Primary challenges with a forest inventory program are surveyors with various levels of experience and the turnover of inexperienced surveyors.Few studies have looked at the consistency of an instrument’s results amo...Primary challenges with a forest inventory program are surveyors with various levels of experience and the turnover of inexperienced surveyors.Few studies have looked at the consistency of an instrument’s results among inexperienced surveyors.Most studies have assessed whether instruments were significantly different.These tests do not indicate whether instruments were statistically equivalent,i.e.,that choosing either one would be acceptable under a certain level of tolerance.This study evaluated the consistency and statistical equivalence among instruments for measuring diameter at breast height(DBH)and for total tree height(HT)among inexperienced surveyors.The study was conducted as a randomized experiment with students from an introductory tree measurement course,using four types of DBH and HT instruments,and with different tree attributes.For DBH,the results show that D-tape was the most consistent across tree attributes and teams of inexperienced surveyors and was only statistically interchangeable with Caliper with a tolerance≥3 cm.For HT,Ultrasound was the most consistent but only statistically interchangeable with Laser with a tolerance≥8 m.A single type of instrument for measuring DBH and for HT is recommended,especially when field crews may be a mixture of experienced and inexperienced surveyors.Our study provides initial recommendations on the choice of instruments when either purchasing new ones or replacing old ones in forest inventories.展开更多
Background:The double sampling method known as“big BAF sampling”has been advocated as a way to reduce sampling effort while still maintaining a reasonably precise estimate of volume.A well-known method for variance ...Background:The double sampling method known as“big BAF sampling”has been advocated as a way to reduce sampling effort while still maintaining a reasonably precise estimate of volume.A well-known method for variance determination,Bruce’s method,is customarily used because the volume estimator takes the form of a product of random variables.However,the genesis of Bruce’s method is not known to most foresters who use the method in practice.Methods:We establish that the Taylor series approximation known as the Delta method provides a plausible explanation for the origins of Bruce’s method.Simulations were conducted on two different tree populations to ascertain the similarities of the Delta method to the exact variance of a product.Additionally,two alternative estimators for the variance of individual tree volume-basal area ratios,which are part of the estimation process,were compared within the overall variance estimation procedure.Results:The simulation results demonstrate that Bruce’s method provides a robust method for estimating the variance of inventories conducted with the big BAF method.The simulations also demonstrate that the variance of the mean volume-basal area ratios can be computed using either the usual sample variance of the mean or the ratio variance estimators with equal accuracy,which had not been shown previously for Big BAF sampling.Conclusions:A plausible explanation for the origins of Bruce’s method has been set forth both historically and mathematically in the Delta Method.In most settings,there is evidently no practical difference between applying the exact variance of a product or the Delta method—either can be used.A caution is articulated concerning the aggregation of tree-wise attributes into point-wise summaries in order to test the correlation between the two as a possible indicator of the need for further covariance augmentation.展开更多
Background:A new variance estimator is derived and tested for big BAF(Basal Area Factor)sampling which is a forest inventory system that utilizes Bitterlich sampling(point sampling)with two BAF sizes,a small BAF for t...Background:A new variance estimator is derived and tested for big BAF(Basal Area Factor)sampling which is a forest inventory system that utilizes Bitterlich sampling(point sampling)with two BAF sizes,a small BAF for tree counts and a larger BAF on which tree measurements are made usually including DBHs and heights needed for volume estimation.Methods:The new estimator is derived using the Delta method from an existing formulation of the big BAF estimator as consisting of three sample means.The new formula is compared to existing big BAF estimators including a popular estimator based on Bruce’s formula.Results:Several computer simulation studies were conducted comparing the new variance estimator to all known variance estimators for big BAF currently in the forest inventory literature.In simulations the new estimator performed well and comparably to existing variance formulas.Conclusions:A possible advantage of the new estimator is that it does not require the assumption of negligible correlation between basal area counts on the small BAF factor and volume-basal area ratios based on the large BAF factor selection trees,an assumption required by all previous big BAF variance estimation formulas.Although this correlation was negligible on the simulation stands used in this study,it is conceivable that the correlation could be significant in some forest types,such as those in which the DBH-height relationship can be affected substantially by density perhaps through competition.We derived a formula that can be used to estimate the covariance between estimates of mean basal area and the ratio of estimates of mean volume and mean basal area.We also mathematically derived expressions for bias in the big BAF estimator that can be used to show the bias approaches zero in large samples on the order of 1n where n is the number of sample points.展开更多
文摘Primary challenges with a forest inventory program are surveyors with various levels of experience and the turnover of inexperienced surveyors.Few studies have looked at the consistency of an instrument’s results among inexperienced surveyors.Most studies have assessed whether instruments were significantly different.These tests do not indicate whether instruments were statistically equivalent,i.e.,that choosing either one would be acceptable under a certain level of tolerance.This study evaluated the consistency and statistical equivalence among instruments for measuring diameter at breast height(DBH)and for total tree height(HT)among inexperienced surveyors.The study was conducted as a randomized experiment with students from an introductory tree measurement course,using four types of DBH and HT instruments,and with different tree attributes.For DBH,the results show that D-tape was the most consistent across tree attributes and teams of inexperienced surveyors and was only statistically interchangeable with Caliper with a tolerance≥3 cm.For HT,Ultrasound was the most consistent but only statistically interchangeable with Laser with a tolerance≥8 m.A single type of instrument for measuring DBH and for HT is recommended,especially when field crews may be a mixture of experienced and inexperienced surveyors.Our study provides initial recommendations on the choice of instruments when either purchasing new ones or replacing old ones in forest inventories.
基金Research Joint Venture Agreement 17-JV-11242306045,“Old Growth Forest Dynamics and Structure,”between the USDA Forest Service and the University of New Hampshire.Additional support to MJD was provided by the USDA National Institute of Food and Agriculture McIntire-Stennis Project Accession Number 1020142,“Forest Structure,Volume,and Biomass in the Northeastern United States.”TBL:This work was supported by the USDA National Institute of Food and Agriculture,McIntire-Stennis project OKL02834 and the Division of Agricultural Sciences and Natural Resources at Oklahoma State University.
文摘Background:The double sampling method known as“big BAF sampling”has been advocated as a way to reduce sampling effort while still maintaining a reasonably precise estimate of volume.A well-known method for variance determination,Bruce’s method,is customarily used because the volume estimator takes the form of a product of random variables.However,the genesis of Bruce’s method is not known to most foresters who use the method in practice.Methods:We establish that the Taylor series approximation known as the Delta method provides a plausible explanation for the origins of Bruce’s method.Simulations were conducted on two different tree populations to ascertain the similarities of the Delta method to the exact variance of a product.Additionally,two alternative estimators for the variance of individual tree volume-basal area ratios,which are part of the estimation process,were compared within the overall variance estimation procedure.Results:The simulation results demonstrate that Bruce’s method provides a robust method for estimating the variance of inventories conducted with the big BAF method.The simulations also demonstrate that the variance of the mean volume-basal area ratios can be computed using either the usual sample variance of the mean or the ratio variance estimators with equal accuracy,which had not been shown previously for Big BAF sampling.Conclusions:A plausible explanation for the origins of Bruce’s method has been set forth both historically and mathematically in the Delta Method.In most settings,there is evidently no practical difference between applying the exact variance of a product or the Delta method—either can be used.A caution is articulated concerning the aggregation of tree-wise attributes into point-wise summaries in order to test the correlation between the two as a possible indicator of the need for further covariance augmentation.
基金Support was provided by Research Joint Venture Agreement 17-JV-11242306045,“Old Growth Forest Dynamics and Structure,”between the USDA Forest Service and the University of New HampshireAdditional support to MJD was provided by the USDA National Institute of Food and Agriculture McIntire-Stennis Project Accession Number 1020142,“Forest Structure,Volume,and Biomass in the Northeastern United States.”+1 种基金supported by the USDA National Institute of Food and Agriculture,McIntire-Stennis project OKL02834the Division of Agricultural Sciences and Natural Resources at Oklahoma State University.
文摘Background:A new variance estimator is derived and tested for big BAF(Basal Area Factor)sampling which is a forest inventory system that utilizes Bitterlich sampling(point sampling)with two BAF sizes,a small BAF for tree counts and a larger BAF on which tree measurements are made usually including DBHs and heights needed for volume estimation.Methods:The new estimator is derived using the Delta method from an existing formulation of the big BAF estimator as consisting of three sample means.The new formula is compared to existing big BAF estimators including a popular estimator based on Bruce’s formula.Results:Several computer simulation studies were conducted comparing the new variance estimator to all known variance estimators for big BAF currently in the forest inventory literature.In simulations the new estimator performed well and comparably to existing variance formulas.Conclusions:A possible advantage of the new estimator is that it does not require the assumption of negligible correlation between basal area counts on the small BAF factor and volume-basal area ratios based on the large BAF factor selection trees,an assumption required by all previous big BAF variance estimation formulas.Although this correlation was negligible on the simulation stands used in this study,it is conceivable that the correlation could be significant in some forest types,such as those in which the DBH-height relationship can be affected substantially by density perhaps through competition.We derived a formula that can be used to estimate the covariance between estimates of mean basal area and the ratio of estimates of mean volume and mean basal area.We also mathematically derived expressions for bias in the big BAF estimator that can be used to show the bias approaches zero in large samples on the order of 1n where n is the number of sample points.
