Evaluating pollination effects and the quantity and quality of beekeeping products in Kenya using earth observation information on flowering activity and landscape structure

Authors and Affiliations: 

Pamela Ochungo1, Tobias Landmann1, David Makori1, Elfatih M. Abel-Rahman1,2

1. International Center for Insect Physiology and Ecology (icipe), P.O. Box 30772, 00100,Nairobi, Kenya; E-Mails: dmakori@icipe.org; eabdel-rahman@icipe.org; pochungo@icipe.org; tlandmann@icipe.org
2. Department of Agronomy, Faculty of Agriculture, University of Khartoum, Khartoum North 13314, Sudan

Corresponding author: 
Tobias Landmann

Knowledge on the distribution, abundance and temporal cycle of flowering plants as well as vegetation structural patterns in agro-ecological landscapes would help bee keepers and decision makers to understand the role of the landscape for hive productivity and pollination efficacy (Landmann et al., 2015). Increasing land transformation decreases the availability of semi-natural habitats which adversely affects the temporal and spatial availability of pollen and nectar for bees (Danner et.al, 2016; Evans and Schwarz, 2011). Given the increasing availability of remote sensing observations with better temporal and spatial resolutions, vegetation flowering cycles and biome fragmentation can be mapped with better accuracies (Abdel-Rahman et al., 2015). In this work, we investigated the possibilities of using high resolution (<1 meter pixel resolution) airborne hyperspectral imagery and multi-spectral high to moderate resolution imagery (10-30-meter pixel resolution) to map flowering activity and landscape structural metrics. Using the hyperspectral imagery a flowering abundance and diversity map for a 100 km2 semi-arid area in central Kenya was produced (overall accuracy >80%). Multi-temporal 30-meter Landsat imagery was used complementary to map fractional landscape components i.e. the fractional coverage of near to natural landscapes with the agro-ecological landscape and landscape fragmentation. Using both remote sensing data sets and novel mapping algorithms, accurate information feeds can be estimated that show the importance of the landscape for bee keeping and pollination.

Keywords: Hyperspectral, Sentinel-2, landscape metrics, bee keeping, pollination


Abdel- Rahman, E. Makori, D. Landmann, T. Raina, S (2015): The Utility of AISA Eagle Hyperspectral Data and Random Forest Classifier for Flower Mapping

Evans,J.D., & Schwarz, R.S. (2011). Bees brought to their knees: Microbes affecting honey bee health. Trends in Microbiology, 19, 614-620

Danner, N., Molitor, A. M., Schiele, S., Härtel, S., & Steffan‐Dewenter, I. (2016). Season and landscape composition affect pollen foraging distances and habitat use of honey bees. Ecological Applications, 26(6), 1920-1929.

Landmann, T., Piiroinen, R., Makori, D. M., Abdel-Rahman, E. M., Makau, S., Pellikka, P., & Raina, S. K. (2015). Application of hyperspectral remote sensing for flower mapping in African savannas. Remote Sensing of Environment, 166, 50-60.

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