Abstract
Climate variability is one of the greatest risks for farmers. The ongoing increase of natural calamities suggests that insurance strategies have to be more dynamic than previously. In this work a remote sensing based service prototype is presented aimed at supporting insurance companies with the aim of defining an operative tool to objectively calibrate insurance annual fares, tending to cost reduction able to attract more potential customers. Methodology was applied to the whole Piemonte region (NW Italy) that is greatly devoted to agriculture. MODIS MOD13Q1-v6 image time series were used for this purpose. MODIS data were used to figure out the ongoing climate change trends at regional scale, looking at the NDVI time series ranging from 2000 to 2018; the average phenological behaviour of the main agriculture classes in the area (CORINE Land Cover classes Level 3, CLC2012) was considered looking at the yearly average NDVI value trend in the analysed period. This analysis was intended to describe the yearly tuning of the average insurance risk factor and fares in respect of the reference year (2000). A patch level investigation comparing the NDVI average value of a single CLC2012 patch with its reference class was differently used to map local differences of crops performance, aimed at locally tuning insurance risk and fares around the average one as resulting from the previous step. Proposed methodology proved to be able to describe the average temporal evolution of crop classes performances and to locally tune, at single field and crop type level, the agronomic performances of insured areas.
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Borgogno-Mondino, E., Sarvia, F., Gomarasca, M.A. (2019). Supporting Insurance Strategies in Agriculture by Remote Sensing: A Possible Approach at Regional Level. In: Misra, S., et al. Computational Science and Its Applications – ICCSA 2019. ICCSA 2019. Lecture Notes in Computer Science(), vol 11622. Springer, Cham. https://doi.org/10.1007/978-3-030-24305-0_15
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DOI: https://doi.org/10.1007/978-3-030-24305-0_15
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