Use of models in large-area forest surveys: comparing model-assisted, model-based and hybrid estimation

Abstract

This paper focuses on the use of models for increasing the precision of estimators in large-area forest surveys. It is motivated by the increasing availability of remotely sensed data, which facilitates the development of models predicting the variables of interest in forest surveys. We present, review and compare three different estimation frameworks where models play a core role: model-assisted, model-based, and hybrid estimation. The first two are well known, whereas the third has only recently been introduced in forest surveys. Hybrid inference mixes designbased and model-based inference, since it relies on a probability sample of auxiliary data and a model predicting the target variable from the auxiliary data..We review studies on large-area forest surveys based on model-assisted, modelbased, and hybrid estimation, and discuss advantages and disadvantages of the approaches. We conclude that no general recommendations can be made about whether model-assisted, model-based, or hybrid estimation should be preferred. The choice depends on the objective of the survey and the possibilities to acquire appropriate field and remotely sensed data. We also conclude that modelling approaches can only be successfully applied for estimating target variables such as growing stock volume or biomass, which are adequately related to commonly available remotely sensed data, and thus purely field based surveys remain important for several important forest parameters.

Benefits of past inventory data as prior information for the current inventory

“…If the errors in the within-plot estimations (such as the allometric biomass models) were included, all the cases considered would be hybrid estimators (e.g. Ståhl et al 2014, Corona et al 2014, Fortin et al 2016, Ståhl et al 2016, Holm et al 2017). In the case of hybrid estimators, the differences in the theoretical foundations of the design-based and model-based approach are ignored.…”

Carbon storage potential in degraded forests of Kalimantan, Indonesia

“…The derived AGB distribution in Kalimantan forests is based on a probabilistic airborne lidar sampling that ensures unbiased estimates of mean and total forest AGB subject to the choice of the lidar-AGB model (Ståhl et al 2016), similar to ground inventory sample measurements that can produce unbiased estimates given the right biomass allometry. However, due to requirements for planning airborne flights in the region, the lidar sampling design provided only 29 lidar scenes for a total of 29 000 ha.…”

Multidimensional scaling of first-return airborne laser echoes for prediction and model-assisted estimation of a distribution of tree stem diameters

“…6). If a census of X is not available, but instead a large sample estimate of X, a model-assisted approach to inference is still possible with a few simple modifications (Mandallaz et al 2013;Ståhl et al 2016). Cited alternatives to MDS may claim a design-based empirical difference estimator (Baffetta et al 2009;Magnussen 2013) disregarding a critical requirement for predictions to be generated independently of the observed sample (Särndal et al 1992, ch.…”