Agricultural Land Use Maps at RapidEye (5m) Scale for Dassari Watershed (Benin), 2012 and 2013

These LULC maps were created through automatic digital classification of RapidEye imagery acquired in November 2012. Reference (or field) data on which the classification was based were acquired through a field campaign that lasted from June to November 2012. Standard image pre-processing techniques such as geometric and radiometric correction were conducted on the data prior to classification. The Random Forest classification algorithm was used for classification. A single level classification was conducted to reveal six LULC classes. Discrimination between different crop types was not possible due to the use of a mono-temporal image

Agricultural Land Use Maps at RapidEye (5m) Scale for Dano Watershed (Burkina Faso) 2012 and 2013

These LULC maps were created through automatic digital classification of RapidEye imagery acquired during the cropping season of 2012 and early dry season of 2013. Three monthly time-steps (June and October, 2012; February 2013) were analyzed.Reference (or field) data on which the classification was based were acquired through a field campaign that lasted from May to October 2012. Standard image pre-processing techniques such as geometric and radiometric correction were conducted on the data prior to classification. The Random Forest classification algorithm was used for classification. Two levels of classification were conducted: (1) a level 1 classification which includes four broad LULC classes and (2) a level 2 classification which comprises of nine LULC classes. The poor temporal coverage of the RapidEye imagery made the accurate delineation of certain crop classes (e.g. groundnuts) very challenging. Nonetheless, an overall accuracy of 70% was obtained

Agricultural Land Use Maps at RapidEye (5m) Scale for Vea Watershed (Ghana) 2012 and 2013

These LULC maps were created through automatic digital classification of RapidEye imagery acquired during the cropping season of 2012. Two monthly time-steps (June and October) were analyzed.Reference (or field) data on which the classification was based were acquired through a field campaign that lasted from May to October 2012. Standard image pre-processing techniques such as geometric and radiometric correction were conducted on the data prior to classification. The Random Forest classification algorithm was used for classification. Two levels of classification were conducted: (1) a level 1 classification which includes four broad LULC classes and (2) a level 2 classification which comprises of nine LULC classes. The poor temporal coverage of the RapidEye imagery made the accurate delineation of certain crop classes (e.g. groundnuts) very challenging. Nonetheless, an overall accuracy of 79% was obtained

Catchments of Vea, Dano and Dassari (West Africa)

The layers show the boundaries of three watersheds in West-Africa: Vea, Dano, and Dassari. The catchments are key-research areas in the WASCAL Core Research Program.