Forest structural complexity is a key indicator of ecosystem functioning, influencing biodiversity, habitat availability, and forest resilience. Yet, comprehensively capturing it remains challenging, and its temporal dynamics in boreal forests are still poorly understood. Laser scanning technologies enable detailed quantification of forest structure, but significant knowledge gaps remain regarding how different technologies capture structural complexity and its changes over time. This dissertation addresses these gaps by evaluating airborne laser scanning (ALS) and terrestrial laser scanning (TLS): (1) for assessing structural complexity in boreal forests using bi-temporal ALS data; (2) for examining how different scanning technologies and processing approaches capture structural complexity; and (3) for investigating the agreement and consistency of structural complexity metrics derived from bi-temporal point cloud datasets.
Study I assesses the feasibility of bi-temporal low-density ALS (<1 pt m2) for monitoring changes in structural complexity under varying light conditions. Using ALS data from 2012 and 2019, canopy vertical profiles were generated by voxelizing point clouds into 4 × 4 × 1 m units and classifying them into light penetration categories. Forest stands with higher structural complexity showed greater vegetation occupancy and less empty space beneath the canopy. The results demonstrate that low-density ALS can detect structural development over time.
Study II compares ALS- and TLS-derived structural complexity metrics using grid- and object-level processing across three dimensions: vertical, horizontal, and volumetric. Helicopter-borne ALS and multi-scan TLS data were analyzed at both levels. Object-level processing produced greater metric variation for both sensors, better capturing detailed spatial information. High-density ALS effectively characterized vertical and horizontal complexity at the object-level (individual trees), showing strong agreement with TLS. However, differences in measurement geometry reduced consistency in volumetric complexity estimates between sensors.
Study III examines how point cloud characteristics influence structural complexity monitoring over 7–10 years. Three ALS datasets (0.4–1, 15–28, and 200–3600 pts/m2) and TLS data were analyzed to evaluate metric consistency. Gap fraction and Shannon entropy showed consistent trends across datasets, while vegetation occupancy and fractal dimension were more sensitive to point cloud properties. These findings emphasize careful metric selection and indicate that robust indicators enable reliable cross-sensor, large-scale monitoring of boreal forest structural complexity.
Overall, this thesis evaluates how airborne and terrestrial laser scanning technologies capture and monitor forest structural complexity over time, identifying reliable metrics and methods for representing structural changes across varying point cloud types and densities in boreal forests.