Large-Scale, Multi-Temporal Remote Sensing of Palaeo-River Networks: A Case Study from Northwest India and its Implications for the Indus Civilisation

Abstract

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.

Mapping Archaeology While Mapping an Empire: Using Historical Maps to Reconstruct Ancient Settlement Landscapes in Modern India and Pakistan

“…It is important to acknowledge that the methods outlined here for making use of these Survey of India 1" to 1-mile maps are only likely to be useful for identifying mounded sites, and will not be suitable to aid detection of a wide range of other features of archaeological significance. Therefore, it is essential to integrate the use of these historical maps into comprehensive approaches that make use of the full suite of earth observation and remote sensing techniques, potentially integrating open-source multi-spectral data and the computational power of platforms like Google Earth Engine to identify hydrological and topographic features not easily visible on the surface [35,90,91]. It is also imperative that these remote prospection approaches are co-ordinated with large-scale ground-truthing surveys, that will verify which of the mound features are archaeological sites, and establish a reliable chronology for those sites and the associated landscapes.…”

Neoglacial climate anomalies and the Harappan metamorphosis

“…Whether fast or over generations, the bulk of Harappan settlements relocated toward the Himalayan foothills on the plains of the upper G-H interfluve (see Supplement; Possehl, 2002;Kenoyer, 1998;Wright, 2010;Madella and Fuller, 2006;. Abandoned by Himalayan rivers since the early Holocene Clift et al, 2012;Singh et al, 2017;Dave et al, 2018), this region between the Sutlej and Yamuna was watered by orographically enhanced rain feeding an intricate small river network (e.g., Yashpal et al, 1980;van Dijk et al, 2016;Orengo and Petrie, 2017).…”

Using Google Earth Engine to Map Complex Shade-Grown Coffee Landscapes in Northern Nicaragua

“…Currently, KT coefficients for Landsat 8 data are best established for TOA data [52][53][54]. TOA data have also consistently been used to produce multi-temporal image mosaics that resulted in high-accuracy land-cover classifications, particularly when spectral indices or transformations have been used to enhance spectral signals [23,25,31,55,56]. TOA reflectance values for the Tier 1 collection were computed using well-established calibration coefficients from Reference [49].…”