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.

Deadwood is a vital component of forest ecosystems, significantly contributing to biodiversity and carbon storage. Accurate mapping of deadwood is essential for ecological monitoring and sustainable forest management. This study introduces a method for downed deadwood mapping using a convolutional neural network (CNN) applied to very high-resolution UAV RGB imagery. The research was conducted in Hainich National Park, central Germany, aiming to enhance the precision of coarse woody debris (CWD) delineation in a dense and structurally diverse temperate deciduous forest. Key objectives included testing the deep learning (DL) model’s performance at area, length, and object levels and benchmarking its accuracy against a traditional object-based image analysis (OBIA) method. Deadwood volume was calculated from the mapping results. By implementing a U-Net architecture with a ResNet-34 backbone and utilizing data augmentation techniques, the model achieved very high classification performance (F1-scores between 73% and 96%). It provided precise delineation of individual CWD objects from the underlying ground, representing detailed stem forms. High precision values highlight the reliability of the mapping results, while lower recall values indicate that some CWD objects, especially smaller branches, were missed. The DL approach achieved higher accuracy values across all testing methods compared to the OBIA method. The study also addresses the challenges posed by spectral ambiguities in decomposed deadwood and recommends future research directions for enhancing model generalization across diverse forest types and acquisition conditions.

Analyzing habitat conditions and mapping habitat structures are crucial for monitoring ecosystems and implementing effective conservation measures, especially in the context of declining open grassland ecosystems in Europe. The marsh fritillary (Euphydryas aurinia), an endangered butterfly species, depends heavily on specific habitat conditions found in these grasslands, making it vulnerable to environmental changes. To address this, we conducted a comprehensive habitat suitability analysis within the Hainich National Park in Thuringia, Germany, leveraging very high-resolution (VHR) airborne, red-green-blue (RGB), and color-infrared (CIR) remote sensing data and deep learning techniques. We generated habitat suitability models (HSM) to gain insights into the spatial factors influencing the occurrence of E. aurinia and to predict potential habitat suitability for the whole study site. Through a deep learning classification technique, we conducted biotope mapping and generated fine-scale spatial variables to model habitat suitability. By employing various modeling techniques, including Generalized Additive Models (GAM), Generalized Linear Models (GLM), and Random Forest (RF), we assessed the influence of different modeling parameters and pseudo-absence (PA) data generation on model performance. The biotope mapping achieved an overall accuracy of 81.8%, while the subsequent HSMs yielded accuracies ranging from 0.69 to 0.75, with RF showing slightly better performance. The models agree that homogeneous grasslands, paths, hedges, and areas with dense bush encroachment are unsuitable habitats, but they differ in their identification of high-suitability areas. Shrub proximity and density were identified as important factors influencing the occurrence of E. aurinia. Our findings underscore the critical role of human intervention in preserving habitat suitability, particularly in mitigating the adverse effects of natural succession dominated by shrubs and trees. Furthermore, our approach demonstrates the potential of VHR remote sensing data in mapping small-scale butterfly habitats, offering applicability to habitat mapping for various other species.

Arctic communities on permafrost face infrastructure instability from climate-driven thawing, a process exacerbated by the insulating effect of snow accumulation around buildings and roads. To monitor this threat, we assess the feasibility of using a low-cost, consumer-grade unoccupied aerial vehicle (UAV) (DJI Mini 2) with Structure-from-Motion photogrammetry to map snow depth and derive thermal insulation metrics in Aklavik, NT, Canada. By differencing high-resolution snow-covered (March 2024) and snow-free (September 2022) datasets, we found a mean snow depth of 0.65 m across the 0.98 km ² study area. Critically, snow was deeper adjacent to roads (mean: 0.65 m) than buildings (0.59 m), creating stronger thermal insulation along transportation corridors. This translated into a substantial warming bias (Δ T _s > 2.0 °C) for over 45% of road-adjacent and 35% of building-adjacent areas. Validation revealed a slight overestimation on bare ground (+8.24 cm) but a systematic underestimation relative to probe measurements (bias: −17.32 cm), primarily because the snow-free survey captured the top of shrub canopies rather than the true ground surface. This study demonstrates that carefully processed data from accessible UAV technology can provide valuable, high-resolution insights into hazardous snow distribution, offering a cost-effective and scalable tool to support permafrost stability and infrastructure management.

Automated Individual Tree Stem Detection (ITSD) is an important technique for forest management and conservation. In this study, a new method is tested, using unoccupied aerial vehicle (UAV)-derived point clouds from RGB imagery depicting deciduous dense forests in Germany. For the ITSD, the Vertical Complexity Index (VCI) was applied on five different datasets, including four Structure-from-Motion point clouds of a site in the Hainich National Park and one UAV LiDAR point cloud located at the Research Centre Jülich in Germany for comparison. First, four parameters were calibrated on one dataset and then used on the remaining point clouds to test the robustness of the respective method. The results showed that, for the SfM datasets, up to 82% of overstory trees could be detected with a Precision of up to 0.82 and F1-Score of up to 0.78. The LiDAR point cloud achieved an F1-Score of 0.69 using the same parameter set, but performance could be improved up to an F1-Score of 0.86 when adjusting one parameter. False negatives of the SfM datasets can be traced back to leaning trees and a low diameter at breast height (DBH). The application of this new method proved to be robust across different datasets and study sites, showing promising results for RGB-derived point clouds, and considering the low computational power, holding great potential for future analysis. Especially in forest management, the application of this method on low-cost RGB point clouds would be beneficial compared to the more expensive LiDAR imaging.