Multi-Resolution and Multi-Temporal Satellite Remote Sensing Analysis to Understand Human-Induced Changes in the Landscape for the Protection of Cultural Heritage: The Case Study of the MapDam Project, Syria

Nicodemo Abate, Diego Ronchi (), Sara Elettra Zaia, Gabriele Ciccone, Alessia Frisetti, Maria Sileo, Nicola Masini, Rosa Lasaponara, Tatiana Pedrazzi and Marina Pucci
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Nicodemo Abate: Institute of Heritage Science, National Research Council, Via Cardinale Guglielmo Sanfelice 8, 80134 Napoli, Italy
Diego Ronchi: Institute of Heritage Science, National Research Council, Via Cardinale Guglielmo Sanfelice 8, 80134 Napoli, Italy
Sara Elettra Zaia: Institute of Heritage Science, National Research Council, Via Cardinale Guglielmo Sanfelice 8, 80134 Napoli, Italy
Gabriele Ciccone: Institute of Heritage Science, National Research Council, Via Cardinale Guglielmo Sanfelice 8, 80134 Napoli, Italy
Alessia Frisetti: Institute of Heritage Science, National Research Council, Via Cardinale Guglielmo Sanfelice 8, 80134 Napoli, Italy
Maria Sileo: Institute of Heritage Science, National Research Council, Via Cardinale Guglielmo Sanfelice 8, 80134 Napoli, Italy
Nicola Masini: Institute of Heritage Science, National Research Council, Via Cardinale Guglielmo Sanfelice 8, 80134 Napoli, Italy
Rosa Lasaponara: Institute of Methodology of Environmental Analysis, National Research Council, C.da S. Loja sn, 85050 Tito Scalo, Italy
Tatiana Pedrazzi: Institute of Heritage Science, National Research Council, Via Cardinale Guglielmo Sanfelice 8, 80134 Napoli, Italy
Marina Pucci: Department of History, Archaeology, Geography, Art and Entertainment, University of Florence, Via S. Gallo, 10, 50129 Florence, Italy

Land, 2025, vol. 14, issue 11, 1-25

Abstract: This study presents a multi-resolution and multi-temporal remote sensing approach to assess human-induced changes in cultural landscapes, with a focus on the archaeological site of Amrit (Syria) within the MapDam project. By integrating satellite archives (KH, Landsat series, NASADEM) with ancillary geospatial data (OpenStreetMap) and advanced analytical methods, four decades (1984–2024) of land-use/land-cover (LULC) change and shoreline dynamics were reconstructed. Machine learning classification (Random Forest) achieved high accuracy (Test Accuracy = 0.94; Kappa = 0.89), enabling robust LULC mapping, while predictive modelling of urban expansion, calibrated through a Gradient Boosting Machine, attained a Figure of Merit of 0.157, confirming strong predictive reliability. The results reveal path-dependent urban growth concentrated on low-slope terrains (≤5°) and consistent with proximity to infrastructure, alongside significant shoreline regression after 1974. A Business-as-Usual projection for 2024–2034 estimates 8.676 ha of new anthropisation, predominantly along accessible plains and peri-urban fringes. Beyond quantitative outcomes, this study demonstrates the replicability and scalability of open-source, data-driven workflows using Google Earth Engine and Python 3.14, making them applicable to other high-risk heritage contexts. This transparent methodology is particularly critical in conflict zones or in regions where cultural assets are neglected due to economic constraints, political agendas, or governance limitations, offering a powerful tool to document and safeguard endangered archaeological landscapes.

Keywords: remote sensing; SAR; optical; shoreline changes; land use land cover changes; archaeology; machine learning (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2025
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