Measuring Forest Inventory Attributes Using Faro Orbis Mobile Laser Scanner in Managed Boreal Forests

Forest and wood procurement planning requires detailed information about the quantities and characteristics of individual trees within forest stands. Stand-specific forest inventory data is increasingly being produced using remote sensing combined with accurately measured sample plots. In recent years, close-range sensing techniques such as terrestrial laser scanning (TLS) have been recognized as effective tools for providing precise measurements of tree characteristics—including features that cannot be directly measured nondestructively. To replace conventional field inventory methods using calipers and clinometers, there is a growing need for agile techniques that enable rapid and accurate measurements of all trees within inventoried sample plots. Mobile laser scanning (MLS) offers enhanced data acquisition speed by enabling detailed point cloud reconstructions of the surrounding forest environment on the move, making it an attractive technology for operational forest surveying, particularly for measuring forest sample plots. Previous studies have demonstrated the potential of MLS for individual tree characterization, but there remains a gap in understanding how its tree-level performance translates to plot-level accuracy under varying boreal forest conditions, where the presence of evergreen foliage often poses challenges for tree characterization.

 

The aim of this study was to evaluate how accurately MLS can measure forest stand attributes such as mean basal area (BA), tree density (number of trees per hectare; TPH), and basal area-weighted mean tree diameter and -height (Dg, Hg). Additionally, we investigated the scanning setups required to achieve accurate measurements of stand attributes across different forest types. The study was conducted in Heinävesi, Finland, where 50 plots (typically 30 m x 40 m in size) were measured tree by tree (n = 5227) in the field during the autumn of 2023. MLS data from these sample plots were collected using the Faro Orbis scanner in the summer of 2024. Trees were identified and their dimensions extracted from the point clouds, with plot-level forest stand attributes aggregated and compared to those measured using traditional caliper and clinometer methods.

 

Experiences from the data acquisition campaign highlighted the ease of MLS-based forest surveying, enabling agile data collection. Sample plots ranging from 370 to 2000 m² were captured within an average of 21 minutes, although more complex forest structures and walk paths increased the required time. Preliminary results indicate that, using semi-automatic tree detection methods, approximately 99.6% of trees with diameters greater than 5 cm were successfully identified. Diameter at breast height (DBH) and tree height were measured with RMSEs of 15.7% (2.5 cm) and 12.03% (2.9 m), respectively. At the plot level, these measurements provided unbiased estimates of basal area (G) and trees per hectare (TPH), while slightly overestimating Dg and Hg in more complex forests. These findings underscore the potential of MLS for operational forest inventory measurements.