MAPPING BEE-KEEPING FOREST PLANTS FROM MEDIUM SPATIAL RESOLUTION MULTISPECTRAL SATELLITE DATA

Abstract

Apiculture is one of the main branches of agriculture and crucial to rural development since it provides farmers with unique products like honey, wax, pollen, royal jelly, propolis and bee venom. Due to pollination services provided by honeybees, environmental sustainability and diversity are also increased, as well as crop production. In Greece, approximately 2.000.000 bee colonies (second in the E.U.) are reported with a relatively high density per km2. There are more than 20.000 beekeeping operators that produce about 20.000 tons of honey every year, while more than 65% of Greek honey is produced from honey dew. To this end, this study aims to identify and map major honeybee flora in the Greek mainland and the Greek islands (Fir forests, Pine forests and Oak forests) for the year 2019, in order to examine best regions to deploy honeybee colonies. In particular, a classification framework for mapping the main honeybee flora is introduced that is exploiting annual moderate-resolution satellite multi-temporal data. Additionally, a methodology is presented to generate a coarser training dataset by utilizing a high-spatial resolution, detailed land cover map. This process specifically focuses on the integration of honeybee flora classes that are not present in the land cover map but are of significant great importance for honey flora mapping. The goal was to enable large-scale classification without the computational resource constraints typically associated with such national scale frameworks. Experimental results are quite promising with the quantitative validation indicating overall accuracy of more than 85%.