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Abstract
The mapping and characterization of wetlands in semi-arid savannas is challenging due to the large interannual and seasonal flooding variability in these important and highly vulnerable ecosystems. This study shows the possibility of using 250 m Moderate Resolution Imaging Spectroradiometer (MODIS) metrics (from 16 day composites) in change vector analysis (CVA) to map wetland dynamics in the Linyanti wetland (Namibia) between 2001 and 2010. For each pixel, we compute the interannual CVA intensity and the CVA direction, as well as the cumulative change intensity and the overall direction (trend) within the observation period. Both the change vector intensities and the corresponding change directions are necessary to interpret the interannual change and assess the 9 year trend. The interannual CVA intensities show a significant correlation with flooding magnitudes. The flooding magnitudes are derived from the Advanced Microwave Scanning Radiometer-Earth Observing System instrument (AMSR-E) radar observations for a hydrographic station located at the nearest inflow point into the Linyanti wetland. Given that long-term flooding records and satellite observations are available, the approach could be used to detect and interpret climate-induced inundation dynamics within wetlands in semi-arid Africa.
Acknowledgements
This work was funded by the German Federal Ministry of Education and Research (BMBF) in the context of an initiation project to fund the setting up of competency centres on climate change and adapted land use in West and Southern Africa. We thank Guido Langenhove and Mc-cloud Katjizeu from the Ministry of Agriculture, Water & Forestry in Namibia for their valuable comments on the hydrographic data, as well as Tom De Groeve from Joint Research Centre (JRC) of the European Commission in Ispra, Italy, for sharing his hydrographic data and research results. We further thank colleagues, especially Gerald Forkour and Christopher Conrad, at the Department of Remote Sensing of the University of Wuerzburg, Germany, and staff at the German Remote Sensing Data Center of the German Aerospace Center (DLR) for their valuable suggestions.