HONEY CROP ESTIMATION FROM SPACE: DETECTION OF LARGE FLOWERING EVENTS IN WESTERN AUSTRALIAN FORESTS

Abstract

Abstract. Recent studies have shown that in the spectral space there is often a better spectral separation between leaves and flowers and even between flowers of different species than between leaves of different species. In this study we assess the ability of satellite remotely sensed data to detect the flowering of Red Gum trees (Corymbia calophylla) in Western Australia, the state’s largest annual honey crop. Spectroradiometer measurements of flowers, leaves and groundcover from Red Gum forests were subjected to ANOVA analysis, which showed that flowers are spectrally different to their environment for 92 % of the wavelengths between 350 nm and 1800 nm. A more detailed assessment, using the JM Distance calculation, showed that the spectra can be reliably separated using 10 % of the wavelengths, with peak separation between 518 nm and 557 nm. To assess the ability of satellite-borne sensors to detect the presence of flowers, the spectroradiometer data were convolved with satellite instruments’ response curves to create synthetic remotely sensed datasets on which JM Distance analysis was performed. MODIS blue bands achieved a median JM Distance of greater than 1.9 and therefore should be able to detect the presence of flowers from the environment. Further assessment showed that the shortest wavelength bands for MODIS, VIIRS and Sentinel 3 all occur where the flower spectra have lower reflectance than their natural background. A sensitivity analysis of percentage flower cover for a pixel showed that the highest sensitivity was obtained by dividing the band closest to 520 nm by the shortest wavelength band for data from these three sources. The MODIS band 10/band 8 metric was tested for its ability to detect flowers in real-world data using 15 years of qualitative honey harvest data from one apiary site as a proxy for flower density. This test was successful as, while there was some overlap between good, moderate and poor years, the poor years could be separated from the other years with nearly 80 % accuracy.