Multi-temporal RapidEye Tasselled Cap data for land cover classification

Land cover mapping can be seen as a key element to understand the spatial distribution of habitats and thus to sustainable management of natural resources. Multi-temporal remote sensing data are a valuable data source for land cover mapping. However, the increased amount of data requires effective machine learning algorithms and data compression approaches. In this study, the Random Forest and C 5.0 classification algorithms were applied to (1) a multi-temporal Tasselled-Cap-transformed, (2) top of atmosphere and (3) surface reflectance RapidEye time-series. The overall accuracies ranged from 91.44% to 91.80%, with only minor differences between algorithms and datasets. The McNemar test showed, however, significant differences between the Tasselled-Cap-transformed and untransformed mapping results in most cases. The temporal profiles for the Tasselled-Cap-transformed RapidEye data indicated a good separability between considered classes. The phenological profiles of vegetated surfaces followed a typical green-up curve for the Greenness Tasselled-Cap-index. A permutation-based variable importance measure indicated that late autumn should be considered as most important phenological phase contributing to the classification model performance. The results suggested that the RapidEye Tasselled Cap Transformation, which was designed for agricultural applications, can be an effective data compression tool, suitable to map heterogeneous landscapes with no measurable negative impact on classification accuracy.