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Satellite‐derived global digital elevation models (DEMs) are essential for providing the topographic information needed in a wide range of hydrological applications. However, their use is limited by spatial resolution and vertical bias due to sensor limitations in observing bare terrain. Significant efforts have been made to improve the resolution of global DEMs (e.g., TanDEM‐X) and create bare‐earth DEMs (e.g., FABDEM, MERIT, CEDTM). We evaluated the vertical accuracy of bare‐earth and global DEMs in Central European mountains and submontane regions, and assessed how DEM resolution, vegetation offset removal, land cover, and terrain slope affect stream network delineation. Using lidar‐derived DTM and national stream networks as references, we found that: (a) bare‐earth DEMs outperform global DEMs across all land cover types. RMSEs increased with increasing slope for all DEMs in non‐forest areas. In forests, however, the negative effect of the slope was outweighed by the vegetation offset even for bare‐earth DTMs; (b) the accuracy of derived stream networks was affected by terrain slope and land cover more than by the vertical accuracy of DEMs. Stream network delineation performed poorly in non‐forest areas and relatively well in forests. Increasing slope improved the streams delineation performance; (c) using DEMs with higher resolution (e.g., 12 m TanDEM‐X) improved stream network delineation, but increasing resolution also increased the need for effective vegetation bias removal. Our results indicate that vertical accuracy alone does not reflect how well DEMs perform in stream network delineation. This underscores the need to include stream network performance in DEM quality rankings. Plain Language Summary: We evaluated the accuracy of different types of digital elevation models (DEMs) and derived stream networks in Central European mountains. Our focus was on satellite‐derived global DEMs, including bare‐earth DEMs (i.e., DEMs with vegetation and building offsets removed). Using lidar DTM and national stream networks as references, we found that bare‐earth DEMs consistently outperformed other DEMs across all evaluated land cover types. We observed that slope and land cover have contrasting effects on the accuracy of DEMs and delineated stream networks. As slope increases, DEMs accuracy decreases, while for delineated stream networks, the opposite trend is observed. Where land cover is concerned, DEMs' vertical accuracies (i.e., how well they represent the bare‐earth terrain) are lowest in forests, whereas the accuracy of stream network is lowest in non‐forest areas. Furthermore, we demonstrated that slope and land cover type considerably affect the accuracy of stream network delineation, more so than the vertical accuracy of the DEMs. Additionally, we found that using DEMs with higher resolution can improve stream network delineation, but increasing resolution also increases the need for effective vegetation bias removal. Therefore, removing vegetation and buildings offset from Tandem‐X DEM at a 12 m resolution would represent a major next step forward. Key Points: Differences between digital elevation models (DEMs) (i.e., their absolute vertical accuracy) have a lower effect on stream delineation than terrain slope and landcoverThe use of higher‐resolution DEMs to derive stream networks increases the importance of removing vegetation and building biasVertical accuracy alone does not reflect DEMs' performance in stream delineation, emphasizing the need to include this in quality evaluation [ABSTRACT FROM AUTHOR]

Ecosystem structure, especially vertical vegetation structure, is one of the six essential biodiversity variable classes and is an important aspect of habitat heterogeneity, affecting species distributions and diversity by providing shelter, foraging, and nesting sites. Point clouds from airborne laser scanning (ALS) can be used to derive such detailed information on vegetation structure. However, public agencies usually only provide digital elevation models, which do not provide information on vertical vegetation structure. Calculating vertical structure variables from ALS point clouds requires extensive data processing and remote sensing skills that most ecologists do not have. However, such information on vegetation structure is extremely valuable for many analyses of habitat use and species distribution. We here propose 10 variables that should be easily accessible to researchers and stakeholders through national data portals. In addition, we argue for a consistent selection of variables and their systematic testing, which would allow for continuous improvement of such a list to keep it up‐to‐date with the latest evidence. This initiative is particularly needed not only to advance ecological and biodiversity research by providing valuable open datasets but also to guide potential users in the face of increasing availability of global vegetation structure products. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicle (UAV) platforms are rapidly becoming popular in many research and industry sectors. Due to their relatively low purchase price and the fact they can be used to monitor areas that are difficult or even unsafe to access, they have been increasingly used in land surveying and mapping of smaller areas. Numerous UAV platforms equipped with various cameras are increasingly available on the market, differing in their suitability for environmental mapping. Surveyors therefore face a question whether to buy or assemble their own UAV. The objective of this study is to assess the performance of two fixed-wing UAV systems for land survey and mapping applications. In particular, we: (1) compared a commercial eBee platform equipped with a Sony Cybershot DSC-WX220 camera with zoom lens and a home assembled EasyStar II equipped with Nikon Coolpix A with a lens of fixed focal length to find out if a home-assembled solution can compete with specialized commercial platform; (2) investigated the utilization of UAV images acquired under leaf-off conditions for digital terrain model (DTM) generation with respect to vegetation cover (steppes and forests); (3) assessed whether an increase in the image quantity can compensate for a lower quality of images; and (4) compared the DTM derived from UAV imagery with the official Czech Republic airborne laser scanning (ALS)-derived DTM. One flight with Easystar II and two perpendicular flights with eBee were performed. From these three flights, four point clouds were derived (one from each flight, and one resulting from a combination of two eBee flights), supplemented with four ground filtered point clouds. The accuracy of point clouds and DTM was assessed through a comparison with a conventional GNSS survey. We successfully identified the bare ground during the leaf-off period in the deciduous forest using images from both platforms. Point densities of point clouds acquired with Easystar II exceeded the densities of those acquired with eBee even after combining images from two eBee flights. Root mean square error of all derived point clouds ranged between 0.11 and 0.19 m, exceeding the accuracy of a nationwide ALS-derived DTM in both forest and open steppe areas. The most accurate point cloud was acquired using Easystar II. This is likely due to a combined effect of the quality of onboard cameras, camera settings and environmental conditions during the flight. For users who prefer to have greater control over their options rather than being dependent on the commercially available kit solution, home-assembled kits utilizing drones capable of carrying any camera available on the market may be an advantage. [ABSTRACT FROM AUTHOR]

Several global digital elevation models (DEMs) have been developed in the last two decades. The most recent addition to the family of global DEMs is the TanDEM-X DEM. The original version of the TanDEM-X DEM is, however, a nonedited product (i.e., it contains local artefacts such as voids, spikes, and holes). Therefore, subsequent identification of local artefacts and their editing is necessary. In this study, we evaluated the accuracy of the original TanDEM-X DEM and its improved edited version, the Copernicus DEM, in three major European mountain ranges (the Alps, the Carpathians, and the Pyrenees) using a digital surface model derived from airborne laser scanning data as a reference. In addition, to evaluate the applicability of data acquisition characteristics (coverage map, consistency mask, and height error map) and terrain characteristics (slope, aspect, altitude) to the localization of problematic sites, we modeled their associations with the TanDEM-X DEM error. We revealed local occurrences of large errors in the TanDEM-X DEM that were typically found on steep ridges or in canyons, which were largely corrected in the Copernicus DEM. The editing procedure used for the Copernicus DEM construction was evidently successful as the RMSE for the TanDEM-X and Copernicus DEMs at the 90 m resolution improved from 45 m to 12 m, from 16 m to 6 m, and from 24 m to 9 m for the Alps, the Pyrenees, and the Carpathians, respectively. The Copernicus DEM at the 30 m resolution performed similarly well. The boosted regression trees showed that acquisition characteristics provided as auxiliary data are useful for locating problematic sites and explained 28–50% of deviance of the absolute vertical error. The absolute vertical error was strongly related to the height error map. Finally, up to 26% of cells in the Copernicus DEM were filled using DEMs from different time periods and, hence, users performing multitemporal analysis or requiring data from a specific time period in the mountain environment should be wary when using TanDEM-X and its derivations. We suggest that when filling problematic sites using alternative DEMs, more attention should be paid to the period of their collection to minimize the temporal displacement in the final products. [ABSTRACT FROM AUTHOR]

The ICESat-2 ATL08 land and vegetation product includes several flags that can be used for the assessment of LiDAR-environment interactions and can help select data of the highest quality. However, the usability of these flags has not been sufficiently studied to date. Here, we aimed to evaluate the effects of atmospheric scattering, the presence of snow, canopy cover, terrain slope, beam strength, and solar angle on the accuracy of terrain and canopy height of the ATL08 product as well as on providing recommendations on how to filter data in order to minimize errors. We evaluated the vertical accuracy of ATL08 terrain and canopy height in European mountains by comparing them with the digital terrain model and canopy height model derived from airborne laser scanning data. Our results indicate that the assessment of atmospheric effects using the cloud confidence flag (cloud_flag_atm ; i.e. number of cloud layers) is better than the previously used multiple scattering warning flag (msw_flag). Day acquisitions with more than one layer of clouds yielded a terrain elevation RMSE of 3.22 m in forests while night acquisitions with no more than a single layer of clouds resulted in RMSE of 1.73 m. The increasing atmospheric scattering effects increased the photons' path length, resulting in terrain height underestimation. The presence of snow had a strong positive effect on the number of identified ground photons, independently of the canopy cover, but resulted in an overestimation of terrain height in higher altitudes. Accordingly, the presence of snow cover resulted in a significant underestimation of canopy height in forests. The canopy height in broadleaf/mixed as well as coniferous forests was in summer underestimated on average by 2.1 m (%ME of −15.3%) and 1.2 m (%ME of −8.2%), respectively; in winter, however, the underestimation increased to 8.5 m (%ME of −56.8%) and 5.7 m (%ME of −38.3%), respectively. Canopy height estimates had better accuracy for the strong beam (RMSE of 5.09 m; %RMSE of 35.4%) than for the weak beam (RMSE of 7.03 m; %RMSE of 51.3%). Our results show that the ATL08 terrain height accuracy decreases with uneven distribution of signal photons within individual segments and further deteriorates with increasing terrain slope. Filtering out segments with poor distribution of photons, more than one layer of clouds during the day, and snow cover in high altitudes is the best approach for minimizing the error while maximizing the number of segments left for subsequent analysis. [Display omitted] • Cloud confidence flag is useful for assessing atmospheric effects on ATL08 accuracy. • High number of cloud layers led to low number of photons and terrain underestimation. • Error of canopy height estimates is considerably larger than that of terrain height. • Canopy height is underestimated due to snow-cover and loss of tree foliage in winter. • We provide users with an approach for detecting problematic measurements. [ABSTRACT FROM AUTHOR]