Land Cover Map 2020 (1km summary rasters, GB and N. Ireland)
https://doi.org/10.5285/d6f8c045-521b-476e-b0d6-b3b97715c138
Download/Access
PLEASE NOTE:
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.
Licence terms and conditions apply
Download the data
This 1 km summary pixel data set represents the land surface of Great Britain and Northern Ireland, classified using two classification schemas: target and aggregate classes.
The target class schema comprise 21 UKCEH land cover classes based upon Biodiversity Action Plan broad habitats. The aggregate class schema comprises 10 aggregate classes that are groupings of the 21 target classes. The aggregate classes group some of the more specialised target classes into more general classes. For example, the five coastal classes in the target class are grouped into a single aggregate class.
The 1km percentage products describe percentage cover for each of the 21 land cover classes for 1km x 1km pixels. These contain one band per habitat class, producing 21 images for the target class product and 10 images for the aggregate class product.
The 1km dominant coverage products are based on the 1km percentage products, and describe the land cover class with the highest percentage cover for each 1km pixel.
A full description of these and all UKCEH LCM2020 products are available from the LCM2020 product documentation which accompanies the data.
The target class schema comprise 21 UKCEH land cover classes based upon Biodiversity Action Plan broad habitats. The aggregate class schema comprises 10 aggregate classes that are groupings of the 21 target classes. The aggregate classes group some of the more specialised target classes into more general classes. For example, the five coastal classes in the target class are grouped into a single aggregate class.
The 1km percentage products describe percentage cover for each of the 21 land cover classes for 1km x 1km pixels. These contain one band per habitat class, producing 21 images for the target class product and 10 images for the aggregate class product.
The 1km dominant coverage products are based on the 1km percentage products, and describe the land cover class with the highest percentage cover for each 1km pixel.
A full description of these and all UKCEH LCM2020 products are available from the LCM2020 product documentation which accompanies the data.
Publication date: 2022-10-28
View numbers valid from 01 June 2023 Download numbers valid from 20 June 2024 (information prior to this was not collected)
Format
TIFF
Spatial information
Study area
Spatial representation type
Raster
Spatial reference systems
OSGB 1936 / British National Grid
Irish Grid
Irish Grid
Temporal information
Temporal extent
2020-01-01 to 2020-12-31
Provenance & quality
UKCEH's automated land cover algorithms classify 10 m pixels across the whole of UK. 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 LCM2020 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.
The 1 km percentage and dominant products are created by calculating the percentage cover for each land cover classes for every 1km x 1km pixel, then reporting the dominant land cover class with the highest percentage cover for each 1km pixel.
Land cover was validated by organising the 10 m pixel classification into a land parcel framework (the LCM2020 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.
The 1 km percentage and dominant products are created by calculating the percentage cover for each land cover classes for every 1km x 1km pixel, then reporting the dominant land cover class with the highest percentage cover for each 1km pixel.
Licensing and constraints
Licence terms and conditions apply
Cite this dataset as:
Morton, R.D.; Marston, C.G.; O'Neil, A.W.; Rowland, C.S. (2022). Land Cover Map 2020 (1km summary rasters, GB and N. Ireland). NERC EDS Environmental Information Data Centre. https://doi.org/10.5285/d6f8c045-521b-476e-b0d6-b3b97715c138
Related
This dataset is included in the following collections
Citations
Arsevska, E., Hengl, T., Singleton, D. A., Noble, P.-J. M., Caminade, C., Eneanya, O. A., Jones, P. H., Medlock, J. M., Hansford, K. M., Bonannella, C., & Radford, A. D. (2024). Risk factors for tick attachment in companion animals in Great Britain: a spatiotemporal analysis covering 2014–2021. In Parasites & Vectors (Vol. 17, Issue 1). Springer Science and Business Media LLC. https://doi.org/10.1186/s13071-023-06094-4
Cordero, J. P., Donkers, K., Harrison, I., Bridle, S. L., Frankowska, A., Cain, M., Ward, N., Frendenburgh, J., Pope, E., Kluczkovski, A., Schmidt, X., Silva, J., Reynolds, C., Denby, K., Doherty, B., & Jones, A. (2024). AgriFoodPy: a package for modelling food systems. In Journal of Open Source Software (Vol. 9, Issue 97, p. 6305). The Open Journal. https://doi.org/10.21105/joss.06305
Correspondence/contact details
Marston, C.G.
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
https://orcid.org/0000-0003-3947-6463 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