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.
Highlights
African savannahs are characterized by unreliable and erratic rainfall with low and dispersed forest pockets
Both AISA Eagle hyperspectral images captured in January 2014 and February 2013 were resampled to WorldView-2, RapidEye, Spot-6 and Sentinel-2 multispectral spatial and spectral sensor specifications using the spectral resampling tool in Environment for Visualizing Images (ENVI) 5.3 software [55]
Flowering fobs had the highest classification accuracy compared to the other flower classes (Table 6)
Summary
African savannahs are characterized by unreliable and erratic rainfall with low and dispersed forest pockets. Ecosystem services such as pollinator activities boosts local economies, food and nutritional security and improve biodiversity, valuable and sustainable socio-ecological practices in the African savannah [3,4] Beneficial insects such as honeybees, stingless bees, wasps, butterflies, mulberry and wild silk moths [2,5], are important income sources to local communities and living adjacent to the forest, paramount in pollination and pivotal incentives in forest conservation. The Hughes effect and the high redundancy rates of some bands in models developed using hyperspectral data impede landscape classification [33,34,35,36] In this regard, it is paramount to explore the utility of Mapping flowering plants in the Kenyan savannah multispectral images with fewer broad bands for optimal discrimination of functional flowering groups [29,37,38,39]. We explored the utility of four simulated multispectral data (i.e. WorldView-2, RapidEye, Spot-6 and Sentinel-2) for detecting and mapping functional flowering groups in the African savannahs during the beginning and peak flowering seasons
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