The dead fuel moisture content(DFMC)is the key driver leading to fire occurrence.Accurately estimating the DFMC could help identify locations facing fire risks,prioritise areas for fire monitoring,and facilitate timel...The dead fuel moisture content(DFMC)is the key driver leading to fire occurrence.Accurately estimating the DFMC could help identify locations facing fire risks,prioritise areas for fire monitoring,and facilitate timely deployment of fire-suppression resources.In this study,the DFMC and environmental variables,including air temperature,relative humidity,wind speed,solar radiation,rainfall,atmospheric pressure,soil temperature,and soil humidity,were simultaneously measured in a grassland of Ergun City,Inner Mongolia Autonomous Region of China in 2021.We chose three regression models,i.e.,random forest(RF)model,extreme gradient boosting(XGB)model,and boosted regression tree(BRT)model,to model the seasonal DFMC according to the data collected.To ensure accuracy,we added time-lag variables of 3 d to the models.The results showed that the RF model had the best fitting effect with an R2value of 0.847 and a prediction accuracy with a mean absolute error score of 4.764%among the three models.The accuracies of the models in spring and autumn were higher than those in the other two seasons.In addition,different seasons had different key influencing factors,and the degree of influence of these factors on the DFMC changed with time lags.Moreover,time-lag variables within 44 h clearly improved the fitting effect and prediction accuracy,indicating that environmental conditions within approximately 48 h greatly influence the DFMC.This study highlights the importance of considering 48 h time-lagged variables when predicting the DFMC of grassland fuels and mapping grassland fire risks based on the DFMC to help locate high-priority areas for grassland fire monitoring and prevention.展开更多
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
The rate of fire spread is a key indicator for assessing forest fire risk and developing fire management plans.The Rothermel model is the most widely used fire spread model,established through laboratory experiments o...The rate of fire spread is a key indicator for assessing forest fire risk and developing fire management plans.The Rothermel model is the most widely used fire spread model,established through laboratory experiments on homogeneous fuels but has not been validated for conifer-deciduous mixed fuel.In this study,Pinus koraiensis and Quercus mongolica litter was used in a laboratory burning experiment to simulate surface fire spread in the field.The effects of fuel moisture content,mixed fuel ratio and slope on spread rate were analyzed.The optimum packing ratio,moisture-damping coefficient and slope parameters in the Rothermel model were modified using the measured spread rate which was positively correlated with slope and negatively with fuel moisture content.As the Q.mongolica load increased,the spread rate increased and was highest at a fuel ratio of 4:6.The model with modified optimal packing ratio and slope parameters has a significantly lower spread rate prediction error than the unmodified model.The spread rate prediction accuracy was significantly improved after modifying the model parameters based on spread rates from laboratory burning simulations.展开更多
Forest fuel investigations in central and southern Siberian taiga of Scots pine forest stands dominated by lichen and feather moss ground vegetation cover revealed that total aboveground biomass varied from 13.1 to 21...Forest fuel investigations in central and southern Siberian taiga of Scots pine forest stands dominated by lichen and feather moss ground vegetation cover revealed that total aboveground biomass varied from 13.1 to 21.0 kg/m 2.Stand biomass was higher in plots in the southern taiga,while ground fuel loads were higher in the central taiga.We developed equations for fuel biomass(both aerial and ground)that could be applicable to similar pine forest sites of Central Siberia.Fuel loading variability found among plots is related to the impact and recovery time since the last wildfi re and the mosaic distribution of living vegetation.Fuel consumption due to surface fi res of low to high-intensities ranged from 0.95 to 3.08 kg/m 2,that is,18–74%from prefi re values.The total amount of fuels available to burn in case of fi re was up to 4.5–6.5 kg/m 2.Moisture content of fuels(litter,lichen,feather moss)was related to weather conditions characterized by the Russian Fire Danger Index(PV-1)and FWI code of the Canadian Forest Fire Weather Index System.The data obtained provide a strong foundation for understanding and modeling fi re behavior,emissions,and fi re eff ects on ecosystem processes and carbon stocks and could be used to improve existing global and regional models that incorporate biomass and fuel characteristics.展开更多
[Objective] The aim was to study the regression relationship between water index （WI） and fuel moisture content （FMC） of different growth periods of sawtooth oaks leaf.[Method] Taking sawtooth oaks in Huaguo Mount...[Objective] The aim was to study the regression relationship between water index （WI） and fuel moisture content （FMC） of different growth periods of sawtooth oaks leaf.[Method] Taking sawtooth oaks in Huaguo Mountain,Lianyungang City as research object,the sensitivity of WI to leaf FMC was studied at leaf level,and statistical characteristics were analyzed.[Result] The WI of sawtooth oaks leaves was sensitive to the changes of FMC,and the line regression level between them was significant.A fitting curve between leaf FMC and WI was obtained.[Conclusion] The research provides reference for acquisition methods of vegetation water remote sensing within the range of study area.展开更多
Based on the physical concept of heat energy of pre-ignition,a new fire susceptibility index (FSI) is used to estimate forest fire risk. This physical basis allows calculation of ignition probabilities and comparisons...Based on the physical concept of heat energy of pre-ignition,a new fire susceptibility index (FSI) is used to estimate forest fire risk. This physical basis allows calculation of ignition probabilities and comparisons of fire risk across eco-regions. The computation of the index requires inputs of fuel temperature and fuel moisture content (FMC),both of which can be estimated using remote sensing data. While ASTER data for land surface temperatures (LST) was used as proxys for fuel temperatures,fuel moisture content is estimated by regression technique utilizing the ratio NDVI/LST of ASTER data. FSIs are computed in peninsular Malaysia for nine days before the fires of 2004 and 2005 and validated with fire occurrence data. Results show that the FSI increases as the day approaches the fire day. This trend can be observed clearly about four days before the day of fire. It suggests that FSI can be a good estimator of fire risk. The physical basis provides a more meaningful FSI,allows calculation of ignition probabilities and facilitates the development of a future class of fire risk models. FSI can be used to compare fire risk across different eco-regions and time periods. FSI retains the flexibility to be localized to a vegetation type or eco-regions for improved performance.展开更多
The use of the power tillers （walking tractors） are increasingly popular in Nigeria among farmers adopting Sawah rice production technology. This study compares the effects of two types of power tillers on physical ...The use of the power tillers （walking tractors） are increasingly popular in Nigeria among farmers adopting Sawah rice production technology. This study compares the effects of two types of power tillers on physical properties of soil and rice yield. because soil properties determine to a large extents rice yield. Data on soil properties and rice yield were collected and subjected to t-test statistics. The results show that, significant differences exit for all the physical properties of soils rice yields from fields where the two power tiller were used with yields from rice field where SHAKTI was used was higher than KUBOTA. It is important that farmers using these models of power tillers for rice production should not only focus on the purchase cost of these power tillers but their overall efficiency in order to achieve the desired high level of yield.展开更多
Introduction:The Canadian Forest Fire Danger Rating System(CFFDRS)is a globally known wildland fire risk assessment system,and two major components,the fire weather index system and the fire behavior prediction system...Introduction:The Canadian Forest Fire Danger Rating System(CFFDRS)is a globally known wildland fire risk assessment system,and two major components,the fire weather index system and the fire behavior prediction system,have been extensively used both nationally and internationally to aid operational wildland fire decision making.Methods:In this paper,we present an overview of an R package cffdrs,which is developed to calculate components of the CFFDRS,and highlight some of its functionality.In particular,we demonstrate how these functions could be used for large data analysis.Results and Discussion:With this cffdrs package,we provide a portal for not only a collection of R functions dealing with all available components in CFFDRS but also a platform for various additional developments that are useful for the understanding of fire occurrence and behavior.This is the first time that all relevant CFFDRS methods are incorporated into the same platform,which can be accessed by both the management and research communities.展开更多
Wildfires over permafrost put perennially frozen carbon at risk.However,wildfire emissions from biomass burning over the diverse range of permafrost regions and their share in global wildfire emissions have not been r...Wildfires over permafrost put perennially frozen carbon at risk.However,wildfire emissions from biomass burning over the diverse range of permafrost regions and their share in global wildfire emissions have not been revealed.The results showed a dramatic increase in wildfire carbon emissions from permafrost regions over the period 1997–2021.The share of permafrost in global wildfire CO_(2) emissions increased from 2.42%in 1997 to 20.86%in 2021.Accelerating wildfire emissions from continuous permafrost region is the single largest contributor to increased emissions in northern permafrost regions.Fire-induced emissions from 2019 to 2021 alone accounted for approximately 40%of the 25-year total CO_(2) emissions from continuous permafrost regions.The rise in wildfire emissions from continuous permafrost regions is explained by desiccation within a 5–10 cm soil depth,where wildfires combust belowground fuel.These findings highlight the acceleration of fire-induced carbon emissions from continuous permafrost regions,which disturb the organic carbon stock and accelerate the positive feedback between permafrost degradation and climate warming,thus stimulating permafrost towards a climatic tipping point.展开更多
基金funded by the National Key Research and Development Program of China Strategic International Cooperation in Science and Technology Innovation Program (2018YFE0207800)the National Natural Science Foundation of China (31971483)。
文摘The dead fuel moisture content(DFMC)is the key driver leading to fire occurrence.Accurately estimating the DFMC could help identify locations facing fire risks,prioritise areas for fire monitoring,and facilitate timely deployment of fire-suppression resources.In this study,the DFMC and environmental variables,including air temperature,relative humidity,wind speed,solar radiation,rainfall,atmospheric pressure,soil temperature,and soil humidity,were simultaneously measured in a grassland of Ergun City,Inner Mongolia Autonomous Region of China in 2021.We chose three regression models,i.e.,random forest(RF)model,extreme gradient boosting(XGB)model,and boosted regression tree(BRT)model,to model the seasonal DFMC according to the data collected.To ensure accuracy,we added time-lag variables of 3 d to the models.The results showed that the RF model had the best fitting effect with an R2value of 0.847 and a prediction accuracy with a mean absolute error score of 4.764%among the three models.The accuracies of the models in spring and autumn were higher than those in the other two seasons.In addition,different seasons had different key influencing factors,and the degree of influence of these factors on the DFMC changed with time lags.Moreover,time-lag variables within 44 h clearly improved the fitting effect and prediction accuracy,indicating that environmental conditions within approximately 48 h greatly influence the DFMC.This study highlights the importance of considering 48 h time-lagged variables when predicting the DFMC of grassland fuels and mapping grassland fire risks based on the DFMC to help locate high-priority areas for grassland fire monitoring and prevention.
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supported by the National Key Research and Development Program of China(Grant No.2020YFC1511603)the Fundamental Research Funds for the Central Universities(2572021BA04).
文摘The rate of fire spread is a key indicator for assessing forest fire risk and developing fire management plans.The Rothermel model is the most widely used fire spread model,established through laboratory experiments on homogeneous fuels but has not been validated for conifer-deciduous mixed fuel.In this study,Pinus koraiensis and Quercus mongolica litter was used in a laboratory burning experiment to simulate surface fire spread in the field.The effects of fuel moisture content,mixed fuel ratio and slope on spread rate were analyzed.The optimum packing ratio,moisture-damping coefficient and slope parameters in the Rothermel model were modified using the measured spread rate which was positively correlated with slope and negatively with fuel moisture content.As the Q.mongolica load increased,the spread rate increased and was highest at a fuel ratio of 4:6.The model with modified optimal packing ratio and slope parameters has a significantly lower spread rate prediction error than the unmodified model.The spread rate prediction accuracy was significantly improved after modifying the model parameters based on spread rates from laboratory burning simulations.
基金Cooperation and logistical support of the Russian Aerial Forest Protection Service(Avialesookhrana)and Russian Forest Service(Regional and Local Forestry Committees)is greatly appreciated.A special thanks to L.Bobkova,N.Koshurnikova,and E.Krasnoshchekova for their assistance in fuel sampling and to D.Randall for statistical analysis of tree data.
文摘Forest fuel investigations in central and southern Siberian taiga of Scots pine forest stands dominated by lichen and feather moss ground vegetation cover revealed that total aboveground biomass varied from 13.1 to 21.0 kg/m 2.Stand biomass was higher in plots in the southern taiga,while ground fuel loads were higher in the central taiga.We developed equations for fuel biomass(both aerial and ground)that could be applicable to similar pine forest sites of Central Siberia.Fuel loading variability found among plots is related to the impact and recovery time since the last wildfi re and the mosaic distribution of living vegetation.Fuel consumption due to surface fi res of low to high-intensities ranged from 0.95 to 3.08 kg/m 2,that is,18–74%from prefi re values.The total amount of fuels available to burn in case of fi re was up to 4.5–6.5 kg/m 2.Moisture content of fuels(litter,lichen,feather moss)was related to weather conditions characterized by the Russian Fire Danger Index(PV-1)and FWI code of the Canadian Forest Fire Weather Index System.The data obtained provide a strong foundation for understanding and modeling fi re behavior,emissions,and fi re eff ects on ecosystem processes and carbon stocks and could be used to improve existing global and regional models that incorporate biomass and fuel characteristics.
基金Supported by Natural Science Foundation of Jiangsu Province(BK2009627)~~
文摘[Objective] The aim was to study the regression relationship between water index （WI） and fuel moisture content （FMC） of different growth periods of sawtooth oaks leaf.[Method] Taking sawtooth oaks in Huaguo Mountain,Lianyungang City as research object,the sensitivity of WI to leaf FMC was studied at leaf level,and statistical characteristics were analyzed.[Result] The WI of sawtooth oaks leaves was sensitive to the changes of FMC,and the line regression level between them was significant.A fitting curve between leaf FMC and WI was obtained.[Conclusion] The research provides reference for acquisition methods of vegetation water remote sensing within the range of study area.
基金Projects KSTAS/MACRES/T/2/2004 supported by the Airborne Remote Sensing (MARS) Program of Malaysia, 4067113040671122 by the National Natural Science Foundation of China
文摘Based on the physical concept of heat energy of pre-ignition,a new fire susceptibility index (FSI) is used to estimate forest fire risk. This physical basis allows calculation of ignition probabilities and comparisons of fire risk across eco-regions. The computation of the index requires inputs of fuel temperature and fuel moisture content (FMC),both of which can be estimated using remote sensing data. While ASTER data for land surface temperatures (LST) was used as proxys for fuel temperatures,fuel moisture content is estimated by regression technique utilizing the ratio NDVI/LST of ASTER data. FSIs are computed in peninsular Malaysia for nine days before the fires of 2004 and 2005 and validated with fire occurrence data. Results show that the FSI increases as the day approaches the fire day. This trend can be observed clearly about four days before the day of fire. It suggests that FSI can be a good estimator of fire risk. The physical basis provides a more meaningful FSI,allows calculation of ignition probabilities and facilitates the development of a future class of fire risk models. FSI can be used to compare fire risk across different eco-regions and time periods. FSI retains the flexibility to be localized to a vegetation type or eco-regions for improved performance.
文摘The use of the power tillers （walking tractors） are increasingly popular in Nigeria among farmers adopting Sawah rice production technology. This study compares the effects of two types of power tillers on physical properties of soil and rice yield. because soil properties determine to a large extents rice yield. Data on soil properties and rice yield were collected and subjected to t-test statistics. The results show that, significant differences exit for all the physical properties of soils rice yields from fields where the two power tiller were used with yields from rice field where SHAKTI was used was higher than KUBOTA. It is important that farmers using these models of power tillers for rice production should not only focus on the purchase cost of these power tillers but their overall efficiency in order to achieve the desired high level of yield.
文摘Introduction:The Canadian Forest Fire Danger Rating System(CFFDRS)is a globally known wildland fire risk assessment system,and two major components,the fire weather index system and the fire behavior prediction system,have been extensively used both nationally and internationally to aid operational wildland fire decision making.Methods:In this paper,we present an overview of an R package cffdrs,which is developed to calculate components of the CFFDRS,and highlight some of its functionality.In particular,we demonstrate how these functions could be used for large data analysis.Results and Discussion:With this cffdrs package,we provide a portal for not only a collection of R functions dealing with all available components in CFFDRS but also a platform for various additional developments that are useful for the understanding of fire occurrence and behavior.This is the first time that all relevant CFFDRS methods are incorporated into the same platform,which can be accessed by both the management and research communities.
基金supported by the National Key R&D Program of China(2022YFF0801904)。
文摘Wildfires over permafrost put perennially frozen carbon at risk.However,wildfire emissions from biomass burning over the diverse range of permafrost regions and their share in global wildfire emissions have not been revealed.The results showed a dramatic increase in wildfire carbon emissions from permafrost regions over the period 1997–2021.The share of permafrost in global wildfire CO_(2) emissions increased from 2.42%in 1997 to 20.86%in 2021.Accelerating wildfire emissions from continuous permafrost region is the single largest contributor to increased emissions in northern permafrost regions.Fire-induced emissions from 2019 to 2021 alone accounted for approximately 40%of the 25-year total CO_(2) emissions from continuous permafrost regions.The rise in wildfire emissions from continuous permafrost regions is explained by desiccation within a 5–10 cm soil depth,where wildfires combust belowground fuel.These findings highlight the acceleration of fire-induced carbon emissions from continuous permafrost regions,which disturb the organic carbon stock and accelerate the positive feedback between permafrost degradation and climate warming,thus stimulating permafrost towards a climatic tipping point.
