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Investigation of Maize Lethal Necrosis (MLN) severity and cropping systems mapping in agro-ecological maize systems in Bomet, Kenya utilizing RapidEye and Landsat-8 Imagery.

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dc.contributor.author Jafari Jozani, H.
dc.contributor.author Thiel, M.
dc.contributor.author Abdel-Rahman, E.M
dc.contributor.author Kyalo, R.
dc.contributor.author Landmann, T.
dc.contributor.author Subramanian, S.
dc.contributor.author Hahn, M.
dc.date.accessioned 2021-06-09T10:56:48Z
dc.date.available 2021-06-09T10:56:48Z
dc.date.issued 2020
dc.identifier.uri http://hdl.handle.net/123456789/1418
dc.description Research Article en_US
dc.description.abstract Spatiotemporal information on crops and cropping systems can provide useful insights into disease outbreak mechanisms in croplands. In September 2011, a severe outbreak of Maize Lethal Necrosis (MLN) disease was reported in Bomet County, Kenya. We aimed to detect severely MLN-infected fields and discriminate mono, inter, and continuous maize cropping systems. We collected in-situ MLN severity observations and acquired multi-date and multi-sensor data viz., RapidEye (RE), Sentinel-1 (S1), digital elevation model (DEM), and Landsat-8 (L8) imagery. A hierarchical classification approach was used to map the cropping systems and severely MLN-infected fields during the short rainy season (September 2014–February 2015) using the random forest (RF), one-class support vector machine (OCSVM) and biased SVM (BSVM) classifiers. RF showed better performance when a balanced multi-class dataset was available. Both OCSVM and BSVM did not lead to an accurate high severity MLN class separation. Moreover, the BSVM classifier was able to separate the mono and intercropping systems. During the long rainy season (March–August 2015), only maize crop data were available, hence the BSVM as one class classifiers (OCC) was used and maize fields were successfully mapped even with high confusion rate. Furthermore, the distribution of maize intercropping system increased in low rainfall sites, and the continuous cropping system limited to only 31% of total maize cropland en_US
dc.description.sponsorship Deutsche Gesellschaft für Internationale Zusammenarbeit. en_US
dc.publisher Geology, Ecology, and Landscapes, en_US
dc.rights Attribution-NonCommercial-ShareAlike 3.0 United States *
dc.rights.uri http://creativecommons.org/licenses/by-nc-sa/3.0/us/ *
dc.subject Maize Lethal Necrosis disease en_US
dc.subject cropping systems en_US
dc.subject Machine learning en_US
dc.title Investigation of Maize Lethal Necrosis (MLN) severity and cropping systems mapping in agro-ecological maize systems in Bomet, Kenya utilizing RapidEye and Landsat-8 Imagery. en_US
dc.type Article en_US


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