Data provided are monthly surface water layers extracted from Sentinel1A SAR data for 3 districts in India (Shivamogga, Sindhudurg, Wayanad) for the year 2017 and 2018. Surface water body layers were mapped using an average monthly threshold value extracted from the image backscatter histogram. The average threshold value excluded the monsoon months due to the difference in water and not water area. The threshold value was slightly lesser than the mean threshold value. The end product was validated using field data which resulted in user and producer accuracies. Monthly surface water body layers were not produced for a few months due to the non-availability of Sentinel 1 data. The work was supported by MRC, AHRC, BBSRC, ESRC and NERC [grant number MR/P024335/1] and NERC - SUNRISE project [grant number NE/R000131/1] Full details about this dataset can be found at https://doi.org/10.5285/3c23fea1-5b27-4b01-b9ef-fc13346cfedc

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 data comprise Sentinel-2 derived burn severity rasters covering restored and unrestored reaches of the South Fork McKenzie river, Oregon USA. The data were collected in order to quantify differences in burn severity in restored and unrestored river reaches following the Holiday Farm wildfire in 2020. Raw satellite imagery acquired in June 2020 and June 2021 was processed to calculate Normalised Burn Ratio (NBR), giving pre- and post-fire burn severity information. Data consist of 10 m .TIF raster imagery where a digital number gives a measure of burn severity; high NBR values indicate healthy vegetation, whereas lower values indicate burnt areas or bare ground. The study was conducted by the University of Nottingham, in partnership with the US Forest Service, Portland State University, Washington State University and Colorado State University. Funding for the work was received from the Natural Environment Research Council. Full details about this dataset can be found at https://doi.org/10.5285/8162887a-5481-440f-a7f2-427eee793efd

Gridded land use map of Peninsular Malaysia with a resolution of approximate 25 meters for the year 2018. The map includes nine different classes: 1) non-paddy agriculture, 2) paddy fields, 3) rural residential, 4) urban residential, 5) commercial/institutional, 6) industrial/infrastructure, 7) roads, 8) urban and 9) others. The land use map was created as part of the project “Malaysia - Flood Impact Across Scales”. The project is funded under the Newton-Ungku Omar Fund ‘Understanding of the Impacts of Hydrometeorological Hazards in South East Asia’ call. The grant was jointly awarded by the Natural Environment Research Council and the MYPAIR Scheme under the Ministry of Higher Education of Malaysia. Full details about this dataset can be found at https://doi.org/10.5285/36df244e-11c8-44bc-aa9b-79427123c42c

The data set contains multi-temporal aerial imagery for two river segments in the Philippines. Imagery covers: (i) the downstream segment of the Bislak River and (ii) the confluence of the Abuan, Bintacan and Pinacanauan de Ilagan Rivers (referred to as ‘Ilagan’ in this data resource). Repeat aerial surveys were completed in 2019 and 2020. The data coverage includes the river channels, floodplains and surrounding areas. Raw aerial images were processed to produce spatially corrected orthoimagery (see supporting documentation). The resulting orthoimagery has a 0.2 m spatial resolution, containing information on the red, green and blue (RGB) bands. The work was supported by the Natural Environment Research Council (NERC) and Department of Science and Technology - Philippine Council for Industry, Energy and Emerging Technology Research and Development (DOST-PCIEERD) – Newton Fund grant NE/S003312. Full details about this dataset can be found at https://doi.org/10.5285/e040ff39-2176-4ed4-9e5d-861bdae8a030

The data was produced as part of a study to determine human impacts on river planform change within the context of short- and long-term river channel dynamics. To this end, the Himalayan Sutlej-Beas River system was used as a case study to (i) systematically assess changes in river planform characteristics over centennial, annual, seasonal, and episodic timescales; (ii) connect the observed patterns of planform change to human-environment drivers and interactions; and (iii) conceptualise these geomorphic changes in terms of timescale-dependant evolutionary trajectories. The dataset was derived from historic maps (1847-1850) and remote sensing data (Landsat over a 30-year period). It comprises post monsoon season wet river area annually 1989-2018, post monsoon season active gravel bars annually 1989-2018, active channel area (maximum extent between 1989-2018), active channel width annually 1989-2018, active channel width assessed from historic map (1847–1850), and the Anabranching index, annually 1989-2018. The work was supported by the Natural Environment Research Council (Grant NE/S01232X/1). Full details about this dataset can be found at https://doi.org/10.5285/f7aada06-7352-44c0-988e-2f4b31690189

This 1 km summary pixel data set represents the land surface of Great Britain and Northern Ireland, classified using two classification schemas: target and aggregate classes. The target class schema comprise 21 UKCEH land cover classes based upon Biodiversity Action Plan broad habitats. The aggregate class schema comprises 10 aggregate classes that are groupings of the 21 target classes. The aggregate classes group some of the more specialised target classes into more general classes. For example, the five coastal classes in the target class are grouped into a single aggregate class. The 1km percentage products describe percentage cover for each of the 21 land cover classes for 1km x 1km pixels. These contain one band per habitat class, producing 21 images for the target class product and 10 images for the aggregate class product. The 1km dominant coverage products are based on the 1km percentage products, and describe the land cover class with the highest percentage cover for each 1km pixel. A full description of these and all UKCEH LCM2020 products are available from the LCM2020 product documentation which accompanies the data. Full details about this dataset can be found at https://doi.org/10.5285/d6f8c045-521b-476e-b0d6-b3b97715c138

The data describe vegetation outlines and tree tops above 1m in height as polylines and points. Data have been processed from a digital terrain model (DTM) and digital surface model (DSM), converted from raw LiDAR data. The LiDAR dataset was acquired for Cornwall and Devon (all the land west of Exmouth) during the months of July and August 2013. The data were created as part of the Tellus South West project. Full details about this dataset can be found at https://doi.org/10.5285/78dba959-989b-43d4-b4da-efd2506e0c8e

The data contains Aerial imagery of Ynyslas Dunes, Wales saved in a GeoTiff format. The imagery covers 8000 m2 of a discrete coastal sand dune at northern distal end of a spit in Dyfi National Nature Reserve. Data was collected during a six-minute flight on 5th February 2020 made by a DJI Mavic Pro 2 uncrewed aerial vehicle (UAV). The flight was planned with Pix4DCapture based on a ground pixel resolution of 0.01 m. Lateral and longitudinal overlap was set to 80%. Prior to flying, eight (5.8 per 100 photos) Ground Control Points (GCPs) were evenly distributed throughout the dune and their location surveyed using a differential global positioning system (DGPS). Orthorectification and mosaicking of the aerial imagery collected was performed using Pix4Dmapper utilising a fully automated workflow based on Structure-from-Motion (SFM) digital photogrammetry algorithms. The data was collected to test the accuracy and repeatability of bare sand and vegetation cover in dunes mapped from aerial imagery. Data was collected and processed by Dr Ryan Wilson (University of Huddersfield) and interpreted by Dr Thomas Smyth (University of Huddersfield). The work was supported by the Natural Environment Research Council NE/T00410X/1. Full details about this dataset can be found at https://doi.org/10.5285/ac7071cb-79a3-400d-9f17-13dc4a657083

This 1 km summary pixel data set represents the land surface of Great Britain and Northern Ireland, classified using two classification schemas, target and aggregate classes. The target class schema comprise 21 UKCEH land cover classes based upon Biodiversity Action Plan broad habitats. The aggregate class schema comprises 10 aggregate classes that are groupings of the 21 target classes. The aggregate classes group some of the more specialised target classes into more general classes. For example, the five coastal classes in the target class are grouped into a single aggregate class. The 1km percentage product provides the percentage cover for each of the 21 land cover classes for 1km x 1km pixels. This product contains one band per habitat class, producing 21 and 10 band images for the target and aggregate class products respectively. The 1km dominant coverage product is based on the 1km percentage product, and reports the land cover class with the highest percentage cover for each 1km pixel. A full description of this and all UKCEH LCM2021 products are available from the LCM2021 product documentation. Full details about this dataset can be found at https://doi.org/10.5285/a3ff9411-3a7a-47e1-9b3e-79f21648237d