Browsing by Author "Becker, Eemil"

Tree microhabitats are important in assessing forest biodiversity and ecological value. Knowledge of what types of forests and trees bear the most microhabitats can be useful in sustainable forest management and conservation. Based on research that has been done on assessing the main drivers of microhabitats, tree age, size and developmental stage have been established as the main driving factors for microhabitats. Terrestrial laser scanning data has been used in microhabitat modelling, either at forest stand scale, or by directly detecting certain microhabitats from the data using machine learning. However, high precision individual tree level architectural data facilitated by terrestrial laser scanning and quantitative structure modelling has not yet been tested in microhabitat related research. In this thesis I use the detailed tree-level information that the quantitative structure models provide to determine whether high precision tree architectural data improves the modelling performance of tree microhabitats. Terrestrial laser scanning data was collected from ten plots in a hardwood riparian forest area near Leipzig, Germany. Two tree microhabitat inventories from these ten plots collected in the winter of 2020-2021 and January 2023 respectively as reference data sets. Running increasingly complex models for predicting microhabitat richness for the two datasets I found that 1) While the performance gets better with addition of more tree structural variables, the difference was not substantial between models using only variables attainable by less labour-intensive methods and models with more complex, TLS-derived variables. 2) Comparing the performance metrics between the two datasets showed that the models performed very differently between the inventories, suggesting that microhabitat inventory data collection is susceptible to subjectivity, and the surveys are prone to having observer biases. These discrepancies between the datasets further reinforce the established consensus that tree microhabitats are hard to detect. Difficulties with creating reliable reference data sets, along with the inherently random nature of tree microhabitat occurrence can make microhabitat modelling difficult. This thesis concludes that high precision tree structural data might not increase the microhabitat prediction power in a way that justifies the cost. The results support the established consensus that microhabitat occurrence is mainly driven by tree size and living status. Variables such as species, tree neighbourhood metrics and stand level metrics were not included in this study, but should be researched more, as for example, tree structural characteristics and compartmentalization capacity vary between tree species. Additionally, based on the findings in this thesis, further effort into the standardisation of microhabitat inventories and their collection should be done. There should be little to no room for interpretation regarding the typologies of microhabitats and their related field work methodologies, leading to less observer biases and more comparable and comprehensive data.