This web map service (WMS) is the 25m raster version of the Land Cover Map 2015 (LCM2015) for Great Britain and Northern Ireland. It shows the target habitat class with the highest percentage cover in each 25m x 25m pixel. The 21 target classes are based on the Joint Nature Conservation Committee (JNCC) Broad Habitats, which encompass the entire range of UK habitats.The 25m raster web map service is the most detailed of the LCM2015 raster products, both thematically and spatially, and it is derived from the LCM2015 vector product. For LCM2015 per-pixel classifications were conducted, using a random forest classification algorithm. The resultant classifications were then mosaicked together, with the best classifications taking priority. This produced a per-pixel classification of the UK, which was then 'imported' into the spatial framework, recording a number of attributes, including the majority class per polygon which is the Land Cover class for each polygon.Find out more about Land Cover Map 2015 at ceh.ac.uk.LCM2015 is available for download to Catchment Based Approach (CaBA) Partnerships in the desktop GIS data package. Please contact your CaBA catchment host for further information.

This is the land parcels (polygon) dataset for the UKCEH Land Cover Map of 2019 (LCM2019) representing Great Britain. It describes Great Britain's land cover in 2019 using UKCEH Land Cover Classes, which are based on UK Biodiversity Action Plan broad habitats. This dataset was derived from the corresponding LCM2019 20m classified pixels dataset. All further LCM2019 datasets for Great Britain are derived from this land parcel product. A range of land parcel attributes are provided. These include the dominant UKCEH Land Cover Class given as an integer value, and a range of per-parcel pixel statistics to help to assess classification confidence and accuracy; for a full explanation please refer to the dataset documentation. LCM2019 represents a suite of geospatial land cover datasets (raster and polygon) describing the UK land surface in 2019. These were produced at the UK Centre for Ecology & Hydrology by classifying satellite images from 2019. LCM2019 was simultaneously released with LCM2017 and LCM2018. These are the latest in a series of UKCEH land cover maps, which began with the 1990 Land Cover Map of Great Britain (now usually referred to as LCM1990) followed by UK-wide land cover maps LCM2000, LCM2007 and LCM2015. 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 is the 20m classified pixels dataset for the UKCEH Land Cover Map of 2019 (LCM2019) representing Great Britain. It describes Great Britain's land cover in 2019 using UKCEH Land Cover Classes, which are based on UK Biodiversity Action Plan broad habitats. This dataset is the Random Forest classification result from classifying a 20m pixel raster containing multi-season spectral information combined with context layers, which help to resolve spectral confusion. It is provided as a two-band, 8-bit integer raster. Band 1 is the UKCEH Land Cover Class identifier, band 2 is an indicator of classification confidence. For a fuller description please refer to the product documentation. LCM2019 represents a suite of geospatial land cover datasets (raster and polygon) describing the UK land surface in 2019. These were produced at the UK Centre for Ecology & Hydrology by classifying satellite images from 2019. LCM2019 was simultaneously released with LCM2017 and LCM2018. These are one in a series of UKCEH land cover maps, which began with the 1990 Land Cover Map of Great Britain (now usually referred to as LCM1990) followed by UK-wide land cover maps LCM2000, LCM2007 and LCM2015. 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.

About this layerThe Land Use Database held by the Northern Ireland Environment Agency (NIEA) provides a record of approximately 14,000 sites that have had previous industrial land use(s).What can you do with the layer?Visualisation: This layer can be used for visualisation online in web maps and in ArcGIS Pro.Analysis: This layer can be used in dashboards.Download: The data is downloadable.This layer is part of the Living Atlas of the World that provides access to thousands of beautiful and authoritative layers, web maps and apps.

This is the land parcels (polygon) dataset for the UKCEH Land Cover Map of 2017 (LCM2017) representing Northern Ireland. It describes Northern Ireland's land cover in 2017 using UKCEH Land Cover Classes, which are based on UK Biodiversity Action Plan broad habitats. This dataset was derived from the corresponding LCM2017 20m classified pixels dataset. All further LCM2017 datasets for Northern Ireland are derived from this land parcel product. A range of land parcel attributes are provided. These include the dominant UKCEH Land Cover Class given as an integer value, and a range of per-parcel pixel statistics to help to assess classification confidence and accuracy; for a full explanation please refer to the dataset documentation. LCM2017 represents a suite of geospatial land cover datasets (raster and polygon) describing the UK land surface in 2017. These were produced at the UK Centre for Ecology & Hydrology by classifying satellite images from 2017. LCM2017 was simultaneously released with LCM2018 and LCM2019. These are the latest in a series of UKCEH land cover maps, which began with the 1990 Land Cover Map of Great Britain (now usually referred to as LCM1990) followed by UK-wide land cover maps LCM2000, LCM2007 and LCM2015. 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 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

Habitat and land cover map created using AI to classify satellite data to EUNIS by Space Intelligence in partnership with NatureScot. This dataset contains three layers: - A EUNIS level 1 classification of Scotland's land cover for the year 2022 which uses 5 different classes; - A EUNIS level 2 classification of Scotland's land cover for the year 2022 which uses 28 different classes; - A change map, which provides predicted land cover changes that occurred between 2020 and 2022. The map is produced through a simple comparison between the 2020 and 2022 maps, where each instance of change identified is interpreted and assigned one of the following descriptors: (i) Afforestation (ii) Tree removal (iii) Agriculture related (iv) Urban development (v) Forest growth (vi) Water gain (vii) Water loss (viii) Other changes Please note, we believe these predicted changes, and others, are inaccurate, mainly due to inaccuracies we have identified in the 2020 map, along with improved methodologies and processes developed at Space Intelligence since the creation of the 2020 map.

The dataset is part of a time series of 20m resolution raster layers, the others being from 2019 and 2020 and the change layer of predicted land cover changes that occurred between 2019 and 2020.

This dataset contains a model of the extent and height class of woody linear features on field boundaries in England, including hedgerows, tree lines and semi-natural thickets of shrubs and trees. The model was derived from processing of the Environment Agency lidar product (National Lidar Programme), captured in 2016-2021, and the linework is consistent with the polygon boundaries used in the UK Centre for Ecology & Hydrology (UKCEH) Land Cover Map (LCM). Due to expected low densities of woody linear features, and potential uncertainty, areas excluded from the coverage include LCM classes of mountain/moor/heath, open water and the coastal zone, urban/suburban areas and woodlands (i.e., areas of continuous non-linear woody cover). An area of approximately 24 x 25 km on the North Yorkshire Moors was also excluded from the model, as this area was not mapped by the EA lidar. Full details about this dataset can be found at https://doi.org/10.5285/d90a3733-2949-4dfa-8ac2-a88aef8699be

This dataset contains gridded human population with a spatial resolution of 1 km x 1 km for the UK based on Census 2011 and Land Cover Map 2015 input data. Data on population distribution for the United Kingdom is available from statistical offices in England, Wales, Northern Ireland and Scotland and provided to the public e.g. via the Office for National Statistics (ONS). Population data is typically provided in tabular form or, based on a range of different geographical units, in file types for geographical information systems (GIS), for instance as ESRI Shapefiles. The geographical units reflect administrative boundaries at different levels of detail, from Devolved Administration to Output Areas (OA), wards or intermediate geographies. While the presentation of data on the level of these geographical units is useful for statistical purposes, accounting for spatial variability for instance of environmental determinants of public health requires a more spatially homogeneous population distribution. For this purpose, the dataset presented here combines 2011 UK Census population data on Output Area level with Land Cover Map 2015 land-use classes 'urban' and 'suburban' to create a consistent and comprehensive gridded population data product at 1 km x 1 km spatial resolution. The mapping product is based on British National Grid (OSGB36 datum).

All organisations hold information about the core of their business. Forestry England holds information on trees and forests. We use this information to help us run our business and make decisions.

The role of the Forest Inventory (the Sub-compartment Database (SCDB) and the stock maps) is to be our authoritative data source, giving us information for recording, monitoring, analysis and reporting. Through this it supports decision-making on the whole of the FE estate. Information from the Inventory is used by FE, wider government, industry and the public for economic, environmental and social forest-related decision-making.

Furthermore, it supports forest-related national policy development and government initiatives, and helps us meet our national and international forest-related reporting responsibilities. Information on our current forest resource, and the future expansion and availability of wood products from our forests, is vital for planners both in and outside FE. It is used when looking at the development of processing industries, regional infrastructure, the effect upon communities of our actions, and to prepare and monitor government policies. The Inventory (SCDB and stock maps), with ‘Future Forest Structure’ and the ‘rollback’ functionality of Forester, will help provide a definitive measure of trends in extent, structure, composition, health, status, use, and management of all FE land holdings.

We require this to meet national and international commitments, to report on the sustainable management of forests as well as to help us through the process of business and Forest Design Planning. As well as helping with the above, the SCDB helps us address detailed requests from industry, government, non-government organisations and the public for information on our estate. FE's growing national and international responsibilities and the requirements for monitoring and reporting on a range of forest statistics have highlighted the technical challenges we face in providing consistent, national level data. A well kept and managed SCDB and GIS (Geographical Information System - Forester) will provide the best solution for this and assist countries in evidence-based policy making. Looking ahead at international reporting commitments; one example of an area where requirements look set to increase will be reporting on our work to combat climate change and how our estate contributes to carbon sequestration. We have put in place processes to ensure that at least the basics of our inventory are covered:

1. The inventory of forests;
2. The land-uses;
3. The land we own ( Deeds);
4. The roads we manage.

We depend on others to allow us to manage the forests and to provide us with funds and in doing so we need to be seen to be responsible and accountable for our actions. A foundation of achieving this is good record keeping. A subcompartment should be recognisable on the ground. It will be similar enough in land use, species or habitat composition, yield class, age, condition, thinning history etc. to be treated as a single unit. They will generally be contiguous in nature and will not be split by roads, rivers, open space etc. Distinct boundaries are required, and these will often change as crops are felled, thinned, replanted and resurveyed. In some parts of the country foresters used historical and topographical features to delineate subcompartment boundaries, such as hedges, walls and escarpments. In other areas no account of the history and topography of the site was taken, with field boundaries, hedges, walls, streams etc. being subsumed into the sub-compartment. Also, these features may or may not appear on the OS backdrop, again this was dependent on the staff involved and what they felt was relevant to the map. The main point is that, as managers we may find such obvious features in the middle of a subcompartment when nothing is indicated on the stock map, while the same thing would be indicated elsewhere.

Attributes;

FOREST Cost centre Nos. COMPTMENT Compartment Nos. SUBCOMPT Sub-compartment letter BLOCK Block nos. CULTCODE Cultivation Code CULTIVATN Cultivation PRIHABCODE Primary Habitat Code PRIHABITAT Primary Habitat PRILANDUSE Land Use of primary component PRISPECIES Primary component tree species PRI_PLYEAR prim. component year planted PRIPCTAREA Prim. component %Area of sub-compartment SECHABCODE Secondary Habitat Code SECHABITAT Secondary Habitat SECLANDUSE Land Use of secondary component SECSPECIES Secondary component tree species SEC_PLYEAR Secondary component year planted SECPCTAREA Secondary component %Area of sub-compartment TERLANDUSE Land Use of tertiary component TERSPECIES Tertiary component tree species TER_PLYEAR Tertiary component year planted TERPCTAREA Tertiary component %Area of sub-compartment TERHABITAT Tertiary Habitat TERHABCODE Tertiary Habitat Code.

Any maps produced using this data should contain the following Forestry Commission acknowledgement: “Contains, or is based on, information supplied by the Forestry Commission. © Crown copyright and database right 2024 Ordnance Survey AC0000814847”.

Unlock precise, high-quality GIS data covering 164M+ verified locations across 220+ countries. With 50+ enriched attributes including coordinates, building structures, and spatial geometry our dataset provides the granularity and accuracy needed for in-depth spatial analysis. Powered by AI-driven enrichment and deduplication, and backed by 30+ years of expertise, our GIS solutions support industries ranging from mapping and navigation to urban planning and market analysis, helping businesses and organizations make smarter, data-driven decisions.

Key use cases of GIS Data helping our customers :

1. Optimize Mapping & Spatial Analysis : Use GIS data to analyse landscapes, urban infrastructure, and competitor locations, ensuring data-driven planning and decision-making.
2. Enhance Navigation & Location-Based Services : Improve real-time route planning, asset tracking, and EV charging station discovery for seamless location-based experiences.
3. Identify Strategic Sites for Business Expansion : Leverage GIS intelligence to select optimal retail sites, franchise locations, and warehouses with precision.
4. Improve Logistics & Address Accuracy : Streamline delivery networks, validate addresses, and optimize courier routes to boost efficiency and customer satisfaction.
5. Support Environmental & Urban Development Initiatives : Utilize GIS insights for disaster preparedness, sustainable city planning, and land-use management.

GIS-based computer generated real-time landscape models, and other computer generated static images were produced and used alongside photographs in more in-depth interviews and focus groups. (Some elements of this dataset are not part of this data submission due to copyright restrictions, though images may be included in the report). The study is part of the NERC Rural Economy and Land Use (RELU) programme. Future policies are likely to encourage more land use under energy crops: principally willow, grown as short rotation coppice, and a tall exotic grass Miscanthus. These crops will contribute to the UK's commitment to reduce CO2 emissions. However, it is not clear how decisions about appropriate areas for growing the crops, based on climate, soil and water, should be balanced against impacts on the landscape, social acceptance, biodiversity and the rural economy. This project integrated social, economic, hydrological and biodiversity studies in an interdisciplinary approach to assessing the impact of converting land to Miscanthus grass and short-rotation coppice (SRC) willows. Two contrasting farming systems were focused on: the arable-dominated East Midlands; and grassland-dominated South West England. The public attitudes questionnaire data from this study are available at the UK Data Archive under study number 6615 (see online resources). Further documentation for this study may be found through the RELU Knowledge Portal and the project's ESRC funding award web page (see online resources).

Individual Land Management Initiative boundaries. The LMIs worked with farmers, local communities and other interested organisations, to investigate the problems faced by land managers.The aim was to research and demonstrate innovative practical solutions that would maintain viable farm businesses, while providing a wide range of economic, environmental and social benefits. The aims of each LMI were defined locally, in partnership with stakeholders, and were guided by the objectives of the national programme. A national advisory group helped to guide the development of the overall programme.More information about the LMIs can be found at http://ukeconet.org/wp-content/uploads/2009/10/Experiences_from_the_land_management_initiatives.pdf.Full metadata can be viewed on data.gov.uk.

As part of the Centre for Ecology and Hydrology Land Cover 2007 Pilot Project, a reconnaissance survey was undertaken on 12th May 2006 in a 60 x 60 km area (bounded by Ordnance Survey National Grid Reference X = 400000 to 460000, Y = 095000 to 155000) which included the Network for Calibration and Validation in Earth Observation (NCAVEO) test site. A recording tablet device was used for acquiring ground data for sample points in the defined area. The dataset consists of an ESRI shape file of point data, containing all the points recorded on a tablet device. Each point has a British National Grid X and Y co-ordinate and a class code. The dataset has not been checked or edited yet and a few of the records will be erroneous. The most obvious errors will be two or more points with identical locations but different codes, the final code will be the correct one. Some of the points for Salisbury Plain lie just outside the test area boundaries. A key to abbreviations used for field recording is also included and a list of thematic land cover classes and their codes to aid field reconnaissance, as used for Land Cover Map 2000.

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 data-set contains all data resources, either directly downloadable via this platform or as links to external databases, to execute the generic modeling tool as described in D5.4

• 0 generic model
  file: d5.4-generic-model_february-2020.pdf
  Report with methodology and source code for the generic model
• 1.1 COPERNICUS
  https://land.copernicus.eu/local/urban-atlas/urban-atlas-2012?tab=download.%20
  The European Urban Atlas provides reliable, inter-comparable, high-resolution land use and land cover data for 800 Functional Urban Area (FUA) for the 2012 reference year in EEA39 countries.
• 1.2a 1km grid shapefiles for NOx emissions in EU
  The shapefiles are: nox.dbf nox.prj nox.shp nox.shx
• 1.2b 1km grid shapefiles for PM10 emissions in EU
  The shapefiles are: pm10.dbf pm10.prj pm10.shp pm10.shx
• 2.1 Eurostat energy consumption data
  https://ec.europa.eu/eurostat/web/energy/data/database?p_p_id=NavTreeportletprod_WAR_NavTreeportletprod_INSTANCE_QAMy7Pe6HwI1&p_p_lifecycle=0&p_p_state=normal&p_p_mode=view&p_p_col_id=column-2&p_p_col_count=1
• 2.2 JODI gas consumption data
  https://www.jodidata.org/gas/database/data-downloads.aspx
  Complete data series for all products, flows and countries, from January 2009 to one month-old can be downloaded, for free, in a beta version of .csv format.
• 2.3 Meteo data UK
  https://www.metoffice.gov.uk/services/data/datapoint/datapoint-products
• 2.4 Meteo data NL
  https://www.knmi.nl/nederland-nu/klimatologie/daggegevens
• 2.5 hourly electricity load profiles
  https://data.open-power-system-data.org/time_series/
• 3.1 fleet data
  file: 200210_summary_fleet_2016.csv
  Summary of historical fleet for EU28+NO by age and vehicle type: Diesel, CNG, Petrol, Electric, PHEV, LPG
• 3.2 modal shares
  file: 200210_summary_shares.csv
  This data set holds the modal share of different modes, for EU cities at level of vehicle kilometers. Modes considered: bus car heavy duty truck >32t heavy duty truck 16-32t heavy duty truck 3_5-7_5t heavy duty truck 7_5-16t light duty truck metro/tram moped motorcycle passenger train slow mode van
• 3.3 emission factors
  file: 200212_summary_copert_ef.xlsx
  Emission factor for passenger cars (in g/km )from COPERT V, urban conditions. Data dimensions: 1. type: diesel, petrol, CNG, LPG, PHEV (in line with fleet data) 2. pollutant: NOx or PM 3. Peak/off-peak: (P/OP) 4. year of built: YOB
• 5.1 Crude death rates
  http://apps.who.int/healthinfo/statistics/mortality/causeofdeath_query/start.php
  the total number of deaths in a country per year per 1,000 people broken down by age cohort and sex
• 5.2 UN population data
  https://population.un.org/wpp/Download/Standard/Population/
  the number of inhabitants at country level broken down by age cohort and sex the time a person has lived broken down by age cohort and sex, and country

** Important Note **

The Brownfield data was handed over from LDA to the Homes and Communities Agency so that HCA could maintain it as part of the National Land Use Database (NLUD). The HCA’s online mapping site displays a points only version of NLUD from 2010 (password protected):

https://signet.hca-online.org.uk/live/custom/login/SIGnet.aspx

The links to the files below will remain here as a matter of historical record.

Polygons showing the boundaries of Brownfield land in London along with their addresses.

This database of Brownfield land replaces in more detail and accuracy the EP National Land Use Database (NLUD) for London. The current NLUD assessment covers sites in excess of 0.25ha. This project validates, checks and updates this information for existing NLUD sites plus new sites down to a smaller threshold of 0.1 hectares and above.

The Database records over 2,000 Brownfield sites across London, equivalent to more than 2% of the land in Greater London (an increase of over 1,000 sites than recorded on the previous system). The Homes and Communities Agency will use the database as their preferred platform for boroughs to record brownfield sites.

The London Database uses Geographic Information Systems (GIS) mapping. It includes transport routes, deprivation, social infrastructure, as well as heritage and natural environment assets that can be overlaid over the dataset of brownfield land. Visitors to the Database website can identify sites suitable for development, and better explore and understand a site’s context.

For more information visit the HCA website

Files in this archive

London_landcover.zip

Land-cover fractions in 500-m grid boxes covering Greater London, UK

Polygons, ESRI shapefiles (*.shp, *.shx, *.cpg, *.dbf, *.prj)

code.zip

Code to process land cover (Python3)

urbisphere_gb-london_UR-5.pdf

Documentation

Data purpose

The data support APEx, urbisphere-London and ASSURE modelling activities, including simulations with the Surface Urban Energy and Water Balance Scheme (SUEWS).

Linked with

Hertwig et al. 2024. urbisphere_gb-london_UR-1: Processing and modelling grid of 500-m horizontal resolution for London, UK. urbisphere–London Data Release and Technical Documentation [Dataset]. Zenodo. https://doi.org/10.5281/zenodo.10889756

This page is an entry point to the principal LUCAS 2018 TOPSOIL data . In order to access the data, please register using the Request Form tab. (18/07/2022) Topsoil data for 18,984 samples from LUCAS 2018 are available as a CSV file and, to facilitate use of the data, an ESRI shapefile containing the theoretical points to which the taken samples should be associated; you may preview the readme file to find a description of these data. Documentation: these data are accompanied by a report describing the 2018 dataset. Geographical extent: European Union plus UK. Format: CSV (with soil property data) + shapefile (theoretical sampling points, based on LUCAS Grid) Measured properties: pH (CaCl2 and H2O), organic carbon content, CaCO3, nitrogen, phosphorous, potassium, EC (Electrical conductivity), Oxalate extractable Fe and Al . Clay, silt, sand content and coarse fragments have been measured in previous surveys - these data will be made available in a single file in the very near future).

Reference Year: samples taken in 2018 .

Previous LUCAS campaigns topsoil data are found here (2009/2012) and here (2015) Note that for this dataset of basic soil properties, data of physical and chemical properties were analysed in samples taken at various depths in 18,984 LUCAS points as follows:

Samples taken from 0-20 cm depth in 18,744 points (in 141 of these points, all taken in Portugal, OC and CaCO3 were also analysed in additional samples taken from 20-30-cm depth) Samples taken from 0-10 cm depth in 232 points, Samples taken from 10-20 cm depth in 8 points

Further information from this LUCAS SOIL 2018 campaign is in preparation. These include:

As assessment of changes between 2009/2012 and 2018: report and summary data. Heavy metals: a summary of descriptive statistics of 997 LUCAS points with high and low concentrations of metals; the point data will not be made publicly available. Erosion assessment: information recorded on gully erosion in 211 LUCAS points. Assessment of depths of organic soils: measurements of organic horizon from 1,042 LUCAS points. Biodiversity and plant protection products: report and summary statistics. Environmental conditions: reference ancillary data describing a range of environmental conditions for all LUCAS points of the soil module; these data are the same as those distributed with the LUCAS SOIL 2015 data.

Updates on LUCAS 2018 (21/06/2023) Soil biodiversity - DNA Bacteria and Fungi: The dataset includes bacterial 16S data and fungal ITS raw DNA sequences for 885 samples collected as part of LUCAS 2018 Soil survey (Biodiversity module). Additional details on the DNA extraction, amplification and sequencing, and the protocols for measuring soil properties and sources of climatic data can be found in “Patterns in soil microbial diversity across Europe" in Nature Communications: https://www.nature.com/articles/s41467-023-37937-4. Dataset available: https://esdac.jrc.ec.europa.eu/content/soil-biodiversity-dna-bacteria-and-fungi

(19/06/2023) Bulk density is part of the principal LUCAS 2018 TOPSOIL data (as distributed since 18/07/2022).These bulk density data were used to derive a new set of data "Fine Earth Soil Bulk Density at 0.2m depth from Land Use and Coverage Area frame Survey (LUCAS) soil 2018", as described in the research paper https://bsssjournals.onlinelibrary.wiley.com/doi/10.1111/ejss.13391. The data come as a CSV file (containing for each point POINT_ID, Eurostat LandCover category, BDsample_0_20_2018, coarse_mass_fraction, BDfine_0_20_2018_approx, coarse_volume_fraction_approx), an explanation of the columns and a shapefile with same attributes. These new data are now available from the LUCAS 2018 TOPSOIL data distribution page, after registration. Please note that the official Bulk density dataset is located here and includes both the 5659 samples for 0-20cm (Quality Checked) and the bulk density assessment in Europe (as in the research paper https://www.sciencedirect.com/science/article/pii/S0167880924000252. (15/02/2022) We integrated a soil erosion module in LUCAS Topsoils survey fo the EU and UK. The 2018 LUCAS Topsoil Survey consisted of soil sampling (0–20 cm depth) and erosion observations conducted in ca. 10% (n = 24,759) of the 238,077 Land Use/Cover Area frame Survey (LUCAS) 2018 in-field survey sites. Gully erosion channels were detected for ca. 1% (211 sites) of the visited LUCAS Topsoil sites. Overall, the findings indicate that the tested 2018 LUCAS Topsoil in-field gully erosion monitoring system is effective for detecting the incidence of gully erosion. The morphogenesis of the mapped gullies suggests that the approach is an effective tool to map permanent gullies, whereas it appears less effective to detect short-lived forms like ephemeral gullies. The research on the module of gullies in LUCAS can be found in the research paper "Monitoring gully erosion in the European Union: A novel approach based on the Land Use/Cover Area frame survey (LUCAS)" link: https://www.sciencedirect.com/science/article/pii/S2095633921000897. Data are available as well: https://esdac.jrc.ec.europa.eu/content/gully-erosion-based-lucas

Note that on the Eurostat website https://ec.europa.eu/eurostat/web/lucas/data/primary-data/2018, you can find the general LUCAS 2018 data (such as observed LandUse and LandCover, and point coordinates) which can be combined with the LUCAS SOIL 2018 data using the POINT_ID field)

This spatial dataset contains the digital boundaries of Local Nature Reserves (LNRs) in Wales. Local Nature Reserves are established and managed by local authorities, following consultation with Natural Resources Wales (NRW) under the National Parks and Access to the Countryside Act 1949. For a site to become an LNR it must have natural features of special interest to the local area, and the authority must either have a legal interest in the land or have an agreement with the owner to manage the land as a reserve. LNR prove to be useful not only to protect habitats and wildlife but increase people's awareness of their environment. They are places where children can learn about nature, and they are often situated in or near urban areas. Please refer to Countryside Act 1949 for reasons for capture of original designation. With the coming of GIS and its use in government departments and commercial companies mobilising the data in this way, advances the protection of these sites and the efficiency of decision making. The LNRs have been designated over a number of years, from 1970 to the present day, and are on-going. The data has been held digitally since mid1990s. This data can be subject to change by the local authority at anytime.