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  • Topographical and orthophotograph data sets created using Structure from Motion (SfM) from Unmanned Aerial Vehicle (UAV) data, presented as Orthophotographs (image and world file), Digital Surface Models (DSM) (image and world file), and point clouds (LAS format) using EPSG 32620 projection. The data was collected at selected sites on Dominica, Caribbean in January/February 2018 as part of a NERC funded project (NE/RO16968/1) to conduct geomorphological change and infrastructure damage baseline surveys following hurricane Maria. The data was flown using either a DJI Phantom 3 or 4, as indicated by the file name. If the file name includes 'NoGCP' in the file name the data uses the internal GPS and altitude of the DJI UAV. This means the data is not positionally accurate in absolute terms and should not be used in direct comparison to other georeferenced data. If the file name includes 'GCP' then the data was georeferenced using ground control derived from UAV data provided by the University of Michigan. This data is deemed accurate in absolute terms. (World Bank. 2018 Aug 31; Geotechnical Engineering Research Report(UMGE-2018/01))

  • Airborne remote-sensed hyperspectral in-situ radiometry data and hyperspectral imagery collected by the NERC Field Spectroscopy Facility (FSF) Headwall Co-aligned VNIR and SWIR imager (450-2500 nm) with LiDAR instruments mounted on a drone platform. These hyperspectral data collected over a sandy and rocky shore have associated uncertainty estimations that will be used to develop of radiometric proxies for plastics detection and assess future mission requirements. This dataset was collected on 22nd July 2021 from Oban airport's shore using a range of different plastic targets

  • Airborne remote-sensed hyperspectral in-situ radiometry data and hyperspectral imagery collected by the NERC Field Spectroscopy Facility (FSF) Headwall Co-aligned VNIR and SWIR imager (450-2500 nm) with LiDAR instruments mounted on a drone platform. These hyperspectral data collected over a sandy and rocky shore have associated uncertainty estimations that will be used to develop of radiometric proxies for plastics detection and assess future mission requirements. This dataset was collected on 29th September 2020 at Tyninghame beach, East Lothian, Scotland using a range of different plastic targets.

  • This dataset consists of the unprocessed radiance measurements downloaded directly from the unmanned aerial system imaging platform used to image the ice sheet surface near UPE_U in the north-west of the Greenland Ice Sheet, along with captures of reflectance panels and sensor calibration parameters which enable these imagery to be transformed to reflectance measurements. Funding was provided by the NERC standard grant NE/M021025/1.

  • This dataset consists of orthomosaics created from flights of an unmanned aerial system imaging platform at UPE_U in north-west Greenland on 24 July 2018. The Level-2 orthomosaics consist of (1) ground reflectance at 5 spectral bands, and (2) a digital elevation model. Level-3 orthomosaics consist of (1) broadband albedo calculated using a narrowband-to-broadband approximation and (2) surface type classification into snow, clean ice, light algae, heavy algae, cryoconite and water, as determined by a supervised classification algorithm which was trained on measurements collected at S6, K-transect, south-west Greenland. Funding was provided by the NERC standard grant NE/M021025/1.

  • This dataset consists of orthomosaics created from flights of an unmanned aerial system imaging platform at S6 on the south-west Greenland K-transect during July 2017. Level-2 orthomosaics consist of (1) ground reflectance at 5 spectral bands, and (2) digital elevation models (only for 2017-07-20 and 2017-07-21). Level-3 orthomosaics consist of (1) broadband albedo calculated using a narrowband-to-broadband approximation and (2) surface type classification into snow, clean ice, light algae, heavy algae, cryoconite and water, as determined by a supervised classification algorithm. Training data ingested by the classification algorithm are also provided. Funding was provided by the NERC standard grant NE/M021025/1.

  • This dataset consists of (1) broadband albedo calculated using a narrowband-to-broadband approximation and (2) surface type classification into snow, clean ice, light algae, heavy algae, cryoconite and water, as determined by a supervised classification algorithm, as applied to Sentinel-2 overpasses of S6, K-transect, south-west Greenland on 20 and 21 July 2017. Funding was provided by the NERC standard grant NE/M021025/1.

  • This dataset consists of the unprocessed radiance measurements downloaded directly from the unmanned aerial system imaging platform used to image the ice sheet surface at S6 on the south-west Greenland K-transect during July 2017, along with captures of reflectance panels and sensor calibration parameters which enable these imagery to be transformed to reflectance measurements. Funding was provided by the NERC standard grant NE/M021025/1.