Use of UAVs for geological risk management and analysis: The case study of the mud volcano of Villaggio Santa Barbara, Caltanissetta (Sicily).

Abstract

<p>Deformation effects on surface due to active geological processes are several (e.g., uplift, subsidence and shear discontinuities), these are strictly related to the source parameters and to the geomechanical properties of the surrounding rocks. In the last 20 years, remote sensing represents a key tool for the evaluation and monitoring of the natural hazards. Disasters occur when hazard and vulnerability match.</p> <p>The risk is proportional to the magnitude of the hazards and the vulnerability of the involved population. Among the deformation monitoring systems, photogrammetry technique from Unmanned Aerial Vehicles (UAVs) is spreading thanks to the high efficiency in data acquisition (time span, resource, and operators), low cost and the capability to acquire high-resolution images. The use of UAVs in contexts of natural hazard presents three main steps for risk assessment: pre-post event data acquisition, emergency support and monitoring. The mud volcano of Santa Barbara (Municipality of Caltanissetta, Sicily, Italy) represents a potentially dangerous site. On 11th August 2008 a paroxysmal event caused serious damage to infrastructures for a range of about 2 km. The main clues of mud volcano paroxysmal events are the uplift and the development of structural features with dimensions ranging from centimetre to decimetre. Here we present a methodology for monitoring of deformation processes that may be precursors of the mud volcano unrest period. This methodology is based on: i) the data collection, ii) the Structure from Motion (SfM) processing chain and iii) the M3C2-PM algorithm for the comparison between point clouds and uncertainty analysis with a statistical approach. This methodology is useful to detect hazard precursors by monitoring of deformation processes with centimetre precision and a temporal frequency of 1 - 2 months. Precision maps and the M3C2-PM algorithm are used to determine surface variations. The statistical analysis allows us to verify i) the uncertainty between the different surveys ii) the spatial variability of the accuracy; iii) the quality of the georeferencing of the surveys based on the number of GCPs (ground control points).</p>