Assessing Uncertainty: Sample Size Trade-Offs in the Development and Application of Carbon Stock Models
Many parties to the United Nation's Framework Convention on Climate Change (UNFCCC) base their reporting of change in Land Use, Land-Use Change and Forestry (LULUCF) sector carbon pools on national forest inventories. A strong feature of sample-based inventories is that very detailed
measurements can be made at the level of plots. Uncertainty regarding the results stems primarily from the fact that only a sample, and not the entire population, is measured. However, tree biomass on sample plots is not directly measured but rather estimated using regression models based
on allometric features such as tree diameter and height. Estimators of model parameters are random variables that exhibit different values depending on which sample is used for estimating model parameters. Although sampling error is strongly influenced by the sample size when the model is
applied, modeling error is strongly influenced by the sample size when the model is under development. Thus, there is a trade-off between which sample sizes to use when applying and developing models. This trade-off has not been studied before and is of specific interest for countries developing
new national forest inventories and biomass models in the REDD+ context. This study considers a specific sample design and population. This fact should be considered when extrapolating results to other locations and populations.
Keywords: Land Use; Land-Use Change and Forestry (LULUCF); UN Framework Convention on Climate Change (UNFCCC); model error; model-dependent inference; national forest inventory
Document Type: Research Article
Publication date: 09 August 2017
This article was made available online on 04 May 2017 as a Fast Track article with title: "Assessing Uncertainty—Sample Size Trade-Offs in the Development and Application of Carbon Stock Models".
- Access Key
- Free content
- Partial Free content
- New content
- Open access content
- Partial Open access content
- Subscribed content
- Partial Subscribed content
- Free trial content