Mapping hard-to-access and hazardous parts of forests by terrestrial surveying methods is a challenging task. Remote sensing techniques can provide an alternative solution to such cases. Unmanned aerial vehicles (UAVs) can provide on-demand data and higher flexibility in comparison to other remote sensing techniques. However, traditional georeferencing of imagery acquired by UAVs involves the use of ground control points (GCPs), thus negating the benefits of rapid and efficient mapping in remote areas. The aim of this study was to evaluate the accuracy of RTK/PPK (real-time kinematic, post-processed kinematic) solution used with a UAV to acquire camera positions through post-processed and corrected measurements by global navigation satellite systems (GNSS). To compare this solution with approaches involving GCPs, the accuracies of two GCP setup designs (4 GCPs and 9 GCPs) were evaluated. Additional factors, which can significantly influence accuracies were also introduced and evaluated: type of photogrammetric product (point cloud, orthoimages and DEM) vegetation leaf-off and leaf-on seasonal variation and flight patterns (evaluated individually and as a combination). The most accurate results for both horizontal (X and Y dimensions) and vertical (Z dimension) accuracies were acquired by the UAV RTK/PPK technology with RMSEs of 0.026 m, 0.035 m and 0.082 m, respectively. The PPK horizontal accuracy was significantly higher when compared to the 4GCP and 9GCP georeferencing approach (p < 0.05). The PPK vertical accuracy was significantly higher than 4 GCP approach accuracy, while PPK and 9 GCP approach vertical accuracies did not differ significantly (p = 0.96). Furthermore, the UAV RTK/PPK accuracy was not influenced by vegetation seasonal variation, whereas the GCP georeferencing approaches during the vegetation leaf-off season had lower accuracy. The use of the combined flight pattern resulted in higher horizontal accuracy; the influence on vertical accuracy was insignificant. Overall, the RTK/PPK technology in combination with UAVs is a feasible and appropriately accurate solution for various mapping tasks in forests.

The potential of close-range photogrammetry (CRP) to compete with terrestrial laser scanning (TLS) to produce dense and accurate point clouds has increased in recent years. The use of CRP for estimating tree diameter at breast height (DBH) has multiple advantages over TLS. For example, point clouds from CRP are similar to TLS, but hardware costs are significantly lower. However, a number of data collection issues need to be clarified before the use of CRP in forested areas is considered effective. In this paper we focused on different CRP data collection methods to estimate DBH. We present seven methods that differ in camera orientation, shooting mode, data collection path, and other important factors. The methods were tested on a research plot comprised of European beeches (Fagus sylvatica L.). The circle-fitting algorithm was used to estimate DBH. Four of the seven methods were capable of producing a dense point cloud. The tree detection rate varied from 49% to 81%. Estimates of DBH produced a root mean square error that varied from 4.41 cm to 5.98 cm. The most accurate method was achieved using a vertical camera orientation, stop-and-go shooting mode, and a path leading around the plot with two diagonal paths through the plot. This method also had the highest rate of tree detection (81%).

Forest models are instrumental for understanding and projecting the impact of climate change on forests. A considerable number of forest models have been developed in the last decades. However, few systematic and comprehensive model comparisons have been performed in Europe that combine an evaluation of modelled carbon and water fluxes and forest structure. We evaluate 13 widely used, state‐of‐the‐art, stand‐scale forest models against field measurements of forest structure and eddy‐covariance data of carbon and water fluxes over multiple decades across an environmental gradient at nine typical European forest stands. We test the models' performance in three dimensions:accuracy of local predictions(agreement of modelled and observed annual data),realism of environmental responses(agreement of modelled and observed responses of daily gross primary productivity to temperature, radiation and vapour pressure deficit) andgeneral applicability(proportion of European tree species covered). We find that multiple models are available that excel according to our three dimensions of model performance. For the accuracy of local predictions, variables related to forest structure have lower random and systematic errors than annual carbon and water flux variables. Moreover, the multi‐model ensemble mean provided overall more realistic daily productivity responses to environmental drivers across all sites than any single individual model. The general applicability of the models is high, as almost all models are currently able to cover Europe's common tree species. We show that forest models complement each other in their response to environmental drivers and that there are several cases in which individual models outperform the model ensemble. Our framework provides a first step to capturing essential differences between forest models that go beyond the most commonly used accuracy of predictions. Overall, this study provides a point of reference for future model work aimed at predicting climate impacts and supporting climate mitigation and adaptation measures in forests.

Tree regeneration is a key process in forest dynamics, particularly in the context of forest resilience and climate change. Models are pivotal for assessing long‐term forest dynamics, and they have been in use for more than 50 years. However, there is a need to evaluate their capacity to accurately represent tree regeneration. We assess how well current models capture the overall abundance, species composition, and mortality of tree regeneration. Using 15 models built to capture long‐term forest dynamics at the stand, landscape, and global levels, we simulate tree regeneration at 200 sites representing large environmental gradients across Central Europe. The results are evaluated against extensive data from unmanaged forests. Most of the models overestimate recruitment levels, which is compensated only in some models by high simulated mortality rates in the early stages of individual‐tree dynamics. Simulated species diversity of recruitment generally matches observed ranges. Models simulating higher stand‐level species diversity do not feature higher species diversity in the recruitment layer. The effect of light availability on recruitment levels is captured better than the effects of temperature and soil moisture, but patterns are not consistent across models. Increasing complexity in the tree regeneration modules is not related to higher accuracy of simulated tree recruitment. Furthermore, individual model design is more important than scale (stand, landscape, and global) and approach (empirical and process‐based) for accurately capturing tree regeneration. Despite the mismatches between simulation results and data, it is remarkable that most models capture the essential features of the highly complex process of tree regeneration, while not having been parameterized with such data. We conclude that much can be gained by evaluating and refining the modeling of tree regeneration processes. This has the potential to render long‐term projections of forest dynamics under changing environmental conditions much more robust.

Close-range photogrammetry (CRP) can be used to provide precise and detailed three-dimensional data of objects. For several years, CRP has been a subject of research in forestry. Several studies have focused on tree reconstruction at the forest stand, plot, and tree levels. In our study, we focused on the reconstruction of trees separately within the forest stand. We investigated the influence of camera lens, tree species, and height of diameter on the accuracy of the tree perimeter and diameter estimation. Furthermore, we investigated the variance of the perimeter and diameter reference measurements. We chose four tree species (Fagus sylvatica L., Quercus petraea (Matt.) Liebl., Picea abies (L.) H. Karst. and Abies alba Mill.). The perimeters and diameters were measured at three height levels (0.8 m, 1.3 m, and 1.8 m) and two types of lenses were used. The data acquisition followed a circle around the tree at a 3 m radius. The highest accuracy of the perimeter estimation was achieved when a fisheye lens was used at a height of 1.3 m for Fagus sylvatica (root mean square error of 0.25 cm). Alternatively, the worst accuracy was achieved when a non-fisheye lens was used at 1.3 m for Quercus petraea (root mean square error of 1.27 cm). The tree species affected the estimation accuracy for both diameters and perimeters.

Strong wind disturbances can affect large forested areas and often occur irregularly within a forest. Due to this, identifying damaged sites and estimating the extent of these losses are crucial for the harvesting management of salvage logging. Furthermore, the location should be surveyed as soon as possible after the disturbance to prevent the degradation of fallen trees. A fixed-wing type of unmanned aircraft system (UAS) with a compact digital camera was used in this study. The imagery was acquired on approximately 200 hectares where five large windthrow areas had occurred. The objective of the study was to determine the location of the windthrow areas using a semi-automatic approach based on the UAS imagery, and on the combination of UAS imagery with airborne laser scanning (ALS). The results were compared with reference data measured by global navigation satellite system (GNSS) devices. At the same time, windthrow areas were derived from Landsat imagery to investigate whether the UAS imagery would have significantly more accurate results. GNSS measurements and Landsat imagery are currently used in forestry on an operational level. The salvage logging was estimated for each forest stand based on the estimated areas and volume per hectare obtained from the forest management plan. The results from the UAS (25.09 ha) and the combined UAS/ALS (25.56 ha) methods were statistically similar to the reference GNSS measurements (25.39 ha). The result from Landsat, at 19.8 ha, was not statistically similar to the reference GNSS measurements or to the UAS and UAS/ALS methods. The estimate of salvage logging for the whole area, from UAS imagery and the forest management plan, overestimated the actual salvage logging measured by foresters by 4.93% (525 m³), when only the most represented tree species were considered. The UAS/ALS combination improved the preliminary results of determining windthrow areas which lead to decreased editing time for all operators. The UAS imagery shows potential for application to early-stage surveys of windthrow areas in forests. The advantages of this method are that it provides the ability to conduct flights immediately after the disturbance, the foresters do not need to walk within the affected areas which decreases the risk of injury, and allows flights to be conducted on cloudy days. The orthomosaic of the windthrow areas, as a by-product of data processing in combination with forest maps and forest road maps, can be used as a tool to plan salvage logging.