Quantitative Tree Reconstruction from Terrestrial Laser Scanning Data and Applications

Understanding the structure and dynamics of trees and forest is key in studying the environment and understanding current and future climates. Development has been fast in measurement technology for these purposes, as it is currently possible to measure forest terrestrially with photography-based instruments or either static or mobile laser scanning, and airborne using drones, helicopters or aeroplanes, and even from space using satellitemounted instruments. However, as all these measurements are indirect presentations of the key attributes to study, they require powerful analysis methods to accompany them. This thesis focuses on terrestrial laser scanning data and presents a method for reconstructing comprehensive, quantitative structure models of trees from such data. The method is designed to be a tool for understanding tree and forest structure, as well as, dynamics and functionality, without the need for destructive measurements. The reconstructed models provide access to tree attributes previously impossible or laborious to measure, either at a single tree-scale, at forest-plot-scale or even at forest-scale. The thesis will present the reconstruction method and will focus on two of its applications: automatic tree species recognition and augmenting the produced structure models with leaves or needles, enabling more accurate simulations involving light propagation and plant interaction with the atmosphere. Additionally, parts of the thesis describe forms of dissemination used to promote the reconstruction method and its applications, increasing the rate of adoption into operational use. The dissemination approaches include several animations, interactive 3D models and open-source software.