Forest tree species distribution for Europe 2000-2020: mapping potential and realized distributions using spatiotemporal Machine Learning
Authors: Bonannella Carmelo , Hengl Tomislav , Heisig Johannes , Parente Leandro , Wright Marvin N , Herold Martin , de Bruin Sytze 
Published: May 2022

This paper describes a data-driven framework based on spatiotemporal machine learning to producedistribution maps for 16 tree species (Abies alba Mill., Castanea sativa Mill., Corylus avellana L., Fagussylvatica 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 roburL., Quercus suber L. and Salix caprea L.) at high spatial resolution (30 m). Tree occurrence data for atotal of 3 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 coarseand high resolution covariates representing spectral reflectance, different biophysical conditions and bioticcompetition was used as predictors for realized distributions, while potential distribution was modelled withenvironmental predictors only. Logloss and computing time were used to select the three best algorithms totune and train an ensemble model based on stacking with a logistic regressor as a meta-learner. An ensemblemodel was trained for each species: probability and model uncertainty maps of realized distribution wereproduced for each species using a time window of 4 years for a total of 6 distribution maps per species, whilefor potential distributions only one map per species was produced. Results of spatial cross validation showthat the ensemble model consistently outperformed or performed as good as the best individual model inboth potential and realized distribution tasks, with potential distribution models achieving higher predictiveperformances (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 differedacross 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-resolutionmodels 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 seriesdata with state of the art machine learning can be used to derive dynamic distribution maps. The producedpredictions can be used to quantify temporal trends of potential forest degradation and species compositionchange.