Evaluation of most similar neighbour and random forest methods for imputing forest inventory variables using data from target and auxiliary stands

We report the results from modelling standing volume, above-ground biomass and stem count with the aim of exploring the potential of two non-parametric approaches to estimate forest attributes. The models were built based on spectral and 3D information extracted from airborne optical and laser scanner data. The survey was completed across two geographically adjacent temperate forest sites in southwestern Germany, using spatially and temporally comparable remote-sensing data collected by similar instruments. Samples from the auxiliary reference stands (called off-site samples) were combined with random, random stratified and systematically stratified samples from the target area for prediction of standing volume, above-ground biomass and stem count in the target area. A range of combinations was used for the modelling process, comprising the most similar neighbour (MSN) and random forest (RF) imputation methods, three sampling designs and two predictor subset sizes. An evolutionary genetic algorithm (GA) was applied to prune the predictor variables. Diagnostic tools, including root mean square error (RMSE), bias and standard error of imputation, were employed to evaluate the results. The results showed that RF produced more accurate results than MSN (average improvement of 3.5% for a single-neighbour case with selected predictors), yet was more biased than MSN (average bias of 5.13% with RF compared to 2.44% with MSN for stem volume in a single-neighbour case with selected predictors). Combining systematically stratified auxiliary samples from the target data set with the reference data set yielded more accurate results compared to those from random and stratified random samples. Combining additional data was most influential when an intensity of up to 40% of supplementary samples was appended to the reference set. The use of GA-selected predictors resulted in reduced bias of the models. By means of bootstrap simulations of RMSE, the simulations were shown to lie within the applied non-parametric confidence intervals. The achieved results are concluded to be helpful for modelling the mentioned forest attributes by means of airborne remote-sensing data.