## 1 Introduction  
Airborne laser scanning (ALS) is increasingly being used in forest mapping [1; 2; 3]. In certain types of mature stands, even individual trees can be inventoried using ALS data [4; 5; 6]. This kind of individual-tree-level inventory is usually based on the detection of trees from an ALS point cloud. The estimation of the tree variables is based on ALS-derived point cloud metrics and ground-truth data corresponding to the same trees [7; 8; 9], thus creating a geodatabase for accurate tree inventory. In Finland, 96% of annual timber felling in standing sales on privately-owned forestland is done using harvesters [10]. As part of the harvesting process, a harvester collects detailed data on the logged trees, such as length of the utilizable stem part and stem diameter at intervals of 10 cm; and the length, top diameter, and volume of each log [11]. The majority of harvesters are equipped with a global navigation satellite system (GNSS), in addition to having onboard geographic information system (GIS) software. Currently, the location of a harvester under the forest canopy is recorded with an accuracy of about 3 m [12], but the location of the harvester head, _i.e._, the location of the processed tree, is not recorded. However, only the roundwood trade and haulage are based on these harvester measurements. Information collected by means of the harvester head could partly replace the field measurements needed for calibrating remotely-sensed inventory data, and thereby provide both less costly and more detailed reference data required in various forest inventory and mapping applications. [PERSON] _et al._[13] proposed that after harvesting a few trees, local inventory data can be updated in real time. However, the positional accuracy of the current harvester-based tree data is not sufficient to match the trees detected by ALS and therefore harvester head positioning requires technical improvements. There are technical solutions that could be used for positioning the harvester head relative to the harvester, but so far there have not been enough incentives for machine manufacturers to add this new functionality to new machines. If the harvester is also equipped with a MLS (Mobile Laser Scanning) system, then trees possessing the desired characteristics could be selected using the stem curve information available from the MLS data. Moreover, the direct georeferencing solution of GNSS/IMU sensors could be used in the positioning of such a harvester. Thus, improved performance in forest inventory is expected to be achieved if the position of the harvesster head is known so that the harvested trees and the ALS data can be linked. Some studies have been conducted explaining the usability of the approach. [PERSON] _et al._[6] conducted the first tests where ALS data were used in making pre-harvest measurements. An inventory based on individual tree crowns (ITC) was carried out in two mature conifer stands (density \(\sim\)465 stems per hectare) prior to logging. The major problem in ITC is deemed to be tree detection (e.g., [14, 15]), but on a managed forest site tested in the study the number of harvestable trees was underestimated by less than 3%. In general, the ITC approach is at its best in single-layered mature stands. [PERSON] _et al._[16] showed that if the harvester measurements are used as reference data for ALS-based tree-level inventories, highly accurate estimates can be obtained. Their imputation accuracies (root mean squared error percentage RMSE-%) for stem volume, mean tree height, mean stem diameter, and stem density were 11%, 8%, 12%, and 19%, respectively. These validations were executed using aggregated plots, which suggests that stand-level estimates would be even more accurate. Since tree positions were missing from operational harvester data, [PERSON] _et al._[16] used manual measurements to match the logged trees with the ALS data.