The Forest Fire Danger Index (FFDI) is the main measure used in Australia for estimating fire risk. Recent work by the authors showed that the FFDI forms stable state regimes, nominated as fire climate regimes. These regimes shifted to greater intensity in southern and eastern Australia around the year 2000 and, a decade later, further north. Reductions in atmospheric moisture were the primary contributor. These changes have not been fully incorporated into future projections. This paper compares the recent regime shifts with the most recent national projections of FFDI, published in 2015. They show that for most states and regions, the 2030 upper limit is approached or exceeded by the recent shift, except for two states with large arid zones, South Australia and Western Australia. Methods for attributing past changes, constructing projections, and the inability of climate models to reproduce the recent decreases in atmospheric moisture, all contribute to these underestimates. To address these shortcomings, we make some suggestions to modify efforts aiming to develop seamless predictions and projections of future fire risk.
{"title":"Comparing Observed and Projected Changes in Australian Fire Climates","authors":"Roger N. Jones, J. Ricketts","doi":"10.3390/fire7040113","DOIUrl":"https://doi.org/10.3390/fire7040113","url":null,"abstract":"The Forest Fire Danger Index (FFDI) is the main measure used in Australia for estimating fire risk. Recent work by the authors showed that the FFDI forms stable state regimes, nominated as fire climate regimes. These regimes shifted to greater intensity in southern and eastern Australia around the year 2000 and, a decade later, further north. Reductions in atmospheric moisture were the primary contributor. These changes have not been fully incorporated into future projections. This paper compares the recent regime shifts with the most recent national projections of FFDI, published in 2015. They show that for most states and regions, the 2030 upper limit is approached or exceeded by the recent shift, except for two states with large arid zones, South Australia and Western Australia. Methods for attributing past changes, constructing projections, and the inability of climate models to reproduce the recent decreases in atmospheric moisture, all contribute to these underestimates. To address these shortcomings, we make some suggestions to modify efforts aiming to develop seamless predictions and projections of future fire risk.","PeriodicalId":12279,"journal":{"name":"Fire","volume":"32 9","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140359687","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}
Prescribed burning is a management tool commonly used in forested ecosystems in the southeastern United States, but the influence of this method on grassland vegetation and wildlife in this geographic region is unknown. During 2009–2015, we conducted a study to determine if the application of prescribed burning and/or long-term biosolid applications alter plant communities and/or wildlife use of grassland areas at Marine Corps Air Station Cherry Point, Havelock, NC. We monitored vegetation growth, measured plant community composition, and documented wildlife activity in four study plots for 3 years after the implementation of annual winter prescribed burns. Prescribed burning reduced the amount of litter, increased bare ground during spring, and altered the plant community composition relative to areas that were not burned. Overall, prescribed burning did not alter (F1,803 = 0.37, p = 0.54) bird use of the airfield grasslands, while the long-term application of biosolids resulted in higher (F1,803 = 17.61, p < 0.01) bird use. Few species-specific differences in avian use of prescribed burned and unburned grasslands were found. In contrast, white-tailed deer (Odocoileus virginianus) use of areas that were burned in winter, as well as the adjacent unburned areas, was drastically reduced. Winter prescribed burning appeared to remove forage plants at the time of year deer would use them the most. Our findings suggest that prescribed burning and biosolid applications, used alone and in combination, might be viable grassland management tools for altering wildlife use of grassland areas, specifically white-tailed deer; however, similar research at additional locations should be conducted.
{"title":"Using Prescribed Fire and Biosolids Applications as Grassland Management Tools: Do Wildlife Respond?","authors":"Brian Washburn, Michael Begier","doi":"10.3390/fire7040112","DOIUrl":"https://doi.org/10.3390/fire7040112","url":null,"abstract":"Prescribed burning is a management tool commonly used in forested ecosystems in the southeastern United States, but the influence of this method on grassland vegetation and wildlife in this geographic region is unknown. During 2009–2015, we conducted a study to determine if the application of prescribed burning and/or long-term biosolid applications alter plant communities and/or wildlife use of grassland areas at Marine Corps Air Station Cherry Point, Havelock, NC. We monitored vegetation growth, measured plant community composition, and documented wildlife activity in four study plots for 3 years after the implementation of annual winter prescribed burns. Prescribed burning reduced the amount of litter, increased bare ground during spring, and altered the plant community composition relative to areas that were not burned. Overall, prescribed burning did not alter (F1,803 = 0.37, p = 0.54) bird use of the airfield grasslands, while the long-term application of biosolids resulted in higher (F1,803 = 17.61, p < 0.01) bird use. Few species-specific differences in avian use of prescribed burned and unburned grasslands were found. In contrast, white-tailed deer (Odocoileus virginianus) use of areas that were burned in winter, as well as the adjacent unburned areas, was drastically reduced. Winter prescribed burning appeared to remove forage plants at the time of year deer would use them the most. Our findings suggest that prescribed burning and biosolid applications, used alone and in combination, might be viable grassland management tools for altering wildlife use of grassland areas, specifically white-tailed deer; however, similar research at additional locations should be conducted.","PeriodicalId":12279,"journal":{"name":"Fire","volume":"26 19","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140358677","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}
Lightning is responsible for the most area annually burned by wildfires in the extratropical region of the Northern Hemisphere. Hence, predicting the occurrence of wildfires requires reliable forecasting of the chance of cloud-to-ground lightning strikes during storms. Here, we describe the development and verification of a probabilistic lightning-strike algorithm running on a uniform 20 km grid over the continental USA and Alaska. This is the first and only high-resolution lightning forecasting model for North America derived from 29-year-long data records. The algorithm consists of a large set of regional logistic equations parameterized on the long-term data records of observed lightning strikes and meteorological reanalysis fields from NOAA. Principal Component Analysis was employed to extract 13 principal components from a list of 611 potential predictors. Our analysis revealed that the occurrence of cloud-to-ground lightning strikes primarily depends on three factors: the temperature and geopotential heights across vertical pressure levels, the amount of low-level atmospheric moisture, and wind vectors. These physical variables isolate the conditions that are favorable for the development of thunderstorms and impact the vertical separation of electric charges in the lower troposphere during storms, which causes the voltage potential between the ground and the cloud deck to increase to a level that triggers electrical discharges. The results from a forecast verification using independent data showed excellent model performance, thus making this algorithm suitable for incorporation into models designed to forecast the chance of wildfire ignitions.
{"title":"Probabilistic Forecasting of Lightning Strikes over the Continental USA and Alaska: Model Development and Verification","authors":"Ned Nikolov, Phillip Bothwell, John Snook","doi":"10.3390/fire7040111","DOIUrl":"https://doi.org/10.3390/fire7040111","url":null,"abstract":"Lightning is responsible for the most area annually burned by wildfires in the extratropical region of the Northern Hemisphere. Hence, predicting the occurrence of wildfires requires reliable forecasting of the chance of cloud-to-ground lightning strikes during storms. Here, we describe the development and verification of a probabilistic lightning-strike algorithm running on a uniform 20 km grid over the continental USA and Alaska. This is the first and only high-resolution lightning forecasting model for North America derived from 29-year-long data records. The algorithm consists of a large set of regional logistic equations parameterized on the long-term data records of observed lightning strikes and meteorological reanalysis fields from NOAA. Principal Component Analysis was employed to extract 13 principal components from a list of 611 potential predictors. Our analysis revealed that the occurrence of cloud-to-ground lightning strikes primarily depends on three factors: the temperature and geopotential heights across vertical pressure levels, the amount of low-level atmospheric moisture, and wind vectors. These physical variables isolate the conditions that are favorable for the development of thunderstorms and impact the vertical separation of electric charges in the lower troposphere during storms, which causes the voltage potential between the ground and the cloud deck to increase to a level that triggers electrical discharges. The results from a forecast verification using independent data showed excellent model performance, thus making this algorithm suitable for incorporation into models designed to forecast the chance of wildfire ignitions.","PeriodicalId":12279,"journal":{"name":"Fire","volume":"32 17","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140372837","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}
This paper explores the temperature distribution (TD) and maximum temperature (MT) below the ceiling induced by the ceiling jet of an asymmetric dual fire sources in a naturally ventilated tunnel. Considering strong plumes, this study investigates the effects of fire size and spacing of asymmetric dual fire sources on TD and MT. With the same power of fire source, when the size of one of the fire sources increases, the corresponding maximum temperature beneath ceiling decreases. Additionally, the temperature peak below the ceiling shifts from one to two, and the peak temperature of the larger fire source is lower compared to that of smaller one. When the fire sources distance increases, the maximum temperature initially decreases and then increases. Beyond a certain distance, the maximum temperature no longer changes with increasing distance. In this study, we investigated the effect of fire source size and spacing on the MT of the tunnel ceiling for asymmetric dual fire sources. A new model for predicting the MT underneath the tunnel ceiling was developed, taking into account the factors as fire spacing and fire size. The model is able to make effective predictions of the simulation results.
{"title":"A Study on the Maximum Temperature of a Ceiling Jet of Asymmetric Dual Strong Plumes in a Naturally Ventilated Tunnel","authors":"Shenghao Zhang, Na Meng","doi":"10.3390/fire7040110","DOIUrl":"https://doi.org/10.3390/fire7040110","url":null,"abstract":"This paper explores the temperature distribution (TD) and maximum temperature (MT) below the ceiling induced by the ceiling jet of an asymmetric dual fire sources in a naturally ventilated tunnel. Considering strong plumes, this study investigates the effects of fire size and spacing of asymmetric dual fire sources on TD and MT. With the same power of fire source, when the size of one of the fire sources increases, the corresponding maximum temperature beneath ceiling decreases. Additionally, the temperature peak below the ceiling shifts from one to two, and the peak temperature of the larger fire source is lower compared to that of smaller one. When the fire sources distance increases, the maximum temperature initially decreases and then increases. Beyond a certain distance, the maximum temperature no longer changes with increasing distance. In this study, we investigated the effect of fire source size and spacing on the MT of the tunnel ceiling for asymmetric dual fire sources. A new model for predicting the MT underneath the tunnel ceiling was developed, taking into account the factors as fire spacing and fire size. The model is able to make effective predictions of the simulation results.","PeriodicalId":12279,"journal":{"name":"Fire","volume":"121 48","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140380057","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}
Roman Berčák, J. Holuša, J. Trombik, K. Resnerová, T. Hlásny
Central Europe is not a typical wildfire region; however, an increasingly warm and dry climate and model-based projections indicate that the number of forest fires are increasing. This study provides new insights into the drivers of forest fire occurrence in the Czech Republic, during the period 2006 to 2015, by focusing on climate, land cover, and human activity factors. The average annual number of forest fires during the study period was 728, with a median burned area of 0.01 ha. Forest fire incidence showed distinct spring (April) and summer (July to August) peaks, with median burned areas of 0.04 ha and 0.005 ha, respectively. Relationships between the predictors (climate data, forest-related data, socioeconomic data, and landscape-context data) and the number of forest fires in individual municipality districts were analyzed using Generalized Additive Models (GAM) on three time scales (annually, monthly, and during the summer season). The constructed GAMs explained 48.7 and 53.8% of forest fire variability when fire occurrence was analyzed on a monthly scale and during the summer season, respectively. On an annual scale, the models explained 71.4% of the observed forest fire variability. The number of forest fires was related to the number of residents and overnight tourists in the area. The effect of climate was manifested on monthly and summer season scales only, with warmer and drier conditions associated with higher forest fire frequency. A higher proportion of conifers and the length of the wildland–urban interface were also positively associated with forest fire occurrence. Forest fire occurrence was influenced by a combination of climatic, forest-related, and social activity factors. The effect of climate was most pronounced on a monthly scale, corresponding with the presence of two distinct seasonal peaks of forest fire occurrence. The significant effect of factors related to human activity suggests that measures to increase public awareness about fire risk and targeted activity regulation are essential in controlling the risk of fire occurrence in Central Europe. An increasing frequency of fire-conducive weather, forest structure transformations due to excessive tree mortality, and changing patterns of human activity on the landscape require permanent monitoring and assessment of possible shifts in forest fire risk.
{"title":"A Combination of Human Activity and Climate Drives Forest Fire Occurrence in Central Europe: The Case of the Czech Republic","authors":"Roman Berčák, J. Holuša, J. Trombik, K. Resnerová, T. Hlásny","doi":"10.3390/fire7040109","DOIUrl":"https://doi.org/10.3390/fire7040109","url":null,"abstract":"Central Europe is not a typical wildfire region; however, an increasingly warm and dry climate and model-based projections indicate that the number of forest fires are increasing. This study provides new insights into the drivers of forest fire occurrence in the Czech Republic, during the period 2006 to 2015, by focusing on climate, land cover, and human activity factors. The average annual number of forest fires during the study period was 728, with a median burned area of 0.01 ha. Forest fire incidence showed distinct spring (April) and summer (July to August) peaks, with median burned areas of 0.04 ha and 0.005 ha, respectively. Relationships between the predictors (climate data, forest-related data, socioeconomic data, and landscape-context data) and the number of forest fires in individual municipality districts were analyzed using Generalized Additive Models (GAM) on three time scales (annually, monthly, and during the summer season). The constructed GAMs explained 48.7 and 53.8% of forest fire variability when fire occurrence was analyzed on a monthly scale and during the summer season, respectively. On an annual scale, the models explained 71.4% of the observed forest fire variability. The number of forest fires was related to the number of residents and overnight tourists in the area. The effect of climate was manifested on monthly and summer season scales only, with warmer and drier conditions associated with higher forest fire frequency. A higher proportion of conifers and the length of the wildland–urban interface were also positively associated with forest fire occurrence. Forest fire occurrence was influenced by a combination of climatic, forest-related, and social activity factors. The effect of climate was most pronounced on a monthly scale, corresponding with the presence of two distinct seasonal peaks of forest fire occurrence. The significant effect of factors related to human activity suggests that measures to increase public awareness about fire risk and targeted activity regulation are essential in controlling the risk of fire occurrence in Central Europe. An increasing frequency of fire-conducive weather, forest structure transformations due to excessive tree mortality, and changing patterns of human activity on the landscape require permanent monitoring and assessment of possible shifts in forest fire risk.","PeriodicalId":12279,"journal":{"name":"Fire","volume":"123 8","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140380317","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}
In South Korea, the need to link fire and evacuation simulations to compare the available safety egress time (ASET) and required safety egress time (RSET) in real time when implementing performance-based design in buildings is increasing. Accordingly, the Consolidated Model of Fire Growth and Smoke Transport (CFAST) has been discussed as an alternative to the fire dynamics simulator, which requires high computational costs, sufficient experience in fire dynamics numerical calculations, and various input parameters and faces limitations in integration with evacuation simulations. A method for establishing a reasonable computational domain to predict the activation times of smoke and heat detectors has been proposed. This study examined the validity of using CFAST to predict factors relevant to the ASET evaluation. The results showed that CFAST, which solved empirical correlations based on heat release rates, predicted high gas temperatures similarly. Moreover, the applicability of the visibility distance calculation method using smoke concentration outputs from CFAST was examined. The results suggest that despite the limitations of the zone model, CFAST can produce reasonable ASET results. These results are expected to enhance the usability of CFAST in terms of understanding general fire engineering technology and simple fire dynamics trends.
在韩国，在建筑中实施基于性能的设计时，越来越需要将火灾和疏散模拟联系起来，以实时比较可用安全疏散时间（ASET）和所需安全疏散时间（RSET）。火灾动力学模拟器需要较高的计算成本、足够的火灾动力学数值计算经验和各种输入参数，而且在与疏散模拟集成时面临限制。有人提出了一种建立合理计算域的方法，用于预测烟感和热感探测器的启动时间。本研究考察了使用 CFAST 预测 ASET 评估相关因素的有效性。结果表明，CFAST 解决了基于热释放率的经验相关性问题，对高气体温度的预测结果类似。此外，还考察了利用 CFAST 输出的烟雾浓度计算能见度距离方法的适用性。结果表明，尽管区域模型存在局限性，CFAST 仍能得出合理的 ASET 结果。这些结果有望提高 CFAST 在理解一般消防工程技术和简单火灾动力学趋势方面的可用性。
{"title":"Evaluation of Available Safety Egress Time (ASET) in Performance-Based Design (PBD) Using CFAST","authors":"Hyo-Yeon Jang, Cheol-Hong Hwang","doi":"10.3390/fire7040108","DOIUrl":"https://doi.org/10.3390/fire7040108","url":null,"abstract":"In South Korea, the need to link fire and evacuation simulations to compare the available safety egress time (ASET) and required safety egress time (RSET) in real time when implementing performance-based design in buildings is increasing. Accordingly, the Consolidated Model of Fire Growth and Smoke Transport (CFAST) has been discussed as an alternative to the fire dynamics simulator, which requires high computational costs, sufficient experience in fire dynamics numerical calculations, and various input parameters and faces limitations in integration with evacuation simulations. A method for establishing a reasonable computational domain to predict the activation times of smoke and heat detectors has been proposed. This study examined the validity of using CFAST to predict factors relevant to the ASET evaluation. The results showed that CFAST, which solved empirical correlations based on heat release rates, predicted high gas temperatures similarly. Moreover, the applicability of the visibility distance calculation method using smoke concentration outputs from CFAST was examined. The results suggest that despite the limitations of the zone model, CFAST can produce reasonable ASET results. These results are expected to enhance the usability of CFAST in terms of understanding general fire engineering technology and simple fire dynamics trends.","PeriodicalId":12279,"journal":{"name":"Fire","volume":" 13","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140384195","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}
Accurate prediction of the coal spontaneous combustion hazard grades is of great significance to ensure the safe production of coal mines. However, traditional coal temperature prediction models have low accuracy and do not predict the coal spontaneous combustion hazard grades. In order to accurately predict coal spontaneous combustion hazard grades, a prediction model of coal spontaneous combustion based on principal component analysis (PCA), case-based reasoning (CBR), fuzzy clustering (FM), and the snake optimization (SO) algorithm was proposed in this manuscript. Firstly, based on the change rule of the concentration of signature gases in the process of coal warming, a new method of classifying the risk of spontaneous combustion of coal was established. Secondly, MeanRadius-SMOTE was adopted to balance the data structure. The weights of the prediction indicators were calculated through PCA to enhance the prediction precision of the CBR model. Then, by employing FM in the case base, the computational cost of CBR was reduced and its computational efficiency was improved. The SO algorithm was used to determine the hyperparameters in the PCA-FM-CBR model. In addition, multiple comparative experiments were conducted to verify the superiority of the model proposed in this manuscript. The results indicated that SO-PCA-FM-CBR possesses good prediction performance and also improves computational efficiency. Finally, the authors of this manuscript adopted the Random Balance Designs—Fourier Amplitude Sensitivity Test (RBD-FAST) to explain the output of the model and analyzed the global importance of input variables. The results demonstrated that CO is the most important variable affecting the coal spontaneous combustion hazard grades.
{"title":"Prediction of Coal Spontaneous Combustion Hazard Grades Based on Fuzzy Clustered Case-Based Reasoning","authors":"Qiuyan Pei, Zhichao Jia, Jia Liu, Yi Wang, Junhui Wang, Yanqi Zhang","doi":"10.3390/fire7040107","DOIUrl":"https://doi.org/10.3390/fire7040107","url":null,"abstract":"Accurate prediction of the coal spontaneous combustion hazard grades is of great significance to ensure the safe production of coal mines. However, traditional coal temperature prediction models have low accuracy and do not predict the coal spontaneous combustion hazard grades. In order to accurately predict coal spontaneous combustion hazard grades, a prediction model of coal spontaneous combustion based on principal component analysis (PCA), case-based reasoning (CBR), fuzzy clustering (FM), and the snake optimization (SO) algorithm was proposed in this manuscript. Firstly, based on the change rule of the concentration of signature gases in the process of coal warming, a new method of classifying the risk of spontaneous combustion of coal was established. Secondly, MeanRadius-SMOTE was adopted to balance the data structure. The weights of the prediction indicators were calculated through PCA to enhance the prediction precision of the CBR model. Then, by employing FM in the case base, the computational cost of CBR was reduced and its computational efficiency was improved. The SO algorithm was used to determine the hyperparameters in the PCA-FM-CBR model. In addition, multiple comparative experiments were conducted to verify the superiority of the model proposed in this manuscript. The results indicated that SO-PCA-FM-CBR possesses good prediction performance and also improves computational efficiency. Finally, the authors of this manuscript adopted the Random Balance Designs—Fourier Amplitude Sensitivity Test (RBD-FAST) to explain the output of the model and analyzed the global importance of input variables. The results demonstrated that CO is the most important variable affecting the coal spontaneous combustion hazard grades.","PeriodicalId":12279,"journal":{"name":"Fire","volume":" 20","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140385398","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}
Since Euro-American settlement and associated fire exclusion, grasslands and open forests have converted to forests throughout the United States. Contributing to the weight of evidence, we determined if forestation also occurred in forests and grasslands of Colorado. Our study extent encompassed landscapes of the 0.5 million ha Arapaho and Roosevelt National Forests in the northern Front Range (eastern side) of the southern Rocky Mountains and the 1 million ha Weld County, which contains Pawnee National Grassland, in the Great Plains grasslands. We quantified tree composition, cover, and densities from historical (years 1863 to 1886) tree surveys, current surveys (2002 to 2011), and land cover (2016) to identify departures. In the Arapaho and Roosevelt, historical lack of tree presence and overall low tree densities suggested an open landscape, due to about 70% of 7134 survey points without two trees within 60 m. The treed landscape, which was not continuously forested, had density estimates of about 153 trees/ha. In contrast, the current landscape was 68% forested with high tree densities; fire-dependent pines decreased relative to subalpine fir (Abies lasiocarpa) increases. In Weld County, seven trees were surveyed historically, whereas currently, woody cover totaled 2555 ha. Uniquely applying historical surveys at landscape scales, we documented an open landscape in the northern Front Range, unlike previous research, and rare tree presence in the relatively understudied grasslands of Colorado. Forestation corresponded with changes in U.S. grasslands and forests following Euro-American settlement and associated fire exclusion.
{"title":"Exploring Tree Density Increases after Fire Exclusion in the Northern Front Range and Great Plains, Colorado, USA","authors":"B. Hanberry, Jacob M. Seidel, Phillip DeLeon","doi":"10.3390/fire7040103","DOIUrl":"https://doi.org/10.3390/fire7040103","url":null,"abstract":"Since Euro-American settlement and associated fire exclusion, grasslands and open forests have converted to forests throughout the United States. Contributing to the weight of evidence, we determined if forestation also occurred in forests and grasslands of Colorado. Our study extent encompassed landscapes of the 0.5 million ha Arapaho and Roosevelt National Forests in the northern Front Range (eastern side) of the southern Rocky Mountains and the 1 million ha Weld County, which contains Pawnee National Grassland, in the Great Plains grasslands. We quantified tree composition, cover, and densities from historical (years 1863 to 1886) tree surveys, current surveys (2002 to 2011), and land cover (2016) to identify departures. In the Arapaho and Roosevelt, historical lack of tree presence and overall low tree densities suggested an open landscape, due to about 70% of 7134 survey points without two trees within 60 m. The treed landscape, which was not continuously forested, had density estimates of about 153 trees/ha. In contrast, the current landscape was 68% forested with high tree densities; fire-dependent pines decreased relative to subalpine fir (Abies lasiocarpa) increases. In Weld County, seven trees were surveyed historically, whereas currently, woody cover totaled 2555 ha. Uniquely applying historical surveys at landscape scales, we documented an open landscape in the northern Front Range, unlike previous research, and rare tree presence in the relatively understudied grasslands of Colorado. Forestation corresponded with changes in U.S. grasslands and forests following Euro-American settlement and associated fire exclusion.","PeriodicalId":12279,"journal":{"name":"Fire","volume":" 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140214584","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 chemical sensing for indoor detection of fire plays an essential role because it can detect chemical volatiles before smoke particles, providing a faster and more reliable method for early fire detection. A thermal imaging camera and seven distinct fire-detecting sensors were used simultaneously to acquire the multimodal fire data that is the subject of this paper. The low-cost sensors typically have lower sensitivity and reliability, making it impossible for them to detect fire at greater distances. To go beyond the limitation of using solely sensors for identifying fire, the multimodal dataset is collected using a thermal camera that can detect temperature changes. The proposed pipeline uses image data from thermal cameras to train convolutional neural networks (CNNs) and their many versions. The training of sensors data (from fire sensors) uses bidirectional long-short memory (BiLSTM-Dense) and dense and long-short memory (LSTM-DenseDenseNet201), and the merging of both datasets demonstrates the performance of multimodal data. Researchers and system developers can use the dataset to create and hone cutting-edge artificial intelligence models and systems. Initial evaluation of the image dataset has shown densenet201 as the best approach with the highest validation parameters (0.99, 0.99, 0.99, and 0.08), i.e., Accuracy, Precision, Recall, and Loss, respectively. However, the sensors dataset has also shown the highest parameters with the BILSTM-Dense approach (0.95, 0.95, 0.95, 0.14). In a multimodal data approach, image and sensors deployed with a multimodal algorithm (densenet201 for image data and Bi LSTM- Dense for Sensors Data) has shown other parameters (1.0, 1.0, 1.0, 0.06). This work demonstrates that, in comparison to the conventional deep learning approach, the federated learning (FL) approach performs privacy-protected fire leakage classification without significantly sacrificing accuracy and other validation parameters.
{"title":"Fire Detection in Urban Areas Using Multimodal Data and Federated Learning","authors":"Ashutosh Sharma, Raj Kumar, I. Kansal, Renu Popli, Vikas Khullar, Jyoti Verma, Sunil Kumar","doi":"10.3390/fire7040104","DOIUrl":"https://doi.org/10.3390/fire7040104","url":null,"abstract":"Fire chemical sensing for indoor detection of fire plays an essential role because it can detect chemical volatiles before smoke particles, providing a faster and more reliable method for early fire detection. A thermal imaging camera and seven distinct fire-detecting sensors were used simultaneously to acquire the multimodal fire data that is the subject of this paper. The low-cost sensors typically have lower sensitivity and reliability, making it impossible for them to detect fire at greater distances. To go beyond the limitation of using solely sensors for identifying fire, the multimodal dataset is collected using a thermal camera that can detect temperature changes. The proposed pipeline uses image data from thermal cameras to train convolutional neural networks (CNNs) and their many versions. The training of sensors data (from fire sensors) uses bidirectional long-short memory (BiLSTM-Dense) and dense and long-short memory (LSTM-DenseDenseNet201), and the merging of both datasets demonstrates the performance of multimodal data. Researchers and system developers can use the dataset to create and hone cutting-edge artificial intelligence models and systems. Initial evaluation of the image dataset has shown densenet201 as the best approach with the highest validation parameters (0.99, 0.99, 0.99, and 0.08), i.e., Accuracy, Precision, Recall, and Loss, respectively. However, the sensors dataset has also shown the highest parameters with the BILSTM-Dense approach (0.95, 0.95, 0.95, 0.14). In a multimodal data approach, image and sensors deployed with a multimodal algorithm (densenet201 for image data and Bi LSTM- Dense for Sensors Data) has shown other parameters (1.0, 1.0, 1.0, 0.06). This work demonstrates that, in comparison to the conventional deep learning approach, the federated learning (FL) approach performs privacy-protected fire leakage classification without significantly sacrificing accuracy and other validation parameters.","PeriodicalId":12279,"journal":{"name":"Fire","volume":" 32","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140219179","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}
During the process of coal spontaneous combustion (CSC), a plethora of combustible gases alongside inert gases, such as CO2, are copiously generated. However, prior investigations have regrettably overlooked the pivotal influence of inert gas production on the propensity for methane explosions during CSC. To investigate the impact of the flue gas environment generated by CSC, containing both combustible and inert gases, on the risk of methane explosion, a high-temperature programmed heating test system for CSC was employed to analyze the generation pattern of flue gas. It was found that CO, CO2, and CH4 were continuously generated in large quantities during the process of CSC, which are the main components of CSC flue gas. The effect of the concentration and component ratio (CCO2/CCO) of the flue gas on the methane explosion limit was tested. It was found that the CSC flue gas led to a decrease in the methane explosion limit, and that the explosion limit range was facilitated at 0 < CCO2/CCO < 0.543 and suppressed at CCO2/CCO > 0.543. As the temperature of CSC increases, the risk of methane explosion is initially suppressed. When the coal temperature exceeds 330~410 °C, the explosion risk rapidly expands.
{"title":"Investigating the Influence of Flue Gas Induced by Coal Spontaneous Combustion on Methane Explosion Risk","authors":"Sijia Hu, Yanjun Li, Chuanjie Zhu, Baiquan Lin, Qingzhao Li, Baolin Li, Zichao Huang","doi":"10.3390/fire7040105","DOIUrl":"https://doi.org/10.3390/fire7040105","url":null,"abstract":"During the process of coal spontaneous combustion (CSC), a plethora of combustible gases alongside inert gases, such as CO2, are copiously generated. However, prior investigations have regrettably overlooked the pivotal influence of inert gas production on the propensity for methane explosions during CSC. To investigate the impact of the flue gas environment generated by CSC, containing both combustible and inert gases, on the risk of methane explosion, a high-temperature programmed heating test system for CSC was employed to analyze the generation pattern of flue gas. It was found that CO, CO2, and CH4 were continuously generated in large quantities during the process of CSC, which are the main components of CSC flue gas. The effect of the concentration and component ratio (CCO2/CCO) of the flue gas on the methane explosion limit was tested. It was found that the CSC flue gas led to a decrease in the methane explosion limit, and that the explosion limit range was facilitated at 0 < CCO2/CCO < 0.543 and suppressed at CCO2/CCO > 0.543. As the temperature of CSC increases, the risk of methane explosion is initially suppressed. When the coal temperature exceeds 330~410 °C, the explosion risk rapidly expands.","PeriodicalId":12279,"journal":{"name":"Fire","volume":" 7","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140220409","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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