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Silva Fennica 1926-1997
Acta Forestalia Fennica

Articles containing the keyword 'bias'

Category: Research article

article id 232, category Research article
Thomas Wutzler. (2008). Effect of the aggregation of multi-cohort mixed stands on modeling forest ecosystem carbon stocks. Silva Fennica vol. 42 no. 4 article id 232.
Keywords: stand structure; thinning; inventory; scale; model; stratification; bias; inventorystand structure
Abstract | View details | Full text in PDF | Author Info
Studies of the carbon sink of forest ecosystems often stratify the studied stands by the dominating species and thereby abstract from differences in the mixed-species, multi-cohort structure of many forests. This case study infers whether the aggregation of forestry data introduces a bias in the estimates of carbon stocks and their changes at the scale of individual stands and the scale of a forest district. The empirical TreeGrOSS-C model was applied to 1616 plots of a forest district in Central Germany to simulate carbon dynamics in biomass, woody debris, and soil. In a first approach each stand was explicitly simulated with all cohorts. In three other approaches the forest inventory data were aggregated in several ways, including a stratification of the stands to 110 classes according to the dominating species, age class, and site conditions. A small but significant bias was confirmed. At stand scale the initial ecosystem carbon stocks by the aggregated approach differed from that of the detailed approach by 2.3%, but at the district scale only by 0.05%. The differences in age between interspersed and dominant cohorts as well as differences in litter production were important for the differences in initial carbon stocks. The amounts of wood extracted by thinning operations were important for the differences in the projection of the carbon stocks over 100 years. Because of the smallness of bias, this case study collects evidence that the approaches, that represent stands or stratums by a single cohort, are valid at the scale of a forest district or larger.
  • Wutzler, Max Planck Institute for Biogeochemistry, Hans Knöll Str. 10, DE-07745, Jena, Germany E-mail: (email)
article id 478, category Research article
Ronald E. McRoberts, Daniel G. Wendt, Greg C. Liknes. (2005). Stratified estimation of forest inventory variables using spatially summarized stratifications. Silva Fennica vol. 39 no. 4 article id 478.
Keywords: bias; precision; classified satellite imagery; Internet; variance
Abstract | View details | Full text in PDF | Author Info
Large area natural resource inventory programs typically report estimates for selected geographic areas such as states or provinces, counties, and municipalities. To increase the precision of estimates, inventory programs may use stratified estimation, with classified satellite imagery having been found to be an efficient and effective basis for stratification. For the benefit of users who desire additional analyses, the inventory programs often make data and estimation procedures available via the Internet. For their own analyses, users frequently request access to stratifications used by the inventory programs. When data analysis is via the Internet and stratifications are based on classifications of even medium resolution satellite imagery, the memory requirements for storing the stratifications and the online time for processing them may be excessive. One solution is to summarize the stratifications at coarser spatial scales, thus reducing both storage requirements and processing time. If the bias and loss of precision resulting from using summaries of stratifications is acceptably small, then this approach is viable. Methods were investigated for using summaries of stratifications that do not require storing and processing the entire pixel-level stratifications. Methods that summarized satellite image-based 30 m x 30 m pixel stratifications at spatial scales up to 2400 ha produced stratified estimates of the mean that were generally within 5-percent of estimates for the same areas obtained using the pixel stratifications. In addition, stratified estimates of variances using summarized stratifications realized nearly all the gain in precision that was obtained with the underlying pixel stratifications.
  • McRoberts, North Central Research Station, USDA Forest Service, 1992 Folwell Avenue, Saint Paul, Minnesota, USA 5510 E-mail: (email)
  • Wendt, Region 9, USDA Forest Service, 626 East Wisconsin Avenue, Milwaukee, Wisconsin 53202, USA E-mail:
  • Liknes, North Central Research Station, USDA Forest Service, 1992 Folwell Avenue, Saint Paul, Minnesota, USA 5510 E-mail:

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