The doctoral dissertation in the field of Forestry will be examined at the Faculty of Science, Forestry and Technology, Joensuu campus.
What is the topic of your doctoral research? Why is it important to study the topic?
My doctoral research focuses on assessing tree competition and growth using laser scanning technologies, particularly terrestrial laser scanning (TLS) and low-altitude airborne laser scanning (ALS), in boreal forest ecosystems.
Across three studies, I developed and applied novel methods to evaluate how different thinning treatments affect stem- and crown-based competition, analyze the relationship between tree crown structural changes, in addition to their initial state and stem volume growth over time, and assess competitive stress using object-based and point cloud-based approaches derived from TLS and ALS data.
Sustainable forest management depends on understanding how trees grow and interact, especially in the face of climate change, biodiversity decline, and increasing disturbances. My research demonstrates that TLS and ALS provide detailed data that significantly improve the assessment of tree competition and growth, surpassing the limitations of traditional field methods.
What are the key findings or observations of your doctoral research?
My doctoral research demonstrated that TLS and ALS are effective tools for quantifying tree competition and growth dynamics in boreal forests. Thinning treatments, particularly thinning from below, significantly reduced tree competition, as revealed by TLS-derived stem and crown metrics. I also found that changes in crown structure over time are closely correlated with stem volume growth, especially in Scots pine, suggesting that 3D crown metrics derived from laser scanning can serve as indicators of tree vitality and growth potential.
Furthermore, I compared object-based and point cloud-based approaches to measuring competition and found that object-based indices were more strongly aligned with traditional field-based metrics. This research introduces innovative, non-destructive methods for monitoring forest structure and dynamics using laser scanning. It demonstrates the feasibility of TLS and ALS data to assess tree competition and growth in ways that are not possible with traditional methods. A better understanding of how forest management (e.g., thinning) affects competition and helps in developing data-driven, climate-resilient forest practices. These advances are important not only for the scientific community but also for forest managers and policymakers aiming for more sustainable and efficient forest management in the face of environmental change.
How can the results of your doctoral research be utilised in practice?
The findings of these three studies underscore the value of laser scanning technologies in advancing forest ecology research and supporting sustainable forest management practices. By bridging gaps in competition assessment, growth prediction, and large-scale inventory methods, this research provides valuable insights for developing sustainable forest management strategies, particularly in the boreal context. Future improvement in scanning setup and data affordability could further enhance the practical applications of these methodologies.
What are the key research methods and materials used in your doctoral research?
The key research methods included the use of TLS and ALS to derive both object-based and point cloud-based competition indices (CIs). Object-based CIs were calculated using the distance and size of neighboring trees within an 8-meter radius. Point cloud-based CIs utilized geometric volumes like cones and cylinders around each tree to quantify the surrounding vegetation structure from the TLS data. I used a nested linear mixed-effects model to analyze the effect of thinning treatments on object-based CIs based on stem and crown characteristics derived from TLS. To identify which thinning treatments resulted in statistically significant differences in CIs, the Tukey’s honest significance test was applied following the linear mixed-effects model.
The comparisons were based on TLS-derived object-based CIs, allowing pairwise evaluation of treatment effects relative to the control and to each other. The laser-derived indices, either object-based or point cloud-based, were also compared with traditional in situ CIs and tree growth, measured as basal area increment. The agreement was assessed using correlation strength, with particular attention to how tree density and stem detection rates affected the outcomes.
Additionally, a multisensorial approach combining ALS and TLS point clouds was also applied to comprehensively characterize the crown structure and its growth over time. Correlation coefficients and Random Forest regression were used to assess the species-specific dependencies between stem volume growth and extracted metrics.
The doctoral dissertation of Ghasem Ronoud, MSc, entitled Assessing tree growth and competition using laser scanning will be examined at the Faculty of Science, Forestry and Technology, Joensuu campus. The opponent will be Staff Scientist, Docent Aarne Hovi, Aalto University, and the custos will be Professor Mikko Vastaranta, 91. Language of the public defence is English.
- Public examination
- (PDF)
