Supporting Insurance Strategies in Agriculture by Remote Sensing: A Possible Approach at Regional Level

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.

Exploring Climate Change Effects on Vegetation Phenology by MOD13Q1 Data: The Piemonte Region Case Study in the Period 2001–2019

“…The Global Climate Observing System (GCOS) has reported 26 of the 50 key climate variables (ECVs) as significantly dependent on satellite observations [33]. Remote sensing data are also largely used to develop prevention, mitigation, and adaptation measures to cope with the impact of climate change [34], to take out insurance policies based on vegetation trends [35][36][37][38], to support Common Agricultural Policy control in agriculture [39], to support wildlife diseases assessment [40], to manage the risk of falling trees and heat islands monitoring [41][42][43]. Studies on climate change, in fact, need continuous calibration and validation data and appropriate temporal and spatial sampling over a long period of time [44].…”

Geomatics and epidemiology: Associating oxidative stress and greenness in urban areas

“…Multispectral imagery are widely used to detect and characterise vegetation in urban contexts, permitting to locally map vegetated areas (Mudele and Gamba, 2019;Rosina and Kopecká, 2016). We assumed that the maximum of vegetation vigour, in the area, occurs in the summer period (June-July, Zhou et al 2016;De Petris S. et al 2019;Borgogno-Mondino, Sarvia, and Gomarasca 2019 1. It is worth to remind that the minimum mapping unit from S2 imagery is 100 m 2 ; this size was retained appropriate if compared with the average dimension of green areas in Torino.…”

System for Monitoring and Warning of the Ultraviolet Radiation Index: A Study Case in Ecuador Elementary Schools

“…Remote monitoring can be applied in various fields of science, obtaining exponential development thanks to continuous advances in computing and connectivity [ 14 – 19 ]. Semlali et al [ 20 ] describes the development of a software that collects, processes, and displays environmental and pollution data.…”