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Our programme image

Our programme image this year comes from research conducted by the McDonald Institute for Archaeological Research, University of Cambridge.

The image, a RGB composite of seasonal multi-temporal Landsat 5 enhanced vegetation indices, by Dr Hector Orengo reflects a series of relict rivers in north-west India, of which no trace can be seen today on the surface or in satellite imagery. The image forms part of an open access research article, published in Remote Sensing, one of the top journals in the field. The paper was selected from a pool of 125 articles as the July 2017 issue cover story.

The ERC-funded project ‘Winter Rain, Summer Rain: Adaptation, Climate Change, Resilience and the Indus Civilisation (TwoRains)’, directed by Cameron A. Petrie (Dept. of Archaeology), is exploring the mechanisms involved in the human adaptation to, and management of, variable and changing water availability. In this regard, the reconstruction of the palaeo-hydrological network of northwest India, which is no longer visible, is instrumental for modelling and testing hypotheses on water movement, accumulation, seasonality, and availability in relation to changing climate and rainfall patterns.

Hector A. Orengo, TwoRains Postdoctoral Research Associate (McDonald Institute for Archaeological Research) is in charge of the remote sensing for the project, and has employed 28 years of Landsat 5 multispectral imagery to create this image. An algorithm was written in JavaScript to group 1254 available 8-day EVI (a multispectral band ratio designed to enhance the vegetation signal) composites into single mean two-month images. Each of these was the average of all 8-day EVI composites for the same two months, from 1984 to 2012, for the study area. Every multiyear bimensal image was the result of averaging around 200 8-day composite images. The RGB composite includes the images corresponding to the Indian Winter rainy season and Summer Monsoon months (January–February, March–April and July–August). The image was calculated using Google Earth Engine, which is a cloud parallel computing platform and programming environment for the analysis of remote sensing data.

Image copyright: Dr Hector Orengo, McDonald Institute for Archaeological Research, University of Cambridge