The application of Very-High-Resolution satellite imagery for the purpose of studying wildlife, particularly in remote regions, has gained significant traction in recent years. With this there has been an exponential increase in the volume of data, which has fostered a shift towards the use of automated systems to increase processing efficiency. However, these systems require manually annotated data on which to be trained, which is lacking. This dataset describes a total of 819 annotated and classified whale Features of Interest (FOIs) from a multi-season survey of Wilhelmina Bay on the Western Antarctic Peninsula (WAP). These data are comprised of FOIs that have been annotated and classified based on existing protocols by seven individual observers who scanned ~1,900 km2 of WorldView-03 imagery acquired between 2018/2019 and 2021/2022. This work was supported by an Innovation Voucher from the British Antarctic Survey and grants from the World Wildlife Fund (GB107301) and NC-International NERC (NE/T012439/1).

This dataset provides daily, 8-day, and monthly Arctic melt pond fractions and binary classification, from 2000-06-01 to 2020-08-31. Level-2 MODerate resolution Imaging Spectroradiometer (MODIS) top-of-the-atmosphere (TOA) reflectances for bands 1-4 were obtained, to which two machine learning algorithms such as multi-layer neural networks and logistic regression were applied to map melt pond fraction and binary melt pond/ice classification. This work was funded by NERC standard grant NE/R017123/1.

This dataset provides daily, 8-day, and monthly Arctic melt pond fractions and binary classification, from 2021-05-01 to 2022-08-31. Level-2 MODerate resolution Imaging Spectroradiometer (MODIS) top-of-the-atmosphere (TOA) reflectances for bands 1-4 were obtained, to which two machine learning algorithms such as multi-layer neural networks and logistic regression were applied to map melt pond fraction and binary melt pond/ice classification. This work was funded by NERC standard grant NE/R017123/1.

Aerial survey using a thermal sensor of the Grytviken / King Edward Point fur seal colony during the 2022/23 season conducted using a RPAS (remotely piloted aerial system). The aerial survey was conducted as part of the Darwin Plus (DPLUS) 109 project: Initiating Monitoring Support for the Government of South Georgia and the South Sandwich Islands Marine Protected Area (SGSSI-MPA) Research and Monitoring Plan for the purpose of establishing a baseline population estimate of key indicator species around South Georgia. Dataset includes the original images and log files, the original RINEX GNSS (global navigation satellite system) base station files, the post-processed GNSS and imagery data, along with the resulting georeferenced DSM (digital surface model) and an orthorectified mosaic of the imagery. This dataset is from flight number DPLUS_109_45 which was carried out on 2022-11-15. This work was supported by the Overseas Territories Environment and Climate Fund under Grant Darwin Plus ref: DPLUS109: Initiating monitoring support for the SGSSI-MPA Research and Monitoring Plan. Funding for this work was also received from the UK Blue Belt fund through GSGSSI.

This dataset provides the data produced as part of the work published in: Leeson, A. A., Foster, E., Rice, A., Gourmelen, N. and van Wessem, J. M.. 2019. 'Evolution of supraglacial lakes on the Larsen B ice shelf in the decades before it collapsed' Geophysical Research Letters. It includes 1) shapefiles of supraglacial lakes mapped in both optical (Landsat) and SAR (ERS) satellite imagery, 2) rasters of lake depth, derived from Landsat TM and ETM+ images acquired in 1988 and 2000 and 3) shapefiles of the study area considered in the paper. Funding was provided by ERPSRC grant EP/R01860X/1.

Meteorological variables (wind speed, air temperature and wind direction) were collected using two wind towers. Photogrammetric data were collected using a pole-mounted digital camera and DJI Phantom 3 UAV. Sites were Storglaciaren and Sydostra Kaskasatjakkaglaciaren, both in the Tarfala Valley in Arctic Sweden. Fieldwork was carried out between the 8th and 20th of July 2017, by Mark Smith, Duncan Quincey and Jonathan Carrivick. Wind towers recorded data continuously for the study period, and photogrammetric data were collected from each site on alternate days. Data from both sources were used to estimate glacier aerodynamic roughness (z0) for a method comparison. Funding was provided by NERC DTP grant NE/L002574/1

This dataset contains model input and output data on emperor penguin population dynamics for a Bayesian analysis carried out on multivariate classification results. Model input data comprises multivariate classification analysis results derived from very-high resolution (VHR) satellite imagery pertaining to 16 emperor penguin colonies, spanning the Bellingshausen Sea to the Weddell Sea between 2009 to 2023. Model output data comprises population estimates for each year for each colony, global trends per year, global change for the dataset overall, global abundance pertaining to individual colonies, as well as statistical parameter estimates provided by the model. Data collection was carried out by personnel at BAS. Funding from WWF UK (GB095701), project NE/Y00115X/1 "Understanding emperor penguin populations in the Weddell Sea and Antarctic Peninsula" and previous WWF funding over the 15 year period.

Datasets from the Resolving subglacial properties, hydrological networks and dynamic evolution of ice flow on the Greenland Ice Sheet (RESPONDER) project as published in the paper by Chudley et al. entitled "Supraglacial lake drainage at a fast-flowing Greenlandic outlet glacier". Please cite this paper if using this data. This dataset consists of observations of the rapid drainage of a supraglacial lake on Store Glacier, a marine-terminating outlet glacier of the west Greenland Ice Sheet. 'Lake 028', located 70.57degN, 50.08degW, drained on 2018-07-07 and was recorded using a variety of geophysical instrumentation. The dataset presented here includes all data necessary to replicate the findings presented in the main paper, including UAV photogrammetry-derived raster data (producing a series of orthophotos, digital elevation models, and velocity fields) and time-series records from in-situ geophysical instrumentation (GPS receiver, geophone, and water pressure sensor). Funding was provided by NERC DTP grant NE/L002507/1 and ERC Horizon 2020 grant 683043.

This dataset provides supraglacial lake extents and depths as included in the paper by Arthur et al. (in review, Nature Comms.) entitled " Large interannual variability in supraglacial lakes around East Antarctica". Please cite this paper if using this data. This dataset consists of (1) shapefiles of supraglacial lake extents around the East Antarctic Ice Sheet derived from Landsat-8 imagery acquired between January 2014 and 2020 and (2) rasters of supraglacial lake depths derived from Landast-8 imagery acquired over the same period. The datasets presented here were used to analyse the spatial distribution and interannual variability in lake distributions and volume. Funding was provided by NERC DTP grant NE/L002590/1 and NERC grant NE/R000824/1.

This dataset presents point annotations of stranded whale (Sperm whales, Physeter macrocephalus) and dolphin (Pilot whales, Globicephala melas edwardii) species identified in very high-resolution (VHR) optical and SAR satellite imagery, along offshore islands of New Zealand and Tasmania, between 2018-2023. Cetacean strandings offer significant conservation value for the assessment of ecosystems and serve as early warning of emerging concerns regarding animal, ocean, and human health. However stranding monitoring programmes are scarce or non-existent along minimally populated areas, coastlines with limited economic resources, geographically remote areas, complex coastlines and areas of geopolitical unrest. VHR satellite imagery offers the prospect of improving monitoring in these regions. While VHR satellite imagery is able to detect large baleen whale strandings, mass strandings are predominantly smaller-sized odontocetes (toothed whale and dolphin species). Detecting odontocetes is therefore crucial for VHR satellites to be useful for monitoring strandings globally. In addition, scaling up the use of VHR optical satellite imagery is limited by cloud cover, the primary environmental condition governing successful imagery collection. Synthetic Aperture Radar (SAR) satellites enable VHR imaging of Earth in cloudy regions and in darkness. This approach could facilitate strandings detection in cloudy regions and independent of daylight hours, which is critical for enabling timely emergency responses to unfolding stranding events. Here, we present data from four smaller odontocete mass strandings of long-finned pilot whale (LFPW), on Chatham, Pitt and Stewart Island, New Zealand, and one large odontocete (sperm whale) mass stranding on King Island, Tasmania, Australia between 2018-2023, to successfully detect and quantify large and small odontocete strandings in VHR optical and SAR satellite imagery. This research has been supported by the Natural Environment Research Council (NERC) through a SENSE CDT studentship (grant no. NE/T00939X/1). The research was further supported by additional funding provided through, the British Antarctic Survey (BAS) Innovation Voucher, Sentinel Hub and their #30MapChallenge competition, BAS Ecosystems, and the support and cooperation of Airbus and Maxar Technologies Ltd, for their rapid response and efforts to enable successful collection of the imagery analysed here.