Skip to Main Content
Change detection for soil carbon in the forest inventory and analysisAuthor(s): An-Min Wu; Edward A. Nater; Charles H. Perry; Brent J. Dalzell; Barry T. Wilson
Source: In: Stanton, Sharon M.; Christensen, Glenn A., comps. 2015. Pushing boundaries: new directions in inventory techniques and applications: Forest Inventory and Analysis (FIA) symposium 2015. 2015 December 8–10; Portland, Oregon. Gen. Tech. Rep. PNW-GTR-931. Portland, OR: U.S. Department of Agriculture, Forest Service, Pacific Northwest Research Station: 159-162.
Publication Series: General Technical Report (GTR)
Station: Pacific Northwest Research Station
PDF: Download Publication (307.0 KB)
DescriptionEstimates of carbon stocks and stock changes in the U.S. Department of Agriculture Forest Service’s Forest Inventory and Analysis (FIA) Program are reported as the official United States submission to the UN Framework Convention on Climate Change. Soil, as a critical component of the forest carbon stocks, has been sampled in about 10-year intervals in FIA with the re-measurement underway. However, the magnitude of detectable change in soil organic carbon (SOC) with the current sampling scheme is unknown. We aim to identify SOC variability and to best determine minimum detectable changes in SOC under the current sampling scheme. The project seeks to: identify statistical relationships between SOC and environmental covariates; normalize SOC data for main forest-type groups (FTGs) using identified covariates; and determine the minimum detectable change in the normalized SOC using power analysis. We investigated SOC variability for 8 FTGs: Oak-Hickory, Maple-Beech-Birch, Pinyon-Juniper, Loblolly-Shortleaf Pine, Aspen-Birch, Douglas-Fir, Fir-Spruce-Mountain Hemlock and Woodland Hardwoods. Relationships between SOC and environmental covariates (biomass/soil properties in FIA, PRISM climate data, and DEM-derived terrain attributes) are determined by multiple linear regression and are used to normalize SOC variability. The results showed that terrain attributes were not significant in explaining SOC in the FIA dataset and climate data were only significant in certain FTGs locations. Except for Oak-Hickory, Maple-Beech-Birch and Pinyon-Juniper groups, sample numbers are insufficient to detect a change in SOC less than 10 percent (%) of the mean. To guide future sampling efforts, we will continue our study on detecting minimal change in SOC and to explore sample number and sampling frequency scenarios to inform future soil sampling protocols.
- Visit PNW's Publication Request Page to request a hard copy of this publication.
- We recommend that you also print this page and attach it to the printout of the article, to retain the full citation information.
- This article was written and prepared by U.S. Government employees on official time, and is therefore in the public domain.
CitationWu, An-Min; Nater, Edward A.; Perry, Charles H.; Dalzell, Brent J.; Wilson, Barry T. 2015. Change detection for soil carbon in the forest inventory and analysis. In: Stanton, Sharon M.; Christensen, Glenn A., comps. 2015. Pushing boundaries: new directions in inventory techniques and applications: Forest Inventory and Analysis (FIA) symposium 2015. 2015 December 8–10; Portland, Oregon. Gen. Tech. Rep. PNW-GTR-931. Portland, OR: U.S. Department of Agriculture, Forest Service, Pacific Northwest Research Station: 159-162.
- Promotion of adventitious root formation of difficult-to-root hardwood tree species
- Silviculture and stand dynamics of hemlock-dominated stands in southern New England: some lessons from early research
- Effects of elevated CO2 and shade on the decomposition of senesced tree foliage: impacts on the growth and survival of treehole mosquitoes
XML: View XML