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 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

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 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

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

This dataset consists of the vector version of the Land Cover Map 2000 for Northern Ireland, containing individual parcels of land cover (the highest available resolution). Level 2 & Level 3 attributes are available. Level 2, the standard level of detail, provides 26 LCM2000 target or ('sub') classes. This is the most widely used version of the dataset. Level 3 gives higher class detail. However, the quality of this level of detail may vary in different areas of the country, requiring expert interpretation. The dataset is part of a series of data products produced by the Centre for Ecology & Hydrology known as LCM2000. LCM2000 is a parcel-based thematic classification of satellite image data covering the entire United Kingdom. LCM2000 is derived from a computer classification of satellite scenes obtained mainly from Landsat, IRS and SPOT sensors and also incorporates information derived from other ancillary datasets. LCM2000 was classified using a nomenclature corresponding to the Joint Nature Conservation Committee (JNCC) Broad Habitats, which encompasses the entire range of UK habitats. In addition, it recorded further detail where possible. The series of LCM2000 products includes vector and raster formats, with a number of different versions containing varying levels of detail and at different spatial resolutions. Full details about this dataset can be found at https://doi.org/10.5285/9f043047-d1c7-4852-b513-aa00204022a8

This dataset consists of a 1km resolution raster version of the Land Cover Map 2000 for Great Britain. Each 1km pixel represents the dominant aggregate class across the 1km area. The aggregate classes are aggregations of the target classes, broadly representing Broad Habitats (see below). The dataset is part of a series of data products produced by the Centre for Ecology & Hydrology known as LCM2000. LCM2000 is a parcel-based thematic classification of satellite image data covering the entire United Kingdom. The map updates and upgrades the Land Cover Map of Great Britain (LCMGB). Like the earlier 1990 products, LCM2000 is derived from a computer classification of satellite scenes obtained mainly from Landsat, IRS and SPOT sensors and also incorporates information derived from other ancillary datasets. LCM2000 was classified using a nomenclature corresponding to the Joint Nature Conservation Committee (JNCC) Broad Habitats, which encompasses the entire range of UK habitats. In addition, it recorded further detail where possible. The series of LCM2000 products includes vector and raster formats, with a number of different versions containing varying levels of detail and at different spatial resolutions. Full details about this dataset can be found at https://doi.org/10.5285/b8b8a266-9162-40d8-98a6-f44178d31543

This dataset contains polylines depicting non-woodland linear tree and shrub features in Cornwall and much of Devon, derived from lidar data collected by the Tellus South West project. Data from a lidar (light detection and ranging) survey of South West England was used with existing open source GIS datasets to map non-woodland linear features consisting of woody vegetation. The output dataset is the product of several steps of filtering and masking the lidar data using GIS landscape feature datasets available from the Tellus South West project (digital terrain model (DTM) and digital surface model (DSM)), the Ordnance Survey (OS VectorMap District and OpenMap Local, to remove buildings) and the Forestry Commission (Forestry Commission National Forest Inventory Great Britain 2015, to remove woodland parcels). The dataset was tiled as 20 x 20 km shapefiles, coded by the bottom-left 10 km hectad name. Ground-truthing suggests an accuracy of 73.2% for hedgerow height classes. Full details about this dataset can be found at https://doi.org/10.5285/4b5680d9-fdbc-40c0-96a1-4c022185303f

Dataset contains the Land Use/Land Cover (LULC) map under four scenarios (Trend, Expansion, Sustainability, and Conservation) in 2030 in the Luanhe River Basin (LRB), China, with a resolution of 1km. The scenarios were based on different socio-economic development and environmental protection targets, local plans and policies, and the information from a stakeholders’ workshop, to explore land system evolution trajectories of the LRB and major challenges that the river basin may face in the future. The map includes nine different land use classes: 1) Extensive cropland, 2) Medium intensive cropland, 3) Intensive cropland, 4) Forest, 5) Grassland with low livestock, 6) Grassland with high livestock, 7) Water, 8) Built-up area and 9) Unused land. The land system classification is based on three main classification factors: (1) land use and cover, (2) livestock, and (3) agricultural intensity. The data was funded by UK Research and Innovation (UKRI) through the Natural Environment Research Council’s (NERC) Towards a Sustainable Earth (TaSE) programme, for the project "River basins as 'living laboratories' for achieving sustainable development goals across national and sub-national scales" (Grant no. NE/S012427/1) . Full details about this dataset can be found at https://doi.org/10.5285/a94640dc-fe21-4c38-936b-d62dfca0c952