Large-scale, multi-temporal remote sensing of palaeo-river networks

DOI

JavaScript code to be implemented in Google Earth Engine(c) for large-scale, multi-temporal remote sensing of palaeo-river networks.

This research presents a seasonal multi-temporal approach to the detection of palaeo-rivers over large areas based on long-term vegetation dynamics and spectral decomposition techniques. Twenty-eight years of Landsat 5 data, a total of 1711 multi-spectral images, have been bulk processed using Google Earth Engine© Code Editor and cloud computing infrastructure.

The research presented has been carried out as part of the TwoRains projectt, which is a multi-disciplinary investigation of climate change and the Indus civilization in northwest India.

Identifier
DOI https://doi.org/10.34810/data240
Related Identifier IsCitedBy https://doi.org/10.3390/rs9070735
Metadata Access https://dataverse.csuc.cat/oai?verb=GetRecord&metadataPrefix=oai_datacite&identifier=doi:10.34810/data240
Provenance
Creator Orengo Romeu, Hèctor A. ORCID logo
Publisher CORA.Repositori de Dades de Recerca
Contributor Orengo Romeu, Hèctor A.
Publication Year 2022
Funding Reference Horizon 2020 European Research Council (ERC) H2020, 648609
Rights Custom Dataset Terms; info:eu-repo/semantics/openAccess; https://dataverse.csuc.cat/api/datasets/:persistentId/versions/1.0/customlicense?persistentId=doi:10.34810/data240
OpenAccess true
Contact Orengo Romeu, Hèctor A. (Institut Català d’Arqueologia Clàssica (ICAC))
Representation
Resource Type Program source code; Dataset
Format text/plain; charset=US-ASCII
Size 19027; 19659
Version 1.0
Discipline Ancient Cultures; Archaeology; Humanities