Marston, C.; Rowland, C.S.; O'Neil, A.W.; Morton, R.D.
        
        Land Cover Map 2021 (10m classified pixels, GB)
         https://doi.org/10.5285/a22baa7c-5809-4a02-87e0-3cf87d4e223a
        
       
            Cite this dataset as: 
            
           
          Marston, C.; Rowland, C.S.; O'Neil, A.W.; Morton, R.D. (2022). Land Cover Map 2021 (10m classified pixels, GB). NERC EDS Environmental Information Data Centre. https://doi.org/10.5285/a22baa7c-5809-4a02-87e0-3cf87d4e223a
             
             
            
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         By accessing or using this dataset, you agree to the terms of the relevant licence agreement(s). You will ensure that this dataset is cited in any publication that describes research in which the data have been used.
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          https://catalogue.ceh.ac.uk/maps/2ad19a50-b940-469e-a40d-17818b77020c?request=getCapabilities&service=WMS&cache=false&
         
        
          This is a 10m pixel data set representing the land surface of Great Britain, classified into 21 UKCEH land cover classes, based upon Biodiversity Action Plan broad habitats. It is a two-band raster in GeoTiff format. The first band gives the most likely land cover type; the second band gives the per-parcel probability of the land cover. A full description of this and all UKCEH LCM2021 products are available from the LCM2021 product documentation accompanying this dataset. 
          
         
           Publication date: 2022-08-03
          
         View numbers valid from 01 June 2023 Download numbers valid from 03 August 2022 (information prior to this was not collected)
           
          Format
TIFF
Spatial information
          Study area
         
         
          Spatial representation type
         
         
          Raster
         
        
          Spatial reference system
         
         
          OSGB 1936 / British National Grid
         
        Temporal information
          Temporal extent
         
         2021-01-01    to    2021-12-31
          
         Provenance & quality
         UKCEH's automated land cover algorithms classify 10 m pixels across the whole of UK. Training data were automatically selected from stable land covers over the interval of 2018 to 2020. A Random Forest classifier used these to classify four composite images representing per season median surface reflectance. Seasonal images were integrated with context layers (e.g., height, aspect, slope, coastal proximity, urban proximity and so forth) to reduce confusion among classes with similar spectra. 
 
Land cover was validated by organising the 10 m pixel classification into a land parcel framework (the LCM2021 classified land parcels product). The classified land parcels were compared to known land cover producing a confusion matrix to determine overall and per class accuracy. Details are available from the product documentation accompanying this dataset.
 
The 25 m rasterised land parcels product is created by pixelating the corresponding land parcel product.
       Land cover was validated by organising the 10 m pixel classification into a land parcel framework (the LCM2021 classified land parcels product). The classified land parcels were compared to known land cover producing a confusion matrix to determine overall and per class accuracy. Details are available from the product documentation accompanying this dataset.
The 25 m rasterised land parcels product is created by pixelating the corresponding land parcel product.
Licensing and constraints
Licence terms and conditions apply
         Cite this dataset as: 
         
       Marston, C.; Rowland, C.S.; O'Neil, A.W.; Morton, R.D. (2022). Land Cover Map 2021 (10m classified pixels, GB). NERC EDS Environmental Information Data Centre. https://doi.org/10.5285/a22baa7c-5809-4a02-87e0-3cf87d4e223a
          
          
         
        Related
Services associated with this dataset
UK gridded population at 1 km resolution for 2021 based on Census 2021/2022 and Land Cover Map 2021
This dataset is included in the following collections
Citations
Price, B., Huber, N., Nussbaumer, A., & Ginzler, C. (2023). The Habitat Map of Switzerland: A Remote Sensing, Composite Approach for a High Spatial and Thematic Resolution Product. In Remote Sensing (Vol. 15, Issue 3, p. 643)  https://doi.org/10.3390/rs15030643
        
        Marston, C.G., O'Neil, A.W., Morton, R.D., Wood, C.M., & Rowland, C.S. (2023). LCM2021 – the UK Land Cover Map 2021. In Earth System Science Data (Vol. 15, Issue 10, pp. 4631–4649). Copernicus GmbH.  https://doi.org/10.5194/essd-15-4631-2023
        
        Filippelli, S.K., Schleeweis, K., Nelson, M.D., Fekety, P.A., & Vogeler, J.C. (2024). Testing temporal transferability of remote sensing models for large area monitoring. In Science of Remote Sensing (Vol. 9, p. 100119). Elsevier BV.   https://doi.org/10.1016/j.srs.2024.100119
        
        van der Plas, T.L., Geikie, S.T., Alexander, D.G., & Simms, D.M. (2023). Multi-Stage Semantic Segmentation Quantifies Fragmentation of Small Habitats at a Landscape Scale. In Remote Sensing (Vol. 15, Issue 22, p. 5277). MDPI AG.   https://doi.org/10.3390/rs15225277
        
        Hooftman, D.A.P., Ridding, L.E., Redhead, J.W., & Willcock, S. (2023). National scale mapping of supply and demand for recreational ecosystem services. In Ecological Indicators (Vol. 154, p. 110779). Elsevier BV.   https://doi.org/10.1016/j.ecolind.2023.110779
        
        Lewis, C.H.M., Little, K., Graham, L.J., Kettridge, N., & Ivison, K. (2024). Diurnal fuel moisture content variations of live and dead Calluna vegetation in a temperate peatland. In Scientific Reports (Vol. 14, Issue 1). Springer Science and Business Media LLC.   https://doi.org/10.1038/s41598-024-55322-z
        
        Tomkins, M., McDonald, H., Huck, J.J., Tippet, J., Elliot, S., Harris, E., & Maxwell, C. (2024). Modelling the cooling effects of urban canals. Zenodo.  https://doi.org/10.5281/ZENODO.10927598
        
        Dennis, M., Huck, J. ., Holt, C. D., Bispo, P. da C., McHenry, E., Speak, A., & James, P. (2024). Land-cover gradients determine alternate drivers of mammalian species richness in fragmented landscapes. In Landscape Ecology (Vol. 39, Issue 8). Springer Science and Business Media LLC.  https://doi.org/10.1007/s10980-024-01952-7
        
        Rong, Y., Bates, P., & Neal, J. (2024). GPU‐Accelerated Urban Flood Modeling Using a Nonuniform Structured Grid and a Super Grid Scale River Channel. In Water Resources Research (Vol. 60, Issue 6). American Geophysical Union (AGU).  https://doi.org/10.1029/2023wr036128
        
        Blaydes, H., Whyatt, J.D., Carvalho, F., Lee, H.K., McCann, K., Silveira, J.M., & Armstrong, A. (2025). Shedding light on land use change for solar farms. Progress in Energy, 7(3), 033001.   https://doi.org/10.1088/2516-1083/adc9f5
        
        Land Use in England Committee (2022). Making the most out of England’s land. UK Parliament Select Committee Publications  https://committees.parliament.uk/publications/33168/documents/179645/default/
        
        Greater Manchester Combined Authority (2024). Greater Manchester State of Nature. Greater Manchester Combined Authority  https://www.greatermanchester-ca.gov.uk/media/9526/gm-state-of-nature-report.pdf
        
       Correspondence/contact details
          Marston, C.
         
         
          UK Centre for Ecology & Hydrology
         
         
          Lancaster Environment Centre, Library Avenue, Bailrigg
Lancaster
Lancashire
LA1 4AP
UNITED KINGDOM
         
  enquiries@ceh.ac.uk
        Lancaster
Lancashire
LA1 4AP
UNITED KINGDOM
Authors
Other contacts
          Rights holder
         
         
           UK Centre for Ecology & Hydrology
          
         
          Custodian
         
         
            NERC EDS Environmental Information Data Centre
           
  info@eidc.ac.uk
          
          Publisher
         
         
            NERC EDS Environmental Information Data Centre
           
  info@eidc.ac.uk
          Additional metadata
          Funding
         
          Natural Environment Research Council  Award: NE/R016429/1  
         
         
      
 https://orcid.org/0000-0002-2070-2187
 https://orcid.org/0000-0002-2070-2187