Authors : Adama Ouattara, E.D. Zenobe, C.Y. Sangne, B.E. Frazier, M.C. Azagoh and N.A. Konan
Abstract: In Cote d’Ivoire, agricultural statistics about some major crops are just not available because government agencies in charge of survey have all been dismantled. This lack of data greatly affects planning and management of available land resources, estimation of agricultural output and proper decision making process. Cashew farming has recently been widely cultivated across large regions in the north of the country and its widespread adoption by the farmers raise question of arable land management. The main goal of the present study was to evaluate the spatial extent of this crop and by in large the main agricultural land cover types in the savannah region of northern Cote d’Ivoire. The objective was to determine which of the object-based and the pixel of classification methods could provide better estimates of agricultural land cover types in the study zone. In the fragmented landscapes observed in this region using the spatial attributes undoubtedly improves classification accuracy, thus leading us to favor the object-based classification approach over classical pixel-based classification. In this non-mechanized agriculture, small holdings dominated the agricultural landscape with yams farms varying between 0.8 and 2.5 ha. The object-based method predicted that 2.5% of the study area was occupied by cashew orchards or about 22,400 ha. With a better description of farm geometries, the object-based classification can be refined to yields accurate estimates and thus to be an efficient tool in agricultural data gathering process across large zones and particularly so when ground collection methods are inexistent.
Adama Ouattara, E.D. Zenobe, C.Y. Sangne, B.E. Frazier, M.C. Azagoh and N.A. Konan, 2010. Determining Cashew Acreages in a Fragmented Landscape: Object vs. Pixel Based Classification. Online Journal of Earth Sciences, 4: 13-18.