Improving Woody Biomass Estimation Efficiency Using Double Sampling

David L Parrott,Songlin Fei,B Scott Shouse,John M Lhotka

doi:10.3390/f3020179

Abstract

Although double sampling has been shown to be an effective method to estimate timber volume in forest inventories, only a limited body of research has tested the effectiveness of double sampling on forest biomass estimation. From forest biomass inventories collected over 9,683 ha using systematic point sampling, we examined how a double sampling scheme would have affected precision and efficiency in these biomass inventories. Our results indicated that double sample methods would have yielded biomass estimations with similar precision as systematic point sampling when the small sample was ≥ 20% of the large sample. When the small to large sample time ratio was 3:1, relative efficiency (a combined measure of time and precision) was highest when the small sample was a 30% subsample of the large sample. At a 30% double sample intensity, there was a < 3% deviation from the original percent margin of error and almost half the required time. Results suggest that double sampling can be an efficient tool for natural resource managers to estimate forest biomass.

Highlights

The introduction of carbon markets and the potential for future bioenergy markets have heightened interest in quantifying forest biomass
Evaluation of different time requirement ratios indicate that double sampling provides little improvement in inventory efficiency when the time ratio between small and large sample points is 2:1 and that efficiency is maximized at 20% to 30% subsampling intensity when ratios are 4:1 and 6:1
These double sampling results were derived from systematic point sample inventories that were successful in estimating mean property basal area with a margin of error ≤10%

Summary

Introduction

The introduction of carbon markets and the potential for future bioenergy markets have heightened interest in quantifying forest biomass. Double sampling is designed to lower inventory times by providing an estimate of a target variable by utilizing a highly correlated auxiliary variable that is easy to measure [2]. Double sampling requires the sampling of two sets of points: a small set of sample points where the target and auxiliary variables are measured and a large set of sample points where only the auxiliary variable is measured. These two sets of points can be separate, or the small sample can be a subset of the large sample. Once the relationship between the target and auxiliary variable is calculated from the small sample data, regression analysis or a ratio estimator can be used to estimate the target variable from the auxiliary variable collected in the large sample

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