As ecological research continues to expand across temporal and spatial scales, it generates immense amounts of data. The ever-growing volume and complexity of this data pose a major bottleneck in the scientific process.

The main objective of this interdisciplinary work was to accelerate several data analysis tasks in ecological research that are too time-consuming to be performed manually. In a first step, the feasibility of this goal was evaluated using existing generic methods. For some specific tasks, such as instance segmentation of tree rings or estimation of root turnover, the detection performance of these generic methods did not meet the desired level of accuracy due to unique requirements that are not adequately addressed in generic image processing research.

This led to the development of new algorithms to address these novel challenges, which constitute the main part of this work. The incorporation of domain-specific knowledge about the underlying biological systems and processes has proven essential for the development of these algorithms. The presented algorithms are packaged as user-friendly analysis tools that do not require a high level of technical expertise. They have already demonstrated their usability in several ecological studies and have led to new follow-up projects.