Crop mapping in countries with small-scale farming: a case study for West Shewa, Ethiopia

Abstract

Remote sensing is nowadays considered to be a valuable input for the annual collection of crop statistics. Derived crop maps can serve as a baseline for yield or area estimation or to target next year's census. For subsistence farming, where small parcels are mixed with other land use, crop mapping remains very challenging. This article evaluates the potential of discriminating crops in West Shewa, an area with small-scale farming in central Ethiopia. A hard classification of high-resolution (30 m) images, yielding good results for commercial farming, could not deal with mixed pixels due to the small parcels. Very high resolution (4 m) images have a more appropriate pixel size, although they only cover subsets of the region. The very high resolution classification was used to calibrate a neural network for sub-pixel classification of the high resolution images. The accuracies were not satisfactory, but did at least demonstrate the potential of this approach.

Acknowledgements

This research was carried out in the frame of the Global Monitoring for Food Security (GMFS) projects funded by the European Space Agency and Global Agricultural Monitoring (GLOBAM) funded by the Belgian Science Policy. Bert Bossyns and Jasper Van Doninck carried out the 2007 field survey. Ato Matewos Hunde, director of the Disaster Risk Management and Food Security Sector (DRMFSS), kindly granted access to the statistics from the MoARD. Beletu Tefera from DRMFSS and Guluma Sobokssa contributed to the 2009 field survey. The KOMPSAT-2 and DMC images were provided by GMFS and the Joint Research Centre (JRC) of the European Commission, respectively. ASTER GDEM is a product of the Ministry of Economy, Trade and Industry of Japan (METI) and NASA; Landsat 7 is provided by the United States Geological Survey (USGS). Furthermore, the author wishes to thank Dr Jan Plue for carefully reading and helping to improve the manuscript.

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