Search

The World Heritage Committee has been meeting to discuss the arrangements of existing World Heritage Sites, and, on 22–26 October, the area from Giza to the Dahshur was included in the list of World Heritage Sites. According to the Egyptian Antiquities Authority (EAA), the groundwater levels at the Pyramids Plateau are too shallow, which threatens the ancient Sphinx and Pyramids in Giza, Egypt. In addition, many geophysical studies have been carried out in the archaeological area of Giza, which prove that the area is facing the risk of a high level of groundwater, specifically threatening the Sphinx. Recent developments in Earth observation have helped in the field of land monitoring such as land use changes, risk observation, and the creation of models for protecting cultural heritage sites. This study aimed to examine the impact of land use changes on on the archaeological sites of the Giza Necropolis area by integrating various data sources including optical satellite imagery and SRTM data during the period of 1965–2019. A historical database of Corona 1965 and Landsat 2009 data was investigated along with the new acquisitions of Sentinel-2 2016 and Sentinel-1 2016 and 2019. In addition, the radar Sentinel-1 SLC data were collected and analyzed for calculating the land subsidence value in the area of interest through two periods between 6–30 July 2016 and 30 July–15 December 2016. Various methods were implemented, including cluster outliers, the Moran index, and spatial autocorrelation to examine the changes in urban masses. Additionally, the relationship between groundwater leakage and land subsidence in the region was investigated. The analysis was carried out using Envi5.3, ArcMap10.6.1, and SNAP6.0 software to extract spatial data from the raw data. The results from our investigation highlighted rapid changes in urban areas between 1965 and 2019. The data obtained and analyzed from optical and radar satellite imagery showed that changes in land use can cause changes in the topographic situation by decreasing the level of groundwater, which adversely affects Egyptian monumental pyramids and the Sphinx. Land use analysis showed that the urban area represented 7.63% of the total area of the study area in 1965, however it reached 32.72% in 2009, approximately half of the total area in 2016, and in 2019, the urban mass area increased to nearly two-thirds of the total area. The annual growth rate between 1965 and 2019 was estimated by nearly 0.642 km2/year. These land-use changes possibly affected the land subsidence value (−0.0138 m), causing the rising groundwater level close to the Sphinx. Using the information obtained from our RS- and GIS-based analysis, mitigation strategies have also been identified to support archaeological area preservation. [ABSTRACT FROM AUTHOR]

Xylella Fastidiosa has been recently detected for the first time in southern Italy, representing a very dangerous phytobacterium capable of inducing severe diseases in many plants. In particular, the disease induced in olive trees is called olive quick decline syndrome (OQDS), which provokes the rapid desiccation and, ultimately, death of the infected plants. In this paper, we analyse about two thousands pixels of MODIS satellite evapotranspiration time series, covering infected and uninfected olive groves in southern Italy. Our aim is the identification of Xylella Fastidiosa-linked patterns in the statistical features of evapotranspiration data. The adopted methodology is the well-known Fisher–Shannon analysis that allows one to characterize the time dynamics of complex time series by means of two informational quantities, the Fisher information measure (FIM) and the Shannon entropy power (SEP). On average, the evapotranspiration of Xylella Fastidiosa-infected sites is characterized by a larger SEP and lower FIM compared to uninfected sites. The analysis of the receiver operating characteristic curve suggests that SEP and FIM can be considered binary classifiers with good discrimination performance that, moreover, improves if the yearly cycle, very likely linked with the meteo-climatic variability of the investigated areas, is removed from the data. Furthermore, it indicated that FIM exhibits superior effectiveness compared to SEP in discerning healthy and infected pixels. [ABSTRACT FROM AUTHOR]

This study aimed to evaluate the impact of using an AI model, specifically ChatGPT-3.5, in remote sensing (RS) applied to archaeological research. It assessed the model's abilities in several aspects, in accordance with a multi-level analysis of its usefulness: providing answers to both general and specific questions related to archaeological research; identifying and referencing the sources of information it uses; recommending appropriate tools based on the user's desired outcome; assisting users in performing basic functions and processes in RS for archaeology (RSA); assisting users in carrying out complex processes for advanced RSA; and integrating with the tools and libraries commonly used in RSA. ChatGPT-3.5 was selected due to its availability as a free resource. The research also aimed to analyse the user's prior skills, competencies, and language proficiency required to effectively utilise the model for achieving their research goals. Additionally, the study involved generating JavaScript code for interacting with the free Google Earth Engine tool as part of its research objectives. Use of these free tools, it was possible to demonstrate the impact that ChatGPT-3.5 can have when embedded in an archaeological RS flowchart on different levels. In particular, it was shown to be useful both for the theoretical part and for the generation of simple and complex processes and elaborations. [ABSTRACT FROM AUTHOR]

The purpose of this work was to evaluate the potential of Singular Spectrum Analysis (SSA) and the Fisher–Shannon method to analyse NDVI MODIS time series and to capture and estimate inner vegetation anomalies in forest covers. In particular, the Fisher–Shannon method allows to calculate two quantities, the Fisher Information Measure (FIM) and the Shannon entropy power (SEP), which are used to characterise the complexity of a time series in terms of organisation/disorder. Pilot sites located both in urban (Milano, Torino, and Roma) and peri-urban areas (Appia Park, Castel Porziano, and Castel Volturno) were selected. Among the six sites, Roma, Castel Porziano, and Castel Volturno are affected by the parasite Toumeyella parvicornis. The time series was analysed using the products available in Google Earth Engine. To explore and characterise long-term vegetation dynamics, the time series was analysed using a multistep processing chain based on the (i) normalisation of the satellite time series, (ii) removal of seasonality and any other periodical cycles using SSA, (iii) analysis of the de-trended data using the Fisher–Shannon statistical method, and (iv) validation through comparison with independent data and ancillary information. Our findings point out to a clear discrimination between healthy and unhealthy sites, being the first (Milano, Torino, Appia) characterised by a larger FIM (lower SEP) and the second (Roma, Castel Porziano, Castel Volturno) by a lower FIM (larger SEP). The results of the investigations showed that the use of the SSA and Fisher–Shannon statistical methods coupled with the NDVI time series of the MODIS satellite made it possible to effectively identify and characterise subtle but physically significant signals veiled by seasonality and annual cycles. [ABSTRACT FROM AUTHOR]

Climate change effects along with anthropogenic activities present the main factors that threaten the existence of heritage sites across the north Nile Delta of Egypt close to the coastline of the Mediterranean Sea. Observing the changes in the landscape close to the archaeological sites is an important issue for decision‐makers in terms of reducing the negative impact of natural events and human activities. The coastal heritage sites are becoming strongly threatened by the rising sea level phenomena that will happen due to global warming. Focusing on the distribution of the archaeological sites, this study aims to detect the areas at risk of shoreline erosion or accretion in the northern shoreline of the Nile Delta. In this study, the changes in the northern shoreline of the Nile Delta were observed and calculated during the last hundred years based on the integration between the old topographic maps from surveys in 1900, 1925 and 1945, optical satellite images captured by Landsat in 1972, 1986 and 2000; Sentinel2 2021; and the Radar SRTM data. The results of this study showed that the changes were enormous with a great shoreline erosion process over the last 121 years recorded along the shoreline in the periods between 1900–1925, 1925–1945, 1945–1972, 1972–1986, 1986–2000 and 2000–2021. The areas eroded were about 5.3, 4.7, 5.6, 8.9, 2.5 and 5.4 km2, respectively. Such negative movements caused the loss of two heritage sites, and the expected changes will lead to the loss of additional heritage sites in the next 500 years. Furthermore, a model was suggested for protecting the coastal heritage sites threatened by the risk of submergence. This study can help the decision‐makers to detect the coastal archaeological sites at risk and create innovative solutions for protecting these irreplaceable heritage sites. [ABSTRACT FROM AUTHOR]

The study aims to explore the added value of the joint use of Sentinel-1 (S1) and Sentinel-2 (S2) data for assessing burn severity in heterogeneous, fragmented ecosystems. The importance of this aim lies in the fact that for both S2 and S1 (as for all the synthetic aperture radar (SAR) C-bands), the impact of fire was found to cause ambiguous effects in complex and fragmented ecosystems. For our investigation, the effectiveness of S1 and S2 fire metrics was first statistically analyzed using ISODATA coupled with field surveys conducted for a fire that occurred on 13 July 2019 in Sardinia. Later, to automatically map burn areas and categorize fire severity, S1 and S2 fire metrics were integrated through a multilevel classification performed at a pixel and feature level. Results were successful (accuracy higher than 94%) compared with independent data sets and in situ investigations. [ABSTRACT FROM AUTHOR]

Xylella fastidiosa is a phytobacterium able to provoke severe diseases in many species. When it infects olive trees, it induces the olive quick decline syndrome that leads the tree to a rapid desiccation and then to the death. This phytobacterium has been recently detected in olive groves in southern Italy, representing an important threat to the olive growing of the area. In this paper, in order to identify patterns revealing the presence of Xylella fastidiosa, several hundreds pixels of MODIS satellite evapostranspiration covering infected and healthy olive groves in southern Italy were analyzed by means of the Fisher–Shannon method and the multifractal detrended fluctuation analysis. The analysis of the receiver operating characteric curve indicates that the two informational quantities (the Fisher information measure and the Shannon entropy) and the three multifractal parameters (the range of generalized Hurst exponents and the width and the maximum of the multifractal spectrum) are well suited to discriminate between infected and healthy sites, although the maximum of the multifractal spectrum performs better than the others. These results could suggest the use of both the methods as an operational tool for early detection of plant diseases. [ABSTRACT FROM AUTHOR]

Applications of non-invasive sensing techniques to investigate the internal structure and surface of precious and delicate objects represent a very important and consolidated research field in the scientific domain of cultural heritage knowledge and conservation. The present article is the first of three reviews focused on contact and non-contact imaging techniques applied to surveying cultural heritage at micro- (i.e., manufacts), meso- (sites) and macro-scales (landscapes). The capability to infer variations in geometrical and physical properties across the inspected surfaces or volumes is the unifying factor of these techniques, allowing scientists to discover new historical sites or to image their spatial extent and material features at different scales, from landscape to artifact. This first part concentrates on the micro-scale, i.e., inspection, study and characterization of small objects (ancient papers, paintings, statues, archaeological findings, architectural elements, etc.) from surface to internal properties. [ABSTRACT FROM AUTHOR]

Soil conventional tillage has been associated with deterioration of its characteristics, while organic farming has been promoted as an approach to conserve a favorable soil environment. With the interest in nominating the tillage strategies without ploughing for maintaining long-term soil quality and subsequently increasing yields, this study set to identify if and how conservation tillage practices in organic management (OM) do improve soil physical properties compared to conventional management (CM). This study was conducted on matched field pairs in Baden-Württemberg, Germany. The conservation tillage treatment effects of OM (superficial tillage using chisel at 10 cm depth) was compared with conventional tillage practices CM (mouldboard ploughing at 30 cm depth). The field pairs were homogenous in most respects that would reflect tillage impacts. Measurements included soil infiltration capacity, saturated hydraulic conductivity, penetration resistance, and effective bulk density. Infiltration rate, measured using a hood infiltrometer at 10 parcels, was computed using Wooding's analytical method, while Gardner's equation was used to calculate the saturated hydraulic conductivity (Ks). The steady infiltration rate qs (h) was two times higher under OM than under CM with an average of 624 mm/h and 303 mm/h, respectively. Penetration resistances of OM were lower than under CM irrespective of the clay content. The degree of compactness (effective bulk density) was greater under CM than OM. That small change in soil compactness affects the water infiltration rate and the hydraulic properties rather than intrinsic soil matrix such as texture. Numerical model Hydrus-1D results were more representative for simulating the soil water transfer and hydraulic parameters under tillage changes. [ABSTRACT FROM AUTHOR]

The main goal of this study was to evaluate the potential of the Fisher-Shannon statistical method applied to the MODIS satellite time series to search for and explore any small multiyear trends and changes (herein also denoted as inner anomalies) in vegetation cover. For the purpose of our investigation, we focused on the vegetation cover of three peri-urban parks close to Rome and Naples (Italy). For each of these three areas, we analyzed the 2000–2020 time variation of four MODIS-based vegetation indices: evapotranspiration (ET), normalized difference vegetation index (NDVI), leaf area index (LAI), and enhanced vegetation index (EVI). These data sets are available in the Google Earth Engine (GEE) and were selected because they are related to the interactions between soil, water, atmosphere, and plants. To account for the great variability exhibited by the seasonal variations while identifying small multiyear trends and changes, we devised a procedure composed of two steps: (i) application of the Singular Spectrum Analysis (SSA) to each satellite-based time series to detect and remove the annual cycle including the seasonality and then (ii) analysis of the detrended signals using the Fisher-Shannon method, which combines the Shannon entropy and the Fisher Information Measure (FIM). Our results indicate that among all the three pilot test areas, Castel Volturno is characterized by the highest Shannon entropy and the lowest FIM that indicate a low level of order and organization of vegetation time series. This behaviour can be linked to the degradation phenomena induced by the parasite (Toumeyella parvicornis) that has affected dramatically the area in recent years. Our results were nicely confirmed by the comparison with in situ analyzed and independent data sets revealing the existence of subtle, small multiyear trends and changes in MODIS-based vegetation indices. [ABSTRACT FROM AUTHOR]

Both in the literature and in practical applications, several works have dealt with rockfall analysis and the planning of mitigation measures. It is also possible to find inventories and papers that describe historical events. However, it is challenging to find methodologies or studies about inventorying rockfall mitigation or their efficiency over time. In Italy, many rockfall barriers and other mitigation solutions have been built in the last decades, and one of the most urgent problems is their correct management and maintenance. Lauria, a small town in southern Italy, can be considered an example of this common condition exacerbated by a wildfire in 2017. This work presents a methodology for assessing rockfall risk and creating a geodatabase of mitigation structures focused on small communities. We used digitalization of archival sources to reconstruct and geocode the record of mitigation works. An available database of historical landslides was used to reconstruct the most relevant rockfall events. Moreover, we coupled this with Sentinel-2 images and high-resolution orthophotos to map the wildfire area. Data obtained from the UAV-LiDAR survey were used to map the mitigation structures. The aim was to create a reliable state-of-the-art method, described in an operational monograph, to be used by experts for the design of new rockfall mitigation structures in both an affordable and efficient way. Moreover, we created a simple webGIS and a 3-D interactive view, helpful in disseminating rockfall hazards and mitigation strategies among the population at risk. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

This study focuses on the use of radar and optical satellite imagery for flood hazard mapping and monitoring around the archaeological sites of the Wadi Baba area, situated at Sinai (Egypt) and well known for its heritage treasures belonging to diverse historical periods and civilizations from the Pharaonic, Nabateans, Christian, and Islamic eras. Although this area is located in an arid to semi-arid climatic region, it is intermittently flooded due to torrential rainstorms. To assess the amount of rainfall expected and its impacts on heritage sites, satellite Sentinel-1 (C-Band) and Tropical Rainfall Monitoring Mission (TRMM) data were jointly used with measurements from meteorological stations and the Digital Elevation Model (DEM) from Shuttle Radar Topography Mission (SRTM). Envi5.1, ArcGIS 10.4.1, Snap 6.0, and the GEE platform were used to process optical and radar data, which were further analysed using the ArcHydro model. In this study, the TRMM accumulated rainfall data acquired on 17 January 2010, Sentinel-1 radar images between 2017 and 2019, and Sentinel-1 data captured from 1 to 30 March 2020 processed by GEE platform were chosen to assess the effects of flood events on the archaeological sites in the study area. The results indicated that the study area is exposed to flood risk that significantly threatens these heritage sites. Based on that, mitigation strategies were devised and recommended to mitigate the flood hazard impact around the archaeological areas. [ABSTRACT FROM AUTHOR]

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%. [ABSTRACT FROM AUTHOR]

The currently available tools and services as open and free cloud resources to process big satellite data opened up a new frontier of possibilities and applications including archeological research. These new research opportunities also pose several challenges to be faced, as, for example, the data processing and interpretation. This letter is about the assessment of different methods and data sources to support a visual interpretation of EO imagery. Multitemporal Sentinel 1 and Sentinel 2 data sets have been processed to assess their capability in the detection of buried archeological remains related to some lost sections of the ancient Via Appia road (herein selected as case study). The very subtle and nonpermanent features linked to buried archeological remains can be captured using multitemporal (intra- and inter-year) satellite acquisitions, but this requires strong hardware infrastructures or cloud facilities, today also available as open and free tools as Google Earth Engine (GEE). In this study, a total of 2948 Sentinel 1 and 743 Sentinel 2 images were selected (from February 2017 to August 2020) and processed using GEE to enhance and unveil archeological features. Outputs obtained from both Sentinel 1 and Sentinel 2 have been successfully compared with in situ analysis and high-resolution Google Earth images. [ABSTRACT FROM AUTHOR]