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