Recent advances in LiDAR sensors and robotic technologies have raised the question of whether handheld mobile laser scanning (HMLS) systems can allow for the performing of forest inventories (FIs) without the use of conventional ground measurement (CGM) techniques. However, the reliability of such an approach for forest planning applications, particularly in non-uniform forests under mountainous conditions, remains underexplored. This study aims to address these issues by assessing the accuracy of HMLS-derived data based on the calculation of basic forest attributes such as the number of trees, dominant height and basal area. To this end, near-natural forests of a national park (NE Türkiye) were surveyed using the HMLS and CGM techniques for a management plan renewal project. Taking CGM results as reference, we compared each forest attribute pair based on two datasets collected from 39 sample plots at the forest (landscape) scale. Diameter distributions and the influence of stand characteristics on HMLS data accuracy were also analyzed at the plot scale. The statistical results showed no significant difference between the two datasets for any investigated forest attributes (P >0.05). The most and the least accurately calculated attributes were quadratic mean diameter (root mean square error (RMSE) = 1.3 cm, 4.5 per cent) and stand volume (RMSE = 93.7 m3 ha−1, 16.4 per cent), respectively. The stand volume bias was minimal at the forest scale (15.65 m3 ha−1, 3.11 per cent), but the relative bias increased to 72.1 per cent in a mixed forest plot with many small and multiple-stemmed trees. On the other hand, a strong negative relationship was detected between stand maturation and estimation errors. The accuracy of HMLS data considerably improved with increased mean diameter, basal area and stand volume values. Eventually, we conclude that many forest attributes can be quantified using HMLS at an accuracy level required by forest planning and management-related decision making. However, there is still a need for CGM in FIs to capture qualitative attributes, such as species mix and stem quality.