This practical session will base on the introductory lecture on machine-learning based modelling of spatial and spatio-temporal data held on Monday. Two examples will be provided to dive into machine learning for spatial and spatio-temporal data in R:
The first example is a classic remote sensing example dealing with land cover classification at the example of the Banks Peninsula in New Zealand that suffers from spread of the invasive gorse. In this example we will use the random forest classifier via the caret package to learn the relationships between spectral satellite information and provided reference data on the land cover classes. Spatial predictions will then be made to create a map of land use/cover based on the trained model.
As second example, the vignette "Introduction to CAST" is taken from the CAST package. In this example the aim is to model soil moisture in a spatio-temporal way for the cookfarm (http://gsif.r-forge.r-project.org/cookfarm.html). In this example we focus on the differences between different cross-validation strategies for error assessment of spatio-temporal prediction models as well as on the need of a careful selection of predictor variables to avoid overfitting.
- Slides: https://github.com/HannaMeyer/Geostat2018/tree/master/slides
- Exercise A: https://github.com/HannaMeyer/Geostat2018/tree/master/practice/LUCmodelling.html
- Exercise B: https://github.com/HannaMeyer/Geostat2018/tree/master/practice/CAST-intro.html
- Data for Exercise A: https://github.com/HannaMeyer/Geostat2018/tree/master/practice/data/
Required Packages: caret, randomForest, CAST, raster
Suggested further packages: mapview