ANV - Probability distribution for Castanea sativa

Overview:

Actual Natural Vegetation (ANV): probability of occurrence for the Sweet chestnut in its realized environment for the period 2000 - 2021

Traceability (lineage):

This is an original dataset produced with a machine learning framework which used a combination of point datasets and raster datasets as inputs. Point dataset is a harmonized collection of tree occurrence data, comprising observations from National Forest Inventories (EU-Forest), GBIF and LUCAS. The complete dataset is available on Zenodo. Raster datasets used as input are: harmonized and gapfilled time series of seasonal aggregates of the Landsat GLAD ARD dataset (bands and spectral indices); monthly time series air and surface temperature and precipitation from a reprocessed version of the Copernicus ERA5 dataset; long term averages of bioclimatic variables from CHELSA, tree species distribution maps from the European Atlas of Forest Tree Species; elevation, slope and other elevation-derived metrics; long term monthly averages snow probability and long term monthly averages of cloud fraction from MODIS. For a more comprehensive list refer to Bonannella et al. (2022) (in review, preprint available at: https://doi.org/10.21203/rs.3.rs-1252972/v1 ).

Scientific methodology:

Probability and uncertainty maps were the output of a spatiotemporal ensemble machine learning framework based on stacked regularization. Three base models (random forest, gradient boosted trees and generalized linear models) were first trained on the input dataset and their predictions were used to train an additional model (logistic regression) which provided the final predictions. More details on the whole workflow are available in the listed publication.

Usability:

Probability maps can be used to detect potential forest degradation and compositional change across the time period analyzed. Some possible applications for these topics are explained in the listed publication.

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:

Distribution maps were validated using a spatial 5-fold cross validation following the workflow detailed in the listed publication.

Completeness:

The raster files perfectly cover the entire Geo-harmonizer region as defined by the landmask raster dataset available here.

Consistency:

Areas which are outside of the calibration area of the point dataset (Iceland, Norway) usually have high uncertainty values. This is not only a problem of extrapolation but also of poor representation in the feature space available to the model of the conditions that are present in this countries.

Positional accuracy:

The rasters have a spatial resolution of 30m.

Temporal accuracy:

The maps cover the period 2000 - 2020, each map covers a certain number of years according to the following scheme: (1) 2000--2002, (2) 2002--2006, (3) 2006--2010, (4) 2010--2014, (5) 2014--2018 and (6) 2018--2020

Thematic accuracy:

Both probability and uncertainty maps contain values from 0 to 100: in the case of probability maps, they indicate the probability of occurrence of a single individual of the target species, while uncertainty maps indicate the standard deviation of the ensemble model.

Simple

Date ( Publication )
NaT
Identifier
unknown
Status
Under development
Author
- Carmelo Bonannella ( )

Point of contact
- Carmelo Bonannella

Maintenance and update frequency
As needed
Keywords ( Theme )
  • Vegetation
  • 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
2000-2002-01-01
End date
2018-2020-12-31
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:veg-castaneaXsativa-anvXeml-p-30m-0XX0cm--eumap-epsg3035-v0X2 ( OGC:WMS )

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

OnLine resource
https://s3.eu-central-1.wasabisys.com/eumap/veg/veg_castanea.sativa_anv.eml_p_30m_0..0cm_2018..2020_eumap_epsg3035_v0.2.tif ( WWW:DOWNLOAD-1.0-http--download )
OnLine resource
https://s3.eu-central-1.wasabisys.com/eumap/veg/veg_castanea.sativa_anv.eml_p_30m_0..0cm_2014..2018_eumap_epsg3035_v0.2.tif ( WWW:DOWNLOAD-1.0-http--download )
OnLine resource
https://s3.eu-central-1.wasabisys.com/eumap/veg/veg_castanea.sativa_anv.eml_p_30m_0..0cm_2010..2014_eumap_epsg3035_v0.2.tif ( WWW:DOWNLOAD-1.0-http--download )
OnLine resource
https://s3.eu-central-1.wasabisys.com/eumap/veg/veg_castanea.sativa_anv.eml_p_30m_0..0cm_2006..2010_eumap_epsg3035_v0.2.tif ( WWW:DOWNLOAD-1.0-http--download )
OnLine resource
https://s3.eu-central-1.wasabisys.com/eumap/veg/veg_castanea.sativa_anv.eml_p_30m_0..0cm_2002..2006_eumap_epsg3035_v0.2.tif ( WWW:DOWNLOAD-1.0-http--download )
OnLine resource
https://s3.eu-central-1.wasabisys.com/eumap/veg/veg_castanea.sativa_anv.eml_p_30m_0..0cm_2000..2002_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
4c430057-6c29-4984-be3e-7a6a63b52f32 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
- Carmelo Bonannella ( )

Citation proposal


Carmelo Bonannella () (NaT) . ANV - Probability distribution for Castanea sativa.
https://data.opendatascience.eu/geonetwork/srv/api/records/4c430057-6c29-4984-be3e-7a6a63b52f32

Overviews

overview

Provided by

Share on social sites

Associated resources

Not available