Aki Suvanto , Matti Maltamo

Using mixed estimation for combining airborne laser scanning data in two different forest areas

Suvanto A., Maltamo M. (2010). Using mixed estimation for combining airborne laser scanning data in two different forest areas. Silva Fennica vol. 44 no. 1 article id 164. https://doi.org/10.14214/sf.164

Abstract

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.

Keywords
airborne laser scanning; area-based method; mixed estimation; regression models

Received 31 January 2008 Accepted 19 January 2010 Published 31 December 2010

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