Continental-Scale Land Cover Mapping at 10 m Resolution Over Europe (ELC10)

Land cover maps are important tools for quantifying the human footprint on the environment and facilitate reporting and accounting to international agreements addressing the Sustainable Development Goals. Widely used European land cover maps such as CORINE (Coordination of Information on the Environ...

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Bibliographic Details
Published in:Remote sensing (Basel, Switzerland) Switzerland), 2021-06, Vol.13 (12), p.2301
Main Authors: Venter, Zander S., Sydenham, Markus A. K.
Format: Article
Language:English
Subjects:
Accuracy
Atmospheric correction
Centroids
Classification
Climate change
Cloud computing
CORINE
Data processing
Datasets
Frequency
International agreements
Land cover
Land use
Learning algorithms
Machine learning
Mapping
Polls & surveys
Polygons
Radar imaging
Remote sensing
sar
Satellites
Sensors
Sentinel-1
Sentinel-2
Sustainable development
Temporal variations
Time series
Training
Tree planting
Workflow
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Summary:Land cover maps are important tools for quantifying the human footprint on the environment and facilitate reporting and accounting to international agreements addressing the Sustainable Development Goals. Widely used European land cover maps such as CORINE (Coordination of Information on the Environment) are produced at medium spatial resolutions (100 m) and rely on diverse data with complex workflows requiring significant institutional capacity. We present a 10 m resolution land cover map (ELC10) of Europe based on a satellite-driven machine learning workflow that is annually updatable. A random forest classification model was trained on 70K ground-truth points from the LUCAS (Land Use/Cover Area Frame Survey) dataset. Within the Google Earth Engine cloud computing environment, the ELC10 map can be generated from approx. 700 TB of Sentinel imagery within approx. 4 days from a single research user account. The map achieved an overall accuracy of 90% across eight land cover classes and could account for statistical unit land cover proportions within 3.9% (R2 = 0.83) of the actual value. These accuracies are higher than that of CORINE (100 m) and other 10 m land cover maps including S2GLC and FROM-GLC10. Spectro-temporal metrics that capture the phenology of land cover classes were most important in producing high mapping accuracies. We found that the atmospheric correction of Sentinel-2 and the speckle filtering of Sentinel-1 imagery had a minimal effect on enhancing the classification accuracy (
ISSN:2072-4292
2072-4292
DOI:10.3390/rs13122301