Нижневолжский археологический вестник (Jun 2024)

Geoinformation Modeling of Terrain to Identify Promising Areas for Archaeological Research Using the Example of Monuments in Saratov Region

  • Vladimir A. Danilov,
  • Vladimir A. Lopatin,
  • Valeriya A. Morozova,
  • Alexey V. Fedorov

DOI
https://doi.org/10.15688/nav.jvolsu.2024.2.1
Journal volume & issue
Vol. 23, no. 2
pp. 5 – 24

Abstract

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Abstract. This article provides an overview of modern remote sensing techniques in archaeology and their practical applications. The widespread use of GIS technologies and remote sensing methods such as photogrammetry and laser scanning is a distinguishing characteristic of contemporary archaeology. Remote sensing data is employed not only for the analysis of 3D archaeological objects and territories but also in the digital terrain models (DTMs) analysis to search for and identify potential archaeological excavation sites. The introduction of remote sensing methods in archaeology has brought about a change in the approach to conducting archaeological studies. In the field of international research, a distinct stage known as predictive archaeology, which involves preliminary reconnaissance of an area before excavation, has emerged. The study is focused on the archaeological sites of Stantsiya Krasavka and Akhmatskoe Gorodishche, located in the Atkarsky and Krasnoarmeysky municipal districts of the Saratov region. The selected study areas applied the DTM analysis, specifically using the “Hillshade” technique (analytical shading relief), which allows for the detection of previously overlooked terrain features. Based on the results, the potential of this technology for identifying individual archaeological objects using contemporary open DTMs and field geodetic survey data was analyzed. Experimental determination of the optimal DTM resolution for the identification and analysis of objects was conducted in areas previously subject to archaeological research. The experiments and comparative analysis of various laser scanning technologies led to the identification of optimal methods and filtering parameters to “exclude” vegetation and generate DTMs.

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