3D digital elevation models of Tsho Rolpa glacier lake, Nepal, generated from unmanned aerial vehicle (UAV) imagery, with a spatial resolution of 10 centimetres. It is combined with bathymetry data so that both the lakebed elevation (DTM) and the lake surface elevation (DSM) are obtained. Full details about this dataset can be found at https://doi.org/10.5285/8e483692-3b65-41d2-a7fd-5a3cd589a71c

The dataset contains three modelled estimates of global ammonia emissions from seabird colonies, at a spatial resolution of 0.1 degrees latitude/longitude. The model estimates were derived with a) detailed global seabird population data collated from a large number of sources (data sources date from 1980-2010 for different parts of the world) b) climate data (source: High-resolution Gridded Datasets, Climatic Research Unit, University of East Anglia, UK. http://www.cru.uea.ac.uk/cru/data/hrg/ last updated by Harris, I. (2007), date: 1995) c) emission model derived by Riddick et al. (2012) with funding for the project from the CEH Integrating Fund (NERC). A detailed description and discussion of the datasets, including methodology and uncertainties, can be found in the following peer-reviewed article: S. N. Riddick, U. Dragosits, T. D. Blackall, F. Daunt, S. Wanless and M. A. Sutton (2012) The global distribution of ammonia emissions from seabird colonies. Atmospheric Environment, 55 (2012), pp. 319-327 https://doi.org/10.1016/j.atmosenv.2012.02.052 Full details about this dataset can be found at https://doi.org/10.5285/c9e802b3-43c8-4b36-a3a3-8861d9da8ea9

This dataset comprises co-aligned hyperspectral and LiDAR data collected of European beech (Fagus sylvatica) forest within core protected areas of the UNESCO Rhӧn Biosphere Reserve, Germany. Data was collected using the Headwall Hyperspec Nano sensor flown from a unmanned aerial vehicle (UAV) in September 2020. The dataset comprises image and LiDAR data of four sites, each approximately 8ha in size. The study forests were subject to the extreme drought event that impacted central Europe in 2018/2019 and this project sought to collect data to enable individual tree and stand level assessment of the response (canopy damage and defoliation) of European beech trees to extreme drought events. The hyperspectral images available in this dataset have approx. 5cm pixel size with an associated LiDAR dataset and are suitable for identifying individual trees and the degree of canopy damage (defoliation, discolouration, and mortality) sustained by individuals/stands within the forest. The work was supported by the Natural Environment Research Council (Grant NE/V00929X/1). Full details about this dataset can be found at https://doi.org/10.5285/23d6a61c-c1cf-4c1b-a65c-f3fe42fc0e76

This dataset comprises four RGB unmanned aerial vehicle (UAV) images collected in September 2020 of European beech (Fagus sylvatica) forest within core protected areas of the UNESCO Rhӧn Biosphere Reserve, Germany. The study forests were subject to the extreme drought event that impacted central Europe in 2018/2019 and this project sought to collect data to enable the analysis of individual tree and stand level response (canopy damage and defoliation) of European beech trees to extreme drought events. The RGB images available in this dataset have approx. 3cm pixel size with an associated 10cm pixel digital elevation model (DEM) and are suitable for identifying individual trees and the degree of canopy damage (defoliation, discolouration, and mortality) sustained by individuals/stands within the forest. The work was supported by the Natural Environment Research Council (Grant NE/V00929X/1). Full details about this dataset can be found at https://doi.org/10.5285/b2d17962-3c7f-4193-a180-cde885d1a83e