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  • This dataset for the UK, Jersey and Guernsey contains the Corine Land Cover (CLC) revised for 2006. This shapefile has been created from combining the 2006 land cover layers from the individual CLC database files for the UK, Jersey and Guernsey. CLC is a dataset produced within the frame of the Initial Operations of the Copernicus programme (the European Earth monitoring programme previously known as GMES) on land monitoring. CLC provides consistent information on land cover and land cover changes across Europe. This inventory was initiated in 1985 (initial year 1990) and then established a time series of land cover information with updates in 2000 and 2006 the last one being the 2012 reference year. CLC products are based on the analysis of satellite images by national teams of participating countries - the EEA member and cooperating countries - following a standard methodology and nomenclature with the following base parameters: * 44 classes in the hierarchical three level Corine nomenclature; * Minimum mapping unit (MMU) for status layers is 25 hectares; * Minimum width of linear elements is 100 metres; The resulting national land cover inventories are further integrated into a seamless land cover map of Europe. Land cover and land use (LCLU) information is important not only for land change research, but also more broadly for the monitoring of environmental change, policy support, the creation of environmental indicators and reporting. CLC datasets provide important datasets supporting the implementation of key priority areas of the Environment Action Programmes of the European Union as protecting ecosystems, halting the loss of biological diversity, tracking the impacts of climate change, assessing developments in agriculture and implementing the EU Water Framework Directive, among others. Full details about this dataset can be found at https://doi.org/10.5285/2d0cf17f-aabd-4be6-859b-55c3403bbd9a

  • Erosion risk mapping showing river channel concentrations modelled using SCIMAP for the Yorkshire River Derwent, UK. Scenario mapping has been carried out and the dataset includes the following scenarios to assess variation in model output: 1) traditional land use map; 2) satellite derived land use maps; 3) long term rainfall averages; 4) integrating the artificial drainage network and 5) incorporating future climate change. Full details about this dataset can be found at https://doi.org/10.5285/331dd8ca-a4ff-40e6-b753-1b68468d8996

  • This is a digital map containing polygons representing areas of vegetation within Roudsea Wood National Nature Reserve (NNR), Cumbria. Vegetation was mapped in the field on a basemap as parcels according to tree cover type, tree stocking rates and ground flora communities. The map covers the western side of the reserve (the woodland). The field map was originally created by staff at the Nature Conservancy’s Merlewood Research Station, Grange-over-Sands, Cumbria in 1962 and digitized by the Centre for Ecology & Hydrology from the original field map in 2019. Full details about this dataset can be found at https://doi.org/10.5285/a8d710fb-177d-467c-b2c1-2b215f582d2c

  • This dataset models positive plant habitat condition indicators across Great Britain (GB). This data provides a metric of plant diversity weighted by the species that you would expect and desire to have in a particular habitat type so indicates habitat condition. In each Countryside Survey 2007 area vegetation plot the number of positive plant habitat indicators (taken from a list created from Common Standards Monitoring Guidance and consultation with the Botanical society of the British Isles (BSBI)) for the habitat type in which the plot is located are counted. This count is then divided by the possible indicators for that habitat type (and multiplied by 100) to get a percentage value. This is extrapolated to 1km squares across GB using a generalised additive mixed model. Co-variables used in the model are Broad Habitat (the dominant broad habitat of the 1km square), air temperature, nitrogen deposition, sulphur deposition, precipitation and whether the plot is located in a Site of Special Scientific Interest (SSSI) (presence or absence data). Full details about this dataset can be found at https://doi.org/10.5285/cc5ae9b1-43a0-475e-9157-a9b7fccb24e7

  • This data set provides a spatial stratification of forest cover into discrete vegetation classes according to the High Carbon Stock (HCS) Approach. The data set covers the Stability of Altered Forest Ecosystems (SAFE) project site located in Sabah, Malaysian Borneo. Data were collected in 2015 during a project which was included in the NERC Human-modified tropical forest (HMTF) programme. Full details about this dataset can be found at https://doi.org/10.5285/81cad1ef-b5cc-4592-a71f-204a5d04b700

  • These spatial layers contain the predicted occurrence and abundance of three heathland shrubs, Arctostaphylos uva-ursi, Vaccinium myrtillus and Vaccinium vitis-idaea identified as susceptible host species for Phytophthora ramorum and Phytophthora kernoviae in Scotland. The distribution models were developed from quadrat vegetation data kindly provided by Scottish Natural Heritage combined with data on climate and soil conditions as well as deer abundance and were fitted using a Bayesian Generalised Mixed Modelling approach adapted for input data on the DOMIN scale. This research was funded by the Scottish Government under research contract CR/2008/55, 'Study of the epidemiology of Phytophthora ramorum and Phytophthora kernoviae in managed gardens and heathlands in Scotland' and involved collaborators from St Andrews University, Science and Advice for Scottish Agriculture (SASA), Scottish Natural Heritage (SNH), Forestry Commission, the Food and Environment Research Agency (FERA) and the Centre for Ecology & Hydrology (CEH). Full details about this dataset can be found at https://doi.org/10.5285/5749df3d-000c-445e-a37f-dc0763b4d5ec

  • This dataset comprises forest stand and species occurrence data for a selection of non-native species collected in the UK Sovereign Base Areas (SBA) of Cyprus in October 2015 and March 2017, with a particular focus on the area surrounding Lake Akrotiri in the Western SBA. The main focus for mapping was stands of Acacia saligna, Casuarina cunninghamiana, the eucalypts Eucalyptus camaldulensis and E. gomphocephala, and the forb Symphyotrichum squamatum. The typical accuracy of data capture was around 10-15 m precision, varying according to the presence of forest canopy. Full details about this dataset can be found at https://doi.org/10.5285/7c84e06d-bb1a-4aac-b1d7-33c11310d8a0

  • The dataset consists of a distribution map of ash trees (Fraxinus excelsior) within woody linear features across Great Britain. The data is derived from Countryside Survey 2007 and includes trees recorded in lines of trees of a natural shape and lines of trees of an unnatural shape. Trees were mapped in 569 1km sample squares across Britain, and this national estimate dataset was derived from the sample data using ITE Land Classes. Full details about this dataset can be found at https://doi.org/10.5285/05e5d538-6be7-476d-9141-76d9328738a4

  • 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

  • Modelled annual average production loss (thousand tonnes per 1 degree by 1 degree grid cell) due to ground-level ozone pollution is presented for the crops maize (Zea mays), rice (Oryza sativa), soybean (Glycine max) and wheat (Triticum aestivum), for the period 2010-2012. Data are on a global scale, based on the distribution of production for each crop, according to the Food and Agriculture Organisation’s (FAO) Global Agro-Ecological Zones (GAEZ) crop production data for the year 2000. Modelled ozone data (2010-2012) needed for production loss calculations were derived from the EMEP MSC-W (European Monitoring and Evaluation Programme, Meteorological Synthesising Centre-West) chemical transport model (version 4.16). Mapping the global crop production losses due to ozone highlights the impact of ozone on crops and allows areas at high risk of ozone damage to be identified, which is a step towards mitigation of the problem. The production loss calculations were done as part of the NERC funded SUNRISE project (NEC06476) and National Capability Project NC-Air quality impacts on food security, ecosystems and health (NEC05574). Full details about this dataset can be found at https://doi.org/10.5285/0aa7911a-ab5f-4b08-a225-28b1e8344d01