NFIs are typically based on approximate systematic grids of sample plots which generally produce conservative (i.e., too large) estimates of uncertainty if design-based estimators assuming simple random sampling (SRS) are used. Magnussen et al. (2020) document the 100-year long quest of improving variance estimation in systematic sampling using model-based methods and add previously untested estimators to the set of alternatives to using simple expansion estimators with SRS. Of importance for NFIs, they conclude “In large populations, and a low sampling intensity, the performance of the investigated estimators becomes more similar” (Magnussen et al. 2020).
The local pivotal method (LPM) is a form of balanced sampling method that produces small uncertainties with a minimum number of sample plots which, of course, is of considerable relevance for NFI programs. In the context of the Finnish NFI, Räty et al. (2020) found, however, that LPM-sampling could not markedly improve estimates based on systematic sampling when considering several variables of interest as is typical in NFIs. Complementing the study by Magnussen et al. (2020), Räty et al. (2020) identify a variance estimator originally developed for LPM that is well-suited for systematic sampling.
In a simulation study, Kangas et al. (2020) show that old measurements on permanent sample plots can constitute valuable source of auxiliary information for augmenting and complementing high-quality airborne laser scanning (ALS) data. The study highlights data-fusion opportunities with model-assisted and model-based estimators.
The hierarchical model based approach (HMB) is a method for propagating uncertainties from multiple regression models when combining multiple remotely sensed data layers. Saarela et al. (2020) advanced an analytical HMB method for the important class of non-linear models. In an ALS-based application, they show the close connection between fine resolution mapping and model-based inference for estimators for areas that aggregate arbitrary numbers of mapped pixels. At the scale of their study area, the use of HMB revealed that 75% of the uncertainty in biomass estimates was caused by uncertainties in tree-level biomass model parameter estimates.
Kleinn et al. (2020) describe how a new perspective on continuous landscape variables, such as full-tree biomass, can reduce uncertainty in estimates using common field sampling. With their continuous approach, the spatial, 2-dimensional biomass distribution of trees is modelled, instead of aggregating all biomass to the point of the stem position as with the traditional approach. The surface that is surveyed using sample plots is smoother in the continuous approach relative to the traditional approach and, thereby, reduces the sample variance. New measurement methods such as terrestrial laser scanning (TLS) make the continuous approach an interesting option.
The age of forest stands is critical information for forest management and decision-making. However, this information is usually not available at fine resolution for large geographic scales. Two studies in this Collection describe the development of regression models for large-area mapping of forest age using a combination of NFI, ALS, and other data (Maltamo et al. 2020; Schumacher et al. 2020). Using Norwegian NFI data, Schumacher et al. (2020) model stand age by exploiting tree height predicted from ALS, a site index prediction map, and Sentinel-2 data as predictor variables. Satisfactory results were obtained, especially for stands with large site indices. Using Finnish NFI data, Maltamo et al. (2020) exploit ALS and geographical data to model stand age. They highlight that the utility of age predictions varies according to applications. In contract to Schumacher et al. (2020), Maltamo et al. (2020) focus on managed forests younger than 100 years of age.