Abstract: Forest management alters the spatial structure of forests, which directly shapes the biodiversity, processes and functioning of such ecosystems. While forest structure is commonly quantified using field-based forest management metrics, close range (distances 150m) remote sensing techniques are able to describe the distribution of material in 3D space with very high detail and precision. Since manual inventories of forest structure are labor intensive and suffer from observer biases remote sensing techniques offer new possibilities for efficient and objective quantifications of forest structure. To investigate the impacts of forest management on stand structure, new indices and metrics based on 3D point clouds have been developed and validated. This dissertation is structured in three publications (Chapters 4 - 6), starting with a methods paper on UAV flight planning optimization, followed by a comparison of tree related microhabitat inventories and close range remote sensing indices for stand structure quantification, and ending with a validation of a remote sensing index based on a forest expert survey.brFor the first paper, a technical flight planning optimization was conducted for unmanned aerial vehicles structure from motion as a basis for optimal data acquisition (Chapter 4). The image forward overlap and ground sampling distance were varied, and the parameters of the resulting geometric model completeness in 2D and 3D space that differed could be independently quantified. While finer resolutions led to a better representation of smaller forest details and a better representation of the understory, the model completeness suffered from it. A higher forward image overlap can compensate for this if the overlap is very high (95%). Tree related microhabitat (TreM) inventories are unlike remote sensing based indices but have a similar aim of quantifying forest structures, which can be accumulated to a stand level. TreMs themselves are special tree level structures such as forks, cavities and fungi. While both approaches have different perspectives on forest structure, their common goal of forest stand structure quantification make the respective insights from these methods worth comparing (Chapter 5). A significant correlation with a weak R2 (0.30) indicate that these two measures are linked but that their representation of stand structure is complementary. Foresters and other forest experts are of major relevance to the topic of implementation of retention management and the selection of retention patches, since they are the decision makers and practitioners in this sector. The judgement of those experts could be biased by additional objectives and individual preferences. In an extensive online survey (n=444), experts were asked to quantify stand structure on 360 degree panoramic images in an interactive viewer. The expert responses were compared to stand structural complexity metrics derived from terrestrial laser scans, which were taken at the same location from the same viewpoint. The standard deviation of the expert judgements were high, which indicates the necessity of objective measurements. The laser scanning based index significantly correlated with the expert judgements, which shows that neither the expert ratings nor the scanning index are random and that laser scanning is an option for more objective decision making processes. However, experts in the field might take a variety of additionally relevant criteria (e.g. rareness of the habitat in landscape, special tree features, breeding places) into account, which should not been overlooked. In summary, this dissertation validated and adapted several indices based on close range remote sensing techniques and showed their potential as monitoring tools and assistance for the forest management decision-making processes