Estimation of stem attributes using a combination of terrestrial and airborne laser scanning

Properties of individual trees can be estimated from airborne laser scanning (ALS) data provided that the scanning is dense enough and the positions of field-measured trees are available as training data. However, such detailed manual field measurements are laborious. This paper presents new methods to use terrestrial laser scanning (TLS) for automatic measurements of tree stems and to further link these ground measurements to ALS data analyzed at the single tree level. The methods have been validated in six 80 9 80 m field plots in spruce-dominated forest (lat. 58 degrees N, long. 13 degrees E). In a first step, individual tree stems were automatically detected from TLS data. The root mean square error (RMSE) for DBH was 38.0 mm (13.1 %), and the bias was 1.6 mm (0.5 %). In a second step, trees detected from the TLS data were automatically co-registered and linked with the corresponding trees detected from the ALS data. In a third step, tree level regression models were created for stem attributes derived from the TLS data using independent variables derived from trees detected from the ALS data. Leave-one-out cross-validation for one field plot at a time provided an RMSE for tree level ALS estimates trained with TLS data of 46.0 mm (15.4 %) for DBH, 9.4 dm (3.7 %) for tree height, and 197.4 dm(3) (34.0 %) for stem volume, which was nearly as accurate as when data from manual field inventory were used for training.