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
Models of above-ground tree biomass have been widely used to estimate forest biomass using national forest inventory data.However,many sources of uncertainty affect above-ground biomass estimation and are challenging ...Models of above-ground tree biomass have been widely used to estimate forest biomass using national forest inventory data.However,many sources of uncertainty affect above-ground biomass estimation and are challenging to assess.In this study,the uncertainties associated with the measurement error in independent variables(diameter at breast height,tree height),residual variability,variances of the parameter estimates,and the sampling variability of national inventory data are estimated for five above-ground biomass models.The results show sampling variability is the most significant source of uncertainty.The measurement error and residual variability have negligible effects on forests above-ground biomass estimations.Thus,a reduction in the uncertainty of the sampling variability has the greatest potential to decrease the overall uncertainty.The power model containing only the diameter at breast height has the smallest uncertainty.The findings of this study provide suggestions to achieve a trade-off between accuracy and cost for above-ground biomass estimation using field work.展开更多
We contrast a new continuous approach(CA)for estimating plot-level above-ground biomass(AGB)in forest inventories with the current approach of estimating AGB exclusively from the tree-level AGB predicted for each tree...We contrast a new continuous approach(CA)for estimating plot-level above-ground biomass(AGB)in forest inventories with the current approach of estimating AGB exclusively from the tree-level AGB predicted for each tree in a plot,henceforth called DA(discrete approach).With the CA,the AGB in a forest is modelled as a continuous surface and the AGB estimate for a fixed-area plot is computed as the integral of the AGB surface taken over the plot area.Hence with the CA,the portion of the biomass of in-plot trees that extends across the plot perimeter is ignored while the biomass from trees outside of the plot reaching inside the plot is added.We use a sampling simulation with data from a fully mapped two hectare area to illustrate that important differences in plot-level AGB estimates can emerge.Ideally CA-based estimates of mean AGB should be less variable than those derived from the DA.If realized,this difference translates to a higher precision from field sampling,or a lower required sample size.In our case study with a target precision of 5%(i.e.relative standard error of the estimated mean AGB),the CA required a 27.1%lower sample size for small plots of 100 m2 and a 10.4%lower sample size for larger plots of 1700 m2.We examined sampling induced errors only and did not yet consider model errors.We discuss practical issues in implementing the CA in field inventories and the potential in applications that model biomass with remote sensing data.The CA is a variation on a plot design for above-ground forest biomass;as such it can be applied in combination with any forest inventory sampling design.展开更多
针对正交频分复用(OFDM)系统的时延(the time of arrival,TOA)估计未充分利用OFDM信号的时频特性及其精度较低的问题,根据OFDM信号的时频特性提出一种基于频域相偏的多径时延估计模型(Multipath Delay Estimation Based on Frequency Do...针对正交频分复用(OFDM)系统的时延(the time of arrival,TOA)估计未充分利用OFDM信号的时频特性及其精度较低的问题,根据OFDM信号的时频特性提出一种基于频域相偏的多径时延估计模型(Multipath Delay Estimation Based on Frequency Domain Phase-offset,FDP-MDE).并在此基础上,结合LTE(Long Term Evolution)系统的实际特点提出一种分组联合时延估计算法(Grouped Joint Time Delay Estimation,GJ-TDE).该算法首先将OFDM系统的时域接收信号转换为频域信号,然后对频域信号采样分成多组低维的接收数据矩阵并利用各采样数据矩阵组分级估计时延,最后取各时延估计值的平均作为定位时延值.仿真结果表明:在信噪比(SNR)为0dB、采样间隔为8的条件下,GJ-TDE算法的均方根误差(RMSE)比基于时域同步的Mensing算法降低了5.503 4m.展开更多
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.
基金supported financially by the National Key R&D Program of China(Grant No.2017YFC0506503-02)。
文摘Models of above-ground tree biomass have been widely used to estimate forest biomass using national forest inventory data.However,many sources of uncertainty affect above-ground biomass estimation and are challenging to assess.In this study,the uncertainties associated with the measurement error in independent variables(diameter at breast height,tree height),residual variability,variances of the parameter estimates,and the sampling variability of national inventory data are estimated for five above-ground biomass models.The results show sampling variability is the most significant source of uncertainty.The measurement error and residual variability have negligible effects on forests above-ground biomass estimations.Thus,a reduction in the uncertainty of the sampling variability has the greatest potential to decrease the overall uncertainty.The power model containing only the diameter at breast height has the smallest uncertainty.The findings of this study provide suggestions to achieve a trade-off between accuracy and cost for above-ground biomass estimation using field work.
文摘We contrast a new continuous approach(CA)for estimating plot-level above-ground biomass(AGB)in forest inventories with the current approach of estimating AGB exclusively from the tree-level AGB predicted for each tree in a plot,henceforth called DA(discrete approach).With the CA,the AGB in a forest is modelled as a continuous surface and the AGB estimate for a fixed-area plot is computed as the integral of the AGB surface taken over the plot area.Hence with the CA,the portion of the biomass of in-plot trees that extends across the plot perimeter is ignored while the biomass from trees outside of the plot reaching inside the plot is added.We use a sampling simulation with data from a fully mapped two hectare area to illustrate that important differences in plot-level AGB estimates can emerge.Ideally CA-based estimates of mean AGB should be less variable than those derived from the DA.If realized,this difference translates to a higher precision from field sampling,or a lower required sample size.In our case study with a target precision of 5%(i.e.relative standard error of the estimated mean AGB),the CA required a 27.1%lower sample size for small plots of 100 m2 and a 10.4%lower sample size for larger plots of 1700 m2.We examined sampling induced errors only and did not yet consider model errors.We discuss practical issues in implementing the CA in field inventories and the potential in applications that model biomass with remote sensing data.The CA is a variation on a plot design for above-ground forest biomass;as such it can be applied in combination with any forest inventory sampling design.
文摘针对正交频分复用(OFDM)系统的时延(the time of arrival,TOA)估计未充分利用OFDM信号的时频特性及其精度较低的问题,根据OFDM信号的时频特性提出一种基于频域相偏的多径时延估计模型(Multipath Delay Estimation Based on Frequency Domain Phase-offset,FDP-MDE).并在此基础上,结合LTE(Long Term Evolution)系统的实际特点提出一种分组联合时延估计算法(Grouped Joint Time Delay Estimation,GJ-TDE).该算法首先将OFDM系统的时域接收信号转换为频域信号,然后对频域信号采样分成多组低维的接收数据矩阵并利用各采样数据矩阵组分级估计时延,最后取各时延估计值的平均作为定位时延值.仿真结果表明:在信噪比(SNR)为0dB、采样间隔为8的条件下,GJ-TDE算法的均方根误差(RMSE)比基于时域同步的Mensing算法降低了5.503 4m.
