Journal
REMOTE SENSING
Volume 9, Issue 7, Pages -Publisher
MDPI AG
DOI: 10.3390/rs9070735
Keywords
multi-temporal; seasonal; vegetation; palaeo-river; Indus Civilisation; archaeology
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Funding
- Horizon European Research Council (ERC) Consolidator Grant [H2020, 648609]
- McDonald Institute for Archaeological Research at the University of Cambridge
- European Research Council (ERC) [648609] Funding Source: European Research Council (ERC)
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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((C)) 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.
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