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A Google Earth Engine-enabled Python approach to improve identification of anthropogenic palaeo-landscape features [version 2; peer review: 2 approved, 1 approved with reservations]

Lookup NU author(s): Dr Filippo Brandolini, Professor Sam Turner

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This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).


Abstract

The necessity of sustainable development for landscapes has emerged as an important theme in recent decades. Current approaches 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 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.


Publication metadata

Author(s): Brandolini F, Ribas A, Zerboni A, Turner S

Publication type: Article

Publication status: Published

Journal: Open Research Europe

Year: 2021

Volume: 1

Online publication date: 03/09/2021

Acceptance date: 18/01/2021

Date deposited: 05/01/2021

ISSN (electronic): 2732-5121

Publisher: European Commission

URL: https://doi.org/10.12688/openreseurope.13135.2

DOI: 10.12688/openreseurope.13135.2


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