Coniferous forest

Overview:

312: Vegetation formation composed principally of trees, including shrub and bush understorey, where coniferous species predominate. The predominant classifying parameter for this class is a crown cover density of > 30 % or a minimum 500 subjects/ha density, with coniferous trees representing > 75 % of the formation. The minimum tree height is 5 m (with the exception of Christmas tree plantations).

Traceability (lineage):

This dataset was produced with a machine learning framework with several input datasets, specified in detail in Witjes et al., 2022 (in review, preprint available at https://doi.org/10.21203/rs.3.rs-561383/v3 )

Scientific methodology:

The single-class probability layers were generated with a spatiotemporal ensemble machine learning framework detailed in Witjes et al., 2022 (in review, preprint available at https://doi.org/10.21203/rs.3.rs-561383/v3 ). The single-class uncertainty layers were calculated by taking the standard deviation of the three single-class probabilities predicted by the three components of the ensemble. The HCL (hard class) layers represents the class with the highest probability as predicted by the ensemble.

Usability:

The HCL layers have a decreasing average accuracy (weighted F1-score) at each subsequent level in the CLC hierarchy. These metrics are 0.83 at level 1 (5 classes):, 0.63 at level 2 (14 classes), and 0.49 at level 3 (43 classes). This means that the hard-class maps are more reliable when aggregating classes to a higher level in the hierarchy (e.g. 'Discontinuous Urban Fabric' and 'Continuous Urban Fabric' to 'Urban Fabric'). Some single-class probabilities may more closely represent actual patterns for some classes that were overshadowed by unequal sample point distributions. Users are encouraged to set their own thresholds when postprocessing these datasets to optimize the accuracy for their specific use case.

Uncertainty quantification:

Uncertainty is quantified by taking the standard deviation of the probabilities predicted by the three components of the spatiotemporal ensemble model.

Data validation approaches:

The LULC classification was validated through spatial 5-fold cross-validation as detailed in the accompanying publication.

Completeness:

The dataset has chunks of empty predictions in regions with complex coast lines (e.g. the Zeeland province in the Netherlands and the Mar da Palha bay area in Portugal). These are artifacts that will be avoided in subsequent versions of the LULC product.

Consistency:

The accuracy of the predictions was compared per year and per 30km*30km tile across europe to derive temporal and spatial consistency by calculating the standard deviation. The standard deviation of annual weighted F1-score was 0.135, while the standard deviation of weighted F1-score per tile was 0.150. This means the dataset is more consistent through time than through space: Predictions are notably less accurate along the Mediterrranean coast. The accompanying publication contains additional information and visualisations.

Positional accuracy:

The raster layers have a resolution of 30m, identical to that of the Landsat data cube used as input features for the machine learning framework that predicted it.

Temporal accuracy:

The dataset contains predictions and uncertainty layers for each year between 2000 and 2019.

Thematic accuracy:

The maps reproduce the Corine Land Cover classification system, a hierarchical legend that consists of 5 classes at the highest level, 14 classes at the second level, and 44 classes at the third level. Class 523: Oceans was omitted due to computational constraints.

Date ( Publication ): 2022-03-03

Identifier: unknown

Status: Under development

Author

- Martijn Witjes ( )

Point of contact

- Martijn Witjes

Maintenance and update frequency: As needed

Keywords ( Theme )

Land cover, land use and administrative data
Geoharmonizer
geoharvester
geodata

Keywords ( Place )

Europe

GEMET - INSPIRE themes, version 1.0 ( Theme )

Geographical grid systems

Use limitation: None

Access constraints: Copyright

Use constraints: otherRestictions

Distance: 30 m

Metadata language: eng

Character set: UTF8

Topic category

Environment

Begin date: 2000-01-01

End date: 2019-12-31

Author

- Leandro Parente ( )

Reference system identifier: EPSG:3035

Number of dimensions: 2

Dimension name: Row

Resolution: 30 m

Dimension name: Column

Resolution: 30 m

Cell geometry: Area

Distribution format

GeoTIFF ( XY )
OGC WMS ( 1.3 )

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WMS browse (add eg. "&DIM_YEAR=2019" for year 2019)

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OnLine resource: https://s3.eu-central-1.wasabisys.com/eumap/lcv/lcv_landcover.312_lucas.corine.eml_p_30m_0..0cm_2002_eumap_epsg3035_v0.2.tif ( WWW:DOWNLOAD-1.0-http--download )

OnLine resource: https://s3.eu-central-1.wasabisys.com/eumap/lcv/lcv_landcover.312_lucas.corine.eml_p_30m_0..0cm_2001_eumap_epsg3035_v0.2.tif ( WWW:DOWNLOAD-1.0-http--download )

OnLine resource: https://s3.eu-central-1.wasabisys.com/eumap/lcv/lcv_landcover.312_lucas.corine.eml_p_30m_0..0cm_2000_eumap_epsg3035_v0.2.tif ( WWW:DOWNLOAD-1.0-http--download )

Hierarchy level: Dataset

Conformance result

Date: 2010-12-08

Explanation: See specified reference

Pass: Yes

Statement: derived from XY

gmd:MD_Metadata

File identifier: 6ecade73-d26a-4cca-81cf-fc0dbe8475b1 XML

Metadata language: en

Character set: UTF8

Date stamp: 2021-01-14T10:37:57

Metadata standard name: ISO 19115:2003/19139

Metadata standard version: 1.0

Point of contact

- Martijn Witjes ( )

Citation proposal

Martijn Witjes () - Leandro Parente () (2022) . Coniferous forest.
https://data.opendatascience.eu/geonetwork/srv/api/records/6ecade73-d26a-4cca-81cf-fc0dbe8475b1

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