A Google Earth Engine-enabled Python approach to improve identification of anthropogenic palaeo-landscape features

TL;DR

This study demonstrates how a Python-based approach using Google Earth Engine and Sentinel-2 data can effectively identify ancient landscape features, aiding sustainable landscape management and heritage preservation.

Contribution

It introduces a novel GEE Python API workflow for detecting buried palaeo-landscape features using multi-temporal satellite imagery in landscape research.

Findings

Successful detection of palaeo-riverscape features in the Po Plain

Effective use of Spectral Index and Decomposition analysis

First application of GEE Python API in landscape heritage studies

Abstract

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