Extensive ground surveys of forest resources are expensive, and remote sensing is commonly used to extend surveys to large areas for which no ground data are available to provide more accurate estimates for forest management decisions. Remote-sensing data for tree type classification are usually analysed at the individual tree level (object-based). However, due to computational challenges, most object-based studies cover only smaller areas and experience of larger areas is lacking. We present an approach for an object-based, unsupervised classification of trees into broadleaf or conifer using airborne light detection and ranging (lidar) data and colour infrared (CIR) images on a countrywide scale. We adjusted the classification procedure using field data from countrywide tree species trial (TST) plots, and verified it on data from the National Forest Inventory (NFI). Results of the object-based classification of the TST plots showed an overall accuracy of 84% and a kappa coefficient () of 0.61 when using all plots, and 92% and 0.79, respectively, when leaving out plots with larch. NFI plots were assigned to conifer- or broadleaf-dominated or mixed depending on the area covered by the segments of the two tree types. In areas where lidar data were collected specifically during leaf-off conditions, 71% of the NFI plots were assigned correctly into the three categories with = 0.53. Using only NFI plots dominated by one type (broadleaf or conifer), 78% were categorized correctly with = 0.61. These results demonstrate that using wall-to-wall remote-sensing data, unsupervised classification of forest stands into broadleaf, conifer, or mixed is possible with an accuracy comparable to that of limited area studies. However, challenges and restrictions of using countrywide airborne remote sensing data lie in the costs associated with data collection and the data processing time.