%0 Research article
%T Using mixed estimation for combining airborne laser scanning data in two different forest areas
%A Suvanto, Aki
%A Maltamo, Matti
%D 2010
%J Silva Fennica
%V 44
%N 1
%R doi:10.14214/sf.164
%U https://silvafennica.fi/article/164
%X Airborne laser scanning (ALS) data have become the most accurate remote  sensing technology for forest inventories. When planning new inventories  the costs of fieldwork could be reduced if datasets of old inventory  areas are effectively reused in the new area. The aim of this study was  to apply mixed estimation using a combination of existing and new field  datasets in area-based approach. Additionally, combining datasets with  mixed estimation was compared with constructing new local models with  smaller datasets. The two forest study areas were in Juuka and  Matalansalo, which are located about 120 km apart in eastern Finland.  ALS-based regression models were constructed using datasets of  Matalansalo (472 reference plots) and Juuka (10–212 reference plots).  Models were developed for the basal area median tree diameter and  height, mean tree height, stem number, basal area and volume. The work  was based on a simulation approach which involved five methods for  approximating the regression coefficients. The first method merged the  datasets using ordinary least squares (OLS) regression models, whereas  the second and third methods combined datasets using mixed estimation on  different weighting principles, and the final two estimated local  models with predetermined and new independent variables. The results  indicate that mixed estimation can improve the accuracy of derived stand  variables compared with basic OLS models. Additionally, a sample of  40–50 plots was enough to build local models for basal area and volume  and produce at least the equal accuracy of results than any other  methods in this study.