The R Package forestinventory: Design-Based Global and Small Area Estimations for Multiphase Forest Inventories

Abstract

Forest inventories provide reliable evidence-based information to assess the state and development of forests over time. They typically consist of a random sample of plot locations in the forest that are assessed individually by field crews. Due to the high costs of these terrestrial campaigns, remote sensing information available in high quantity and low costs is frequently incorporated in the estimation process in order to reduce inventory costs or improve estimation precision. With respect to this objective, the application of multiphase forest inventory methods (e.g., double-and triple-sampling regression estimators) has proved to be efficient. While these methods have been successfully applied in practice, the availability of open-source software has been rare if not non-existent. The R package forestinventory provides a comprehensive set of global and small area regression estimators for multiphase forest inventories under simple and cluster sampling. The implemented methods have been demonstrated in various scientific studies ranging from small to large scale forest inventories, and can be used for post-stratification, regression and regression within strata. This article gives an extensive review of the mathematical theory of this family of design-based estimators, puts them into a common framework of forest inventory scenarios and demonstrates their application in the R environment.

A design-based assessment of an expanded set of auxiliary information for forest growth estimation

“…The estimators described in this study are presented in the model-assisted design-based framework in the context of two-phase estimation (also known as double sampling). Although the ideas borrow heavily from Särndal et al ( 2003), which uses the finite population approach where the sampling units are plots, the approach here is rooted in the infinite population approach (Mandallaz 2008;Hill et al 2021) where the fundamental sampling units are points rather than plots. This avoids the need for circular plots to be approximated by hexagons in order to be tessellated over the forest area.…”

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

“…Confidence intervals can be reliably constructed for these estimators, but are usually used for major domains as their variance can be large or unstable in domains having small sample sizes (Särndal, 1984;Lehtonen and Veijanen, 2009). We note that design-based approaches including H-T and model-assisted estimation are sometimes categorized as SAE (see Figure 2.1 in Rahman and Harding, 2017;Hill et al, 2021); however, they are often used where interest lies in only a single population domain, such as in the methods demonstrated by McConville et al (2020) for estimating forest attributes in a single county in Utah, USA.…”

“…Variances for model coefficients and estimates of finitepopulation parameters for design-based direct or model-assisted estimates (Sections 2.1 and 2.2) are in most cases calculable using commercially available statistical software packages (Molina and Marhuenda, 2015;Breidenbach, 2018;McConville et al, 2018;Hill et al, 2021). Other variance estimators are documented in research literature in sufficient detail to facilitate calculation with scientific programming software (McRoberts, 2012;Mandallaz et al, 2013;Babcock et al, 2015;Magnussen et al, 2017;Mauro et al, 2017;Frank et al, 2020).…”

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

“…Two doctoral theses have resulted from this research (Massey, 2015;Hill, 2018). Hill et al (2021) have implemented the estimators discussed above in the R package forestinventory.…”