Unlocking the Australian Landsat Archive – From dark data to High Performance Data infrastructures

Abstract Earth Observation data acquired by the Landsat missions are of immense value to the global community and constitute the world’s longest continuous civilian Earth Observation program. However, because of the costs of data storage infrastructure these data have traditionally been stored in raw form on tape storage infrastructures which introduces a data retrieval and processing overhead that limits the efficiency of use of this data. As a consequence these data have become ‘dark data’ with only limited use in a piece-meal and labor intensive manner. The Unlocking the Landsat Archive project was set up in 2011 to address this issue and to help realize the true value and potential of these data. The key outcome of the project was the migration of the raw Landsat data that was housed in tape archives at Geoscience Australia to High Performance Data facilities hosted by the National Computational Infrastructure (a super computer facility located at the Australian National University). Once this migration was completed the data were calibrated to produce a living and accessible archive of sensor and scene independent data products derived from Landsat-5 and Landsat-7 data for the period 1998–2012. The calibrated data were organized into High Performance Data structures, underpinned by ISO/OGC standards and web services, which have opened up a vast range of opportunities to efficiently apply these data to applications across multiple scientific domains.

[1]  W. Cohen,et al.  Landsat's Role in Ecological Applications of Remote Sensing , 2004 .

[2]  Zhe Zhu,et al.  Object-based cloud and cloud shadow detection in Landsat imagery , 2012 .

[3]  David P. Roy,et al.  Accessing free Landsat data via the Internet: Africa's challenge , 2010 .

[4]  Ben Somers,et al.  Spectral mixture analysis to assess post-fire vegetation regeneration using Landsat Thematic Mapper imagery: Accounting for soil brightness variation , 2012, Int. J. Appl. Earth Obs. Geoinformation.

[5]  Michael A. Wulder,et al.  Opening the archive: How free data has enabled the science and monitoring promise of Landsat , 2012 .

[6]  Adam Lewis,et al.  The variability of satellite derived surface BRDF shape over Australia from 2001 to 2011 , 2013, 2013 IEEE International Geoscience and Remote Sensing Symposium - IGARSS.

[7]  S. Goward,et al.  Characterization of the Landsat-7 ETM Automated Cloud-Cover Assessment (ACCA) Algorithm , 2006 .

[8]  B. Markham,et al.  Summary of Current Radiometric Calibration Coefficients for Landsat MSS, TM, ETM+, and EO-1 ALI Sensors , 2009 .

[9]  D. Jupp,et al.  Characteristics of MODIS BRDF shape and its relationship with land cover classes in Australia , 2013 .

[10]  Shanti Reddy,et al.  An Evaluation of the Use of Atmospheric and BRDF Correction to Standardize Landsat Data , 2010, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[11]  D. Barrett,et al.  Estimating fractional cover of photosynthetic vegetation, non-photosynthetic vegetation and bare soil in the Australian tropical savanna region upscaling the EO-1 Hyperion and MODIS sensors. , 2009 .

[12]  Leo Lymburner,et al.  A hybrid approach to automated landsat pixel quality , 2013, 2013 IEEE International Geoscience and Remote Sensing Symposium - IGARSS.

[13]  Scarth Peter Tracking Grazing Pressure and Climate Interaction - The Role of Landsat Fractional Cover in Time Series Analysis , 2012 .

[14]  Martha C. Anderson,et al.  Free Access to Landsat Imagery , 2008, Science.

[15]  Randy Showstack Award Program Recognizes Efforts to Protect Geoscience Data , 2014 .

[16]  Michael A. Wulder,et al.  Landsat continuity: Issues and opportunities for land cover monitoring , 2008 .

[17]  Medhavy Thankappan,et al.  Creating multi-sensor time series using data from Landsat-5 TM and Landsat-7 ETM+ to characterise vegetation dynamics , 2013, 2013 IEEE International Geoscience and Remote Sensing Symposium - IGARSS.

[18]  D. Jupp,et al.  A physics-based atmospheric and BRDF correction for Landsat data over mountainous terrain , 2012 .