The Coastal Overview data layers identifies the lead authority for the management of discrete stretches of the English coast as defined by the Seaward of the Schedule 4 boundary of the Coastal Protection Act 1949. The data are intended as a reference for GIS users and Coastal Engineers with GIS capability to identify the responsible authority or whether the coast is privately owned. The information has been assigned from the following sources, listed in by preference: Shoreline Management Plans 1; Environment Agency’s RACE database; Consultation with Coastal Business User Group and Local Authority Maritime records where possible. A confidence rating is attributed based on where the data has been attributed from and the entry derived from the source data. The following data is intended as a reference document for GIS users and Coastal Engineers with GIS capability to identify the responsible authority and the assigned EA Coastal Engineer so as to effectively manage the coast for erosion and flooding. The product comprises 3 GIS layers that are based on the OS MasterMap Mean High Watermark and consists of the following data layers that are intended to be displayed as with the confidence factor that the information is correct. Coastal Overview Map [Polyline] –details the Lead Authority, EA Contact and other overview information for coast sections; Coastal Overview Map [Point] – shows the start point of the discrete stretch of coast and the lead authority; and Coastal Legislative Layer [Polyline] - represents the predominant risk; flooding or erosion, which are assigned to each section of the coastline.

The Coastal Overview data layers identifies the lead authority for the management of discrete stretches of the English coast as defined by the Seaward of the Schedule 4 boundary of the Coastal Protection Act 1949. The data are intended as a reference for GIS users and Coastal Engineers with GIS capability to identify the responsible authority or whether the coast is privately owned. The information has been assigned from the following sources, listed in by preference: Shoreline Management Plans 1; Environment Agency’s RACE database; Consultation with Coastal Business User Group and Local Authority Maritime records where possible. A confidence rating is attributed based on where the data has been attributed from and the entry derived from the source data. The following data is intended as a reference document for GIS users and Coastal Engineers with GIS capability to identify the responsible authority and the assigned EA Coastal Engineer so as to effectively manage the coast for erosion and flooding. The product comprises 3 GIS layers that are based on the OS MasterMap Mean High Watermark and consists of the following data layers that are intended to be displayed as with the confidence factor that the information is correct. Coastal Overview Map [Polyline] –details the Lead Authority, EA Contact and other overview information for coast sections; Coastal Overview Map [Point] – shows the start point of the discrete stretch of coast and the lead authority; and Coastal Legislative Layer [Polyline] - represents the predominant risk; flooding or erosion, which are assigned to each section of the coastline. Attribution statement: © Environment Agency copyright and/or database right 2016. All rights reserved.Contains Ordnance Survey data © Crown copyright and database rights

Living England is a multi-year project which delivers a broad habitat map for the whole of England, created using satellite imagery, field data records and other geospatial data in a machine learning framework. The Living England habitat map shows the extent and distribution of broad habitats across England aligned to the UKBAP classification, providing a valuable insight into our natural capital assets and helping to inform land management decisions. Living England is a project within Natural England, funded by and supports the Defra Natural Capital and Ecosystem Assessment (NCEA) Programme and Environmental Land Management (ELM) Schemes to provide an openly available national map of broad habitats across England.This dataset includes very complex geometry with a large number of features so it has a default viewing distance set to 1:80,000 (City in the map viewer).Process Description:A number of data layers are used to develop a ground dataset of habitat reference data, which are then used to inform a machine-learning model and spatial analyses to generate a map of the likely locations and distributions of habitats across England. The main source data layers underpinning the spatial framework and models are Sentinel-2 and Sentinel-1 satellite data from the ESA Copernicus programme, Lidar from the EA's national Lidar Programme and collected data through the project's national survey programme. Additional datasets informing the approach as detailed below and outlined in the accompanying technical user guide.Datasets used:OS MasterMap® Topography Layer; Geology aka BGS Bedrock Mapping 1:50k; Long Term Monitoring Network; Uplands Inventory; Coastal Dune Geomatics Mapping Ground Truthing; Crop Map of England (RPA) CROME; Lowland Heathland Survey; National Grassland Survey; National Plant Monitoring Scheme; NE field Unit Surveys; Northumberland Border Mires Survey; Sentinel-2 multispectral imagery; Sentinel-1 backscatter imagery; Sentinel-1 single look complex (SLC) imagery; National forest inventory (NFI); Cranfield NATMAP; Agri-Environment HLS Monitoring; Living England desktop validation; Priority Habitat Inventory; Space2 Eye Lens: Ainsdale NNR, State of the Bog Bowland Survey, State of the Bog Dark Peak Condition Survey, State of the Bog Manchester Metropolitan University (MMU) Mountain Hare Habitat Survey Dark Peak, State of the Bog; Moors for the Future Dark Peak Survey; West Pennines Designation NVC Survey; Wetland Annex 1 inventory; Soils-BGS Soil Parent Material; Met Office HadUK gridded climate product; Saltmarsh Extent and Zonation; EA LiDAR DSM & DTM; New Forest Mires Wetland Survey; New Forest Mires Wetland Survey; West Cumbria Mires Survey; England Peat Map Vegetation Surveys; NE protected sites monitoring; ERA5; OS Open Built-up Areas; OS Boundaries dataset; EA IHM (Integrated height model) DTM; OS VectorMap District; EA Coastal Flood Boundary: Extreme Sea Levels; AIMS Spatial Sea Defences; LIDAR Sand Dunes 2022; EA Coastal saltmarsh species surveys; Aerial Photography GB (APGB); NASA SRT (Shuttle Radar Topography Mission) M30; Provisional Agricultural Land Classification; Renewable Energy Planning Database (REPD); Open Street Map 2024.Attribute descriptions: Column Heading Full Name Format Description

SegID SegID Character (100) Unique Living England segment identifier. Format is LEZZZZ_BGZXX_YYYYYYY where Z = release year (2223 for this version), X = BGZ and Y = Unique 7-digit number

Prmry_H Primary_Habitat Date Primary Living England Habitat

Relblty Reliability
Character (12) Reliability Metric Score

Mdl_Hbs Model_Habs Interger List of likely habitats output by the Random Forest model.

Mdl_Prb Model_Probs Double (6,2) List of probabilities for habitats listed in ‘Model_Habs’, calculated by the Random Forest model.

Mixd_Sg Mixed_Segment Character (50) Indication of the likelihood a segment contains a mixture of dominant habitats. Either Unlikely or Probable.

Source Source

Description of how the habitat classification was derived. Options are: Random Forest; Vector OSMM Urban; Vector Classified OS Water; Vector EA saltmarsh; LE saltmarsh & QA; Vector RPA Crome, ALC grades 1-4; Vector LE Bare Ground Analysis; LE QA Adjusted

SorcRsn Source_Reason

Reasoning for habitat class adjustment if ‘Source’ equals ‘LE QA Adjusted’

Shap_Ar Shape_Area

Segment area (m2) Full metadata can be viewed on data.gov.uk.

PLEASE NOTE: This data product is not available in Shapefile format or KML at https://naturalengland-defra.opendata.arcgis.com/datasets/Defra::living-england-habitat-map-phase-4/about, as the data exceeds the limits of these formats. Please select an alternative download format.This data product is also available for download in multiple formats via the Defra Data Services Platform at https://environment.data.gov.uk/explore/4aa716ce-f6af-454c-8ba2-833ebc1bde96?download=true.The Living England project, led by Natural England, is a multi-year programme delivering a satellite-derived national habitat layer in support of the Environmental Land Management (ELM) System and the Natural Capital and Ecosystem Assessment (NCEA) Pilot. The project uses a machine learning approach to image classification, developed under the Defra Living Maps project (SD1705 – Kilcoyne et al., 2017). The method first clusters homogeneous areas of habitat into segments, then assigns each segment to a defined list of habitat classes using Random Forest (a machine learning algorithm). The habitat probability map displays modelled likely broad habitat classifications, trained on field surveys and earth observation data from 2021 as well as historic data layers. This map is an output from Phase IV of the Living England project, with future work in Phase V (2022-23) intending to standardise the methodology and Phase VI (2023-24) to implement the agreed standardised methods.The Living England habitat probability map will provide high-accuracy, spatially consistent data for a range of Defra policy delivery needs (e.g. 25YEP indicators and Environment Bill target reporting Natural capital accounting, Nature Strategy, ELM) as well as external users. As a probability map, it allows the extrapolation of data to areas that we do not have data. These data will also support better local and national decision making, policy development and evaluation, especially in areas where other forms of evidence are unavailable. Process Description: A number of data layers are used to inform the model to provide a habitat probability map of England. The main sources layers are Sentinel-2 and Sentinel-1 satellite data from the ESA Copericus programme. Additional datasets were incorporated into the model (as detailed below) to aid the segmentation and classification of specific habitat classes. Datasets used:Agri-Environment Higher Level Stewardship (HLS) Monitoring, British Geological Survey Bedrock Mapping 1:50k, Coastal Dune Geomatics Mapping Ground Truthing, Crop Map of England (RPA), Dark Peak Bog State Survey, Desktop Validation and Manual Points, EA Integrated Height Model 10m, EA Saltmarsh Zonation and Extent, Field Unit NEFU, Living England Collector App NEFU/EES, Long Term Monitoring Network (LTMN), Lowland Heathland Survey, National Forest Inventory (NFI), National Grassland Survey, National Plant Monitoring Scheme, NEFU Surveys, Northumberland Border Mires, OS Vector Map District , Priority Habitats Inventory (PHI) B Button, European Space Agency (ESA) Sentinel-1 and Sentinel-2 , Space2 Eye Lens: Ainsdale NNR, Space2 Eye Lens: State of the Bog Bowland Survey, Space2 Eye Lens: State of the Bog Dark Peak Condition Survey, Space2 Eye Lens: State of the Bog (MMU) Mountain Hare Habitat Survey Dark Peak, Uplands Inventory, West Pennines Designation NVC Survey, Wetland Inventories, WorldClim - Global Climate DataFull metadata can be viewed on data.gov.uk.

The Crop Map of England (CROME) is a polygon vector dataset mainly containing the crop types of England. The dataset contains approximately 32 million hexagonal cells classifying England into over 50 main crop types, grassland, and non-agricultural land covers, such as Trees, Water Bodies, Fallow Land and other non-agricultural land covers. The classification was created automatically using supervised classification (Random Forest Classification) from the combination of Sentinel-1 and Sentinel-2 images during the period late January 2017 – August 2017. The dataset was created to aid the classification of crop types from optical imagery, which can be affected by cloud cover. The results were checked against survey data collected by field inspectors and visually validated. Refer to the CROME specification document.

Areas of Outstanding Natural Beauty (AONBs) are designated areas where protection is afforded to protect and manage the areas for visitors and local residents. AONBs are also known as National Landscapes.Under the Countryside and Rights of Way Act 2000, Natural England has the power to designate AONBs in England that are outside national parks and that are considered to have such natural beauty it is desirable they are conserved and enhanced; issue a variation order to change an existing AONB boundary. It also holds a duty to give advice on developments taking place in an AONB; take into account the conservation and enhancement of AONBs in its work.National Landscapes are living places. Area of Outstanding Natural Beauty is not a nature designation, and caring for the natural beauty of these places involves more than habitat restoration.There are 46 National Landscapes in the UK. These are places with national importance, protected for the nation's benefit, but cared for by local teams with a deep understanding of the distinctive web of interconnecting factors that make these places special.The physical geography in a National Landscape: the unique combination of landform, climate and geology determines which species thrive, which industries grow, and therefore the heritage, language and culture of the individual place.For more information visit https://national-landscapes.org.uk/.Full metadata can be viewed on data.gov.uk.

This dataset consists of the 1km raster, dominant aggregate class version of the Land Cover Map 1990 (LCM1990) for Great Britain. The 1km dominant coverage product is based on the 1km percentage product and reports the aggregated habitat class with the highest percentage cover for each 1km pixel. The 10 aggregate classes are groupings of 21 target classes, which are based on the Joint Nature Conservation Committee (JNCC) Broad Habitats, which encompass the entire range of UK habitats. The aggregate classes group some of the more specialised classes into more general categories. For example, the five coastal classes in the target class are grouped into a single aggregate coastal class. This dataset is derived from the vector version of the Land Cover Map, which contains individual parcels of land cover and is the highest available spatial resolution. LCM1990 is a land cover map of the UK which was produced at the UK Centre for Ecology & Hydrology by classifying satellite images (mainly from 1989 and 1990) into 21 Broad Habitat-based classes. It is the first in a series of land cover maps for the UK, which also includes maps for 2000, 2007, 2015, 2017, 2018 and 2019. LCM1990 consists of a range of raster and vector products and users should familiarise themselves with the full range (see related records, the UK CEH web site and the LCM1990 Dataset documentation) to select the product most suited to their needs. 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.

This service contains various Aquaculture data. This includes Shellfish Production, Optimum Sites of Aquaculture potential (AQ1), Bivalve Classification area and Areas of Future Potential for Aquaculture. ------------------------------------------------------------------------------------------------------------The Shellfish Production dataset shows shellfish farm species production data grouped by water body. Water bodies were taken from the water framework directive (WFD) coastal and transitional water bodies database, and joined with the data from CEFAS. Data contains information on species present and production values. This dataset was created by ABPmer under contract to DEFRA (Contract reference MB106). An Excel spreadsheet was supplied to ABPmer by CEFAS which contained a list of waterbodies with the species cultivated per waterbody, production per waterbody and the number of businesses operating for 2007. The production data was joined to a shapefile containing waterbodies based on name of waterbody, and all sites where no shellfish cultivation occurred were removed. The same procedure was repeated with the data of species present. A shapefile containing both number of species grown and tonnes produced per waterbody was created by merging the two datasets based on waterbody name. ------------------------------------------------------------------------------------------------------------The Optimum Sites of Aquaculture Potential (AQ1) dataset shows areas identified through GIS modelling of suitable environmental conditions in East Coast Inshore and Offshore Marine Plan Areas favourable for macroalgae culture, Bivalve Bottom Culture, Finfish Cage, Lobster Restocking, Rope Cultured Bivalve Shellfish or Trestle/Bag Culture of Bivalves. This dataset has been derived from of a wider study assessing aquaculture potential in the South and East Marine Plan Areas for the Marine Management Organisation, project MMO1040. It was created using the Natural Resource model which forms part of the MMO project 1040 Spatial Trends in Aquaculture Potential in the South and East Coast Inshore and Offshore Marine Plan Areas. The Natural Resource model is made up of three existing environmental datasets: bathymetry derived from the Department of Food and Rural Affairs (Defra) Digital Elevation Model (DEM), predicted seabed sediments and combined seabed energy, both from UKSeaMap 2010 (McBreen, et al., 2010). Suitable environmental conditions applied include - low-moderate seabed energy, any sediment type and 10-25 m water depth for current potential. The depth limitations in this instance are based on the industry current reliance on scuba-divers for maintenance and husbandry. It is anticipated that as the industry develops it will become less reliant on divers and be able to move into deeper waters. Note that although the Natural Resource model used the best environmental data available for use in the study but there are significant limitations and gaps. These are outlined below and are discussed in more detail in the final project report: The model does not contain any measure of water quality (e.g. dissolved oxygen, sediment loading or contaminants) and therefore is likely to overestimate the area deemed suitable for aquaculture developments, particularly fin fish cage culture, rope grown bivalve culture and macroalgae culture. The UKSeaMap 2010 predicted seabed sediment map (McBreen, et al., 2010) is modelled at a coarse scale which has led to inaccuracies in the identification of areas which have potential for aquaculture development. UKSeaMap 2010 is known to under-estimate rock habitats because of the type of sampling data (sediment grabs) used to underpin the model. The MMO is working with JNCC to develop these data to lead to improvements in future models. The UKSeaMap 2010 combined seabed energy map included in the model (McBreen, et al., 2010) provides an approximation of the environmental conditions that are likely to limit aquaculture development (e.g. strong currents and large waves) but more accurate results could be obtained by using more precise component datasets such as the maximum wave height and tidal current range, where these datasets are available and the precise limitations of the aquaculture activities of interest are known. The dataset shows potential based on current technologies as defined in Table 10 of the MMO1040 Aquaculture Potential Final Report which is published on the MMO website's evidence pages. ------------------------------------------------------------------------------------------------------------The Bivalve Classification dataset classifies where the production of shellfish can be commercially harvested. All areas listed are designated for species that may be harvested as well as the classification of the shellfish waters. Classification of harvesting areas is required and implemented directly in England and Wales under European Regulation 854/2004. The co-ordination of the shellfish harvesting area classification monitoring programme in England and Wales is carried out by the Centre for Environment, Fisheries and Aquaculture Science, Weymouth (Cefas) on behalf of the Food Standards Agency (FSA). Cefas will make recommendations on classification according to an agreed protocol with the FSA making all final classification decisions and setting out the overall policy. Shellfish production areas are classified according to the extent to which shellfish sampled from the area are contaminated with E. coli. The Classification Zones/Production areas delineate areas where shellfish may be commercially harvested. Coordinates for the zone boundaries are calculated during a sanitary (ground) survey of the production area and where appropriate they are based on the OS Mastermap Mean High Water Line (coordinate accuracy <10m). The maps/zones are correct at time of publication but are updated when necessary depending on hygiene testing results. The current maps (jpgs) are available from the Cefas website ( https://www.cefas.co.uk/publications-data/food-safety/classification-and-microbiological-monitoring/england-and-wales-classification-and-monitoring/classification-zone-maps ) or a listing is available from the FSA website ( http://www.food.gov.uk/enforcement/monitoring/shellfish/shellharvestareas ) ------------------------------------------------------------------------------------------------------------The Current Aquaculture Potential layer highlights areas identified through GIS modelling of suitable environmental conditions in the South and East Marine Plan Areas favourable for macroalgae culture, Bivalve Bottom Culture, Finfish Cage, Lobster Restocking, Rope Cultured Bivalve Shellfish or Trestle/Bag Culture of Bivalves in the South and East Coast Marine Plan Areas. This dataset forms part of a wider study assessing different aquaculture potential in the South and East Marine Plan Areas for the Marine Management Organisation, project MMO1040. This dataset was created using the Natural Resource model which forms part of the MMO project 1040 Spatial Trends in Aquaculture Potential in the South and East Coast Inshore and Offshore Marine Plan Areas. The Natural Resource model is made up of three existing environmental datasets: bathymetry derived from the Department of Food and Rural Affairs (Defra) Digital Elevation Model (DEM), predicted seabed sediments and combined seabed energy, both from UKSeaMap 2010 (McBreen, et al., 2010). Suitable environmental conditions applied include - low-moderate seabed energy, any sediment type, 10-25 m water depth for current potential and 25-50 m water depth for near future potential). The depth limitations in this instance are based on the industry current reliance on scuba-divers for maintenance and husbandry. It is anticipated that as the industry develops it will become less reliant on divers and be able to move into deeper waters. Note that although the Natural Resource model used the best environmental data available for use in the study, there are significant limitations and gaps. These are outlined below and are discussed in more detail in the final project report: The Natural Resource model does not contain any measure of water quality (e.g. dissolved oxygen, sediment loading or contaminants) and therefore is likely to overestimate the area deemed suitable for aquaculture developments, particularly fin fish cage culture, rope grown bivalve culture and macroalgae culture. The UKSeaMap 2010 predicted seabed sediment map (McBreen, et al., 2010) is modelled at a coarse scale which has led to inaccuracies in the identification of areas which have potential for aquaculture development. UKSeaMap 2010 is known to under-estimate rock habitats because of the type of sampling data (sediment grabs) used to underpin the model. It is recommended that this component of the model is supplemented or replaced by higher resolution sediment maps where they are available for the region of interest. The UKSeaMap 2010 combined seabed energy map included in the model (McBreen, et al., 2010) provides an approximation of the environmental conditions that are likely to limit aquaculture development (e.g. strong currents and large waves) but more accurate results could be obtained by using more precise component datasets such as the maximum wave height and tidal current range, where these datasets are available and the precise limitations of the aquaculture activities of interest are known. The potential for development for the feature is "Current" (0-5 years), "Near Future" (5-10 years) or "Future" (10-20 years), the definitions of which are presented in Table 13 within the main report.

A PDF map showing the Rural Urban Classification (2011) of the OAs in the North East Region. (File Size - 850 KB)

A Special Protection Area (SPA) is the land designated under Directive 2009/147/EC on the Conservation of Wild Birds. SPAs are strictly protected sites classified in accordance with Article 4 of the EC Birds Directive, which came into force in April 1979. They are classified for rare and vulnerable birds (as listed on Annex I of the Directive), and for regularly occurring migratory species. Data supplied has the status "Classified". The data does not include "Potential" sites. Boundaries are mapped against Ordnance Survey MasterMap.Full metadata can be viewed on data.gov.uk.

A PDF map showing the Rural Urban Classification (2011) of the OAs in the East of England Region. (File Size - 3 MB)

This dataset consists of an interactive map (and supporting guidance) containing background information that informs how we understand flood risk across the South East River Basin District. The map shows the River Basin District, component river basins and the coastline together with layers showing land use and topography.

This dataset together with equivalent datasets for each River Basin District, supports the Preliminary Flood Risk Assessment for England report which has been written to meet the requirements of the Flood Risk Regulations (2009) - to complete an assessment of flood risk and produce supporting maps of river catchments.

A PDF map showing the Rural Urban Classification (2011) of the MSOAs in the East of England Region. (File Size - 932 KB)

The Young Trees map was funded by DEFRA through the Natural Capital and Ecosystem Assessment (NCEA) programme. The young trees mapping project developed a machine learning methodology using remote sensing to identify restocked stands where saplings persist in healthy numbers. The approach uses an eight-year timeframe since planting, crucial for verifying government grant compliance. Automating this methodology ensures easy replication and model transferability across years by training on multi-year data, making it resilient to climatic variations. Validation has confirmed the model’s accuracy, recommending high-confidence thresholds for restock classification. In the future, integration with the National Forest Inventory will enhance woodland mapping, accelerating updates and improving national indicators for forest extent and connectivity.

The aim of the young trees mapping project was to develop a machine learning methodology using remote sensing data, to identify stands where trees have been planted and saplings persist in healthy numbers. This was conducted within restock contexts across a specific timeframe, currently eight years since planting. This timeframe is significant because funding provided by government grants for planting can be reclaimed if it can be demonstrated that the funding has not been utilised by the landowner. Furthermore, the restock status of clearfell polygons has the potential to improve the accuracy of extent and connectivity environmental indicators developed as part of the Tree Health Resilience Strategy (THRS). The aim of this part of the project was to automate the methodology in such a way that it can be easily replicated, and to make the model transferable across years. Specifically, to train the model using multiple years of data, which makes the model agnostic to variable annual climactic conditions. The model is both robust and accurate, as demonstrated by the validation. It is recommended that only polygons with over 95% and under 5% confidence are treated as restocked or not restocked with any certainty. Outside of these limits confidence scores are only indicative of the restock status. In the future, the model is likely to be implemented as part of the National Forest Inventory (NFI) woodland map creation procedure. This will result in accelerated turnover of polygon labels from clearfell to young trees, where appropriate and will provide an important improvement to a national indicator for woodland extent and connectivity.

The Crop Map of England (CROME) South East is a polygon vector dataset mainly containing the crop types of England. The dataset contains approximately 32 million hexagonal cells classifying England into over 20 main crop types, grassland, and non-agricultural land covers, such as Woodland, Water Bodies, Fallow Land and other non-agricultural land covers. The classification was created automatically using supervised classification (Random Forest Classification) from the combination of Sentinel-1 and Sentinel-2 images during the period late January 2016 – August 2016. The dataset was created to aid the classification of crop types from optical imagery, which can be affected by cloud cover. The results were checked against survey data collected by field inspectors and visually validated. refer to the CROME specification document Attribution statement:

This record is for Approval for Access product AfA439. A habitat map derived from airborne data, specifically CASI (Compact Airborne Spectrographic Imager) and LIDAR (Light Detection and Ranging) data.

The habitat map is a polygon shapefile showing site relevant habitat classes. Geographical coverage is incomplete because of limits in data available. It includes those areas where the Environment Agency, Natural England and the Regional Coastal Monitoring Programme have carried out sufficient aerial and ground surveys in England.

The habitat map is derived from CASI multispectral data, LIDAR elevation data and other GIS products. The classification uses ground data from sites collected near to the time of CASI capture. We use ground data to identify the characteristics of the different habitats in the CASI and LIDAR data. These characteristics are then used to classify the remaining areas into one of the different habitats.

Habitat maps generated by Geomatics are often derived using multiple data sources (e.g. CASI, LIDAR and OS-base mapping data), which may or may not have been captured coincidentally. In instances where datasets are not coincidentally captured there may be some errors brought about by seasonal, developmental or anthropological change in the habitat.

The collection of ground data used in the classification has some limitations. It has not been collected at the same time as CASI or LIDAR capture; it is normally within a couple of months of CASI capture. Some variations between the CASI data and situation on site at the time of ground data collection are possible. A good spatial coverage of ground data around the site is recommended, although not always practically achievable. For a class to be mapped on site there must have been samples collected for it on site. If the class is not seen on site or samples are not collected for a class, it cannot be mapped.

No quantitative accuracy assessment has been carried out on the habitat map, although the classification was trained using ground data and the final habitat map has been critically evaluated using Aerial Photography captured simultaneously with the CASI data by the processors and independently by habitat specialists. Please note that this content contains Ordnance Survey data © Crown copyright and database right [2014] and you must ensure that a similar attribution statement is contained in any sub-licences of the Information that you grant, together with a requirement that any further sub-licences do the same.

This geolocated dataset derives from several surveys commissioned by the English Crown in 1565, enquiring into the state of the various ports, landing places, and coastal communities of England and Wales.

Please see the GitHub repository for details of the sources used and visualisation of their geographic scope.

A PDF map showing the Rural Urban Classification (2011) of the LSOAs in the East of England Region. (File Size - 2 MB)

Coal resource maps for the whole of the UK have been produced by the British Geological Survey as a result of joint work with Department of Trade and Industry and the Coal Authority. The Coal Resources Map is a Map of Britain depicting the spatial extent of the principal coal resources. The map shows the areas where coal and lignite are present at the surface and also where coal is buried at depth beneath younger rocks. The maps are intended to be used for resource development, energy policy, strategic planning, land-use planning, the indication of hazard in mined areas, environment assessment and as a teaching aid. In addition to a general map of coal resources for Britain data also exists for the six inset maps: Scotland; North-East; North-West; East Pennines; Lancashire, North Wales and the West Midlands; South Wales, Forest of Dean and Bristol. Available as a paper map, flat or folded, from BGS Sales or as a pdf on a CD if requested.

This is the web map service (WMS) for the 25m rasterised land parcels dataset of the UKCEH Land Cover Map of 2019 (LCM2019). It describes Great Britain and Northern Ireland land cover in 2019 using UKCEH Land Cover Classes, which are based on UK Biodiversity Action Plan broad habitats. The data was derived by rasterising the corresponding LCM2019 land parcels datasets into 25m pixels.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.Publication date: 2020-12-10https://catalogue.ceh.ac.uk/documents/4f88a4f0-3bbc-4735-b078-11919d1865e0Morton, R. D.; Marston, C. G.; O’Neil, A. W.; Rowland, C. S. (2020). Land Cover Map 2019 (25m rasterised land parcels, N. Ireland). NERC Environmental Information Data Centre. https://doi.org/10.5285/2f711e25-8043-4a12-ab66-a52d4e649532Morton, R. D.; Marston, C. G.; O’Neil, A. W.; Rowland, C. S. (2020). Land Cover Map 2019 (25m rasterised land parcels, GB). NERC Environmental Information Data Centre. https://doi.org/10.5285/f15289da-6424-4a5e-bd92-48c4d9c830cc