Accurate detection and delineation of individual trees and their crowns in dense forest environments are essential for forest management and ecological applications. This study explores the potential of combining leaf-off and leaf-on structure from motion (SfM) data products from unoccupied aerial vehicles (UAVs) equipped with RGB cameras. The main objective was to develop a reliable method for precise tree stem detection and crown delineation in dense deciduous forests, demonstrated at a structurally diverse old-growth forest in the Hainich National Park, Germany. Stem positions were extracted from the leaf-off point cloud by a clustering algorithm. The accuracy of the derived stem co-ordinates and the overall UAV-SfM point cloud were assessed separately, considering different tree types. Extracted tree stems were used as markers for individual tree crown delineation (ITCD) through a region growing algorithm on the leaf-on data. Stem positioning showed high precision values (0.867). Including leaf-off stem positions enhanced the crown delineation, but crown delineations in dense forest canopies remain challenging. Both the number of stems and crowns were underestimated, suggesting that the number of overstory trees in dense forests tends to be higher than commonly estimated in remote sensing approaches. In general, UAV-SfM point clouds prove to be a cost-effective and accurate alternative to LiDAR data for tree stem detection. The combined datasets provide valuable insights into forest structure, enabling a more comprehensive understanding of the canopy, stems, and forest floor, thus facilitating more reliable forest parameter extraction.

Leveraging low-cost drone technology, specifically the DJI Mini 2, this study presents an innovative method for creating accurate, high-resolution digital surface models (DSMs) to enhance topographic mapping with off-the-shelf components. Our research, conducted near Jena, Germany, introduces two novel flight designs, the “spiral” and “loop” flight designs, devised to mitigate common challenges in structure from motion workflows, such as systematic doming and bowling effects. The analysis, based on height difference products with a lidar-based reference, and curvature estimates, revealed that “loop” and “spiral” flight patterns were successful in substantially reducing these systematic errors. It was observed that the novel flight designs resulted in DSMs with lower curvature values compared to the simple nadir or oblique flight patterns, indicating a significant reduction in distortions. The results imply that the adoption of novel flight designs can lead to substantial improvements in DSM quality, while facilitating shorter flight times and lower computational needs. This work underscores the potential of consumer-grade unoccupied aerial vehicle hardware for scientific applications, especially in remote sensing tasks.

Data on forest parameters defining the structure, health and condition of a forest stand is essential for forest management and conservation. The increasing frequency of forest changes, such as those caused by climate change-related drought and heat events, highlight the importance of having a forest database with high spatial and temporal resolution. Automated forest parameter extraction based on unmanned aerial vehicle (UAV) imagery is a cost-effective way to address the need for accurate and up-to-date forest data.The aim of this project is to develop user-friendly tools based on optical data from UAVs that can be applied to accurately and efficiently conduct digital forest inventories. We are using spectral and geometric information from UAV data to create methods for automated derivation of forest parameters such as diameter at breast height (DBH), tree stem positions, individual tree crown delineation, and coarse wood debris. These methods are being designed with the practical needs of potential users from the forestry sector in mind. Different flight configurations, such as nadir and oblique camera angles, as well as different acquisition times, were combined to generate structure from motion (SfM) data products (dense 3D point clouds, orthomosaics and height models) containing both ground and canopy information. For a study site within the Hainich National Park, Germany, we analyzed how leaf-off and leaf-on data can be combined to improve the derivation of stand parameters, such as tree stem positions and individual tree crowns, using point- and raster-based algorithms. Additionally, DBH on an individual tree basis was derived for the same study site using the cast shadows of tree trunks. To do so, a deep learning model was trained to identify stem shadows based on an orthomosaic of only ground points acquired during sunny and leaf-off conditions.