Rowland, C.S.; O'Neil, A.W.; Hunt, M.L.; Marston, C.G.
Land Cover Map 2025 (LCM2025 - 25m rasterised land parcels, GB)
https://doi.org/10.5285/995f3744-0f95-426f-9ce7-b2fa729f9f4a
Cite this dataset as:
Rowland, C.S.; O'Neil, A.W.; Hunt, M.L.; Marston, C.G. (2026). Land Cover Map 2025 (LCM2025 - 25m rasterised land parcels, GB). NERC EDS Environmental Information Data Centre. https://doi.org/10.5285/995f3744-0f95-426f-9ce7-b2fa729f9f4a
Download/Access
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
This is a 25m pixel dataset representing the land surface of Great Britain, classified into 21 UKCEH land cover classes, based upon Biodiversity Action Plan broad habitats. It is a three-band raster in GeoTiff format, produced by rasterising three properties of the classified land parcels dataset. The first band gives the most likely land cover type; the second band gives the per-parcel probability of the land cover, the third band is a measure of parcel purity. The probability and purity bands (scaled 0 to 100) combine to give an indication of uncertainty. A full description of this and all UKCEH LCM2025 products are available from the LCM2025 product documentation.
Publication date: 2026-10-12
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Format
TIFF
Spatial information
Study area
Spatial representation type
Raster
Spatial reference system
OSGB 1936 / British National Grid
Spatial resolution
25 metres
Temporal information
Temporal extent
2025-01-01 to 2025-12-31
Provenance & quality
UKCEH's automated land cover algorithms classify 10 m pixels across the whole of UK. A Random Forest classifier is used to classify four composite satellite images representing per season median surface reflectance, based on training data comprising locations of known land cover for 21 land cover classes. 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 LCM2025 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 25 m rasterised land parcels product is created by rasterising the corresponding land parcel product.
Land cover was validated by organising the 10 m pixel classification into a land parcel framework (the LCM2025 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 25 m rasterised land parcels product is created by rasterising the corresponding land parcel product.
Licensing and constraints
Licence terms and conditions apply
Cite this dataset as:
Rowland, C.S.; O'Neil, A.W.; Hunt, M.L.; Marston, C.G. (2026). Land Cover Map 2025 (LCM2025 - 25m rasterised land parcels, GB). NERC EDS Environmental Information Data Centre. https://doi.org/10.5285/995f3744-0f95-426f-9ce7-b2fa729f9f4a
Related
Related datasets
Land Cover Map 2025 (LCM2025 - 1km summary rasters, GB and N. Ireland)
Land Cover Map 2025 (LCM2025 - UK land cover statistics)
Land Cover Map 2025 (LCM2025 - 10m classified pixels, GB)
Land Cover Map 2025 (LCM2025 - 10m classified pixels, N. Ireland)
Land Cover Map 2025 (LCM2025 - 25m rasterised land parcels, N. Ireland)
Land Cover Map 2025 (LCM2025 - land parcels, GB)
This dataset is included in the following collections
Correspondence/contact details
Authors
Other contacts
Publisher
NERC EDS Environmental Information Data Centre
info@eidc.ac.uk
Rights holder
UK Centre for Ecology & Hydrology
Custodian
NERC EDS Environmental Information Data Centre
info@eidc.ac.uk
Additional metadata
Keywords
Funding
Natural Environment Research Council Award: NE/Y006208/1
