Abstract Background Mobile ad hoc networks have piqued researchers’ interest in various applications, including forest fire detection. Because of the massive losses caused by this disaster, forest fires necessitate regular monitoring, good communication, and technology. As a result, disaster response and rescue applications are mobile ad hoc network’s primary applications. However, quality of service becomes a significant and difficult issue, and the capabilities of the basic routing protocol limit mobile ad hoc network’s ability to deliver reasonable quality of service. Results The proposed research is for disaster-related scenarios, with nodes representing firefighters and vehicles (ambulances). Mobile nodes moving at 10 m/s are thought to be firefighters, while nodes moving at 20 m/s are thought to be vehicles (ambulances) delivering emergency healthcare. The NS-2 simulator is used in this research for the performance assessment of the two routing protocols, such as Optimized Link State Routing (OLSR) and Temporally Order Routing Algorithm (TORA), in terms of average latency, average throughput, and average packet drop. The simulation was run with varying node velocities and network densities to examine the impact of scalability on the two mobile ad hoc network routing protocols. Conclusions This work presents two main protocols: TORA (for reactive networks) and OLSR (for proactive networks). The proposed methods had no impact on the end-to-end bandwidth delay or the packet delivery delay. The performance is evaluated in terms of varying network density and node speed (firefighter speed), i.e., varying network density and mobility speed. The simulation revealed that in a highly mobile network with varying network densities, OLSR outperforms TORA in terms of overall performance. TORA’s speed may have been enhanced by adding more nodes to the 20 nodes that used a significant amount of transmission control protocol traffic.
{"title":"Analyzing the impacts of node density and speed on routing protocol performance in firefighting applications","authors":"Inam Ullah, Tariq Hussain, Aamir Khan, Iqtidar Ali, Farhad Ali, Chang Choi","doi":"10.1186/s42408-023-00220-4","DOIUrl":"https://doi.org/10.1186/s42408-023-00220-4","url":null,"abstract":"Abstract Background Mobile ad hoc networks have piqued researchers’ interest in various applications, including forest fire detection. Because of the massive losses caused by this disaster, forest fires necessitate regular monitoring, good communication, and technology. As a result, disaster response and rescue applications are mobile ad hoc network’s primary applications. However, quality of service becomes a significant and difficult issue, and the capabilities of the basic routing protocol limit mobile ad hoc network’s ability to deliver reasonable quality of service. Results The proposed research is for disaster-related scenarios, with nodes representing firefighters and vehicles (ambulances). Mobile nodes moving at 10 m/s are thought to be firefighters, while nodes moving at 20 m/s are thought to be vehicles (ambulances) delivering emergency healthcare. The NS-2 simulator is used in this research for the performance assessment of the two routing protocols, such as Optimized Link State Routing (OLSR) and Temporally Order Routing Algorithm (TORA), in terms of average latency, average throughput, and average packet drop. The simulation was run with varying node velocities and network densities to examine the impact of scalability on the two mobile ad hoc network routing protocols. Conclusions This work presents two main protocols: TORA (for reactive networks) and OLSR (for proactive networks). The proposed methods had no impact on the end-to-end bandwidth delay or the packet delivery delay. The performance is evaluated in terms of varying network density and node speed (firefighter speed), i.e., varying network density and mobility speed. The simulation revealed that in a highly mobile network with varying network densities, OLSR outperforms TORA in terms of overall performance. TORA’s speed may have been enhanced by adding more nodes to the 20 nodes that used a significant amount of transmission control protocol traffic.","PeriodicalId":12273,"journal":{"name":"Fire Ecology","volume":"45 26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135778554","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-17DOI: 10.1186/s42408-023-00218-y
Ana Solares-Canal, Laura Alonso, Thais Rincón, Juan Picos, Domingo M. Molina-Terrén, Carmen Becerra, Julia Armesto
Abstract Background In the new era of large, high-intensity wildfire events, new fire prevention and extinction strategies are emerging. Software that simulates fire behavior can play a leading role. In order for these simulators to provide reliable results, updated fuel model maps are required. Previous studies have shown that remote sensing is a useful tool for obtaining information about vegetation structures and types. However, remote sensing technologies have not been evaluated for operational purposes in Atlantic environments. In this study, we describe a methodology based on remote sensing data (Sentinel-2 images and aerial point clouds) to obtain updated fuel model maps of an Atlantic area. These maps could be used directly in wildfire simulation software. Results An automated methodology has been developed that allows for the efficient identification and mapping of fuel models in an Atlantic environment. It mainly consists of processing remote sensing data using supervised classifications to obtain a map with the geographical distribution of the species in the study area and maps with the geographical distribution of the structural characteristics of the forest covers. The relationships between the vegetation species and structures in the study area and the Rothermel fuel models were identified. These relationships enabled the generation of the final fuel model map by combining the different previously obtained maps. The resulting map provides essential information about the geographical distribution of fuels; 32.92% of the study area corresponds to models 4 and 7, which are the two models that tend to develop more dangerous behaviors. The accuracy of the final map is evaluated through validation of the maps that are used to obtain it. The user and producer accuracy ranged between 70 and 100%. Conclusion This paper describes an automated methodology for obtaining updated fuel model maps in Atlantic landscapes using remote sensing data. These maps are crucial in wildfire simulation, which supports the modern wildfire suppression and prevention strategies. Sentinel-2 is a global open access source, and LiDAR is an extensively used technology, meaning that the approach proposed in this study represents a step forward in the efficient transformation of remote sensing data into operational tools for wildfire prevention.
{"title":"Operational fuel model map for Atlantic landscapes using ALS and Sentinel-2 images","authors":"Ana Solares-Canal, Laura Alonso, Thais Rincón, Juan Picos, Domingo M. Molina-Terrén, Carmen Becerra, Julia Armesto","doi":"10.1186/s42408-023-00218-y","DOIUrl":"https://doi.org/10.1186/s42408-023-00218-y","url":null,"abstract":"Abstract Background In the new era of large, high-intensity wildfire events, new fire prevention and extinction strategies are emerging. Software that simulates fire behavior can play a leading role. In order for these simulators to provide reliable results, updated fuel model maps are required. Previous studies have shown that remote sensing is a useful tool for obtaining information about vegetation structures and types. However, remote sensing technologies have not been evaluated for operational purposes in Atlantic environments. In this study, we describe a methodology based on remote sensing data (Sentinel-2 images and aerial point clouds) to obtain updated fuel model maps of an Atlantic area. These maps could be used directly in wildfire simulation software. Results An automated methodology has been developed that allows for the efficient identification and mapping of fuel models in an Atlantic environment. It mainly consists of processing remote sensing data using supervised classifications to obtain a map with the geographical distribution of the species in the study area and maps with the geographical distribution of the structural characteristics of the forest covers. The relationships between the vegetation species and structures in the study area and the Rothermel fuel models were identified. These relationships enabled the generation of the final fuel model map by combining the different previously obtained maps. The resulting map provides essential information about the geographical distribution of fuels; 32.92% of the study area corresponds to models 4 and 7, which are the two models that tend to develop more dangerous behaviors. The accuracy of the final map is evaluated through validation of the maps that are used to obtain it. The user and producer accuracy ranged between 70 and 100%. Conclusion This paper describes an automated methodology for obtaining updated fuel model maps in Atlantic landscapes using remote sensing data. These maps are crucial in wildfire simulation, which supports the modern wildfire suppression and prevention strategies. Sentinel-2 is a global open access source, and LiDAR is an extensively used technology, meaning that the approach proposed in this study represents a step forward in the efficient transformation of remote sensing data into operational tools for wildfire prevention.","PeriodicalId":12273,"journal":{"name":"Fire Ecology","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136033486","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-17DOI: 10.1186/s42408-023-00222-2
Rebekah L. Stanton, Baylie C. Nusink, Kristina L. Cass, Tara B. B. Bishop, Brianna M. Woodbury, David N. Armond, Samuel B. St. Clair
Abstract Background Wildfire regimes are changing dramatically across North American deserts with the spread of invasive grasses. Invasive grass fire cycles in historically fire-resistant deserts are resulting in larger and more frequent wildfire. This study experimentally compared how single and repeat fires influence invasive grass-dominated plant fuels in the Great Basin, a semi-arid, cold desert, and the Mojave, a hyper-arid desert. Both study sites had identical study designs. In the summer of 2011, we experimentally burned half of each experimental block, the other half remaining as an unburned control. Half of the burned plots were reburned 5 years later to simulate increasing burn frequency. We estimated non-woody plant biomass, cover, and density in plots from 2017 to 2020. Results Biomass did not vary between sites, but there was higher plant cover and lower plant density at the Mojave site than at the Great Basin site. Plant biomass, density, and cover varied significantly across the years, with stronger annual fluctuations in the Great Basin. At both desert sites, fire increased plant density and biomass but had no effect on the cover. The effect of fire on plant cover varied significantly between years for both deserts but was greater in the Great Basin than in the Mojave site. Repeat fires did not amplify initial fire effects. Conclusions The results suggest that in general annual fluctuations in fine fuel production and fluctuations in response to fire were more apparent at the Great Basin site than at the Mojave site, with no immediate compounding effect of repeat fires at either site.
{"title":"Fire frequency effects on plant community characteristics in the Great Basin and Mojave deserts of North America","authors":"Rebekah L. Stanton, Baylie C. Nusink, Kristina L. Cass, Tara B. B. Bishop, Brianna M. Woodbury, David N. Armond, Samuel B. St. Clair","doi":"10.1186/s42408-023-00222-2","DOIUrl":"https://doi.org/10.1186/s42408-023-00222-2","url":null,"abstract":"Abstract Background Wildfire regimes are changing dramatically across North American deserts with the spread of invasive grasses. Invasive grass fire cycles in historically fire-resistant deserts are resulting in larger and more frequent wildfire. This study experimentally compared how single and repeat fires influence invasive grass-dominated plant fuels in the Great Basin, a semi-arid, cold desert, and the Mojave, a hyper-arid desert. Both study sites had identical study designs. In the summer of 2011, we experimentally burned half of each experimental block, the other half remaining as an unburned control. Half of the burned plots were reburned 5 years later to simulate increasing burn frequency. We estimated non-woody plant biomass, cover, and density in plots from 2017 to 2020. Results Biomass did not vary between sites, but there was higher plant cover and lower plant density at the Mojave site than at the Great Basin site. Plant biomass, density, and cover varied significantly across the years, with stronger annual fluctuations in the Great Basin. At both desert sites, fire increased plant density and biomass but had no effect on the cover. The effect of fire on plant cover varied significantly between years for both deserts but was greater in the Great Basin than in the Mojave site. Repeat fires did not amplify initial fire effects. Conclusions The results suggest that in general annual fluctuations in fine fuel production and fluctuations in response to fire were more apparent at the Great Basin site than at the Mojave site, with no immediate compounding effect of repeat fires at either site.","PeriodicalId":12273,"journal":{"name":"Fire Ecology","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135995982","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-10DOI: 10.1186/s42408-023-00221-3
Frederick W. Rainsford, Katherine M. Giljohann, Andrew F. Bennett, Michael F. Clarke, Josephine MacHunter, Katharine Senior, Holly Sitters, Simon Watson, Luke T. Kelly
Abstract Background Understanding how temporal and spatial attributes of fire regimes, environmental conditions, and species’ traits interact to shape ecological communities will help improve biodiversity conservation in fire-affected areas. We compared the influence of time since the last fire at a site, and the area and diversity of post-fire successional vegetation surrounding a site (i.e., the “spatial context” of fire), on bird species and functional groups in two ecosystems in south-eastern Australia. These ecosystems, semi-arid “mallee” woodlands and temperate “foothill” forests, differ in stand-regeneration patterns, climate, and topography. For 22 bird species in mallee woodlands, 33 species in foothill forests and four functional groups of birds in both ecosystems, we fitted non-linear models that differed in fire regime predictor variables. Results In foothill forests, models that included both time since fire and a spatial context variable explained more variation in bird abundances than models that included only time since fire or a spatial variable. In mallee woodlands, the addition of spatial attributes of fire helped explain the occurrence of several species, but this finding was muted when measured across all species. There were key differences between ecosystems in functional group responses to fire regimes. Canopy/upper-midstorey foragers were positively associated with the amount of late -successional vegetation in mallee woodlands, but not in foothill forests. Lower-midstorey foragers showed a decline response to the amount of late -successional vegetation in mallee woodlands and a contrasting incline response in foothill forests. However, lower-midstorey foragers showed a similar response to the amount of surrounding early -successional vegetation in both ecosystems—decreasing in abundance when > 50% of the surrounding vegetation was early-successional. Conclusions The influence of fire regimes on birds varies among species within sites, across landscapes and between ecosystems. Species’ foraging traits influence bird associations with fire regimes, and help to make sense of a myriad of relationships, but are usefully understood in the context of ecosystem types and the regeneration patterns of their dominant flora. The spatial context of fire regimes is also important—the amount of successional vegetation surrounding a site influences bird abundance. Fire management strategies that incorporate the spatial contexts of fire regimes, as well as the temporal and ecological contexts of fire regimes, will have the greatest benefits for biodiversity.
{"title":"Ecosystem type and species’ traits help explain bird responses to spatial patterns of fire","authors":"Frederick W. Rainsford, Katherine M. Giljohann, Andrew F. Bennett, Michael F. Clarke, Josephine MacHunter, Katharine Senior, Holly Sitters, Simon Watson, Luke T. Kelly","doi":"10.1186/s42408-023-00221-3","DOIUrl":"https://doi.org/10.1186/s42408-023-00221-3","url":null,"abstract":"Abstract Background Understanding how temporal and spatial attributes of fire regimes, environmental conditions, and species’ traits interact to shape ecological communities will help improve biodiversity conservation in fire-affected areas. We compared the influence of time since the last fire at a site, and the area and diversity of post-fire successional vegetation surrounding a site (i.e., the “spatial context” of fire), on bird species and functional groups in two ecosystems in south-eastern Australia. These ecosystems, semi-arid “mallee” woodlands and temperate “foothill” forests, differ in stand-regeneration patterns, climate, and topography. For 22 bird species in mallee woodlands, 33 species in foothill forests and four functional groups of birds in both ecosystems, we fitted non-linear models that differed in fire regime predictor variables. Results In foothill forests, models that included both time since fire and a spatial context variable explained more variation in bird abundances than models that included only time since fire or a spatial variable. In mallee woodlands, the addition of spatial attributes of fire helped explain the occurrence of several species, but this finding was muted when measured across all species. There were key differences between ecosystems in functional group responses to fire regimes. Canopy/upper-midstorey foragers were positively associated with the amount of late -successional vegetation in mallee woodlands, but not in foothill forests. Lower-midstorey foragers showed a decline response to the amount of late -successional vegetation in mallee woodlands and a contrasting incline response in foothill forests. However, lower-midstorey foragers showed a similar response to the amount of surrounding early -successional vegetation in both ecosystems—decreasing in abundance when > 50% of the surrounding vegetation was early-successional. Conclusions The influence of fire regimes on birds varies among species within sites, across landscapes and between ecosystems. Species’ foraging traits influence bird associations with fire regimes, and help to make sense of a myriad of relationships, but are usefully understood in the context of ecosystem types and the regeneration patterns of their dominant flora. The spatial context of fire regimes is also important—the amount of successional vegetation surrounding a site influences bird abundance. Fire management strategies that incorporate the spatial contexts of fire regimes, as well as the temporal and ecological contexts of fire regimes, will have the greatest benefits for biodiversity.","PeriodicalId":12273,"journal":{"name":"Fire Ecology","volume":"89 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136295682","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-10DOI: 10.1186/s42408-023-00219-x
Sebastian U. Busby, Angela M. Klock, Jeremy S. Fried
Abstract Background Six synchronous, wind-driven, high severity megafires burned over 300,000 hectares of mesic temperate forest in the western Cascades of NW Oregon and SW Washington states in early September 2020. While remote sensing data has been utilized to estimate fire severity across the fires, assessments of fire impacts informed by field observations are missing. We compiled field measurement data, pre- and post-fire, from a statistically representative sample of existing forest inventory analysis (FIA) plots, to estimate stand-level fire effects indices that describe (1) tree survival and its implications for carbon emissions, (2) effects on tree crowns, and (3) effects on soils. Field observations were analyzed in relation to fire weather when plots burned and to evaluate accuracy of remotely sensed burn severity classifications. Results Wind speed strongly interacted with tree size and stand age to influence tree survival. Under high fuel aridity but light winds, young stands composed of small trees, found primarily on private lands, exhibited a much lower survival rate than older stands composed of medium to large trees, found primarily on federal lands. Under moderate to high winds, poor tree survival was characteristic of all forest structures and ownerships. Fire impacts on tree crowns were strongly related to wind speed, while fire impacts on soils were not. These fires transferred nearly 70 MMT CO 2 e from wood in live and growing trees to a combination of immediate smoke and carbon emissions, plus delayed emissions from dead wood, that will release most of the embodied carbon over the next few decades. These emissions will exceed all 2020 anthropogenic emissions in Oregon (64 MMT CO 2 e). Substantial discrepancies were observed between two remotely sensed burn severity products, BAER-SBS and MTBS-TC, and field observed soil organic matter cover and tree mortality, respectively. Conclusions Post-fire FIA plot remeasurements are valuable for understanding fire’s impact on forest ecosystems and as an empirical basis for model validation and hypothesis testing. This continuous forest inventory system will compound the value of these post-fire remeasurements, enabling analysis of post-fire forest ecosystem trajectories in relation to both immediate fire impacts and pre-fire conditions.
{"title":"Inventory analysis of fire effects wrought by wind-driven megafires in relation to weather and pre-fire forest structure in the western Cascades","authors":"Sebastian U. Busby, Angela M. Klock, Jeremy S. Fried","doi":"10.1186/s42408-023-00219-x","DOIUrl":"https://doi.org/10.1186/s42408-023-00219-x","url":null,"abstract":"Abstract Background Six synchronous, wind-driven, high severity megafires burned over 300,000 hectares of mesic temperate forest in the western Cascades of NW Oregon and SW Washington states in early September 2020. While remote sensing data has been utilized to estimate fire severity across the fires, assessments of fire impacts informed by field observations are missing. We compiled field measurement data, pre- and post-fire, from a statistically representative sample of existing forest inventory analysis (FIA) plots, to estimate stand-level fire effects indices that describe (1) tree survival and its implications for carbon emissions, (2) effects on tree crowns, and (3) effects on soils. Field observations were analyzed in relation to fire weather when plots burned and to evaluate accuracy of remotely sensed burn severity classifications. Results Wind speed strongly interacted with tree size and stand age to influence tree survival. Under high fuel aridity but light winds, young stands composed of small trees, found primarily on private lands, exhibited a much lower survival rate than older stands composed of medium to large trees, found primarily on federal lands. Under moderate to high winds, poor tree survival was characteristic of all forest structures and ownerships. Fire impacts on tree crowns were strongly related to wind speed, while fire impacts on soils were not. These fires transferred nearly 70 MMT CO 2 e from wood in live and growing trees to a combination of immediate smoke and carbon emissions, plus delayed emissions from dead wood, that will release most of the embodied carbon over the next few decades. These emissions will exceed all 2020 anthropogenic emissions in Oregon (64 MMT CO 2 e). Substantial discrepancies were observed between two remotely sensed burn severity products, BAER-SBS and MTBS-TC, and field observed soil organic matter cover and tree mortality, respectively. Conclusions Post-fire FIA plot remeasurements are valuable for understanding fire’s impact on forest ecosystems and as an empirical basis for model validation and hypothesis testing. This continuous forest inventory system will compound the value of these post-fire remeasurements, enabling analysis of post-fire forest ecosystem trajectories in relation to both immediate fire impacts and pre-fire conditions.","PeriodicalId":12273,"journal":{"name":"Fire Ecology","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136295315","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-02DOI: 10.1186/s42408-023-00212-4
Rodrigo San Martín, Catherine Ottlé, Anna Sörensson
Abstract Background Wildfires represent an important element in the bio-geophysical cycles of various ecosystems across the globe and are particularly related to land transformation in tropical and subtropical regions. In this study, we analyzed the links between fires, land use (LU), and meteorological variables in the South American Chaco (1.1 million km 2 ), a global deforestation hotspot and fire-exposed region that has recently attracted greater attention as the largest and one of the last tropical dry forests in the world. Results We found that the Dry Chaco (73% of the total area of Chaco) exhibits a unimodal fire seasonality (winter-spring), and the Wet Chaco (the remaining 23%) displays a bimodal seasonality (summer-autumn and winter-spring). While most of the burnt area (BA) was found in the Wet Chaco (113,859 km 2 ; 55% of the entire BA), the Dry Chaco showed the largest fraction of forest loss (93,261 km 2 ; 88% of the entire forest loss). Between 2001 and 2019, 26% of the entire Chaco’s forest loss occurred in areas with BA detections, and this percentage varies regionally and across countries, revealing potential connections to LU and policy. Argentina lost 51,409 km 2 of its Chaco tree cover, surpassing the forest losses of Paraguay and Bolivia, and 40% of this loss was related to fire detections. The effect of meteorological fluctuations on fuel production and flammability varies with land cover (LC), which emerged as the principal factor behind BA. While wet areas covered with herbaceous vegetation showed negative correlations between BA and precipitation, some dry regions below 800 mm/year, and mostly covered by shrublands, showed positive correlations. These results reveal the two different roles of precipitation in (a) moisture content and flammability and (b) production of biomass fuel. Conclusions As fires and deforestation keep expanding in the South American Chaco, our study represents a step forward to understanding their drivers and effects. BA is dependent on LC types, which explains the discrepancies in fire frequency and seasonality between the Wet and Dry Chaco subregions. The links between fires and deforestation also vary between regions and between countries, exposing the role of anthropic forcing, land management, and policy. To better understand the interactions between these drivers, further studies at regional scale combining environmental sciences with social sciences are needed. Such research should help policy makers take action to preserve and protect the remaining forests and wetlands of the Chaco.
野火是全球各种生态系统生物地球物理循环的重要组成部分，尤其与热带和亚热带地区的土地转化有关。在这项研究中，我们分析了南美洲查科(110万平方公里)的火灾、土地利用(LU)和气象变量之间的联系，查科是全球森林砍伐热点和火灾暴露地区，最近作为世界上最大和最后的热带干燥森林之一而引起了更大的关注。结果查科干区(占查科总面积的73%)表现为单峰性(冬春)，湿区(占查科总面积的23%)表现为双峰性(夏秋和冬春)。而大部分燃烧面积(BA)发现在湿查科(113,859 km 2;占整个BA的55%)，干查科的森林损失比例最大(93,261 km2;整个森林损失的88%)。2001年至2019年期间，查科整个森林损失的26%发生在发现BA的地区，这一比例因地区和国家而异，揭示了与LU和政策的潜在联系。阿根廷损失了51,409平方公里的查科树木覆盖面积，超过了巴拉圭和玻利维亚的森林损失，其中40%的损失与火灾探测有关。气象波动对燃料产量和可燃性的影响随土地覆盖(LC)的变化而变化，这是BA背后的主要因素。草本植被覆盖的湿润地区BA与降水呈负相关，而在800mm /年以下以灌丛为主的干旱地区BA与降水呈正相关。这些结果揭示了降水在(a)含水量和可燃性以及(b)生物质燃料生产中的两种不同作用。随着南美查科地区的火灾和森林砍伐不断扩大，我们的研究代表了了解其驱动因素和影响的一步。BA依赖于LC类型，这解释了干湿查科分区之间火灾频率和季节性的差异。火灾和森林砍伐之间的联系也因地区和国家而异，暴露了人为强迫、土地管理和政策的作用。为了更好地理解这些驱动因素之间的相互作用，需要进一步在区域尺度上进行环境科学与社会科学相结合的研究。这样的研究应该有助于决策者采取行动来保存和保护查科剩余的森林和湿地。
{"title":"Fires in the South American Chaco, from dry forests to wetlands: response to climate depends on land cover","authors":"Rodrigo San Martín, Catherine Ottlé, Anna Sörensson","doi":"10.1186/s42408-023-00212-4","DOIUrl":"https://doi.org/10.1186/s42408-023-00212-4","url":null,"abstract":"Abstract Background Wildfires represent an important element in the bio-geophysical cycles of various ecosystems across the globe and are particularly related to land transformation in tropical and subtropical regions. In this study, we analyzed the links between fires, land use (LU), and meteorological variables in the South American Chaco (1.1 million km 2 ), a global deforestation hotspot and fire-exposed region that has recently attracted greater attention as the largest and one of the last tropical dry forests in the world. Results We found that the Dry Chaco (73% of the total area of Chaco) exhibits a unimodal fire seasonality (winter-spring), and the Wet Chaco (the remaining 23%) displays a bimodal seasonality (summer-autumn and winter-spring). While most of the burnt area (BA) was found in the Wet Chaco (113,859 km 2 ; 55% of the entire BA), the Dry Chaco showed the largest fraction of forest loss (93,261 km 2 ; 88% of the entire forest loss). Between 2001 and 2019, 26% of the entire Chaco’s forest loss occurred in areas with BA detections, and this percentage varies regionally and across countries, revealing potential connections to LU and policy. Argentina lost 51,409 km 2 of its Chaco tree cover, surpassing the forest losses of Paraguay and Bolivia, and 40% of this loss was related to fire detections. The effect of meteorological fluctuations on fuel production and flammability varies with land cover (LC), which emerged as the principal factor behind BA. While wet areas covered with herbaceous vegetation showed negative correlations between BA and precipitation, some dry regions below 800 mm/year, and mostly covered by shrublands, showed positive correlations. These results reveal the two different roles of precipitation in (a) moisture content and flammability and (b) production of biomass fuel. Conclusions As fires and deforestation keep expanding in the South American Chaco, our study represents a step forward to understanding their drivers and effects. BA is dependent on LC types, which explains the discrepancies in fire frequency and seasonality between the Wet and Dry Chaco subregions. The links between fires and deforestation also vary between regions and between countries, exposing the role of anthropic forcing, land management, and policy. To better understand the interactions between these drivers, further studies at regional scale combining environmental sciences with social sciences are needed. Such research should help policy makers take action to preserve and protect the remaining forests and wetlands of the Chaco.","PeriodicalId":12273,"journal":{"name":"Fire Ecology","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135895116","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-27DOI: 10.1186/s42408-023-00209-z
Suzanne H. Blaydes, Jeffery B. Cannon, Doug P. Aubrey
Abstract Background Predicting patterns of fire behavior and effects in frequent fire forests relies on an understanding of fine-scale spatial patterns of available fuels. Leaf litter is a significant canopy-derived fine fuel in fire-maintained forests. Litter dispersal is dependent on foliage production, stand structure, and wind direction, but the relative importance of these factors is unknown. Results Using a 10-year litterfall dataset collected within eighteen 4-ha longleaf pine ( Pinus palustris Mill.) plots varying in canopy spatial pattern, we compared four spatially explicit models of annual needle litter dispersal: a model based only on basal area, an overstory abundance index (OAI) model, both isotropic and anisotropic litter kernel models, and a null model that assumed no spatial relationship. The best model was the anisotropic model (R 2 = 0.656) that incorporated tree size, location, and prevailing wind direction, followed by the isotropic model (R 2 = 0.612), basal area model (R 2 = 0.488), OAI model (R 2 = 0.416), and the null model (R 2 = 0.08). Conclusions As with previous studies, the predictive capability of the litter models was robust when internally validated with a subset of the original dataset (R 2 = 0.196–0.549); however, the models were less robust when challenged with an independent dataset (R 2 = 0.122–0.319) from novel forest stands. Our model validation underscores the need for rigorous tests with independent, external datasets to confirm the validity of litter dispersal models. These models can be used in the application of prescribed fire to estimate fuel distribution and loading, as well as aid in the fine tuning of fire behavior models to better understand fire outcomes across a range of forest canopy structures.
{"title":"Modeling spatial patterns of longleaf pine needle dispersal using long-term data","authors":"Suzanne H. Blaydes, Jeffery B. Cannon, Doug P. Aubrey","doi":"10.1186/s42408-023-00209-z","DOIUrl":"https://doi.org/10.1186/s42408-023-00209-z","url":null,"abstract":"Abstract Background Predicting patterns of fire behavior and effects in frequent fire forests relies on an understanding of fine-scale spatial patterns of available fuels. Leaf litter is a significant canopy-derived fine fuel in fire-maintained forests. Litter dispersal is dependent on foliage production, stand structure, and wind direction, but the relative importance of these factors is unknown. Results Using a 10-year litterfall dataset collected within eighteen 4-ha longleaf pine ( Pinus palustris Mill.) plots varying in canopy spatial pattern, we compared four spatially explicit models of annual needle litter dispersal: a model based only on basal area, an overstory abundance index (OAI) model, both isotropic and anisotropic litter kernel models, and a null model that assumed no spatial relationship. The best model was the anisotropic model (R 2 = 0.656) that incorporated tree size, location, and prevailing wind direction, followed by the isotropic model (R 2 = 0.612), basal area model (R 2 = 0.488), OAI model (R 2 = 0.416), and the null model (R 2 = 0.08). Conclusions As with previous studies, the predictive capability of the litter models was robust when internally validated with a subset of the original dataset (R 2 = 0.196–0.549); however, the models were less robust when challenged with an independent dataset (R 2 = 0.122–0.319) from novel forest stands. Our model validation underscores the need for rigorous tests with independent, external datasets to confirm the validity of litter dispersal models. These models can be used in the application of prescribed fire to estimate fuel distribution and loading, as well as aid in the fine tuning of fire behavior models to better understand fire outcomes across a range of forest canopy structures.","PeriodicalId":12273,"journal":{"name":"Fire Ecology","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135535485","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-25DOI: 10.1186/s42408-023-00206-2
Kristin Sweeney, Ruth Dittrich, Spencer Moffat, Chelsea Power, Jeffrey D. Kline
Abstract Background Wildfires are increasingly frequent in the Western US and impose a number of costs including from the instantaneous release of carbon when vegetation burns. Carbon released into the atmosphere aggravates climate change while carbon stored in vegetation helps to mitigate climate change. The need for climate change mitigation is becoming more and more urgent as achieving the Paris climate agreement target of limiting global warming to 1.5 °C seems ever more challenging. A clear understanding of the role of different carbon sources is required for understanding the degree of progress toward meeting mitigation objectives and assessing the cost and benefits of mitigation policies. Results We present an easily replicable approach to calculate the economic cost from carbon released instantaneously from wildfires at state and county level (US). Our approach is straightforward and relies exclusively on publicly available data that can be easily obtained for locations throughout the USA. We also describe how to apply social cost of carbon estimates to the carbon loss estimates to find the economic value of carbon released from wildfires. We demonstrate our approach using a case study of the 2017 Eagle Creek Fire in Oregon. Our estimated value of carbon lost for this medium-sized (19,400 ha) fire is $187.2 million (2020 dollars), which highlights the significant role that wildfires can have in terms of carbon emissions and their associated cost. The emissions from this fire were equivalent to as much as 2.3% of non-fire emissions for the state of Oregon in 2020. Conclusions Our results demonstrate an easily replicable method for estimating the economic cost of instantaneous carbon dioxide emissions for individual wildfires. Estimates of the potential economic costs associated with carbon dioxide emissions help to provide a more complete picture of the true economic costs of wildfires, thus facilitating a more complete picture of the potential benefits of wildfire management efforts.
{"title":"Estimating the economic value of carbon losses from wildfires using publicly available data sources: Eagle Creek Fire, Oregon 2017","authors":"Kristin Sweeney, Ruth Dittrich, Spencer Moffat, Chelsea Power, Jeffrey D. Kline","doi":"10.1186/s42408-023-00206-2","DOIUrl":"https://doi.org/10.1186/s42408-023-00206-2","url":null,"abstract":"Abstract Background Wildfires are increasingly frequent in the Western US and impose a number of costs including from the instantaneous release of carbon when vegetation burns. Carbon released into the atmosphere aggravates climate change while carbon stored in vegetation helps to mitigate climate change. The need for climate change mitigation is becoming more and more urgent as achieving the Paris climate agreement target of limiting global warming to 1.5 °C seems ever more challenging. A clear understanding of the role of different carbon sources is required for understanding the degree of progress toward meeting mitigation objectives and assessing the cost and benefits of mitigation policies. Results We present an easily replicable approach to calculate the economic cost from carbon released instantaneously from wildfires at state and county level (US). Our approach is straightforward and relies exclusively on publicly available data that can be easily obtained for locations throughout the USA. We also describe how to apply social cost of carbon estimates to the carbon loss estimates to find the economic value of carbon released from wildfires. We demonstrate our approach using a case study of the 2017 Eagle Creek Fire in Oregon. Our estimated value of carbon lost for this medium-sized (19,400 ha) fire is $187.2 million (2020 dollars), which highlights the significant role that wildfires can have in terms of carbon emissions and their associated cost. The emissions from this fire were equivalent to as much as 2.3% of non-fire emissions for the state of Oregon in 2020. Conclusions Our results demonstrate an easily replicable method for estimating the economic cost of instantaneous carbon dioxide emissions for individual wildfires. Estimates of the potential economic costs associated with carbon dioxide emissions help to provide a more complete picture of the true economic costs of wildfires, thus facilitating a more complete picture of the potential benefits of wildfire management efforts.","PeriodicalId":12273,"journal":{"name":"Fire Ecology","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135770912","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-20DOI: 10.1186/s42408-023-00216-0
Khubab Ahmad, Muhammad Shahbaz Khan, Fawad Ahmed, Maha Driss, Wadii Boulila, Abdulwahab Alazeb, Mohammad Alsulami, Mohammed S. Alshehri, Yazeed Yasin Ghadi, Jawad Ahmad
Abstract Background Forests cover nearly one-third of the Earth’s land and are some of our most biodiverse ecosystems. Due to climate change, these essential habitats are endangered by increasing wildfires. Wildfires are not just a risk to the environment, but they also pose public health risks. Given these issues, there is an indispensable need for efficient and early detection methods. Conventional detection approaches fall short due to spatial limitations and manual feature engineering, which calls for the exploration and development of data-driven deep learning solutions. This paper, in this regard, proposes 'FireXnet', a tailored deep learning model designed for improved efficiency and accuracy in wildfire detection. FireXnet is tailored to have a lightweight architecture that exhibits high accuracy with significantly less training and testing time. It contains considerably reduced trainable and non-trainable parameters, which makes it suitable for resource-constrained devices. To make the FireXnet model visually explainable and trustable, a powerful explainable artificial intelligence (AI) tool, SHAP (SHapley Additive exPlanations) has been incorporated. It interprets FireXnet’s decisions by computing the contribution of each feature to the prediction. Furthermore, the performance of FireXnet is compared against five pre-trained models — VGG16, InceptionResNetV2, InceptionV3, DenseNet201, and MobileNetV2 — to benchmark its efficiency. For a fair comparison, transfer learning and fine-tuning have been applied to the aforementioned models to retrain the models on our dataset. Results The test accuracy of the proposed FireXnet model is 98.42%, which is greater than all other models used for comparison. Furthermore, results of reliability parameters confirm the model’s reliability, i.e., a confidence interval of [0.97, 1.00] validates the certainty of the proposed model’s estimates and a Cohen’s kappa coefficient of 0.98 proves that decisions of FireXnet are in considerable accordance with the given data. Conclusion The integration of the robust feature extraction of FireXnet with the transparency of explainable AI using SHAP enhances the model’s interpretability and allows for the identification of key characteristics triggering wildfire detections. Extensive experimentation reveals that in addition to being accurate, FireXnet has reduced computational complexity due to considerably fewer training and non-training parameters and has significantly fewer training and testing times.
{"title":"FireXnet: an explainable AI-based tailored deep learning model for wildfire detection on resource-constrained devices","authors":"Khubab Ahmad, Muhammad Shahbaz Khan, Fawad Ahmed, Maha Driss, Wadii Boulila, Abdulwahab Alazeb, Mohammad Alsulami, Mohammed S. Alshehri, Yazeed Yasin Ghadi, Jawad Ahmad","doi":"10.1186/s42408-023-00216-0","DOIUrl":"https://doi.org/10.1186/s42408-023-00216-0","url":null,"abstract":"Abstract Background Forests cover nearly one-third of the Earth’s land and are some of our most biodiverse ecosystems. Due to climate change, these essential habitats are endangered by increasing wildfires. Wildfires are not just a risk to the environment, but they also pose public health risks. Given these issues, there is an indispensable need for efficient and early detection methods. Conventional detection approaches fall short due to spatial limitations and manual feature engineering, which calls for the exploration and development of data-driven deep learning solutions. This paper, in this regard, proposes 'FireXnet', a tailored deep learning model designed for improved efficiency and accuracy in wildfire detection. FireXnet is tailored to have a lightweight architecture that exhibits high accuracy with significantly less training and testing time. It contains considerably reduced trainable and non-trainable parameters, which makes it suitable for resource-constrained devices. To make the FireXnet model visually explainable and trustable, a powerful explainable artificial intelligence (AI) tool, SHAP (SHapley Additive exPlanations) has been incorporated. It interprets FireXnet’s decisions by computing the contribution of each feature to the prediction. Furthermore, the performance of FireXnet is compared against five pre-trained models — VGG16, InceptionResNetV2, InceptionV3, DenseNet201, and MobileNetV2 — to benchmark its efficiency. For a fair comparison, transfer learning and fine-tuning have been applied to the aforementioned models to retrain the models on our dataset. Results The test accuracy of the proposed FireXnet model is 98.42%, which is greater than all other models used for comparison. Furthermore, results of reliability parameters confirm the model’s reliability, i.e., a confidence interval of [0.97, 1.00] validates the certainty of the proposed model’s estimates and a Cohen’s kappa coefficient of 0.98 proves that decisions of FireXnet are in considerable accordance with the given data. Conclusion The integration of the robust feature extraction of FireXnet with the transparency of explainable AI using SHAP enhances the model’s interpretability and allows for the identification of key characteristics triggering wildfire detections. Extensive experimentation reveals that in addition to being accurate, FireXnet has reduced computational complexity due to considerably fewer training and non-training parameters and has significantly fewer training and testing times.","PeriodicalId":12273,"journal":{"name":"Fire Ecology","volume":"161 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136264032","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-19DOI: 10.1186/s42408-023-00214-2
Hope Fillingim, Benjamin O. Knapp, John M. Kabrick, Michael C. Stambaugh, Grant P. Elliott, Daniel C. Dey
Abstract Background Shortleaf pine is a fire-adapted tree species, and prescribed fire is commonly used to increase its regeneration success, improve wildlife habitat, and reach conservation objectives associated with open forest ecosystems. We studied direct effects of heat and smoke on shortleaf pine germination in a greenhouse study and effects of season of burning on the number of new germinants in a field study. Improved understanding of fire effects on shortleaf pine seed and regeneration success can help refine burn prescriptions to better meet specific management objectives. Results Temperatures ≥ 120 °C eliminated germination of shortleaf pine seeds in a greenhouse trial, and exposure of seeds to 60 °C resulted in no reduction in germination compared to the unheated control regardless of duration of exposure. At 80 °C, duration of heat exposure mattered, with exposure for 10 min reducing germination compared to unheated controls. Smoke exposure had no effect on germination. A field experiment showed that fall burns (prior to seedfall) resulted in greater initial germinant counts than early spring burns (after seedfall but before germination) or unburned controls, which both resulted in greater initial germinant counts than late spring burns (after germination). Conclusions Season of prescribed burning can affect the success of shortleaf pine germination. Late spring burning resulted in high mortality of young germinants. Burning in early spring likely resulted in direct damage to some seeds due to heating but may have also had indirect benefit by exposing mineral soil. Fall burning, before the dispersal of shortleaf pine seed, yielded the highest germinant count and is recommended if improving natural regeneration from seed is the primary objective.
{"title":"Direct and indirect effects of fire on germination of shortleaf pine seeds","authors":"Hope Fillingim, Benjamin O. Knapp, John M. Kabrick, Michael C. Stambaugh, Grant P. Elliott, Daniel C. Dey","doi":"10.1186/s42408-023-00214-2","DOIUrl":"https://doi.org/10.1186/s42408-023-00214-2","url":null,"abstract":"Abstract Background Shortleaf pine is a fire-adapted tree species, and prescribed fire is commonly used to increase its regeneration success, improve wildlife habitat, and reach conservation objectives associated with open forest ecosystems. We studied direct effects of heat and smoke on shortleaf pine germination in a greenhouse study and effects of season of burning on the number of new germinants in a field study. Improved understanding of fire effects on shortleaf pine seed and regeneration success can help refine burn prescriptions to better meet specific management objectives. Results Temperatures ≥ 120 °C eliminated germination of shortleaf pine seeds in a greenhouse trial, and exposure of seeds to 60 °C resulted in no reduction in germination compared to the unheated control regardless of duration of exposure. At 80 °C, duration of heat exposure mattered, with exposure for 10 min reducing germination compared to unheated controls. Smoke exposure had no effect on germination. A field experiment showed that fall burns (prior to seedfall) resulted in greater initial germinant counts than early spring burns (after seedfall but before germination) or unburned controls, which both resulted in greater initial germinant counts than late spring burns (after germination). Conclusions Season of prescribed burning can affect the success of shortleaf pine germination. Late spring burning resulted in high mortality of young germinants. Burning in early spring likely resulted in direct damage to some seeds due to heating but may have also had indirect benefit by exposing mineral soil. Fall burning, before the dispersal of shortleaf pine seed, yielded the highest germinant count and is recommended if improving natural regeneration from seed is the primary objective.","PeriodicalId":12273,"journal":{"name":"Fire Ecology","volume":"174 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135010935","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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