Separation of savanna land cover components is challenging due to the high heterogeneity
of this landscape and spectral similarity of compositionally different vegetation types. In this
study, we tested the usability of very high spatial and spectral resolution WorldView-2 (WV-2)
imagery to classify land cover components of African savanna in wet and dry season. We compared
the performance of Object-Based Image Analysis (OBIA) and pixel-based approach with several
algorithms: k-nearest neighbor (k-NN), maximum likelihood (ML), random forests (RF), classification
and regression trees (CART) and support vector machines (SVM). Results showed that classifications
of WV-2 imagery produce high accuracy results (>77%) regardless of the applied classification
approach. However, OBIA had a significantly higher accuracy for almost every classifier with the
highest overall accuracy score of 93%. Amongst tested classifiers, SVM and RF provided highest
accuracies. Overall classifications of the wet season image provided better results with 93% for RF.
However, considering woody leaf-off conditions, the dry season classification also performed well
with overall accuracy of 83% (SVM) and high producer accuracy for the tree cover (91%). Our findings
demonstrate the potential of imagery like WorldView-2 with OBIA and advanced supervised
machine-learning algorithms in seasonal fine-scale land cover classification of African savanna.