%0 Research article
%T Improving multi-source forest inventory by weighting auxiliary data sources
%A Tuominen, Sakari
%A Poso, Simo
%D 2001
%J Silva Fennica
%V 35
%N 2
%R doi:10.14214/sf.596
%U https://silvafennica.fi/article/596
%X A two-phase sampling design has been applied to forest inventory. First, a large number of first phase sample plots were defined with a square grid in a geographic coordinate system for two study areas of about 1800 and 4500 ha. The first phase sample plots were supplied by auxiliary data of Landsat TM and IRS-1C with principal component transformation for stratification and drawing the second phase sample (field sample). Proportional allocation was used to draw the second phase sample. The number of field sample plots in the two study areas was 300 and 380. The local estimates of five continuous forest stand variables, mean diameter, mean height, age, basal area, and stem volume, were calculated for each of the first phase sample plots. This was done separately by using one auxiliary data source at a time together with the field sample information. However, if the first phase sample plot for which the stand variables were to be estimated was also a field sample plot, the information of that field sample plot was eliminated according to the cross validation principle. This was because it was then possible to calculate mean square errors of estimates related to a specific auxiliary data source. The procedure produced as many estimates for each first phase sample plot and forest stand variable as was the number of auxiliary data sources, i.e. seven estimates: These were based on Landsat TM, IRS-1C, digitized aerial photos, ocular stereoscopic interpretation from aerial photographs, data from old forest inventory made by compartments, Landsat TM95–TM89 difference image and IRS96–TM95 difference image. The final estimates were calculated as weighted averages where the weights were inversely proportional to mean square errors. The alternative estimates were calculated by applying simple rules based on knowledge and the outliers were defined. The study shows that this kind of system for finding outliers for elimination and a weighting procedure improves the accuracy of stand variable estimation.