Abstract
BackgroundTo 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.ResultsFrom 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.ConclusionOur 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.
Highlights
Forests are globally significant carbon (C) pools, and can be managed to sequester additional atmospheric C
Total carbon:basal area ratios (CBAR) averaged just above 20% while wood and bark CBARs averaged about 25% across all measure BAF (mBAF) explored in this study
Bias was minimal for total carbon across most combinations of count BAF (cBAF), mBAF, and sample size (Fig. 3)
Summary
Forests are globally significant carbon (C) pools, and can be managed to sequester additional atmospheric C. One of the widely-used indicators of forest ecosystem C is aboveground forest biomass (Case and Hall 2008) To estimate this aboveground forest biomass, a forest inventory is needed to collect relevant data. The use of allometric equations reduces the task of carbon estimation to a standard forest inventory where the main concerns are: how many (2019) 6:13 sample points; how these sample points are located across the area of interest; what types of sample units to use; what tree attributes to measure and record; and whether or not to apply hierarchical or subsampling selection schemes (Kershaw et al 2016). 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. 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
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