Bonannella-et-al_2022_Forest tree species distribution for Europe.pdf 19,89MB
1000 Titel
  • Forest tree species distribution for Europe 2000–2020: mapping potential and realized distributions using spatiotemporal machine learning
1000 Autor/in
  1. Bonannella, Carmelo |
  2. Hengl, Tomislav |
  3. Heisig, Johannes |
  4. Parente, Leandro |
  5. Wright, Marvin |
  6. Herold, Martin |
  7. de Bruin, Sytze |
1000 Erscheinungsjahr 2022
1000 LeibnizOpen
1000 Publikationstyp
  1. Artikel |
1000 Online veröffentlicht
  • 2022-07-25
1000 Erschienen in
1000 Quellenangabe
  • 10:e13728
1000 FRL-Sammlung
1000 Copyrightjahr
  • 2022
1000 Lizenz
1000 Verlagsversion
  • |
  • |
1000 Ergänzendes Material
  • |
1000 Publikationsstatus
1000 Begutachtungsstatus
1000 Sprache der Publikation
1000 Abstract/Summary
  • This article describes a data-driven framework based on spatiotemporal machine learning to produce distribution maps for 16 tree species (Abies alba Mill., Castanea sativa Mill., Corylus avellana L., Fagus sylvatica L., Olea europaea L., Picea abies L. H. Karst., Pinus halepensis Mill., Pinus nigra J. F. Arnold, Pinus pinea L., Pinus sylvestris L., Prunus avium L., Quercus cerris L., Quercus ilex L., Quercus robur L., Quercus suber L. and Salix caprea L.) at high spatial resolution (30 m). Tree occurrence data for a total of three million of points was used to train different algorithms: random forest, gradient-boosted trees, generalized linear models, k-nearest neighbors, CART and an artificial neural network. A stack of 305 coarse and high resolution covariates representing spectral reflectance, different biophysical conditions and biotic competition was used as predictors for realized distributions, while potential distribution was modelled with environmental predictors only. Logloss and computing time were used to select the three best algorithms to tune and train an ensemble model based on stacking with a logistic regressor as a meta-learner. An ensemble model was trained for each species: probability and model uncertainty maps of realized distribution were produced for each species using a time window of 4 years for a total of six distribution maps per species, while for potential distributions only one map per species was produced. Results of spatial cross validation show that the ensemble model consistently outperformed or performed as good as the best individual model in both potential and realized distribution tasks, with potential distribution models achieving higher predictive performances (TSS = 0.898, R2logloss = 0.857) than realized distribution ones on average (TSS = 0.874, R2logloss = 0.839). Ensemble models for Q. suber achieved the best performances in both potential (TSS = 0.968, R2logloss = 0.952) and realized (TSS = 0.959, R2logloss = 0.949) distribution, while P. sylvestris (TSS = 0.731, 0.785, R2logloss = 0.585, 0.670, respectively, for potential and realized distribution) and P. nigra (TSS = 0.658, 0.686, R2logloss = 0.623, 0.664) achieved the worst. Importance of predictor variables differed across species and models, with the green band for summer and the Normalized Difference Vegetation Index (NDVI) for fall for realized distribution and the diffuse irradiation and precipitation of the driest quarter (BIO17) being the most frequent and important for potential distribution. On average, fine-resolution models outperformed coarse resolution models (250 m) for realized distribution (TSS = +6.5%, R2logloss = +7.5%). The framework shows how combining continuous and consistent Earth Observation time series data with state of the art machine learning can be used to derive dynamic distribution maps. The produced predictions can be used to quantify temporal trends of potential forest degradation and species composition change.
1000 Sacherschließung
lokal Species distribution model
lokal Spatiotemporal modeling
lokal Imbalanced data
lokal Machine learning
lokal Stacked generalization
lokal Tree species
lokal Ensemble modeling
lokal Ecological niche
lokal Presence-absence
lokal High resolution
1000 Fächerklassifikation (DDC)
1000 Liste der Beteiligten
1000 (Academic) Editor
1000 Label
1000 Förderer
  1. European Union |
1000 Fördernummer
  1. 2018-EU-IA-0095
1000 Förderprogramm
  1. Grant Agreement Connecting Europe Facility (CEF) Telecom project
1000 Dateien
1000 Förderung
  1. 1000 joinedFunding-child
    1000 Förderer European Union |
    1000 Förderprogramm Grant Agreement Connecting Europe Facility (CEF) Telecom project
    1000 Fördernummer 2018-EU-IA-0095
1000 Objektart article
1000 Beschrieben durch
1000 @id frl:6438299.rdf
1000 Erstellt am 2022-11-08T12:38:55.613+0100
1000 Erstellt von 266
1000 beschreibt frl:6438299
1000 Bearbeitet von 317
1000 Zuletzt bearbeitet Tue Nov 08 13:21:51 CET 2022
1000 Objekt bearb. Tue Nov 08 13:18:38 CET 2022
1000 Vgl. frl:6438299
1000 Oai Id
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1000 Sichtbarkeit Metadaten public
1000 Sichtbarkeit Daten public
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