2022-06-13, 13:30–15:00, Workshop room 2 - C223
In this workshop you will learn how to use ensemble machine learning to predict the realized distribution of forest tree species over Europe in spacetime (2000 — 2020). The lecture will provide some basic concepts of Species Distribution Modeling (SDM), focusing mainly on how to prepare and clean a dataset with the target species occurrence, how to select/include absence data in your model and how to avoid spatial clustering due to preferential sampling.
The ensemble strategy used in this lecture is stacked generalization (Wolpert, 1992): the predictions of all the component models are used to train a meta-learner which then produces the final predictions. After fitting the model and generating predictions, the lecture will provide some additional notes on how to calculate the variable importance and the uncertainty of the ensemble model.
Extrapolation/model transferability (i.e. predictions outside the spatiotemporal range used for model calibration) will not be discussed.
Carmelo has a MSc in forest systems sciences and technologies, with a specialization in forest resources monitoring and management through geospatial data science applications and time series analysis.
Carmelo is a PhD Candidate at Wageningen University and Research (WUR) in the Geo-information Science and Remote Sensing program and works as a Research assistant at the OpenGeoHub Foundation