The influence of number and size of sample plots on modelling growing stock volume based on airborne laser scanning

Current forest growing stock inventory methods used in Poland are based on statistical methods using field measurements of trees on circular sample plots. Such measurements are carried out with traditional equipment, i.e. callipers and range finders. Nowadays, remote sensing based inventory techniques are becoming more popular and have already been applied in North America and some Scandinavian countries. Remote sensing based forest inventories require a certain amount of ground sample plots, which serve either as reference data used for model calibration and/or as a validation dataset for the assessment of the accuracy of modelled variables. Using a set of 900 ground sample plots and Airborne Laser Scanner (ALS) from the Milicz forest district, a statistical model for the estimation of plot growing stock volume was developed. Next, the developed model was once again fitted to different variants of sample plot size and number of sample plots. Each variant was selected from a full 900 sample plot set. The selection started from 800, 700, 600, ... , down to 25 plots, respectively, and was carried out in proportion to the dominant tree age range. To account for the area effect, each plot number variant was similarly tested with various sample plot areas, i.e. 500, 400, ... , 100 m(2). Sampling in each variant was repeated in order to take into account the effect of a single selection. The results showed a strong relationship between obtained modelling errors and the size and number of used sample plots. It has been demonstrated that the number of sample plots has no influence on the accuracy of GSV estimation above about 300-400 sample plots (about 500 sample plots for bias), whereas sample plot size has a visible impact on estimation accuracy, which reduces with decreasing sample plot size, regardless of the number of sample plots. If it is about precision, results showed that the influence of a single selection to be relevant only below 300-400 plots (about 500 for bias) and the same trend can be observed in each sample plot size variant. The results showed it is possible to strongly reduce the number of ground sample plots (minimum 300-400), while still maintaining decent accuracy and precision levels, at least in similarly investigated forest conditions.