Rapid, high-resolution detection of environmental change over continental scales from satellite data – the Earth Observation Data Cube. Issue 1 (2nd January 2016)

• Record Type: Journal Article
• Authors: Lewis, Adam; Lymburner, Leo; Purss, Matthew B. J.; Brooke, Brendan; Evans, Ben; Ip, Alex; Dekker, Arnold G.; Irons, James R.; Minchin, Stuart; Mueller, Norman; Oliver, Simon; Roberts, Dale; Ryan, Barbara; Thankappan, Medhavy; Woodcock, Rob; Wyborn, Lesley
• Is Part Of: International journal of digital earth. Volume 9:Issue 1(2016)
• Page Start: 106
• Page End: 111
• Publication Date: 2016-01-02
• Subjects: Earth Observation Data Cube -- HPD -- HPC -- surface water -- Landsat -- Australia; Geographic information systems -- Periodicals; Sustainable development -- Information technology -- Periodicals; Social planning -- Information technology -- Periodicals; 910.285
• DOI: 10.1080/17538947.2015.1111952
• Languages: English
• ISSNs: 1753-8947

Abstract

ABSTRACT: 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.