This view shows a 1km resolution raster version of the Land Cover Map 2007 for Great Britain. The data consists of 23 bands. Each band represents a target class, broadly representing a Broad Habitat, and within the band each 1km pixel represents a percentage cover value of that class. The dataset is part of a series of data products produced by the Centre for Ecology & Hydrology known as LCM2007. LCM2007 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) 1990 and LCM2000. Like the earlier 1990 and 2000 products, LCM2007 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. LCM2007 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 LCM2007 products includes vector and raster formats, with a number of different versions containing varying levels of detail and at different spatial resolutions.

This data contains values of bare sand area, modelled wind speed, aspect and slope at a 2.5 m spatial resolution for four UK coastal dune fields, Abberfraw (Wales), Ainsdale (England), Morfa Dyffryn (Wales), Penhale (England). Data is stored as a .csv file. Data is available for 620,756.25 m2 of dune at Abberfraw, 550,962.5 m2 of dune at Ainsdale, 1,797,756.25 m2 of dune at Morfa Dyffryn and 2,275,056.25 m2 of dune at Penhale. All values were calculated from aerial imagery and digital terrain models collected between 2014 and 2016. For each location, areas of bare sand were mapped in QGIS using the semi-automatic classification plugin (SCP) and the minimum distance algorithm on true-colour RGB images. The slope and aspect of the dune surface at each site was calculated in QGIS from digital terrain models. Wind speed at 0.4 m above the surface of the digital terrain model at each site was calculated using a steady state computational fluid dynamics (CFD). Data was collected to statistically test the relationship between bare sand and three abiotic physical factors on coastal dunes (wind speed, dune slope and dune slope aspect). Vertical aerial imagery was sourced from EDINA Aerial Digimap Service and digital terrain models from EDINA LIDAR Digimap Service. Wind speed data was generated and interpreted by Dr Thomas Smyth (University of Huddersfield). Full details about this dataset can be found at https://doi.org/10.5285/972599af-0cc3-4e0e-a4dc-2fab7a6dfc85

The dataset contains chemistry data from streambed porewater (10 and 20 cm) and surface water, as well as nitrogen chemistry data at 2.5 cm resolution within the upper 15 cm of the streambed. The dataset includes concentrations of dissolved organic carbon (DOC), carbon dioxide, methane, ammonium, nitrate, nitrite and nitrous oxide, and isotopic ratios of δ13CCO2, δ15NNO3+NO2 and δ18ONO3+NO2. Also included are measurements of dissolved oxygen and temperature. Samples were collected from three reaches within the stream, an upstream sandy reach, a mid-stream sandy reach and a downstream gravel reach. The work was carried out with Natural Environment Research Council (NERC) funding through a PhD (NERC award number 1602135), grant (NE/L004437/1) and Life Sciences Mass Spectrometry Facility grant (CEH_L102_05_2016). Full details about this dataset can be found at https://doi.org/10.5285/00601260-285e-4ffa-b381-340b51a7ec50

This dataset shows potential carbon storage as modelled for the urban areas of Milton Keynes/Newport Pagnell, Bedford, and Luton/Dunstable, UK. The modelling approach used the ‘InVEST (Integrated Valuation of Ecosystem Services and Trade-offs) 3.1.0’ ecosystem service model suite, raster land cover maps at two spatial resolutions (5 m and 25 m) and published literature values for carbon storage by land cover. The resulting data are presented in the form of two ‘GeoTIFF’ raster map files (and associated metadata and spatial information files required by software) that can be viewed and manipulated in Geographic Information Software. The units are kg C per square meter. The purpose of the modelling was to help assess and visualise the value that urban green space represents to urban residents and natural systems in just one of many ecosystem services. This research was conducted as part of the larger 'Fragments, Functions, Flows and Urban Ecosystem Services' (F3UES) programme. Detailed methods and results of this analysis are published in: Grafius DR, Corstanje R, Warren PH, et al (2016) The impact of land use/land cover scale on modelling urban ecosystem services. Landsc Ecol 31:1509–1522. doi: 10.1007/s10980-015-0337-7. Full details about this dataset can be found at https://doi.org/10.5285/9209af2c-24f6-4e37-98fe-550032e97a2c

The dataset contains model output from an agricultural land use model at kilometre scale resolution over Great Britain (GB) for four different climate and policy scenarios. Specifically, arable area is modelled for with or without a climate tipping point (standard (medium emissions scenario SRES-A1B) climate change vs Atlantic Meridional Overturning Circulation (AMOC) collapse) and with or without widespread irrigation use for farmers from 2000 to 2089. Full details about this dataset can be found at https://doi.org/10.5285/e1c1dbcf-2f37-429b-af19-a730f98600f6

This dataset contains information about surface and sub-surface hydraulic and hydrological soil properties across the Thames (UK) catchment. Soil dry bulk density, estimated soil porosity, soil moisture and soil moisture retention (to 100 cm suction) were determined through laboratory analysis of soil samples collected at five depths between the surface and 100 cm below ground level (where possible). Surface soil infiltration rates were measured, and soil saturated hydraulic conductivity was calculated at 25 cm and 45 cm depths (where possible). Field scale point data were collected at seven sites in the Thames Catchment, with three sub-groups of sites under different land use and management practices. The first land management group included three arable fields in the Cotswolds, Gloucestershire, on shallow soils over Limestone with no grass in rotation, herbal leys in rotation or rye and clover in rotation. The second group included two arable fields in near Wantage, Oxfordshire, on free draining loamy soils over chalk with conventional management or controlled traffic. The final group included a permanent grassland and broadleaf woodland on slowly permeable soil over mudstone near Oxford, Oxfordshire. Data were collected in representative infield areas; trafficked areas (e.g. tramlines or animal tracks), and untrafficked margins. Samples and measurements were taken between April 2021 and October 2021, with repeats taken before and after harvest. Soil samples were collected using Eijkelkamp 07.53.SC sample ring kit with closed ring holder and the Edelman auger and Stony auger when required. Infiltration measurements were taken using Mini Disk Infiltrometers. Soil saturated hydraulic conductivity was measured using Guelph permeameters. Soil bulk density and porosity were calculated using oven drying methods. Soil moisture retention was calculated using an Eijelkamp Sandbox. This dataset was collected by UKCEH as part of the 'Land management in lowland catchments for integrated flood risk reduction' (LANDWISE) project. LANDWISE seeks to examine how land use and management can be used to reduce the risk of flooding for communities. LANDWISE is one of three projects comprising the Natural Environment Research Council Natural Flood Management Research Programme. The work was supported by the Natural Environment Research Council Grant NE/R004668/1. Full details about this dataset can be found at https://doi.org/10.5285/a32f775b-34dd-4f31-aafa-f88450eb7a90

This dataset contains information on individual birds caught at nestboxes or via mistnetting at 20 sites along a 35 km urban gradient in Glasgow, Scotland, 2014-2022. For each capture, we recorded the ring number of the individual, morphological parameters, whether samples were obtained and the sample number (blood, feather, faeces). The morphological measurements obtained were: Wing length (total length of the stretched wing, as per BTO guidelines), Weight (to the nearest 0.01 g), Tarsus length (using a caliper with 0.1 cm precision). Data were collected to investigate the effects of urbanisation on daily activity patterns, reproductive traits and population dynamics of passerine birds. Full details about this dataset can be found at https://doi.org/10.5285/9982cf52-7144-4877-9e17-1335f14140d8

This dataset contains information on activity parameters obtained from automated radiotelemetry data collected on individual birds of six passerine species (European robin, Eurasian blackbird, great tit, blue tit, dunnock, common chaffinch). Birds were caught via mistnetting at 4 sites along a 35 km urban gradient in Glasgow, Scotland, in autumn and winter of two years: 2020 and 2021. Once tagged, each bird was monitored for approximately 3-4 weeks. Raw telemetry data was processed and analysed in order to extract activity traits. The activity traits were: onset of morning activity, end of evening activity, total amount of daily activity. Data were collected to investigate the effects of urbanisation on daily activity patterns, reproductive traits and population dynamics of passerine birds. Full details about this dataset can be found at https://doi.org/10.5285/1b55a4eb-30be-4bd1-9144-cb7f8ba83b4e

This dataset contains information about soil near-surface physical and hydrological properties, vegetation observations and land use & management information across the Thames catchment (UK). It was collected during the ‘Landwise' project's ‘Broad-scale field survey' which sampled 1836 location points across a total of 164 fields/land parcels. The aim of the survey was to quantify the impact of innovative land use and management on soil properties, with implications for natural flood management. The surveyed fields were selected to represent four broad land use and management classes (arable with and without grass in rotation, permanent grassland and broadleaf woodland) and five generalised soil/geology classes. Approximately eight fields were sampled for each of the twenty combinations of land use and soil/geology class. The sampled fields cover a range of traditional and innovative agricultural practices. Within each field/parcel, representative sampling locations were selected to cover the anticipated range of soil variability, including typical infield, untrafficked margins and trafficked headlands/tramlines etc. Sampling was undertaken once during the period 2018-2021. Samples were measured and analysed using a range of field and laboratory techniques (see Data Lineage). Point data include: 1. Survey point location (British National Grid coordinates) 2. Soil quantitative measurements (near-surface: 0 – 50 mm below ground level): dry bulk density, volumetric water content, organic matter, derived porosity, derived porosity accounting for variable organic matter, particle size distribution and texture classification 3. Vegetation quantitative measurements: maximum and minimum height 4. Soil qualitative measurements: hand texture classification, aggregate stability test slaking and dispersion results, hydrochloric acid test for calcareous soil, and for a subset of locations Visual Evaluation of Soil Structure (VESS) score 5. Observations (also classified into groups): soil surface condition (e.g. slaked/unslaked/capped/poached etc.), vegetation type Field contextual data include: 1. Land owner/manager responses to a land use and management questionnaire (primary data) including information on: crop types/rotation, cover crops, herbal leys, organic or conventional, organic amendments, lime additions, tillage, last ploughed, tramlines, buffer strips, field drainage, grass species, livestock, last grazed, stocking density, grazing weeks per year, stock out-wintering, mob or paddock grazing, woodland management, tree species, woodland age, path management, land use history, flooding history, waterlogging, water or sediment runoff 2. Classification of selected questionnaire free text responses into categories (derived secondary data) 3. General field observations (primary data) including: slope gradient and shape, surface form, surface water, surface condition (slaking, capped, ruts, wheelings, poaching etc.), soil erosion or deposition features As agreed with the survey participants, this dataset has been anonymised by removing location specific information, such as farm and field names, along with any other personally identifiable information. As also agreed, point data location coordinates have been degraded to the nearest 1 km grid point. The dataset was co-produced by the UK Centre for Ecology and Hydrology and Landwise Partners as part of the Landwise Natural Flood Management project, supported by the Natural Environment Research Council (Grant NE/R004668/1). The participation and assistance of the land owners and managers is gratefully acknowledged. Full details about this dataset can be found at https://doi.org/10.5285/9ab5285f-e9c4-4588-ba21-476e79e87668

The data describes future land use projections at 1 km^2 resolution developed by CRAFTY-GB. For each of six Shared Socioeconomic Pathways (SSP-RCP) scenarios, gridded land use maps for Great Britain are provided, each as a stacked raster file with seven bands representing land use at each decadal timestep, from 2020 to 2080. CRAFTY-GB is a new agent-based model of the British land system operating at a 1 km^2 resolution and based on a broad range of available land system data . The model is based on linked UK-RCP climate scenarios and UK-SSPs socio-economic pathway (SSP) scenarios, based on global SSPs developed by the Intergovernmental Panel on Climate Change (IPCC). It extrapolates the impact of these on the British Land system at decadal timesteps from 2020-2080. Full details about this dataset can be found at https://doi.org/10.5285/f9ab3051-4f85-415f-b691-371ff8e951f2