Different plot selection strategies for field training data in ALS-assisted forest inventory

The aim of this paper was to examine different plot selection strategies of field training plots in forest inventory using airborne laser scanner (ALS) data. The applied plot selection strategies were random selection, random selection within pre-stratification according to forest type, selection of plots according to geographical location and selection of plots based on properties of the ALS data given as a priori information. The study was conducted by means of simulation utilizing existing and independent training and validation plot data and the performance was evaluated by assessing bias and the root mean square error (RMSE). The accuracy of simultaneously derived biophysical stand properties, i.e. volume, number of stems and Lorey’s mean height, was examined using non-parametric modelling. The use of ALS data as a priori information provided the most accurate results in the case of stand volume and number of stems the RMSE being less than 15 and 30 per cent, respectively. For the mean height, also the other selection strategies were as good but the most accurate alternative varied according to number of training plots used. In most cases, the RMSE values for the mean height were between 8 and 9 per cent. The bias of the different strategies followed the same patterns as the corresponding RMSE values.