Background：Depending on tree and site characteristics crown biomass accounts for a significant portion of the total aboveground biomass in the tree.Crown biomass estimation is useful for different purposes including ...Background：Depending on tree and site characteristics crown biomass accounts for a significant portion of the total aboveground biomass in the tree.Crown biomass estimation is useful for different purposes including evaluating the economic feasibility of crown utilization for energy production or forest products,fuel load assessments and fire management strategies,and wildfire modeling.However,crown biomass is difficult to predict because of the variability within and among species and sites.Thus the allometric equations used for predicting crown biomass should be based on data collected with precise and unbiased sampling strategies.In this study,we evaluate the performance different sampling strategies to estimate crown biomass and to evaluate the effect of sample size in estimating crown biomass.Methods：Using data collected from 20 destructively sampled trees,we evaluated 11 different sampling strategies using six evaluation statistics：bias,relative bias,root mean square error（RMSE）,relative RMSE,amount of biomass sampled,and relative biomass sampled.We also evaluated the performance of the selected sampling strategies when different numbers of branches（3,6,9,and 12）are selected from each tree.Tree specific log linear model with branch diameter and branch length as covariates was used to obtain individual branch biomass.Results：Compared to all other methods stratified sampling with probability proportional to size estimation technique produced better results when three or six branches per tree were sampled.However,the systematic sampling with ratio estimation technique was the best when at least nine branches per tree were sampled.Under the stratified sampling strategy,selecting unequal number of branches per stratum produced approximately similar results to simple random sampling,but it further decreased RMSE when information on branch diameter is used in the design and estimation phases.Conclusions：Use of auxiliary information in design or estimation phase reduces the RMSE produced by a sampling strategy.However,this is attained by having to sample larger amount of biomass.Based on our finding we would recommend sampling nine branches per tree to be reasonably efficient and limit the amount of fieldwork.展开更多
基金the Forest Inventory Analysis Unit for funding the data collection and analysis phases of this project
文摘Background：Depending on tree and site characteristics crown biomass accounts for a significant portion of the total aboveground biomass in the tree.Crown biomass estimation is useful for different purposes including evaluating the economic feasibility of crown utilization for energy production or forest products,fuel load assessments and fire management strategies,and wildfire modeling.However,crown biomass is difficult to predict because of the variability within and among species and sites.Thus the allometric equations used for predicting crown biomass should be based on data collected with precise and unbiased sampling strategies.In this study,we evaluate the performance different sampling strategies to estimate crown biomass and to evaluate the effect of sample size in estimating crown biomass.Methods：Using data collected from 20 destructively sampled trees,we evaluated 11 different sampling strategies using six evaluation statistics：bias,relative bias,root mean square error（RMSE）,relative RMSE,amount of biomass sampled,and relative biomass sampled.We also evaluated the performance of the selected sampling strategies when different numbers of branches（3,6,9,and 12）are selected from each tree.Tree specific log linear model with branch diameter and branch length as covariates was used to obtain individual branch biomass.Results：Compared to all other methods stratified sampling with probability proportional to size estimation technique produced better results when three or six branches per tree were sampled.However,the systematic sampling with ratio estimation technique was the best when at least nine branches per tree were sampled.Under the stratified sampling strategy,selecting unequal number of branches per stratum produced approximately similar results to simple random sampling,but it further decreased RMSE when information on branch diameter is used in the design and estimation phases.Conclusions：Use of auxiliary information in design or estimation phase reduces the RMSE produced by a sampling strategy.However,this is attained by having to sample larger amount of biomass.Based on our finding we would recommend sampling nine branches per tree to be reasonably efficient and limit the amount of fieldwork.
