Application of big BAF sampling for estimating carbon on small woodlots
| Autor: | Yung-Han Hsu, Dale Prest, Yingbing Chen, Ting-Ru Yang, John A. Kershaw |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
010504 meteorology & atmospheric sciences
01 natural sciences Horizontal point sampling lcsh:QH540-549.5 Sampling design Statistics Ecology Evolution Behavior and Systematics 0105 earth and related environmental sciences Nature and Landscape Conservation Measure (data warehouse) Forest inventory Inventory costs Ecology Carbon offset Sampling (statistics) Forestry 04 agricultural and veterinary sciences 15. Life on land Above ground carbon stocks Variable (computer science) Standard error 13. Climate action Sample size determination 040103 agronomy & agriculture 0401 agriculture forestry and fisheries Environmental science lcsh:Ecology Subsampling |
| Zdroj: | Forest Ecosystems, Vol 6, Iss 1, Pp 1-11 (2019) |
| ISSN: | 2197-5620 |
| DOI: | 10.1186/s40663-019-0172-4 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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