Abstract
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.
Highlights
The savanna biome covers approximately 25% of the world’s terrestrial landscape, and contributes significantly to the global net vegetation productivity and carbon cycle [1,2]
Considering the performance of each classifier, overall accuracies were between 6% and 8% higher for Object-Based Image Analysis (OBIA) compared to their pixel-based counterpart
Findings of this study revealed that the classification based on WV-2 imagery produces high accuracy results regardless of the used classification approach
Summary
The savanna biome covers approximately 25% of the world’s terrestrial landscape, and contributes significantly to the global net vegetation productivity and carbon cycle [1,2]. These mixed grass–woody ecosystems constitute a multi-scale mosaic of bare soil, patches of grass, shrubs and tree clumps. Detailed mapping of savanna’s land cover components is important in solving fundamental problems in these ecosystems such as soil erosion, bush encroachment, forage and browsing availability. Separation of savanna land cover components is difficult and requires fine-scale analyses. For various specialized studies of savanna ecosystems, there is a need for fine-scale discrimination of the principal land cover components, such as bare soil, grass, shrubs and trees. Limiting confusion between spectrally similar but compositionally different tree canopies, shrubs and grasses can be very challenging [5]
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