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Remote Sensing
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Advances in Remote Sensing for Exploring Ancient History
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Advances in Remote Sensing for Exploring Ancient History

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Environmental Remote Sensing".

Deadline for manuscript submissions: closed (31 March 2023) | Viewed by 11724

Special Issue Editors

Dr. Nicola Masini

E-Mail Website
Guest Editor
CNR-IBAM, 85050 Baragiano Scalo, Italy
Interests: heritage science; remote sensing in archaeology; architectural conservation; archaeological science; humanities
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Apostolos Sarris

E-Mail Website
Guest Editor
Digital Humatities GeoInformatics Lab, Archaeological Research Unit, Department of History and Archaeology, University of Cyprus, 1678 Nicosia, Cyprus
Interests: geophysics and remote sensing; digital heritage; spatial analysis and GIS; landscape archaeology; spatial history; digital humanities; Cultural Resources Management (CRM)
Special Issues, Collections and Topics in MDPI journals
Dr. Rosa Lasaponara

E-Mail Website
Guest Editor
CNR-IMAA (Institute of Environmental Analysis), 85050 Tito Scalo, Italy
Interests: data integration modelling; artificial intelligence for cultural heritage; earth observation technologies for historical landscape
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The study of ancient cultures is fundamental for reconstructing the history of the human past. Toward this end, it is important to analyze the relationship between human attendance, environmental and climatic changes, and socio-cultural evolution using appropriate analysis tools, including Earth observation (EO). The multiscale context and the complexity of the issues to be investigated, along with the heterogeneity and the enormous amount of data (including historical sources and archaeological record), require comprehensive approaches also based on the interrelation of EO-based information, Big Data analysis, and artificial intelligence (AI).

This Special Issue aims to present papers addressing the development of models and approaches based on the use of remote sensing techniques to contribute to answering the questions posed by ancient history.

To extract information useful to improve the knowledge of ancient landscapes, the relationship between humans and the environment over time, particular attention will be paid to the integration of data deriving from different sources (including EO data, historical sources, archaeological and geo-archaeological records, etc.) and to their elaboration and interpretation using spatial analysis and AI techniques.

With reference to the use of EO, the focus will be on the application and development of multi-scale approaches (from spaceborne to geophysics and UAS imagery), with passive and active sensors (including SAR and LiDAR), for the processing of time series of data to identify features, patterns, and predictive indicators of cultural interest.

- Holistic approaches based on the use of historical sources and EO data;

- Remote sensing and geophysics data integration and fusion;

- Machine learning for extracting information of cultural interest from EO data;

- Google Earth Engine applied to EO Big Data to identify cultural patterns of ancient landscapes;

- Automatic procedures vs. visual interpretation;

- Modeling of remote sensing for archaeology, geoarchaeology and paleoevironmental studies;

- Theoretical approaches and EO proxy indicators for archaeology.

Remote sensing datasets: VHR and HR satellite imagery (including multispectral and SAR data); airborne LiDAR; UAS-based passive imagery; geophysical data; historical archive of aerial photographs and declassified satellite images

Ancillary data: historical maps; archaeological and geoarchaeological records; historical documents

Prof. Dr. Nicola Masini
Prof. Dr. Apostolos Sarris
Dr. Rosa Lasaponara
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Archaeological remote sensing
  • Ancient history
  • Environmental changes and ancient landscape evolution
  • Historical data sources analysis
  • EO data integration
  • Big Data
  • Deep and machine learning
  • EO-based archaeological proxy indicators
  • EO-based detection of paleoenvironmental changes

Published Papers (5 papers)

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Research

16 pages, 12513 KiB  
Article
Multisensor Data Fusion by Means of Voxelization: Application to a Construction Element of Historic Heritage
by Javier Raimundo, Serafin Lopez-Cuervo Medina, Julian Aguirre de Mata and Juan F. Prieto
Remote Sens. 2022, 14(17), 4172; https://doi.org/10.3390/rs14174172 - 25 Aug 2022
Cited by 4 | Viewed by 1279
Abstract
Point clouds are very common tools used in the work of documenting historic heritage buildings. These clouds usually comprise millions of unrelated points and are not presented in an efficient data structure, making them complicated to use. Furthermore, point clouds do not contain [...] Read more.
Point clouds are very common tools used in the work of documenting historic heritage buildings. These clouds usually comprise millions of unrelated points and are not presented in an efficient data structure, making them complicated to use. Furthermore, point clouds do not contain topological or semantic information on the elements they represent. Added to these difficulties is the fact that a variety of different kinds of sensors and measurement methods are used in study and documentation work: photogrammetry, LIDAR, etc. Each point cloud must be fused and integrated so that decisions can be taken based on the total information supplied by all the sensors used. A system must be devised to represent the discrete set of points in order to organise, structure and fuse the point clouds. In this work we propose the concept of multispectral voxels to fuse the point clouds, thus integrating multisensor information in an efficient data structure, and applied it to the real case of a building element in an archaeological context. The use of multispectral voxels for the fusion of point clouds integrates all the multisensor information in their structure. This allows the use of very powerful algorithms such as automatic learning and machine learning to interpret the elements studied. Full article
(This article belongs to the Special Issue Advances in Remote Sensing for Exploring Ancient History)
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17 pages, 4619 KiB  
Article
Uncertainties of Global Historical Land Use Datasets in Pasture Reconstruction for the Tibetan Plateau
by Lei Hua, Shicheng Li, Deng Gao and Wangjun Li
Remote Sens. 2022, 14(15), 3777; https://doi.org/10.3390/rs14153777 - 06 Aug 2022
Cited by 2 | Viewed by 2207
Abstract
Global historical land use datasets have been widely used in global or regional environmental change studies. Historical pasture data are essential components of these spatially explicit global datasets, and their uncertainties have not been well evaluated. Using the livestock-based historical pasture dataset for [...] Read more.
Global historical land use datasets have been widely used in global or regional environmental change studies. Historical pasture data are essential components of these spatially explicit global datasets, and their uncertainties have not been well evaluated. Using the livestock-based historical pasture dataset for the Tibetan Plateau (TP), we evaluated the uncertainties of these representative global historical land use datasets in pasture reconstruction for the TP over the past 300 years in terms of pasture area estimation and spatial pattern mapping. We found that only the Sustainability and the Global Environment (SAGE) dataset can roughly reflect the temporal and spatial characteristics of historical pasture changes on the TP. The History Database of the Global Environment (HYDE) version 3.2 and the Pongratz Julia (PJ) datasets overestimated pasture area for the TP dramatically, with a maximum area ratio of about 221% and 291%, respectively, and the Kaplan and Krumhardt 2010 (KK10) dataset underestimated pasture area for the TP dramatically, with a minimum area ratio of only 9%. As for the spatial pattern, all these global datasets overestimated the spatial scope of grazing activities obviously. The KK10 dataset unreasonably allocated pasture to forest areas in southeastern Tibet because only climate and soil factors were considered in assessing land suitability for grazing. Using population to estimate pasture area and only using natural factors to allocate pasture area into grids is unsuitable for the TP historical pasture reconstruction. In the future, more information directly related to grazing activities, e.g., the number of livestock and its spatial distribution, and social-cultural factors, including technology and diet, should be used for area estimation and spatial pattern mapping to improve the accuracy of pasture data in these global datasets. Full article
(This article belongs to the Special Issue Advances in Remote Sensing for Exploring Ancient History)
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16 pages, 5873 KiB  
Article
Application of Convolutional Neural Networks on Digital Terrain Models for Analyzing Spatial Relations in Archaeology
by M. Fabian Meyer-Heß, Ingo Pfeffer and Carsten Juergens
Remote Sens. 2022, 14(11), 2535; https://doi.org/10.3390/rs14112535 - 25 May 2022
Cited by 1 | Viewed by 1673
Abstract
Archaeological research is increasingly embedding individual sites in archaeological contexts and aims at reconstructing entire historical landscapes. In doing so, it benefits from technological developments in the field of archaeological prospection over the last 20 years, including LiDAR-based Digital Terrain Models, special visualizations, [...] Read more.
Archaeological research is increasingly embedding individual sites in archaeological contexts and aims at reconstructing entire historical landscapes. In doing so, it benefits from technological developments in the field of archaeological prospection over the last 20 years, including LiDAR-based Digital Terrain Models, special visualizations, and automated site detection. The latter can generate comprehensive datasets with manageable effort that are useful for answering large-scale archaeological research questions. This article presents a highly automated workflow, in which a Convolutional Neural Network is used to detect burial mounds in the proximity of remotely located hollow ways. Detected mounds are then analyzed with respect to their distribution and a possible spatial relation to hollow ways. The detection works well, produces a reasonable number of results, and achieved a precision of at least 77%. The distribution of mounds shows a clear maximum in the radius of 2000–2500 m. This supports future research such as visibility or cost path analysis. Full article
(This article belongs to the Special Issue Advances in Remote Sensing for Exploring Ancient History)
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18 pages, 13207 KiB  
Article
On the Discovery of a Roman Fortified Site in Gafsa, Southern Tunisia, Based on High-Resolution X-Band Satellite Radar Data
by Nabil Bachagha, Wenbin Xu, Xingjun Luo, Nicola Masini, Mondher Brahmi, Xinyuan Wang, Fatma Souei and Rosa Lasaponora
Remote Sens. 2022, 14(9), 2128; https://doi.org/10.3390/rs14092128 - 28 Apr 2022
Cited by 1 | Viewed by 2051
Abstract
The increasing availability of multiplatform, multiband, very-high-resolution (VHR) satellite synthetic aperture radar (SAR) data has attracted the attention of a growing number of scientists and archeologists. In particular, over the last two decades, archeological research has benefited from SAR development mainly due to [...] Read more.
The increasing availability of multiplatform, multiband, very-high-resolution (VHR) satellite synthetic aperture radar (SAR) data has attracted the attention of a growing number of scientists and archeologists. In particular, over the last two decades, archeological research has benefited from SAR development mainly due to its unique ability to acquire scenes both at night and during the day under all weather conditions, its penetration capability, and the provided polarimetric and interferometric information. This paper explored the potential of a novel method (nonlocal (NL)-SAR) using TerraSAR-X (TSX) and Constellation of Small Satellites for Mediterranean Basin Observation (COSMO)-SkyMed (CSK) data to detect buried archeological remains in steep, rugged terrain. In this investigation, two test sites were selected in southern Tunisia, home to some of the most valuable and well-preserved limes from the Roman Empire. To enhance the subtle signals linked to archeological features, the speckle noise introduced into SAR data by the environment and SAR system must be mitigated. Accordingly, the NL-SAR method was applied to SAR data pertaining to these two significant test sites. Overall, the investigation (i) revealed a fortified settlement from the Roman Empire and (ii) identified an unknown urban area abandoned during this period via a field survey, thus successfully confirming the capability of SAR data to reveal unknown, concealed archeological sites, even in areas with a complex topography. Full article
(This article belongs to the Special Issue Advances in Remote Sensing for Exploring Ancient History)
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18 pages, 4894 KiB  
Article
Pattern Recognition Approach and LiDAR for the Analysis and Mapping of Archaeological Looting: Application to an Etruscan Site
by Maria Danese, Dario Gioia, Valentino Vitale, Nicodemo Abate, Antonio Minervino Amodio, Rosa Lasaponara and Nicola Masini
Remote Sens. 2022, 14(7), 1587; https://doi.org/10.3390/rs14071587 - 25 Mar 2022
Cited by 11 | Viewed by 3073
Abstract
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 [...] Read more.
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%. Full article
(This article belongs to the Special Issue Advances in Remote Sensing for Exploring Ancient History)
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