A Systematic Review of Small Domain Estimation Research in Forestry During the Twenty-First Century From Outside the United States

Abstract

Small domain estimation (SDE) research outside of the United States has been centered in Canada and Europe—both in transnational organizations, such as the European Union, and in the national statistics offices of individual countries. Support for SDE research is driven by government policy-makers responsible for core national statistics across domains. Examples include demographic information about provision of health care or education (a social domain) or business data for a manufacturing sector (economic domain). Small area estimation (SAE) research on forest statistics has typically studied a subset of core environmental statistics for a limited geographic domain. The statistical design and sampling intensity of national forest inventories (NFIs) provide population estimates of acceptable precision at the national level and sometimes for broad sub-national regions. But forest managers responsible for smaller areas—states/provinces, districts, counties—are facing changing market conditions, such as emerging forest carbon markets, and budgetary pressures that limit local forest inventories. They need better estimates of conditions and trends for small sub-sets of a national-scale domain than can be provided at acceptable levels of precision from NFIs. Small area estimation research is how forest biometricians at the science-policy interface build bridges to inform decisions by forest managers, landowners, and investors.

Sampling and Estimation Documentation for the Enhanced Forest Inventory and Analysis Program

“…Synthetic estimators are being implemented through an Amazon Web Services cloud computing environment using Esri's Raster Analytics platform as described in the BIGMAP project in the Computing Resources section. Guldin (2021) and Dettmann et al (2022) provide recent reviews of small area estimation in forest inventory applications, and investigations into improving precision in FIA estimates over small domains continues to rise. For example, in the Pacific Northwest, Bell et al (2022)…”

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

“…SAE techniques have been used to enhance precision of NFI-derived estimates (Breidenbach and Astrup, 2012;Frank et al, 2020), in forest stand inventories (Ver Planck et al, 2018), and from surveys of wood processors or commercial landowner inventories (Green et al, 2020;Coulston et al, 2021), but are not limited to those uses (Affleck and Gregoire, 2015). The ability of SAE to increase estimator precision in small areas where data are otherwise too sparse to satisfy tolerance specifications makes it attractive for applications in forest inventory (Guldin, 2021). For example, the Norwegian NFI has employed national canopy height maps from aerial remote sensing as auxiliary data sources since about 2010 to address needs for better local information in producing municipal forest statistics and forest-management related inventories (Astrup et al, 2019;Breidenbach et al, 2020).…”