A Google Earth Engine-enabled Python approach to improve identification of anthropogenic palaeo-landscape features
| Autor: | Sam Turner, Andrea Zerboni, Filippo Brandolini, Guillem Domingo-Ribas |
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| Rok vydání: | 2021 |
| Předmět: |
FOS: Computer and information sciences
010504 meteorology & atmospheric sciences Computer science Computer Vision and Pattern Recognition (cs.CV) 0211 other engineering and technologies Computer Science - Computer Vision and Pattern Recognition Cloud computing 02 engineering and technology 01 natural sciences World Wide Web Computer Science - Computers and Society Computers and Society (cs.CY) Satellite imagery Spectral decomposition Landscape archaeology Protocol (object-oriented programming) Riverscape 021101 geological & geomatics engineering 0105 earth and related environmental sciences computer.programming_language Application programming interface business.industry Landscape Archaeology Articles 15. Life on land Python (programming language) Data science Fluvial and Alluvial Archaeology Identification (information) Multispectral analysis Geography Buried features Remote sensing (archaeology) 13. Climate action Sentinel-2 business computer Research Article Python |
| Zdroj: | Open Research Europe |
| Popis: | The necessity of sustainable development for landscapes has emerged as an important theme in recent decades. Current methods take a holistic approach to landscape heritage and promote an interdisciplinary dialogue to facilitate complementary landscape management strategies. With the socio-economic values of the natural and cultural landscape heritage increasingly recognised worldwide, remote sensing tools are being used more and more to facilitate the recording and management of landscape heritage. Satellite remote sensing technologies have enabled significant improvements in landscape research. The advent of the cloud-based platform of Google Earth Engine has allowed the rapid exploration and processing of satellite imagery such as the Landsat and Copernicus Sentinel datasets. In this paper, the use of Sentinel-2 satellite data in the identification of palaeo-riverscape features has been assessed in the Po Plain, selected because it is characterized by human exploitation since the Mid-Holocene. A multi-temporal approach has been adopted to investigate the potential of satellite imagery to detect buried hydrological and anthropogenic features along with Spectral Index and Spectral Decomposition analysis. This research represents one of the first applications of the GEE Python API in landscape studies. The complete FOSS-cloud protocol proposed here consists of a Python code script developed in Google Colab which could be simply adapted and replicated in different areas of the world 33 pages, 10 figures, 2 tables |
| Databáze: | OpenAIRE |
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