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  • This dataset is a characterisation of discontinuity types found within rocks and soils in Great Britain. Discontinuities are breaks, fractures or planes of weakness in the rock mass. The dataset includes type, frequency and orientation of discontinuities within rock and soil materials at formation (local to regional) scale. The discontinuities are classified in 3 categories: stratification (bedding planes), foliation (mineral banding) and rock mass description. The dataset aims to facilitate the preliminary research for planning and design of buildings, infrastructure and resource extraction. It forms part of the DiGMap Plus dataset series of GIS layers which describes 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.

  • Waterlines have been extracted to delimit the edge of the Hunga Tonga - Hunga Ha'apai island between April 2017 and April 2022. Waterline is defined as the instantaneous land - water boundary at the time of the imaging process. Waterlines have been generated by BGS - Earth Observation team through a thresholding-based classification based on Sentinel-2 multispectral imagery and developed on Google Earth Engine. Specifically, the thresholding has been applied to the Normalized Difference Water Index (NDWI) has been derived as a basis to discriminate between the land and sea based on their spectral characteristics. Changes in waterlines over volcanic islands can provide key information to understand volcanic processes. For more info on the methodology, see Novellino et al. (2020) https://doi.org/10.3390/app10020536

  • Tsunami trimlines identified across different islands of the Tonga archipelago. Trimlines have been used as a reference land feature following the January 2022 Tonga tsunami event that ripped off vegetation and built-up areas. Trimlines are distinctive limits between an area with sand coverage, vegetation destruction, and soil erosion on the one hand, and the unaffected natural vegetation on the other. This distinction provides a good landmark to map the inundation width and the landward extension of tsunami runup. In this case, the trimlines have been manually delineated by BGS - Earth Observation team using different high-resolution satellite datasets both optical (KompSat, Planet, Pleiades, WorldView) and radar (TerraSAR-X). Trimlines are well known from task-force publications documenting recent tsunami detection efforts and provide key information to support tsunami triggering mechanism models. For more info, see https://www.usgs.gov/media/images/tsunami-terms and Scheffers et al. (2012), https://doi.org/10.1007/s11069-010-9691-6

  • 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 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

  • This dataset consists of a vector layer (based on 1 by 1° grid), of modelled surface ozone concentrations (ppb). The values per cell are daily mean surface ozone for the period 6am – 6pm. The seasonal average has been calculated for the grassland growing season, for the period spanning mid-April to mid-July, for the UK and the USA, for 2018. Full details about this dataset can be found at https://doi.org/10.5285/4b0871a9-196a-48e1-a0c8-c5f53e17e9a7

  • 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 and National Capability Project NC-Air quality impacts on food security, ecosystems and health. Full details about this dataset can be found at https://doi.org/10.5285/2a932995-f040-4724-ad21-3e92ae8a2540

  • 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 https://doi.org/10.5285/2bfdede9-8008-4ba3-ac8e-af4e6ab9888b

  • This dataset is a model output from the European Monitoring and Evaluation Programme (EMEP) model applied to the UK (EMEP4UK) driven by Weather and Research Forecast model meteorology (WRF). It provides annual averages of vegetation specific atmospheric deposition of oxidised sulphur, oxidised nitrogen, and reduced nitrogen on a 1x1 km2 grid for the year 2018. The EMEP4UK model version used here is rv4.36, and the WRF model version is the 4.1.1. This work was supported by the Natural Environment Research Council award number NE/R016429/1 as part of the UK-SCAPE programme delivering National Capability. Full details about this dataset can be found at https://doi.org/10.5285/2adc10bf-e6f4-4e8d-b268-ee5d58d31c50

  • This dataset contains the areas affected by landslides triggered by Typhoon Parma in the area of Itogon (Benguet, Philippines) between the 2nd and 5th October 2009. The polygons were mapped using Google Earth imagery dated 31 December 2003 for pre-event and images and 31 December 2009 for post-event images. The area has an extension of 150 km2. Full details about this dataset can be found at https://doi.org/10.5285/2e15dbd2-71c3-4e86-aa90-6029d37bd417