) and vegetation condition index (VCI) were the most influential factors. Corresponding analysis suggested that the fire regime of the study area is fuel-dominated. Thus, fire danger management strategies within the Golden Gate Highlands National Park (GGHNP) should include fuel management aiming at correctly weighing the effects of fuel in fire ignition and spread.","PeriodicalId":12279,"journal":{"name":"Fire","volume":"33 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140449946","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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