dc.contributor.author |
Orengo Romeu, Hector A. |
dc.contributor.author |
Petrie, Cameron A. |
dc.date.accessioned |
2018-09-05T11:24:47Z |
dc.date.available |
2018-09-05T11:24:47Z |
dc.date.created |
2017-06-06 |
dc.date.issued |
2017-07-16 |
dc.identifier.citation |
Orengo, H., & Petrie, C. (2017). Large-Scale, Multi-Temporal Remote Sensing of Palaeo-River Networks: A Case Study from Northwest India and its Implications for the Indus Civilisation. Remote Sensing, 9(7), 735. MDPI AG. Retrieved from http://dx.doi.org/10.3390/rs9070735 |
dc.identifier.uri |
http://hdl.handle.net/2072/332335 |
dc.format.extent |
20 p. |
dc.language.iso |
eng |
dc.publisher |
MDPI AG |
dc.relation.ispartof |
Remote Sens. 2017, 9. |
dc.rights |
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
dc.source |
RECERCAT (Dipòsit de la Recerca de Catalunya) |
dc.subject.other |
Teledetecció -- Índia |
dc.subject.other |
Índia -- Arqueologia |
dc.title |
Large-Scale, Multi-Temporal Remote Sensing of Palaeo-River Networks: A Case Study from Northwest India and its Implications for the Indus Civilisation |
dc.type |
info:eu-repo/semantics/article |
dc.type |
info:eu-repo/semantics/publishedVersion |
dc.subject.udc |
90 - Arqueologia. Prehistòria |
dc.embargo.terms |
cap |
dc.relation.projectID |
info:eu-repo/grantAgreement/EC/FP7/European Commission (648609) |
dc.identifier.doi |
10.3390/rs9070735 |
dc.rights.accessLevel |
info:eu-repo/semantics/openAccess |
dc.description.abstract |
Remote sensing has considerable potential to contribute to the identification and reconstruction of lost hydrological systems and networks. Remote sensing-based reconstructions of palaeo-river networks have commonly employed single or limited time-span imagery, which limits their capacity to identify features in complex and varied landscape contexts. This paper 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 use of multi-temporal data has allowed us to overcome seasonal cultivation patterns and long-term visibility issues related to recent crop selection, extensive irrigation and land-use patterns. The application of this approach on the Sutlej-Yamuna interfluve (northwest India), a core area for the Bronze Age Indus Civilisation, has enabled the reconstruction of an unsuspectedly complex palaeo-river network comprising more than 8000 km of palaeo-channels. It has also enabled the definition of the morphology of these relict courses, which provides insights into the environmental conditions in which they operated. These new data will contribute to a better understanding of the settlement distribution and environmental settings in which this, often considered riverine, civilisation operated. |