Potential and limits of high-resolution airborne remote sensing data for the derivation of forest parameters

First, this research focuses on the development of new methods for semi-automated extraction of forest parameters such as tree area and species based on airborne remote sensing data and logistic regression models. Second, new image-matching algorithms for the generation of high-resolution digital surface models for the extraction of forest parameters are tested: canopy height models, forest stand structure, degree of composition, forest border, timber volume estimation, tree growth, tree species etc. Third, new methods are developed to detect and simulate changes (forest growth, shrub encroachment) in forested mire vegetation using remotely sensed variables. The project reveals that is possible to estimate and quantify vegetation growth from the past to the future in mire environments.

Contact

• Lars Waser