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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

  • 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

  • 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

  • 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 dataset is the 2012 revised Corine Land Cover (CLC) map, consisting of 44 classes in the hierarchical three level Corine nomenclature, produced during the CLC2018 production to improve the CLC2012 inventory. CLC 2018, CLC change 2012-2018 and CLC 2012 revised are three of the datasets produced within the frame of the Copernicus programme on land monitoring. Corine Land Cover (CLC) provides consistent information on land cover and land cover changes across Europe; these two maps are the UK component of Europe. This inventory was initiated in 1985 (reference year 1990) and established a time series of land cover information with updates in 2000, 2006 and 2012 being the last iteration. CLC products are based on photointerpretation 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) of status layers is 25 hectares; minimum width of linear elements is 100 metres; minimum mapping unit (MMU) for Land Cover Changes (LCC) for the change layers is 5 hectares. 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. More information about the Corine Land Cover (CLC) and Copernicus land monitoring data in general can be found at http://land.copernicus.eu/. Full details about this dataset can be found at https://doi.org/10.5285/9bb7caab-764d-407b-9a81-0d758722d900

  • [THIS DATASET HAS BEEN WITHDRAWN]. Modelled average percentage yield loss due to ground-level ozone pollution (per 1 degree by 1 degree grid cell) are 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 yield 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 yield 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 one of the first steps towards mitigation of the problem. The yield loss calculations were done as part of the NERC funded SUNRISE project (NEC06476). Full details about this dataset can be found at https://doi.org/10.5285/181a7dd5-0fd4-482a-afce-0fa6875b5fb3

  • This dataset contains the areas affected by landslides triggered by Typhoon Mangkhut in the area of Itogon (Benguet, Philippines) between the 13th and 15th of September 2018. The polygons were mapped using very high-resolution satellite imagery from before and after the typhoon. The pre-typhoon images were captured on 18/02/2018 and the post-typhoon images were captured on 02/03/2019 using the World-View 2 satellite. Google Earth imagery was also used as a supplementary source. The study area covers 570 km2. Full details about this dataset can be found at https://doi.org/10.5285/32765a61-8510-4dfc-b7c7-58bad12f8497

  • This dataset consists of the vector version of the Land Cover Map 2000 for Great Britain, 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. The map updates and upgrades the Land Cover Map of Great Britain (LCMGB) 1990. 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/b79e887e-a2a7-4224-8fd7-e78066b950b3

  • Data are presented showing change in saltmarsh extent along 25 estuaries/embayments in six regions across Great Britain, between 1846 and 2016. Data were captured from maps and aerial photographs. Marsh extent was delineated a scale of 1:7,500 by placing vertices every 5 m along the marsh edge. Error introduced from: (i) inaccuracies in the basemap used to georeference maps and aerial photographs; (ii) the georeferencing procedure itself; (iii) the interpreter when placing vertices on the marsh edge; and (iv) map and photo distortions that occurred prior to digitisation were calculated and used to estimate the root mean square error (RMSE) in areal extent of each marsh complex. Measures of marsh extent were only recorded if maps and aerial photographs were available for the entire estuary/embayment. Data was collected as part of a study on the large-scale, long-term trends and causes of lateral saltmarsh change. The data was used in the analysis for Ladd et al. (2019). C. Ladd and M.F. Duggan-Edwards carried out the collection and processing of the saltmarsh extent data. All authors contributed to the interpretation of the data. The work was carried out under the NERC programme - Carbon Storage in Intertidal Environment (C-SIDE), NERC grant reference NE/R010846/1. Full details about this dataset can be found at https://doi.org/10.5285/03b62fd0-41e2-4355-9a06-1697117f0717

  • 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