Testing the capability of spectral resolution of the new multispectral sensors on detecting the severity of grey leaf spot disease in maize crop |
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| Authors: | Inos Dhau Elhadi Adam Onisimo Mutanga Kwabena Ayisi Elfatih Mohamed Abdel-Rahman John Odindi |
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| Institution: | 1. Geography and Environmental Studies, University of Limpopo, Sovenga, South Africainos.dhau@ul.ac.za;3. School of Geography, Archaeology and Environmental Studies, University of Witwatersrand, Johannesburg, South Africa;4. Department of Geography, University of Kwa-Zulu Natal, Pietermaritzburg, South Africa;5. Risk and Vulnerability Science Centre / VLIR-IUC Programme, University of Limpopo, Sovenga, South Africa;6. Geography, University of Kwazulu-Natal, Pietermaritzburg, South Africa |
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| Abstract: | AbstractIn this study, we tested whether GLS field symptoms on maize can be detected using hyperspectral data re-sampled to WorldView-2, Quickbird, RapidEye and Sentinel-2 resolutions. To achieve this objective, Random Forest algorithm was used to classify the 2013 re-sampled spectra to represent the three identified disease severity categories. Results showed that Sentinel-2, with 13 spectral bands, achieved the highest overall accuracy and kappa value of 84% and 0.76, respectively, while the WorldView-2, with eight spectral bands, yielded the second highest overall accuracy and kappa value of 82% and 0.73, respectively. Results also showed that the 705 and 710 nm red edge bands were the most valuable in detecting the GLS for Sentinel-2 and RapidEye, respectively. On the re-sampled WorldView 2 and Quickbird sensor resolutions, the respective 608 and 660 nm in the yellow and red bands were identified as the most valuable for discriminating all categories of infection. |
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| Keywords: | Field spectroscopy grey leaf spot spectral re-sampling multispectral remote sensing |
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