Potential and realized distribution at 30m for Stone pine (Pinus pinea) in Europe for 2000 - 2020
type: Item
instance of: data set
description: Probability and uncertainty maps showing the potential and realized distribution for the stone pine (Pinus pinea, L.) for Europe from the dataset prepared by Bonannella et al. (2022) and predicted using Ensemble Machine Learning (EML). Potential distribution map cover the period 2018 - 2020; realized distribution cover the period 2000 - 2020, split in the following time periods: 2000 - 2002, 2002 - 2006, 2006 - 2010, 2010 - 2014, 2014 - 2018, 2018 - 2020. Files are named according to the following naming convention, e.g: veg_pinus.pinea_anv.eml_md_30m_0..0cm_2000..2002_eumap_epsg3035_v0.3 with the following fields: theme: e.g. veg, species code: e.g. pinus.pinea, species distribution type: e.g. anv (= actual natural vegetation), species estimation method: e.g. eml, species estimation type: e.g. md ( = model deviation), resolution in meters e.g. 30m, reference depths (vertical dimension): e.g. 0..0cm, reference period begin end: e.g. 2000..2002, reference area: e.g. eumap, coordinate system: e.g. epsg3035, data set version: e.g. v0.3. For each species is then easy to identify probability and uncertainty distribution maps: veg_pinus.pinea_anv.eml_md: model uncertainty for realized distribution veg_pinus.pinea_anv.eml_p: probability for realized distribution veg_pinus.pinea_pnv.eml_md: model uncertainty for potential distribution veg_pinus.pinea_pnv.eml_p: probability for potential distribution Files are provided as Cloud Optimized GeoTIFFs and projected in the Coordinate Reference System ETRS89 / LAEA Europe (= EPSG code 3035). Styling files are provided in both SLD and QML format. If you would like to know more about the creation of the maps and the modeling: watch the talk at Open Data Science Workshop 2021 (TIB AV-PORTAL) access the repository with our R/Python scripts and follow the instructions (GitLab) access the repository with the training dataset (Zenodo) read the tutorial with executable code on our GitBook A publication describing, in detail, all processing…
publication date: 2022-07-25T00:00:00Z
author: Carmelo Bonannella
author: Tomislav Hengl
author: Johannes Heisig
author: Leandro Leal Parente
author: Marvin Wright
author: Martin Herold
author: Sytze de Bruin
Zenodo ID: 6966748
DOI: 10.5281/zenodo.6966748
copyright license: Creative Commons Attribution 4.0 International
dataset version identifier: 3
MaRDI profile type: MaRDI dataset profile
description: Dataset published at Zenodo repository.