Fuel moisture content is one of the important factors that determine ignition probability and fire behaviour in forest ecosystems.In this study,ignition and fire spread moisture content thresholds of 40 dead fuel were...Fuel moisture content is one of the important factors that determine ignition probability and fire behaviour in forest ecosystems.In this study,ignition and fire spread moisture content thresholds of 40 dead fuel were performed in laboratory experiments,with a focus on the source of ignition and wind speed.Variability in fuel moisture content at time of ignition and during fire spread was observed for different fuels.Matches were more efficient to result in ignition and spread fire with high values of fuel moisture content compared to the use of cigarette butts.Some fuels did not ignite at 15%moisture content,whereas others ignited at 40%moisture content and fire spread at 38%moisture content in the case of matches,or ignited at 27%moisture content and spread fire at 25%moisture content using cigarette butts.A two-way ANOVA showed that both the source of ignition and the wind speed affected ignition and fire spread threshold significantly,but there was no interaction between these factors.The relationship between ignition and fire spread was strong,with R2=98%for cigarette butts,and 92%for matches.Further information is needed,especially on the density of fuels,fuel proportion(case of mixed fuels),fuel age,and fuel combustibility.展开更多
The moisture content of dead forest fuel is an important indicator of risk levels of forest fires and prediction of fire spread. Moisture distribution is important to determine wild fire rating. However, it is often d...The moisture content of dead forest fuel is an important indicator of risk levels of forest fires and prediction of fire spread. Moisture distribution is important to determine wild fire rating. However, it is often difficult to predict moisture distribution because of a complex terrain, changeable environments and low cover of commercial communication signals inside the forest. This study proposes a moisture content prediction system composed of environmental data collected using a long range radio frequency band 433 MHz wireless sensor network and data processing for moisture prediction based on a BP (back-propagation) neural network. In the fall of 2019, twenty nodes for the collection of environmental data were placed in four forest stands of Maoershan National Forest for a month;7440 sets of data including temperature, humidity, wind speed and air pressure were obtained. Half the data were used as a training set, the other as a testing set for a BP neural network. The results show that the average absolute error between the predicted value and the real value of moisture content of fuels of Larix gmelini, Betula platyphylla, Juglans mandshurica, and Quercus mongolica stands was 0.94%, 0.21%, 0.86%, 0.97%, respectively. The prediction accuracy was relatively high. The proposed distributed moisture content prediction method has the advantages of wide coverage and good real-time performance;at the same time, it is not limited by commercial signals and so it is especially suitable for forest fire prediction in remote mountainous areas.展开更多
In this study,310 destructively sampled plots were used to develop two equation systems for the three main pine species in NW Spain(P.pinaster;P.radiata and P.sylvestris):one for estimating loads of understorey fuel c...In this study,310 destructively sampled plots were used to develop two equation systems for the three main pine species in NW Spain(P.pinaster;P.radiata and P.sylvestris):one for estimating loads of understorey fuel components by size and condition(live and dead)and another one for forest floor fuels.Additive systems of equations were simultaneously fitted for estimating fuel loads using overstorey,understorey and forest floor variables as regressors.The systems of equations included both the effect of pine species and the effect of understorey compositions dominated by ferns-brambles or by woody species,due to their obvious structural and physiological differences.In general,the goodness-of-fit statistics indicated that the estimates were reasonably robust and accurate for all of the fuel fractions.The best results were obtained for total understorey vegetation,total forest floor and raw humus fuel loads,with more than 76%of the observed variability explained,whereas the poorest results were obtained for coarse fuel loads of understory vegetation with a 53%of observed variability explained.To reduce the overall costs associated with the field inventories necessary for operational use of the models,the additive systems were fitted again using only overstorey variables as potential regressors.Only relationships for fine(<6 mm)and total understorey vegetation and total forest floor fuel loads were obtained,indicating the complexity of the forest overstorey-understorey and overstorey-forest floor relationships.Nevertheless,these models explained around 52%of the observed variability.Finally,equations estimating the total understorey vegetation and the total forest floor fuel loads based only on canopy cover were fitted.These models explained only 26%-32%of the observed variability;however,their main advantage is that although understorey vegetation in forested landscapes is largely invisible to remote sensing,canopy cover can be estimated with moderate accuracy,allowing for landscape-scale estimates of total fuel loads.The equations represent an appreciable advance in understorey and forest floor fuel load assessment in the region and areas with similar characteristics and may be instrumental in generating fuel maps,fire management improvement and better C storage assessment by vegetation type,among many other uses.展开更多
基金funded by the National Key Research and Development Program of China(2018YFE0207800)the Fundamental Research Funds for the Central Universities(2572019CP10)+1 种基金the National Innovation Alliance of Wildland Fire Prevention and Control Technology of Chinathe Northern Forest Fire Management Key Laboratory of the State Forestry and Grassland Bureau。
文摘Fuel moisture content is one of the important factors that determine ignition probability and fire behaviour in forest ecosystems.In this study,ignition and fire spread moisture content thresholds of 40 dead fuel were performed in laboratory experiments,with a focus on the source of ignition and wind speed.Variability in fuel moisture content at time of ignition and during fire spread was observed for different fuels.Matches were more efficient to result in ignition and spread fire with high values of fuel moisture content compared to the use of cigarette butts.Some fuels did not ignite at 15%moisture content,whereas others ignited at 40%moisture content and fire spread at 38%moisture content in the case of matches,or ignited at 27%moisture content and spread fire at 25%moisture content using cigarette butts.A two-way ANOVA showed that both the source of ignition and the wind speed affected ignition and fire spread threshold significantly,but there was no interaction between these factors.The relationship between ignition and fire spread was strong,with R2=98%for cigarette butts,and 92%for matches.Further information is needed,especially on the density of fuels,fuel proportion(case of mixed fuels),fuel age,and fuel combustibility.
基金This work was supported by the Fundamental Research Funds for the Central Universities(Grant No.2572020AW43NO.2572019CP19)+2 种基金the National Natural Science Foundation of China(Grant No.31470715)the Natural Science Foundation of Hei-longjiang Province(Grant No.TD2020C001)the project for cultivating excellent doctoral dissertation of forestry engineering(Grant No.LYGCYB202009).
文摘The moisture content of dead forest fuel is an important indicator of risk levels of forest fires and prediction of fire spread. Moisture distribution is important to determine wild fire rating. However, it is often difficult to predict moisture distribution because of a complex terrain, changeable environments and low cover of commercial communication signals inside the forest. This study proposes a moisture content prediction system composed of environmental data collected using a long range radio frequency band 433 MHz wireless sensor network and data processing for moisture prediction based on a BP (back-propagation) neural network. In the fall of 2019, twenty nodes for the collection of environmental data were placed in four forest stands of Maoershan National Forest for a month;7440 sets of data including temperature, humidity, wind speed and air pressure were obtained. Half the data were used as a training set, the other as a testing set for a BP neural network. The results show that the average absolute error between the predicted value and the real value of moisture content of fuels of Larix gmelini, Betula platyphylla, Juglans mandshurica, and Quercus mongolica stands was 0.94%, 0.21%, 0.86%, 0.97%, respectively. The prediction accuracy was relatively high. The proposed distributed moisture content prediction method has the advantages of wide coverage and good real-time performance;at the same time, it is not limited by commercial signals and so it is especially suitable for forest fire prediction in remote mountainous areas.
基金funded by following projects:INIA p5608,INIA p7613,INIA p8038,INIA 9130 and INIA SC96-034 of the Sectorial Research Program of the INIA(Spanish National Institute of Agrarian Research,Ministry of Agriculture),INIA-RTA 2009-00153-C03(INFOCOPAS),INIA-RTA 2014-00011-C06(GEPRIF)and INIA-RTA2017-00042-C05(VIS4FIRE)of the Spanish National Program of Research,Development and Innovation co-funded by the ERDF Program of the European Unionby project CTYO-0087 of the Science and Technology for Environmental Protection Program and projects ENV5V-CT94-0473,ENV4CT98-0701(SALTUS),ENV-CT97-0715(FIRE TORCH),EVG1-CT200100041(FIRESTAR),EVR1-CT-2002-4002(EUFIRELAB)and CTFP6018505(FIRE PARADOX)+1 种基金funded by the Environment Program of the Directorate-General for Research and Innovation,of the European Commission of the European Unionby project PGIDITOSRF050202PR of the Xunta de Galicia。
文摘In this study,310 destructively sampled plots were used to develop two equation systems for the three main pine species in NW Spain(P.pinaster;P.radiata and P.sylvestris):one for estimating loads of understorey fuel components by size and condition(live and dead)and another one for forest floor fuels.Additive systems of equations were simultaneously fitted for estimating fuel loads using overstorey,understorey and forest floor variables as regressors.The systems of equations included both the effect of pine species and the effect of understorey compositions dominated by ferns-brambles or by woody species,due to their obvious structural and physiological differences.In general,the goodness-of-fit statistics indicated that the estimates were reasonably robust and accurate for all of the fuel fractions.The best results were obtained for total understorey vegetation,total forest floor and raw humus fuel loads,with more than 76%of the observed variability explained,whereas the poorest results were obtained for coarse fuel loads of understory vegetation with a 53%of observed variability explained.To reduce the overall costs associated with the field inventories necessary for operational use of the models,the additive systems were fitted again using only overstorey variables as potential regressors.Only relationships for fine(<6 mm)and total understorey vegetation and total forest floor fuel loads were obtained,indicating the complexity of the forest overstorey-understorey and overstorey-forest floor relationships.Nevertheless,these models explained around 52%of the observed variability.Finally,equations estimating the total understorey vegetation and the total forest floor fuel loads based only on canopy cover were fitted.These models explained only 26%-32%of the observed variability;however,their main advantage is that although understorey vegetation in forested landscapes is largely invisible to remote sensing,canopy cover can be estimated with moderate accuracy,allowing for landscape-scale estimates of total fuel loads.The equations represent an appreciable advance in understorey and forest floor fuel load assessment in the region and areas with similar characteristics and may be instrumental in generating fuel maps,fire management improvement and better C storage assessment by vegetation type,among many other uses.
