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  • This dataset consists of change data for areas of Broad Habitats across Great Britain between 1990 and 1998. The data are national estimates generated by analysing the sample data from up to 569 1km squares and scaling up to a national level. The data are summarized as percentage increase or decrease in habitat area per Land Class (areas of similar environmental characteristics) and are in a vector format. The sample sites are chosen from a stratified random sample, based on a 15 by 15 km grid of GB and using the 'ITE Land Classification' as a method of stratification. The data were collected as part of Countryside Survey, a unique study or 'audit' of the natural resources of the UK's countryside. The Survey has been carried out at regular intervals since 1978 by the Centre for Ecology & Hydrology. The countryside is sampled and surveyed using rigorous scientific methods, allowing us to compare new results with those from previous surveys. In this way we can detect the gradual and subtle changes that occur in the UK's countryside over time. Surveys have been carried out in 1978, 1984, 1990, 1998 and 2007 with repeated visits to the majority of squares. In addition to habitat areas, vegetation species data, soil data, linear habitat data, and freshwater habitat data are also gathered by Countryside Survey. Full details about this dataset can be found at

  • 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

  • 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

  • This dataset is a characterisation of the soil and rocks and the potential bulking factor (likely excavated volume increases) at Formation (local to regional) level for Great Britain. The data is categorised into Class, characteristics of similar soils and rocks and Bulking Factor, range or ranges of % bulking. The excavation of rocks or soils is usually accompanied by a change in volume. This change in volume is referred to as ‘bulking’ and the measure of the change is the ‘bulking factor’. The bulking factor is used to estimate the likely excavated volumes that will need to be moved, stored on site, or removed from site. It is envisaged that the 'Engineering Properties: Bulking of soils and rocks' dataset will be of use to companies involved in the estimation of the volume of excavated material for civil engineering operations. These operations may include, but are not limited to, resource estimation, transportation, storage, disposal and the use of excavated materials as engineered fill. It forms part of the DiGMap Plus dataset series of GIS layers which describe the engineering properties of materials from the base of pedological soil down to c. 3m depth (ie the uppermost c.2m of geology). These deposits display a variable degree of weathering, but still exhibit core engineering characteristics relating to their lithologies.

  • 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

  • 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

  • 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

  • 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

  • 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

  • This dataset for the UK, Jersey and Guernsey contains the Corine Land Cover (CLC) changes between 2006 and 2012. This shapefile has been created by combining the land cover change 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 reference year 1990) and then established a time series of land cover information with updates in 2000 and 2006 with the last one being for 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 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 information 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