Mobile laser scanning as reference for estimation of stem attributes from airborne laser scanning

The acquisition of high-quality reference data is essential for effectively modelling forest attributes. Incorporating close-range Light Detection and Ranging (LiDAR) systems into the reference data collection stage of remote sensing-based forest inventories can not only increase data collection efficiency but also increase the number of attributes measured with high quality. Therefore, we propose a model-based forest inventory method that uses reference data collected by a car-mounted mobile laser scanning (MLS) system along boreal forest roads. This approach is used for the estimation of diameter at breast height (DBH) and stem volume at the individual tree-level from airborne laser scanning (ALS) data. In addition, we compare the estimates obtained using the proposed method with the ones derived from reference data collected by traditional field inventory of 265 field plots systematically distributed over the study area. The accuracy of the estimates remained comparable regardless of the reference dataset used for estimation of DBH and stem volume. When using the field inventory dataset for model training, the root mean square error (RMSE) of DBH estimates were 4.06 cm (18.8 %) for Norway spruce trees, 6.3 cm (29.6 %) for Scots pine and 8.61 cm (55.9 %) for deciduous trees. Similarly, when evaluating predictions based on the MLS dataset as reference, RMSEs were equal to 3.97 cm (18.4 %) for Norway spruce, 6.12 cm (28.8 %) for Scots pine, and 8.98 cm (58.3 %) for deciduous trees. In general, biases were below 1 cm for most species classes, with the exception of deciduous trees. The accuracy of stem volume also had RMSEs varying across different tree species. For the estimates based on traditional field inventory, the RMSEs were 0.176 m 3 (38.8 %) for Norway spruce, 0.228 m 3 (52.4 %) for Scots pine and 0.246 m 3 (158 %) for deciduous trees. When using the MLS dataset as a reference, the RMSEs were equal to 0.176 m 3 (38.8 %), 0.228 m 3 (52.4 %), and 0.246 m 3 (158 %) for Norway spruce, Scots pine, and deciduous trees, respectively. Car-mounted MLS demonstrated its potential as an efficient alternative for collecting reference data in remote sensing-based forest inventories, which could complement traditional methods. • We propose a fully remote sensing-based forest inventory method using LiDAR. • Diameter at Breast Height and stem volume are estimated at individual tree-level. • The traditional and LiDAR-based methods yielded similar estimates and accuracy. • Car mounted-MLS is an alternative for data collection in model-based inventories.