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Suitability of resampled multispectral datasets for mapping flowering plants in the Kenyan savannah

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dc.contributor.author Makori, D.M.
dc.contributor.author Abdel-Rahman, E. M.
dc.contributor.author Landmann, T.
dc.contributor.author Mutanga, O.
dc.contributor.author Odindi, J.
dc.contributor.author Nguku, E.
dc.contributor.author Tonnang, E. H. Z.
dc.contributor.author Raina, S.
dc.date.accessioned 2021-06-09T12:23:00Z
dc.date.available 2021-06-09T12:23:00Z
dc.date.issued 2020
dc.identifier.uri http://hdl.handle.net/123456789/1430
dc.description Research Article en_US
dc.description.abstract Pollination services and honeybee health in general are important in the African savannahs particularly to farmers who often rely on honeybee products as a supplementary source of income. Therefore, it is imperative to understand the floral cycle, abundance and spatial distribution of melliferous plants in the African savannah landscapes. Furthermore, placement of apiaries in the landscapes could benefit from information on spatiotemporal patterns of flowering plants, by optimising honeybees’ foraging behaviours, which could improve apiary productivity. This study sought to assess the suitability of simulated multispectral data for mapping melliferous (flowering) plants in the African savannahs. Bi-temporal AISA Eagle hyperspectral images, resampled to four sensors (i.e. WorldView-2, RapidEye, Spot-6 and Sentinel-2) spatial and spectral resolutions, and a 10-cm ultra-high spatial resolution aerial imagery coinciding with onset and peak flowering periods were used in this study. Ground reference data was collected at the time of imagery capture. The advanced machine learning random forest (RF) classifier was used to map the flowering plants at a landscape scale and a classification accuracy validated using 30% independent test samples. The results showed that 93.33%, 69.43%, 67.52% and 82.18% accuracies could be achieved using WorldView-2, RapidEye, Spot-6 and Sentinel-2 data sets respectively, at the peak flowering period. Our study provides a basis for the development of operational and cost-effective approaches for mapping flowering plants in an African semiarid agroecological landscape. Specifically, such mapping approaches are valuable in providing timely and reliable advisory tools for guiding the implementation of beekeeping systems at a landscape scale. en_US
dc.description.sponsorship The European Union funded the project “African reference laboratory (with satellite stations) for the management of pollinator bee diseases and pests for food security” under grant number DCI-FOOD-2011/023-520. The European Union also facilitated the acquisition of the April 2014 WorldView-2 data that was used in this study. The University of Helsinki funded the research community GIMMEC–Geoinformatics for monitoring and modelling of environmental change for facilitating the acquisition and processing of the AISA Eagle hyperspectral data. DMM, EN, SR, and TL's salaries were made possible by the financial support given by the European Union through project DCI-FOOD-2011/023-520. EMAR, EN, HEZT, and TL received salaries from icipe. icipe core funding was provided by UK Aid from the Government of the United Kingdom; Department for International Development Cooperation Agency (Sida); the Swiss Agency for Development for Economic Cooperation and Development (BMZ), Germany Federal Democratic Republic of Ethiopia; and the Kenyan Government. en_US
dc.publisher PLoS ONE 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 datasets en_US
dc.subject plants en_US
dc.subject Kenyan savannah en_US
dc.title Suitability of resampled multispectral datasets for mapping flowering plants in the Kenyan savannah en_US
dc.type Article en_US


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