The Potential of Drones and Sensors to Enhance Detection of Archaeological Cropmarks: A Comparative Study Between Multi-Spectral and Thermal Imagery

Paula Uribe-Agudo, Jorge Angás-Pajas, Fernando Pérez-Cabello, Jaime Vicente-Redón, and Beatriz Ezquerra-Lebrón. 2018. "The Potential of Drones and Sensors to Enhance Detection of Archaeological Cropmarks: A Comparative Study Between Multi-Spectral and Thermal Imagery" Drones 2, no. 3: 29. https://doi.org/10.3390/drones2030029

This paper presents experimentation carried out at the Roman Republican city of La Caridad (Teruel, Spain), where different tools have been applied to obtain multispectral and thermal aerial images to enhance detection of archaeological cropmarks. Two different drone systems were used: a Mikrokopter designed by Tecnitop SA (Zaragoza, Spain) and an eBee produced by SenseFly Company (Cheseaux-sur-Lausanne, Switzerland). Thus, in this study, we have combined in-house manufacturing with commercial products. Six drone sensors were tested and compared in terms of their ability to identify buried remains in archaeological settlements by means of visual recognition. The sensors have different spectral ranges and spatial resolutions. This paper compares the images captured with different spectral range sensors to test the potential of this technology for archaeological benefits. The method used for the comparison of the tools has been based on direct visual inspection, as in traditional aerial archaeology. Through interpretation of the resulting data, our aim has been to determine which drones and sensors obtained the best results in the visualization of archaeological cropmarks. The experiment in La Caridad therefore demonstrates the benefit of using drones with different sensors to monitor archaeological cropmarks for a more cost-effective assessment, best spatial resolution and digital recording of buried archaeological remains.
Keywords: aerial photography; archaeological surveying; buried archaeological remains; drones; multispectral sensors; thermal sensors; index vegetation; PCs