When making assessments of forest resources, there is nearly ubiquitous interest in quantifying current status and trends in tree biomass and carbon stocks. While important at various spatial scales, typical estimations pertinent to broad forest management and policy issues are conducted for large areas such as state, regional, and national perspectives. These assessments are usually accomplished using large-area forest inventory data collected by National Forest Inventory (NFI) programs. While NFI efforts commonly collect size data for individual trees, there is often limited information for tree seedlings, e.g., frequency by species. To fully describe the tree population across the entire range of sizes present, this study proposes methods to predict individual seedling groundline diameter and height using models developed from trees having a diameter at breast height (DBH) less than 7.62 ​cm. These attributes are subsequently used for the prediction of seedling stem volume, total aboveground biomass, and carbon content. The results suggest a smooth transition in tree attributes as size increases to where direct measurement of individual trees and prediction of their volume, biomass, and carbon are implemented as part of standard inventory protocols. Analyses including the full spectrum of tree sizes show that seedlings contribute roughly 0.6%–0.7% of the total tree volume/mass. This additional suite of information provides opportunities for more holistic assessments across the full spectrum of the tree resource or for specialized subdomains that include the seedling component.

Estimating amounts of change in forest resources over time is a key function of most national forest inventories (NFI). As this information is used broadly for many management and policy purposes, it is imperative that accurate estimations are made from the survey sample. Robust sampling designs are often used to help ensure representation of the population, but often the full sample is unrealized due to hazardous conditions or possibly lack of land access permission. Potentially, bias may be imparted to the sample if the nonresponse is nonrandom with respect to forest characteristics, which becomes more difficult to assess for change estimation methods that require measurements of the same sample plots at two points in time, i.e., remeasurement. To examine potential nonresponse bias in change estimates, two synthetic populations were constructed: 1) a typical NFI population consisting of both forest and nonforest plots, and 2) a population that mimics a large catastrophic disturbance event within a forested population. Comparisons of estimates under various nonresponse scenarios were made using a standard implementation of post-stratified estimation as well as an alternative approach that groups plots having similar response probabilities (response homogeneity). When using the post-stratified estimators, the amount of change was overestimated for the NFI population and was underestimated for the disturbance population, whereas the response homogeneity approach produced nearly unbiased estimates under the assumption of equal response probability within groups. These outcomes suggest that formal strategies may be needed to obtain accurate change estimates in the presence of nonrandom nonresponse.