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Rapid, high-resolution detection of environmental change over continental scales from satellite data – the Earth Observation Data Cube

Rapid, high-resolution detection of environmental change

Adam Lewis, Leo Lymburner, Matthew B. J. Purss, Brendan Brookea, Ben Evans, Alex Ip, Arnold G. Dekker, James R. Irons, Stuart Minchin, Norman Mueller, Simon Oliver, Dale Roberts, Barbara Ryan, Medhavy Thankappan, Rob Woodcock & Lesley Wyborn

The effort and cost required to convert satellite Earth Observation (EO) data into meaningful geophysical variables has prevented the systematic analysis of all available observations. To overcome these problems, we utilise an integrated High Performance Computing and Data environment to rapidly process, restructure and analyse the Australian Landsat data archive.

In this approach, the EO data are assigned to a common grid framework that spans the full geospatial and temporal extent of the observations – the EO Data Cube. This approach is pixel-based and incorporates geometric and spectral calibration and quality assurance of each Earth surface reflectance measurement.

We demonstrate the utility of the approach with rapid time-series mapping of surface water across the entire Australian continent using 27 years of continuous, 25 m resolution observations.

Our preliminary analysis of the Landsat archive shows how the EO Data Cube can effectively liberate high-resolution EO data from their complex sensor-specific data structures and revolutionise our ability to measure environmental change.

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Updated:   18 February 2017 / Responsible Officer:  Dean, Business & Economics / Page Contact:  College Web Team