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Research article - Peer-reviewed, 2016

Assessing the potential of multi-seasonal WorldView-2 imagery for mapping West African agroforestry tree species

Karlson, Martin; Ostwald, Madelene; Reese, Heather; Bazié, H. R.; Tankoano, Boalidioa


High resolution satellite systems enable efficient and detailed mapping of tree cover, with high potential to support both natural resource monitoring and ecological research. This study investigates the capability of multi-seasonal WorldView-2 imagery to map five dominant tree species at the individual tree crown level in a parkland landscape in central Burkina Faso. The Random Forest algorithm is used for object based tree species classification and for assessing the relative importance of WorldView-2 predictors. The classification accuracies from using wet season, dry season and multi-seasonal datasets are compared to gain insights about the optimal timing for image acquisition. The multi-seasonal dataset produced the most accurate classifications, with an overall accuracy (OA) of 83.4%. For classifications based on single date imagery, the dry season (OA=78.4%) proved to be more suitable than the wet season (OA=68.1%). The predictors that contributed most to the classification success were based on the red edge band and visible wavelengths, in particular green and yellow. It was therefore conchided that WorldView-2, with its unique band configuration, represents a suitable data source for tree species mapping in West African parklands. These results are particularly promising when considering the recently launched WorldView-3, which provides data both at higher spatial and spectral resolution, including shortwave infrared bands. (C) 2016 Elsevier B.V. All rights reserved.


Tree species mapping; WorldView-2; Agroforestry; Parkland; Sudano-Sahel

Published in

International Journal of Applied Earth Observation and Geoinformation
2016, volume: 50, pages: 80-88

Authors' information

Karlson, Martin
Linköping University
Ostwald, Madelene
Linköping University
Reese, Heather
Swedish University of Agricultural Sciences, Department of Forest Resource Management
Bazié, H. R.
Tankoano, Boalidioa
Polytechnic University of Bobo-Dioulasso

UKÄ Subject classification

Remote Sensing
Forest Science

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