1 dataset found
  1. c

    Land Cover Map (2023)

    • data.catchmentbasedapproach.org
    • river-teme-water-quality-theriverstrust.hub.arcgis.com
    • +1more
    Updated Jul 23, 2024
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    The Rivers Trust (2024). Land Cover Map (2023) [Dataset]. https://data.catchmentbasedapproach.org/maps/88d5846dfe344746906ce93af2b1e1b0
    Explore at:
    Dataset updated
    Jul 23, 2024
    Dataset authored and provided by
    The Rivers Trust
    Area covered
    Description

    This is a web map service (WMS) for the 10-metre Land Cover Map 2023. The map presents the and surface classified into 21 UKCEH land cover classes, based upon Biodiversity Action Plan broad habitats.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 2020 to 2022. 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 LCM2023 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.

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Email
Click to copy link
Link copied
Close
Cite
The Rivers Trust (2024). Land Cover Map (2023) [Dataset]. https://data.catchmentbasedapproach.org/maps/88d5846dfe344746906ce93af2b1e1b0

Land Cover Map (2023)

Explore at:
20 scholarly articles cite this dataset (View in Google Scholar)
Dataset updated
Jul 23, 2024
Dataset authored and provided by
The Rivers Trust
Area covered
Description

This is a web map service (WMS) for the 10-metre Land Cover Map 2023. The map presents the and surface classified into 21 UKCEH land cover classes, based upon Biodiversity Action Plan broad habitats.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 2020 to 2022. 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 LCM2023 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.

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