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A Google Earth Engine-enabled Python approach for the identification of anthropogenic palaeo-landscape features.
Filippo Brandolini1, Guillem Domingo-Ribas1, Andrea Zerboni2
1McCord Centre for Landscape - School of History, Classics and Archaeology, Newcastle University, UK, Newcastle upon Tyne, NE4 5HP, UK.
Open Research Europe
|August 30, 2023
Summary
This study introduces a new, free, and open-source cloud protocol using Google Earth Engine and Sentinel-2 satellite data to map historic landscape features. The method effectively identifies buried hydrological and anthropogenic elements in the Po Plain.
Area of Science:
- Environmental Science
- Archaeology
- Geospatial Analysis
Background:
- Sustainable landscape development is crucial, requiring holistic heritage management and interdisciplinary dialogue.
- Remote sensing, particularly satellite imagery, is increasingly vital for recording and managing natural and cultural landscape heritage.
- Freeware cloud computing platforms like Google Earth Engine (GEE) enhance landscape research capabilities.
Purpose of the Study:
- To present a novel, free, and open-source software (FOSS) cloud protocol for historic landscape analysis.
- To demonstrate the application of the Google Earth Engine (GEE) Python API in historic landscape studies.
- To investigate the potential of Sentinel-2 satellite imagery for detecting buried landscape features.
Main Methods:
- Development of a Python script in Google Colab, utilizing a FOSS cloud protocol.
- Application of a multi-temporal approach with Sentinel-2 satellite imagery.
- Employing spectral index and spectral decomposition analysis to identify landscape features.
Main Results:
- The protocol successfully identified palaeo-riverscape features in the Po Plain, Northern Italy.
- Demonstrated the effectiveness of Sentinel-2 data and GEE for detecting buried hydrological and anthropogenic features.
- Validated the proposed FOSS cloud protocol for historic landscape research.
Conclusions:
- The developed FOSS cloud protocol offers a replicable and adaptable method for historic landscape research globally.
- Google Earth Engine and Sentinel-2 imagery provide powerful tools for uncovering hidden elements of landscape heritage.
- This approach supports sustainable development by enhancing the understanding and management of landscape heritage.
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