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 utili...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.展开更多
The emerging field of Discrete Global Grid Systems(DGGS)provides a way to organise,store and analyse spatio-temporal data at multiple resolutions and scales(from near global scales down to microns).DGGS partition the ...The emerging field of Discrete Global Grid Systems(DGGS)provides a way to organise,store and analyse spatio-temporal data at multiple resolutions and scales(from near global scales down to microns).DGGS partition the entire planet into a discrete hierarchy of global tessellations of progressively finer resolution zones(or cells).Data integration,decomposition and aggregation are optimised by assigning a unique spatio-temporal identifier to each zone.These identifiers are encodings of both the zone’s location and its resolution.As a result,complex multi-dimensional,multi-resolution spatio-temporal operations are simplified into sets of 1D array and filter operations.展开更多
文摘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.
文摘The emerging field of Discrete Global Grid Systems(DGGS)provides a way to organise,store and analyse spatio-temporal data at multiple resolutions and scales(from near global scales down to microns).DGGS partition the entire planet into a discrete hierarchy of global tessellations of progressively finer resolution zones(or cells).Data integration,decomposition and aggregation are optimised by assigning a unique spatio-temporal identifier to each zone.These identifiers are encodings of both the zone’s location and its resolution.As a result,complex multi-dimensional,multi-resolution spatio-temporal operations are simplified into sets of 1D array and filter operations.
