Hail defoliation assessment in corn (Zea mays L.) using airborne LiDAR

The insurance industry reports a pronounced intensification, at the global level, of weather-related events such as droughts, windstorms and hailstorms. As an efficient quantification tool, improved capacities can be built adopting innovative remote sensing methods to map vegetation damage spatial distribution, to quantify its intensity and impact. New airborne LiDAR (Light Detection and Ranging) sensors provide high vertical resolution data, which are potentially suitable not only for forest canopies but also for monitoring shorter crop canopies (e.g. corn − Zea mays L.) for crop injury and lodging assessment. To evaluate the potential of LiDAR metrics to map corn canopy height and hail defoliation, a flight campaign was organized in 2014 in Wampersdorf (Austria) in a cropland area affected by a hailstorm. Ground-truth observations were carried out in 16 plots, where defoliation was assessed both visually (observed range from 0% to 70%) and using a biophysical parameter-based method. The performance of both traditional and newly-introduced metrics (i.e. Canopy Metric, Ground Metric) was assessed at different sampling point densities. The results showed the ability of LiDAR data to map both corn canopy height and defoliation (predicted vs. observed regression: R2 = 0.69 for both canopy height and defoliation; point density 5 and 42 points/m2, respectively). The presented approach has distinct advantages compared to previous remote sensing methods and has a clear application potential for farmers and insurance industries. Larger-scale studies are needed to verify its best implementation strategies and to investigate its economic and logistic benefits.