Aerial and Drone LiDAR Data for Geomorphological Mapping, Landform Extraction and Landscape Evolution

Dear Colleagues,

Recently, the increased availability of ultra-high resolution LIDAR data has favored the spreading of different applications in the field of the quantitative landscape analyses. Such data strongly support traditional geomorphological methods of delineating geomorphological elements and types and rates of surface processes. The aim of this Special Issue is to collect multidisciplinary contributions on the use of airborne and drone LIDAR data to identify geomorphological features and processes, and solve issues of landscape evolution.

We encourage researchers to submit papers dealing with the multitemporal analysis of LIDAR DEMs aimed at the detailed reconstruction of short- and long-term topographic changes. Other relevant topics for this research proposal include the analysis of LIDAR-derived data for geomorphological mapping purposes, modeling of short- and long-term estimation of topographic changes and geomorphological processes in different climate contexts and at different spatial and temporal scales, and quantitative characterization of geomorphological processes and landform changes. Contributions on the high potential of LIDAR surveys for application in the field of landscape archaeology or the identification of small-scale landforms of archaeological significance are also welcomed.

Dr. Dario Gioia
Dr. Nicodemo Abate
Dr. Giuseppe Corrado
Dr. Antonio Minervino Amodio
Prof. Marcello Schiattarella
Guest Editors

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• UAV LiDAR
• geomatics
• geomorphological mapping
• object-based landform extraction
• DEM of difference (Dod)
• short-term geomorphological evolution
• landscape evolution model (LEM)

Title: Enhancing landforms and processes detection using multisource LiDAR data
Authors: Mario Valiante; Alessandro Di Benedetto; Domenico Guida
Affiliation: 1 Department of Civil Engineering, University of Salerno, Fisciano (Italy) 2 C.U.G.RI. - InterUniversity Research Center for Prevision and Prevention of Great Risks, Fisciano (Italy)
Abstract: The automated recognition of landforms holds significant importance within the framework of digital geomorphological mapping, serving as a pivotal focal point for research and practical applications alike. Over the past decade, a multitude of techniques have emerged to address this objective, spanning from grid-based to object-based methodologies, and encompassing a spectrum from supervised to entirely unsupervised methods, with consideration given to the extent of expert judgment involvement as opposed to AI-driven approaches. Furthermore, the vast majority of the methods mentioned depend on Digital Elevation Models (DEMs) as their primary input, highlighting the crucial significance of meticulous preparation and rigorous quality assessment of these datasets. In this study, we compare the outcomes of grid-based methods for landform extraction and surficial process type assessment, leveraging various DEMs as input data. Initially, we employed a photogrammetric Digital Terrain Model (DTM) generated at a regional scale, along with two LiDAR datasets. The first dataset originates from an airborne survey conducted by the national government approximately a decade ago, while the second dataset was generated utilizing UAV technology as part of this study's framework. Results highlights how the higher resolution and level of detail of the LiDAR datasets allow the recognition of a higher number of features at higher scales, but, in contrast, generally to a high level of detail correspond a higher risk of noise within the dataset, mostly due to unwanted natural features or anthropogenic disturbance. Utilizing these datasets for generating geomorphological maps harbors significant potential in the framework of natural hazard assessment, particularly concerning phenomena associated with geo-hydrological processes.

Title: 3D Rockslide analysis using UAV and LIDAR: the Castrocucco case study, Southern Italy
Authors: Antonio Minervino Amodio; Giuseppe Corrado; Ilenia Graziamaria Gallo; Dario Gioia; Marcello Schiattarella; Valentino Vitale; Gaetano Robustelli
Affiliation: Department of Biology, Ecology and Earth Sciences (DIBEST), University of Calabria, Arcavacata di Rende, 87036, Italy
Abstract: Rockslides are one of the most dangerous hazards in mountainous and hilly areas. In this study, a rockslide occurred on 30 November 2022 in Castrocucco, a district located in the Italian municipality of Maratea (Potenza province) in the Basilicata region was investigated by using pre- and post-event high-resolution 3D models. The event caused a great social alarm as some infrastructures were affected. The main road to the tourist hub of Maratea was in fact destroyed and made inaccessible. Rock debris also affected a beach club and an important boat storage for sea excursions to Maratea. This event was investigated using multiscale and multisensor close-range remote sensing (Lidar and SfM) to determine rockslide characteristics. The evolution of the event was determined and a detached limestone volume of approximately 8000 m3 estimated.