DEVELOPING TRANSFERABLE SPATIAL PREDICTION MODELS: A CASE STUDY OF SATELLITE BASED LANDCOVER MAPPING

Spatial proxies such as coordinates and distances are often included as predictors in random forest models for predictive mapping. In a simulation and two case studies, we investigated under which conditions this is appropriate. We found that spatial proxies are not always beneficial and thus we conclude that they should not be used as default approach without careful consideration. We also give insights on the reasons behind their suitability, how to detect it, and potential alternatives.

Estimation of map accuracy based on Cross-Validation (CV) in spatial modeling is pervasive but controversial. Here, we build upon our previous work and propose a novel, prediction-oriented k-fold CV strategy for map accuracy estimation in which the distribution of geographical distances between prediction and training points is taken into account when constructing the CV folds. Our method produces more reliable estimates than other CV methods and can be used for large datasets.