Application of big BAF sampling for estimating carbon on small woodlots

Abstract

Background: To accurately and efficiently quantify forest carbon stocks, a good forest inventory using appropriate sampling that minimizes costs and human effort is needed for landowners who want to enter carbon offset markets. The most commonly used sampling unit is the fixed-area plot; however, it is time consuming, expensive, and is often less accurate than variable probability methods when resources are limited. Previous studies show that big BAF sampling is efficient at estimating volume, therefore, it is interesting to explore whether the efficiency can be extended to carbon. The study is conducted at Noonan Research Forest, which located 30 km northwest of Fredericton, New Brunswick, Canada. In this study, we compared count BAF effects and measure BAF effects on the overall sampling outcome and sampling error for total aboveground C and each C component (wood, bark, branches, and foliage) and explored the minimum sample size requirements and costs for different combinations of count and measure BAFs. Results: From our research, we found that the efficiency gained from estimating volume using big BAF sampling can be extended to carbon estimation. The minimum overall inventory cost from this study is $3500 Canadian, compared to a full Noonan inventory costs of $40,000 with 2% standard error. We also found that, similar to volume, count BAF has a larger effect on carbon estimation than measure BAF and the optimum choice of measure BAF depends on the choice of count BAF. The optimal count BAF and measure BAF combination for Noonan Research Forest was 2/24. Conclusion: Our results show that big BAF sampling was a very efficient sampling design for estimating carbon and significantly reduces overall inventory costs. Although big BAF sampling is not widely used in forest inventory, it should be considered by landowners facing the cost constraint barrier for entering carbon market and seeking a cost-effective inventory system for estimating carbon.

Two Contemporary and Efficient Two-Stage Sampling Methods for Estimating the Volume of Forest Stands: A Brief Overview and Unified Mathematical Description

“…Big BAF has been applied also in mixed species forest structures (Yang et al, 2017), with uneven-age and multistory oak-pine stands (Lindemuth, 2007) and in plantations, like loblolly pine with spatial heterogeneity (Yang & Burkhart, 2018). Geographically, the Big BAF applied in Canada and the western United States (Brooks, 2006;Chen, Yang, Hsu, Kershaw, & Prest, 2019;Corrin, 1998;Desmarais, 2002;Marshall et al, 2004), while studies have been done for potential applicability in northeastern North America (Brooks, 2006;Burk, 2004;Deegan, 2011;Lindemuth, 2007;Yang et al, 2017) and in Europe (Samiotis & Stamatellos, 2011).…”

“…The main applications of Big BAF sampling concerns estimations of stand volume (Lindemuth, 2007;Marshall et al, 2004;Opalach, 2017;Yang & Burkhart, 2018), including total volume, merchantable volume, sawlog volume (saw timber) and pulpwood volume (Kershaw Jr. et al, 2016b;Yang et al, 2017), generally for timber cruising (Corrin, 1998). Current applications of Big BAF turns from estimations of volume to estimations of the average forest biomass (Ellis et al, 2019;Griscom, Ellis, & Putz, 2014) and carbon, demonstrating the efficiency and overall inventory costs reduction of this method (Chen et al, 2019). Additionally, Big BAF is suitable for landowners that seeking a cost-effective method for estimating carbon (Chen et al, 2019).…”

“…Current applications of Big BAF turns from estimations of volume to estimations of the average forest biomass (Ellis et al, 2019;Griscom, Ellis, & Putz, 2014) and carbon, demonstrating the efficiency and overall inventory costs reduction of this method (Chen et al, 2019). Additionally, Big BAF is suitable for landowners that seeking a cost-effective method for estimating carbon (Chen et al, 2019). Currently, Big BAF becomes an option for collecting field data among other sampling methods (Waterman, 2016b).…”

“…In the last few years, Big BAF has a steadily increasing number of research studies in the literature, for example (Chen et al, 2019;Iles, 2012;Kershaw Jr. et al, 2016b;Lei et al, 2019;McTague, 2010;Rice et al, 2014;Samiotis & Stamatellos, 2011;Yang & Burkhart, 2018;Yang, Kershaw, Weiskittel, Lam, & McGarrigle, 2019). Grosenbaugh (1963) introduced a sampling method in which trees are selected for measurement with a probability proportional to some predicted tree value (e.g.…”

A note on the estimation of variance for big BAF sampling

“…Big BAF sampling (Marshall et al 2004) is a simple form of double sampling that can be used in a horizontal point sampling (HPS) inventory. The target attributes are frequently basal area and volume, though more measurements could be taken to estimate the density as well; and recently Chen et al (2019) have demonstrated its use for the estimation of carbon rather than volume. The big BAF method uses two basal area factors (BAFs) on the same full set of sample points in a forest inventory: the smaller BAF is used to select a sample for the estimation of basal area (the BAF c sample), while the larger BAF is used to select trees on which to take more detailed measurements for volume estimation (the BAF v sample).…”