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

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.

Fig 1. The study area indicating the field data sample points collected from the study site in Mwingi in January 2014 and February 2013.

The image on the background is a true colour composite AISA Eagle captured in February 2013 over the Mwingi study site. The country boundary data was downloaded from the World Resource Institute website (https://www.wri.org/resources/data-sets/kenya-gis-data) [51]. The maps were developed using QGIS software version 3.10 (https://qgis.org/en/site/) [52].

Fig 2

January 2014 classification maps of the Mwingi study site obtained using random forest classifier and resampled simulated WorldView-2 image classification (a), RapidEye (b), Spot-6 (c), Sentinel-2 (d) images. The maps were developed using QGIS software version 3.10 (https://qgis.org/en/site/) [52].

Fig 3

February 2013 classification maps of the Mwingi study site obtained using random forest classifier and resampled simulated WorldView-2 image classification (a), RapidEye (b), Spot-6 (c), Sentinel-2 (d) images. The maps were developed using QGIS software version 3.10 (https://qgis.org/en/site/) [52].

Fig 4. April 2014 classification map of the Mwingi study site obtained using random forest classifier and WorldView-2 image.

The map was developed using QGIS software version 3.10 (https://qgis.org/en/site/) [52].

Fig 5. Box plots distribution of the flowering endmember abundancy of the flowering vegetation communities.

Endmember standard deviation (SD) and means are shown for each of the flowering vegetation communities during the onset of flowering in January 2014 (a) and peak flowering season in February 2013 (b). Box plots with different letter(s) are significantly different from each other according to the Duncan’s test (p≦0.05). The circles show outliers.