Background： Currently, the common and feasible way to estimate the most accurate forest biomass requires ground measurements and allometric models.Previous studies have been conducted on allometric equations developm...Background： Currently, the common and feasible way to estimate the most accurate forest biomass requires ground measurements and allometric models.Previous studies have been conducted on allometric equations development for estimating tree aboveground biomass（AGB） of tropical dipterocarp forests（TDFs） in Kalimantan（Indonesian Borneo）.However, before the use of existing equations, a validation for the selection of the best allometric equation is required to assess the model bias and precision.This study aims at evaluating the validity of local and pantropical equations; developing new allometric equations for estimating tree AGB in TDFs of Kalimantan; and validating the new equations using independent datasets.Methods： We used 108 tree samples from destructive sampling to develop the allometric equations, with maximum tree diameter of 175 cm and another 109 samples from previous studies for validating our equations.We performed ordinary least squares linear regression to explore the relationship between the AGB and the predictor variables in the natural logarithmic form.Results： This study found that most of the existing local equations tended to be biased and imprecise, with mean relative error and mean absolute relative error more than 0.1 and 0.3, respectively.We developed new allometric equations for tree AGB estimation in the TDFs of Kalimantan.Through a validation using an independent dataset,we found that our equations were reliable in estimating tree AGB in TDF.The pantropical equation, which includes tree diameter, wood density and total height as predictor variables performed only slightly worse than our new models.Conclusions： Our equations improve the precision and reduce the bias of AGB estimates of TDFs.Local models developed from small samples tend to systematically bias.A validation of existing AGB models is essential before the use of the models.展开更多
基金the GIZ-Forclime project, a bilateral project between Indonesia and German governments, for funding the field measurements
文摘Background： Currently, the common and feasible way to estimate the most accurate forest biomass requires ground measurements and allometric models.Previous studies have been conducted on allometric equations development for estimating tree aboveground biomass（AGB） of tropical dipterocarp forests（TDFs） in Kalimantan（Indonesian Borneo）.However, before the use of existing equations, a validation for the selection of the best allometric equation is required to assess the model bias and precision.This study aims at evaluating the validity of local and pantropical equations; developing new allometric equations for estimating tree AGB in TDFs of Kalimantan; and validating the new equations using independent datasets.Methods： We used 108 tree samples from destructive sampling to develop the allometric equations, with maximum tree diameter of 175 cm and another 109 samples from previous studies for validating our equations.We performed ordinary least squares linear regression to explore the relationship between the AGB and the predictor variables in the natural logarithmic form.Results： This study found that most of the existing local equations tended to be biased and imprecise, with mean relative error and mean absolute relative error more than 0.1 and 0.3, respectively.We developed new allometric equations for tree AGB estimation in the TDFs of Kalimantan.Through a validation using an independent dataset,we found that our equations were reliable in estimating tree AGB in TDF.The pantropical equation, which includes tree diameter, wood density and total height as predictor variables performed only slightly worse than our new models.Conclusions： Our equations improve the precision and reduce the bias of AGB estimates of TDFs.Local models developed from small samples tend to systematically bias.A validation of existing AGB models is essential before the use of the models.
