The warming climate, biodiversity loss, and escalating natural disturbances emphasize the need for sustainable forest management, which relies on understanding tree growth and competition. Laser scanning has opened new possibilities for measuring these processes. This thesis aims to develop approaches to evaluate stem and crown growth and competition using laser scanning point clouds, exploring their utility in assessing and quantifying competition dynamics and growth patterns in forest stands.
Study I developed approaches for assessing stem and crown competition using terrestrial laser scanning (TLS) point clouds and investigated the effect of different thinning treatments on competition in Scots pine (Pinus sylvestris L.)-dominated forests. The results indicated that TLS-derived competition decreased across different thinning methods compared to the control plots for both moderate and intensive thinning. Thinning from below showed the greatest reduction in competition, followed by thinning from above and systematic thinning. Study I demonstrates that TLS provides an advanced solution for assessing tree crown characteristics and growing space, highlighting a novel approach to understanding competition between trees.
Study II investigated the use of bi-temporal TLS and low-altitude airborne laser scanning (ALS), individually and in combination, to assess the relationship between tree stem volume growth (ΔV) and crown structure, including its change (ΔC), over a 7-year monitoring period. The results showed a strong correlation between ΔV and crown metrics (top height, projection area, and perimeter) for Scots pine. For Norway spruce, ΔV weakly correlated with 3D crown area (CA3D), volume (CV), and its change (ΔCV). Birch ΔV showed weak to moderate correlations with CA2D, crown perimeter, and ΔCV. Random Forest (RF) analyses revealed that changes in crown structure were important for explaining ΔV variations for Norway spruce and birch, while top height (CHmax) was the key metric for Scots pine. In conclusion, Study II showed that multisensor laser scanning data can serve to evaluate the relationships between ΔV and tree crown structure.
Study III examined the utility of TLS and low-altitude ALS data in describing the competitive stress of individual trees using two approaches. The object-based approach quantified competition by identifying and characterizing neighboring trees, while the point cloud-based approach evaluated competition through point cloud structures representing competitive vegetation around a target tree. The results showed that object-based competition indices (CIs) correlated more strongly with in situ-based CIs compared to point cloud-based CIs and were more consistent between TLS and ALS. Overall, Study III demonstrated that TLS is effective for small-scale competition assessments, while low-altitude ALS has similar potential for describing competition on a large scale.
This thesis demonstrates the capability of the developed laser scanning-based approaches to assess stem and crown growth and competition. It shows how TLS and ALS enhance our understanding of tree growth and their responses to neighboring trees, helping identify processes driving changes in forest dynamics. These findings offer concrete steps toward more precise and efficient forest management, although further refinement of the methodologies is needed to optimize their use across varying forest ecosystems.