Review and Synthesis of Estimation Strategies to Meet Small Area Needs in Forest Inventory

Simple item page
dc.contributor.authorDettmann, Garret T.en
dc.contributor.authorRadtke, Philip J.en
dc.contributor.authorCoulston, John W.en
dc.contributor.authorGreen, P. Coreyen
dc.contributor.authorWilson, Barry T.en
dc.contributor.authorMoisen, Gretchen G.en
dc.date.accessioned2022-07-12T18:00:56Zen
dc.date.available2022-07-12T18:00:56Zen
dc.date.issued2022-03-16en
dc.description.abstractSmall area estimation is a growing area of research for making inferences over geographic, demographic, or temporal domains smaller than those in which a particular survey data set was originally intended to be used. We aimed to review a body of literature to summarize the breadth and depth of small area estimation and related estimation strategies in forest inventory and management to-date, as well as the current state of terminology, methods, concerns, data sources, research findings, challenges, and opportunities for future work relevant to forestry and forest inventory research. Estimation methodologies explored include direct, indirect, and composite estimation within design-based and model-based inference bases. A variety of estimation methods in forestry have been applied to extensive multi-resource inventory systems like national forest inventories to increase the precision of estimates on small domains or subsets of the overall populations of interest. To avoid instability and large variances associated with small sample sizes when working with small area domains, forest inventory data are often supplemented with information from auxiliary sources, especially from remote sensing platforms and other geospatial, map-based products. Results from many studies show gains in precision compared to direct estimates based only on field inventory data. Gains in precision have been demonstrated in both project-level applications and national forest inventory systems. Potential gains are possible over varying geographic and temporal scales, with the degree of success in reducing variance also dependent on the types of auxiliary information, scale, strength of model relationships, and methodological alternatives, leaving considerable opportunity for future research and growth in small area applications for forest inventory.en
dc.description.notesThis work was supported by USDA Forest Service Southern Research Station and Virginia Tech Department of Forest Resources and Environmental Conservation, Joint Venture Agreement 20-JV-11330145-074. Sponsorship of research into improving estimation of timber damage in tropical storms in the southeastern United States. Funds from the Virginia Tech Library paid for open-access fees.en
dc.description.sponsorshipUSDA Forest Service Southern Research Station and Virginia Tech Department of Forest Resources and Environmental Conservation, Joint Venture Agreement [20-JV-11330145-074]; Virginia Tech Libraryen
dc.description.versionPublished versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.doihttps://doi.org/10.3389/ffgc.2022.813569en
dc.identifier.eissn2624-893Xen
dc.identifier.other813569en
dc.identifier.urihttp://hdl.handle.net/10919/111218en
dc.identifier.volume5en
dc.language.isoenen
dc.publisherFrontiersen
dc.rightsCreative Commons Attribution 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en
dc.subjectsmall area estimationen
dc.subjectmodel-assisted estimationen
dc.subjectforest samplingen
dc.subjectgeospatial dataen
dc.subjectdesign-based inferenceen
dc.subjectmodel-based inferenceen
dc.titleReview and Synthesis of Estimation Strategies to Meet Small Area Needs in Forest Inventoryen
dc.title.serialFrontiers in Forests and Global Changeen
dc.typeArticle - Refereeden
dc.type.dcmitypeTexten

Files

Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
ffgc-05-813569.pdf
Size:
450.08 KB
Format:
Adobe Portable Document Format
Description:
Published version
Download

Collections

Scholarly Works, Forest Resources and Environmental Conservation
Open Access Subvention Fund Articles