Improving the Accuracy of Digital Terrain Models Using Drone-Based LiDAR for the Morpho-Structural Analysis of Active Calderas: The Case of Ischia Island, Italy

Over the past two decades, the airborne Light Detection and Ranging (LiDAR) system has
become a useful tool for acquiring high-resolution topographic data, especially in active tectonics
studies. Analyzing Digital Terrain Models (DTMs) from LiDAR exposes morpho-structural elements,
aiding in the understanding of fault zones, among other applications. Despite its effectiveness,
challenges persist in regions with rapid deformation, dense vegetation, and human impact.
We propose an adapted workflow transitioning from the conventional airborne LiDAR system to
the usage of drone-based LiDAR technology for higher-resolution data acquisition. Additionally,
drones offer a more cost-effective solution, both in an initial investment and ongoing operational
expenses. Our goal is to demonstrate how drone-based LiDAR enhances the identification of active
deformation features, particularly for earthquake-induced surface faulting. To evaluate the potential
of our technique, we conducted a drone-based LiDAR survey in the Casamicciola Terme area,
north of Ischia Island, Italy, known for the occurrence of destructive shallow earthquakes, including
the 2017 Md = 4 event. We assessed the quality of our acquired DTM by comparing it with existing
elevation datasets for the same area. We discuss the advantages and limitations of each DTM product
in relation to our results, particularly when applied to fault mapping. By analyzing derivative
DTM products, we identified the fault scarps within the Casamicciola Holocene Graben (CHG) and
mapped its structural geometry in detail. The analysis of both linear and areal geomorphic features
allowed us to identify the primary factors influencing the current morphological arrangement of
the CHG area. Our detailed map depicts a nested graben formed by two main structures (the Maio
and Sentinella faults) and minor internal faults (the Purgatorio and Nizzola faults). High-resolution
DEMs acquired by drone-based LiDAR facilitated detailed studies of the geomorphology and fault
activity. A similar approach can be applied in regions where the evidence of high slip-rate faults is
difficult to identify due to vegetation cover and inaccessibility.