Comparison of four types of 3D data for timber volume estimation

Abstract

The study compares the accuracy of timber volume prediction based on four different three-dimensional remote sensing data sets in one study area in southern Norway: airborne laser scanning (ALS), stereo aerial photogrammetry (AP), satellite interferometric synthetic aperture radar (InSAR) based on the TanDEM-X mission, and satellite radargrammetry based on the TerraSAR-X mission. We fitted linear mixed effects models with vegetation height and density metrics obtained from the remote sensing data sets as explanatory variables. The cross-validated root mean squared error (RMSE) relative to the observed mean was used as the measure of goodness-of-fit. ALS provided the most accurate prediction at plot level with RMSE=19%, followed by AP (31%), InSAR (42%), and radargrammetry (44%). At stand level the methods' performances were equally ordered, with RMSE values of 12–23%. Including the variables terrain slope and aspect in the models improved the accuracy of AP, InSAR, and radargrammetry slightly.