Ecosystem structure, especially vertical vegetation struc-ture, is one of the six essential biodiversity variable classesand is an important aspect of habitat heterogeneity, affect-ing species distributions and diversity by providing shelter,foraging, and nesting sites. Point clouds from airbornelaser scanning (ALS) can be used to derive such detailed in-formation on vegetation structure. However, public agen-cies usually only provide digital elevation models, which donot provide information on vertical vegetation structure.Calculating vertical structure variables from ALS pointclouds requires extensive data processing and remotesensing skills that most ecologists do not have. However,such information on vegetation structure is extremelyvaluable for many analyses of habitat use and species dis-tribution. We here propose 10 variables that should beeasily accessible to researchers and stakeholders throughnational data portals. In addition, we argue for a consistentselection of variables and their systematic testing, whichwould allow for continuous improvement of such a list tokeep it up-to-date with the latest evidence. This initiativeis particularly needed not only to advance ecological andbiodiversity research by providing valuable open datasetsbut also to guide potential users in the face of increasingavailability of global vegetation structure products.

Most ground filtering algorithms are primarily designed for airborne LiDAR point cloud processing and their successful use in identifying ground points from photogrammetric point clouds remains questionable. We compared six ground filtering algorithms implemented in Metashape, ArcGIS, CloudCompare, LAStools, and PDAL. We used UAV photogrammetry-based (acquired under leaf-off conditions) and airborne LiDAR (leaf-on) point clouds of the same area to: (i) compare accuracy of generated DTMs; (ii) evaluate the effect of vegetation density and terrain slope on filtering accuracy; and (iii) assess which algorithm parameters have the greatest effect on the filtering accuracy. Our results show that the performance of filtering algorithms was affected by the point cloud type, terrain slope and vegetation cover. The results were generally better for LiDAR (RMSE 0.13–0.19 m) than for photogrammetric (RMSE 0.19–0.23 m) point clouds. The behavior in varying vegetation and terrain conditions was consistent for LiDAR point clouds. However, when applied on photogrammetric point clouds, the algorithms’ behavior was inconsistent, especially in areas of steep slope (except for the Progressive Triangulated Irregular Network in LAStools). Parameters related to the selection of the initial minimum elevation ground points were the most influential in all algorithms and point clouds.

With the ever-improving advances in computer vision and Earth observation capabilities, Unmanned Aerial Vehicles (UAVs) allow extensive forest inventory and the description of stand structure indirectly. We performed several flights with different UAVs and popular sensors over two sites with coniferous forests of various ages and flight levels using the custom settings preset by solution suppliers. The data were processed using image-matching techniques, yielding digital surface models, which were further analyzed using the lidR package in R. Consumer-grade RGB cameras were consistently more successful in the identification of individual trees at all of the flight levels (84–77% for Phantom 4), compared to the success of multispectral cameras, which decreased with higher flight levels and smaller crowns (77–54% for RedEdge-M). Regarding the accuracy of the measured crown diameters, RGB cameras yielded satisfactory results (Mean Absolute Error—MAE of 0.79–0.99 m and 0.88–1.16 m for Phantom 4 and Zenmuse X5S, respectively); multispectral cameras overestimated the height, especially in the full-grown forests (MAE = 1.26–1.77 m). We conclude that widely used low-cost RGB cameras yield very satisfactory results for the description of the structural forest information at a 150 m flight altitude. When (multi)spectral information is needed, we recommend reducing the flight level to 100 m in order to acquire sufficient structural forest information. The study contributes to the current knowledge by directly comparing widely used consumer-grade UAV cameras and providing a clear elementary workflow for inexperienced users, thus helping entry-level users with the initial steps and supporting the usability of such data in practice.

Organic amendments are commonly applied singularly to soils to improve physical, biological and chemical properties, but their combination may be even more advantageous than when applied alone. In this study manure was applied singularly and in combination with biochar (90:10 and 50:50 ratios) to a drought prone agricultural Regosol in a field evaluation. Samples were collected twice a year for 2 years and subjected to testing for moisture retention, nutrient status and microbial activity whilst weed growth was monitored by drone. Substantial seasonal variability in all parameters measured was observed, though all amendments increased actual soil moisture content between 18 and 41% initially; without the addition of biochar (i.e., manure alone) this reverted back to reduced moisture content towards the second year of sampling. None of the tested amendment combinations significantly affected soil-saturated hydraulic conductivity. Cation exchange capacity decreased as a result of manure addition alone, the addition of 10% biochar and 50% biochar increased this significantly (23%–54% increase). Though microbial biomass and enzyme soil health indicators showed no decisive changes as a result of amendment application, and plant biomass was variable by ground sampling, drone imagery proved that plant heights and health were generally increased as a result of biochar addition to manure, compared with manured soil alone. In summary, despite much field seasonal variability limiting the interpretation of the data, this study nonetheless demonstrates a useful maintenance of improved soil moisture achieved by adding biochar together with manure to a drought-prone soil agricultural soil.