Assessing the potential of multi-seasonal WorldView-2 imagery for mapping West African agroforestry tree species |
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| Institution: | 1. Department of Forest Sciences, University of Helsinki, P.O. Box 27, FI-00014, Finland;2. School of Forest Sciences, University of Eastern Finland, P.O. Box 111, FI-80101 Joensuu, Finland;1. Institute of Geography and Geoecology, Karlsruhe Institute of Technology (KIT), Kaiserstraße 12, 76131 Karlsruhe, Germany;2. Department of Remote Sensing in Cooperation with German Aerospace Center, University of Wuerzburg, Oswald-Kuelpe-Weg 86, D-97074 Wuerzburg, Germany;3. Department of Forest Resources Management, Forest Research Institute, Sekocin Stary, 3 Braci Lesnej Street, 05-090 Raszyn, Poland;4. Center for Ecological Applications of LiDAR, College of Natural Resources, Colorado State University, 400 University Ave, Fort Collins, USA;5. Swiss Federal Institute for Forest, Snow and Landscape Research WSL, Zuercherstrasse 111, 8903 Birmensdorf, Switzerland;6. Department of Information Technology, Bavarian State Institute of Forestry (LWF), Hans-Carl-von-Carlowitz-Platz 1, D-85354 Freising, Germany;7. Department of Environmental Science and Policy, University of California, 1023 Wickson Hall, Davis, USA |
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| Abstract: | 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 concluded 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. |
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| Keywords: | Tree species mapping WorldView-2 Agroforestry Parkland Sudano-Sahel |
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