A Multisensor Remote Sensing Approach to Archaeological Prospection

Integrating UAV and Google Earth Data in the Bayan Gol Valley, Mongolia

Bibliographic Data

ID12429641
AuthorsPeter Heimermann (0009-0007-5717-0173, Bonn Center for Digital Humanities (BCDH) University of Bonn Bonn Germany, corresponding author), Matthias Lang (0000-0002-0322-5876, Bonn Center for Digital Humanities (BCDH) University of Bonn Bonn Germany), Susanne Reichert (0000-0002-1694-0481, Institute for Archaeology and Cultural Anthropology University of Bonn Bonn Germany), Munkhbayar Lkhagvadorj (0000-0002-4464-3310, Institute of Archaeology Mongolian Academy of Sciences Ulaanbaatar Mongolia), Jan Bemmann (0000-0002-1813-2680, Institute for Archaeology and Cultural Anthropology University of Bonn Bonn Germany)
Year2025
Volume33
Issue1
Pages109-130
Publication date2025-10-06
Open AccessYes
TypeARTICLE
VenueArchaeological Prospection (JOURNAL)
Journal identifiersISSN: 1075-2196 • E-ISSN: 1099-0763
PublisherWiley (PUBLISHER • GB)
DOI10.1002/arp.70011
OpenAlexW4414866900
LanguageEN
Citations received2
References cited55

This study investigates the effectiveness of drone‐based remote sensing and Google Earth satellite imagery for archaeological prospection in the Bayan Gol Valley, Central Mongolia. Utilizing a fixed‐wing unmanned aerial vehicle (UAV) equipped with RGB and multispectral sensors, we surveyed 655 ha to document Mongol‐period settlement structures dating to the 13th and 14th centuries ce . The resulting high‐resolution datasets, including digital surface models, RGB orthomosaics and vegetation index rasters, were compared with five image sets from 2007 to 2021 provided by Google Earth to assess their respective capabilities. The UAV‐derived data proved significantly more effective, identifying 16% more archaeological features than the satellite imagery. However, Google Earth imagery provided valuable cost‐efficient and multitemporal contextual information. The analysis also considers the impact of modern disturbances, such as agricultural activity and road construction, on feature visibility. These findings emphasize the complementary strengths of elevation and vegetation‐based anomaly detection and highlight the value of integrated remote sensing approaches for archaeological mapping in complex and dynamic landscapes. The study contributes to the refinement of UAV methodologies and the broader application of remote sensing in archaeological research

Digital elevation model · Earth observation · Feature (linguistics · Multispectral image · RGB color model · Satellite · Satellite imagery · Thematic map · Vegetation (pathology · Archaeological Research and Protection · Remote Sensing in Agriculture · Remote-Sensing Image Classification

Tools

Open DOIOpen Access