Preventing and suppressing forest fires is one of the main tasks of forestry agencies to reduce resource loss and requires a thorough understanding of the importance of factors affecting their occurrence.This study wa...Preventing and suppressing forest fires is one of the main tasks of forestry agencies to reduce resource loss and requires a thorough understanding of the importance of factors affecting their occurrence.This study was carried out in forest plantations on Maoer Mountain in order to develop models for predicting the moisture content of dead fine fuel using meteorological and soil variables.Models by Nelson(Can J For Res 14:597-600,1984)and Van Wagner and Pickett(Can For Service 33,1985)describing the equilibrium moisture content as a function of relative humidity and temperature were evaluated.A random forest and generalized additive models were built to select the most important meteorological variables affecting fuel moisture content.Nelson’s(Can J For Res 14:597-600,1984)model was accurate for Pinus koraiensis,Pinus sylvestris,Larix gmelinii and mixed Larix gmelinii—Ulmus propinqua fuels.The random forest model showed that temperature and relative humidity were the most important factors affecting fuel moisture content.The generalized additive regression model showed that temperature,relative humidity and rain were the main drivers affecting fuel moisture content.In addition to the combined effects of temperature,rainfall and relative humidity,solar radiation or wind speed were also significant on some sites.In P.koraiensis and P.sylvestris plantations,where soil parameters were measured,rain,soil moisture and temperature were the main factors of fuel moisture content.The accuracies of the random forest model and generalized additive model were similar,however,the random forest model was more accurate but underestimated the effect of rain on fuel moisture.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 lo展开更多
基金the National Key Research and Development Program of ChinaKey Projects for Strategic International Innovative Cooperation in Science and Technology(2018YFE0207800)+1 种基金Fundamental Research Funds for the Central Universities(2572019BA03)partly by the China Scholarship Council(CSC No.2016DFH417)。
文摘Preventing and suppressing forest fires is one of the main tasks of forestry agencies to reduce resource loss and requires a thorough understanding of the importance of factors affecting their occurrence.This study was carried out in forest plantations on Maoer Mountain in order to develop models for predicting the moisture content of dead fine fuel using meteorological and soil variables.Models by Nelson(Can J For Res 14:597-600,1984)and Van Wagner and Pickett(Can For Service 33,1985)describing the equilibrium moisture content as a function of relative humidity and temperature were evaluated.A random forest and generalized additive models were built to select the most important meteorological variables affecting fuel moisture content.Nelson’s(Can J For Res 14:597-600,1984)model was accurate for Pinus koraiensis,Pinus sylvestris,Larix gmelinii and mixed Larix gmelinii—Ulmus propinqua fuels.The random forest model showed that temperature and relative humidity were the most important factors affecting fuel moisture content.The generalized additive regression model showed that temperature,relative humidity and rain were the main drivers affecting fuel moisture content.In addition to the combined effects of temperature,rainfall and relative humidity,solar radiation or wind speed were also significant on some sites.In P.koraiensis and P.sylvestris plantations,where soil parameters were measured,rain,soil moisture and temperature were the main factors of fuel moisture content.The accuracies of the random forest model and generalized additive model were similar,however,the random forest model was more accurate but underestimated the effect of rain on fuel moisture.
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 lo
