This paper deals with a UAV LiDAR methodological approach for the identification and extraction of archaeological features under canopy in hilly Mediterranean environments, characterized by complex topography and strong erosion. The presence of trees and undergrowth makes the reconnaissance of archaeological features and remains very difficult, while the erosion, increased by slope, tends to adversely affect the microtopographical features of potential archaeological interest, thus making them hardly identifiable. For the purpose of our investigations, a UAV LiDAR survey has been carried out at Perticara (located in Basilicata southern Italy), an abandoned medieval village located in a geologically fragile area, characterized by complex topography, strong erosion, and a dense forest cover. All of these characteristics pose serious challenge issues and make this site particularly significant and attractive for the setting and testing of an optimal LiDAR-based approach to analyze hilly forested regions searching for subtle archaeological features. The LiDAR based investigations were based on three steps: (i) field data acquisition and data pre-processing, (ii) data post-processing, and (iii) semi-automatic feature extraction method based on machine learning and local statistics. The results obtained from the LiDAR based analyses (successfully confirmed by the field survey) made it possible to identify the lost medieval village that represents an emblematic case of settlement abandoned during the crisis of the late Middle Ages that affected most regions in southern Italy.

The use of Unmanned Aerial Vehicles (UAVs) represents a rather innovative, quick, and low-cost methodological approach offering applications in several fields of investigation. The present study illustrates the developed method using Digital Elevation Models (DEMs) based on UAV-derived data for evaluating short-term morphological-topographic changes of the beach system and related implications for coastal vulnerability assessment. UAV surveys were performed during the summers of 2019 and 2020 along a beach stretch affected by erosion, located along the central Adriatic coast. Acquired high-resolution aerial photos were used to generate large-scale DEMs as well as orthophotos of the beach using the Structure from Motion (SfM) image processing tool. Comparison of the generated 2019 and 2020 DEMs highlighted significant morphological changes and a sediment volume loss of about 780 m³ within a surface area of about 4400 m². Based on 20 m spaced beach profiles derived from the DEMs, a coastal vulnerability assessment was performed using the CVA approach that highlighted some significant variations in the CVA index between 2019 and 2020. Results evidence that UAV surveys provide high-resolution topographic data, suitable for specific beach monitoring activities and the updating of some parameters that enter in the CVA model contributing to its correct application.

Automatic procedures for landform extraction is a growing research field but extensive quantitative studies of the prediction accuracy of Automatic Landform Classification (ACL) based on a direct comparison with geomorphological maps are rather limited. In this work, we test the accuracy of an algorithm of automatic landform classification on a large sector of the Ionian coast of the southern Italian belt through a quantitative comparison with a detailed geomorphological map. Automatic landform classification was performed by using an algorithm based on the individuation of basic landform classes named geomorphons. Spatial overlay between the main mapped landforms deriving from traditional geomorphological analysis and the automatic landform classification results highlighted a satisfactory percentage of accuracy (higher than 70%) of the geomorphon-based method for the coastal plain area and drainage network. The percentage of accuracy decreased by about 20–30% for marine and fluvial terraces, while the overall accuracy of the ACL map is 69%. Our results suggest that geomorphon-based classification could represent a basic and robust tool to recognize the main geomorphological elements of landscape at a large scale, which can be useful for the advanced steps of geomorphological mapping such as genetic interpretation of landforms and detailed delineation of complex and composite geomorphic elements.

Coastal erosion and its impacts on the involved communities is a topic of great scientific interest that also reflects the need for modern as well as cost and time-effective methodologies to be integrated into or even to substitute traditional investigation methods. The present study is based on an integrated approach that involves the use of data derived from UAV (Unmanned Aerial Vehicle) surveys. The study illustrates the long- to short-term shoreline evolution of the Molise coast (southern Italy) and then focuses on two selected beach stretches (Petacciato and Campomarino beaches), for which annual UAV surveys were performed from 2019 to 2021, to assess their most recent shoreline and morpho-topographical changes and related effects on their coastal vulnerability. UAV data were processed using the Structure from Motion (SfM) image processing tool. Along the beach profiles derived from the produced DEMs, the coastal vulnerability of the selected beach stretches was evaluated by using the Coastal Vulnerability Assessment (CVA) approach. The results obtained highlight some significant worsening of CVA indexes from 2019 to 2021, especially for the Campomarino beach, confirming the importance of the periodic updating of previous data. In conclusion, the easy use of the UAV technology and the good quality of the derived data make it an excellent approach for integration into traditional methodologies for the assessment of short-term shoreline and beach changes as well as for monitoring coastal vulnerability.

Illegal archaeological excavations, generally denoted as looting, is one of the most important damage factors to cultural heritage, as it upsets the human occupation stratigraphy of sites of archaeological interest. Looting identification and monitoring are not an easy task. A consolidated instrument used for the detection of archaeological features in general, and more specifically for the study of looting is remote sensing. Nevertheless, passive optical remote sensing is quite ineffective in dense vegetated areas. For these type of areas, in recent decades, LiDAR data and its derivatives have become an essential tool as they provide fundamental information that can be critical not only for the identification of unknown archaeological remains, but also for monitoring issues. Actually, LiDAR can suitably reveal grave robber devastation, even if, surprisingly, up today LiDAR has been generally unused for the identification of looting phenomenon. Consequently, this paper deals with an approach devised ad hoc for LiDAR data to detect looting. With this aim, some spatial visualization techniques and the geomorphon automatic landform extraction were exploited to enhance and extract features linked to the grave robber devastation. For this paper, the Etruscan site of San Giovenale (Northern Lazio, Italy) was selected as a test area as it is densely vegetated and was deeply plundered throughout the 20th century. Exploiting the LiDAR penetration capability, the prediction ability of the devised approach is highly satisfactory with a high rate of success, varying from 85–95%.

Soil erosion is one of the major natural risk factors for developing high-value crops and an accurate estimation of spatial distribution and rates of soil degradation can be crucial to prevent crop degradation. In this paper, we use comparisons between high-resolution DEMs and soil erosion models to uncover the short-term landscape evolution of hazelnut crop yields, which are affected by incipient processes of rill development. Maps of rill initiation and evolution were extracted from the analysis of UAV-based multitemporal DEMs and the application of soil erosion models. A comparison between such a short-term analysis and historical orthophotos was carried out. Such a comparison shows how the USPED model predicts, very reliably, where linear erosion occurred. In fact, a reliable overlay between the linear erosive forms predicted by the USPED model and those captured by the UAV images can be observed. Furthermore, land use changes from 1974 to 2020 are characterized by a transition from abandoned areas (1974) to areas with high-value cultivation (2020), which has a strong impact on the spatial distribution of erosion processes and landslide occurrence. Such data represent a key tool for both the investigation of the spatial distribution of hot-spots of soil degradation and the identification of effective mitigation practices of soil conservation.