This study aims to assess the potential of Sentinel-2 NDVI time series and Google Earth Engine to detect small land-use/land-cover changes (at the pixel level) in fire-disturbed environs. To capture both slow and fast changes, the investigations focused on the analysis of trends in NDVI time series, selected because they are extensively used for the assessment of post-fire dynamics mainly linked to the monitoring of vegetation recovery and fire resilience. The area considered for this study is the central–southern part of the Italian peninsula, in particular the regions of (i) Campania, (ii) Basilicata, (iii) Calabria, (iv) Toscana, (v) Umbria, and (vi) Lazio. For each fire considered, the study covered the period from the year after the event to the present. The multi-temporal analysis was performed using two main data processing steps (i) linear regression to extract NDVI trends and enhance changes over time and (ii) random forest classification to capture and categorize the various changes. The analysis allowed us to identify changes occurred in the selected case study areas and to understand and evaluate the trend indicators that mark a change in land use/land cover. In particular, different types of changes were identified: (i) woodland felling, (ii) remaking of paths and roads, and (ii) transition from wooded area to cultivated field. The reliability of the changes identified was assessed and confirmed by the high multi-temporal resolution offered by Google Earth. Results of this comparison highlighted that the overall accuracy of the classification was higher than 0.86.

In recent years, the impact of Climate change, anthropogenic and natural hazards (such as earthquakes, landslides, floods, tsunamis, fires) has dramatically increased and adversely affected modern and past human buildings including outstanding cultural properties and UNESCO heritage sites. Research about protection/monitoring of cultural heritage is crucial to preserve our cultural properties and (with them also) our history and identity. This paper is focused on the use of the open-source Google Earth Engine tool herein used to analyze flood and fire events which affected the area of Metaponto (southern Italy), near the homonymous Greek-Roman archaeological site. The use of the Google Earth Engine has allowed the supervised and unsupervised classification of areas affected by flooding (2013–2020) and fire (2017) in the past years, obtaining remarkable results and useful information for setting up strategies to mitigate damage and support the preservation of areas and landscape rich in cultural and natural heritage.

This paper is focused on the use of satellite Sentinel-2 data for assessing their capability in the identification of archaeological buried remains. We selected the “Tavoliere delle Puglie” (Foggia, Italy) as a test area because it is characterized by a long human frequentation and is very rich in archaeological remains. The investigations were performed using multi-temporal Sentinel-2 data and spectral indices, commonly used in satellite-based archaeology, and herein analyzed in known archaeological areas to capture the spectral signatures of soil and crop marks and characterize their temporal behavior using Time Series Analysis and Spectral Un-mixing. Tasseled Cap Transformation and Principal Component Analysis have been also adopted to enhance archaeological features. Results from investigations were compared with independent data sources and enabled us to (i) characterize the spectral signatures of soil and crop marks, (ii) assess the performance of the diverse spectral channels and indices, and (iii) identify the best period of the year to capture the archaeological proxy indicators. Additional very important results of our investigations were (i) the discovery of unknown archaeological areas and (ii) the setup of a database of archaeological features devised ad hoc to characterize and categorize the diverse typologies of archaeological remains detected using Sentinel-2 Data.

The use of satellite radar in landscape archaeology offers great potential for manifold applications, such as the detection of ancient landscape features and anthropogenic transformations. Compared to optical data, the use and interpretation of radar imaging for archaeological investigations is more complex, due to many reasons including that: (i) ancient landscape features and anthropogenic transformations provide subtle signals, which are (ii) often covered by noise; and, (iii) only detectable in specific soil characteristics, moisture content, vegetation phenomenology, and meteorological parameters. In this paper, we assessed the capability of SAR Sentinel 1 in the imaging and detection of palaeo-landscape features in the Mediterranean area of Tavoliere delle Puglie. For the purpose of our investigations, a significant test site (larger than 200 km²) was selected in the Foggia Province (South of Italy) as this area has been characterized for millennia by human frequentation starting from (at least) the Neolithic. The results from the Sentinel 1 (S-1) data were successfully compared with independent data sets, and the comparison clearly showed an excellent match between the S-1 based outputs and ancient anthropogenic transformations and landscape features.

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.

Sentinel-2 data have been used in various fields of human activity. In cultural heritage, their potential is still to be fully explored. This paper aims to illustrate how remote sensing and open source tools are useful for archaeological investigations. The whole issue revolves around the application of satellite (Sentinel-2) and accessory tools for the identification, knowledge and protection of the cultural heritage of two areas of southern Italy: Sant’Arsenio (SA) and Foggia (FG). Both study cases were selected for a specific reason: to demonstrate the usefulness of open data and software for research and preservation of cultural heritage, as in the case of urban sprawl, development of public works (gas- and oil-pipelines, etc.) or intensive use of land for agricultural purposes. The results obtained are relevant for the knowledge improvement and very useful to operate in the field of preventive archaeology, for the evaluation and management of risk, the planning of city-expansion or infrastructures that could damage the buried heritage.