Mapping natura 2000 heathland in Belgium - an evaluation of ensemble classifiers for spaceborne angular CHRIS/Proba imagery

摘要

Natura 2000 is an ecological network of protected areas in the territory of the European Union (EU). With the introduction of the Habitats Directive in 1992, EU member states are obligated to report every six years the status of the Natura 2000 habitats so that better conservation policy can be formulated. This paper examines the use of angular hyperspectral CHRIS/Proba image for the mapping of heathland at a Belgian Natura 2000 site. We find that the use of angular images increases the overall classification rate as compared to using only the nadir image; with the incorporation of angular images the final mapping is also more homogenous with less salt and pepper effect. While the class accuracy of Calluna- and Erica-dominated heathlands are still low, class accuracy of Molinia-dominated heathland is generally more encouraging. Two tree-based ensemble classifiers, Random Forest (RF) and Adaboost, were compared with Support Vector Machines (SVM). When only the nadir image was used, SVM attained the highest accuracy. When angular images were included, all three classifiers obtained comparable accuracies though in general RF and Adaboost had faster training time. We also adopted an assessment approach which repeats the accuracy assessment in ten independent trials, instead of the common practice of having only one trial. Our results show that accuracy attainment can vary significantly among different trials and hence it is recommendable to have more than one trial in order that a more objective characterization of the classifiers is obtained. 1