Non-irrigated arable land prob. slope

Overview:

211: Slope of non-irrigated arable land derived by OLS regression over the probabilities values (2000—2019). The std. error of the model was considered as uncertainty.

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.

Simple

Date ( Publication )
NaT
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 )
Use limitation
None
Access constraints
Copyright
Use constraints
otherRestictions
Distance
30 m
Metadata language
eng
Character set
UTF8
Topic category
  • Environment
Begin date
-
End date
-
N
S
E
W
thumbnail


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 )

OnLine resource
gh:lcv_landcover.211_lucas.corine.slope_p_30m_0..0cm_2000_eumap_epsg3035_v0.1 ( OGC:WMS )
OnLine resource
https://s3.eu-central-1.wasabisys.com/eumap/lcv/lcv_landcover.211_lucas.corine.slope_p_30m_0..0cm_2000_eumap_epsg3035_v0.1.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
05de0e92-620a-4844-963b-0af6ec744db5 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 () (NaT) . Non-irrigated arable land prob. slope.
https://data.opendatascience.eu/geonetwork/srv/api/records/05de0e92-620a-4844-963b-0af6ec744db5

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