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

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.ARTICLE HISTORY

Building an Earth Observations Data Cube: lessons learned from the Swiss Data Cube (SDC) on generating Analysis Ready Data (ARD)

“…space, time, data type) stack of spatially aligned pixels ready for analysis. The concept has been proven and validated by Geoscience Australia together with CSIRO and the National Computing Infrastructure of Australia (NCI) who implemented the Australian Geoscience Data Cube, a national/continental scale DC of thousands of terabytes of EO data (Landsat, MODIS, Sentinel-2) making it quicker and easier to provide information on environmental issues that can affect all Australians Lewis et al, 2016;Purss et al, 2015). It has allowed mapping the extent of surface water across the entire Australian continent using 27 years of Landsat imagery (Mueller et al, 2016), gaining knowledge on flood dynamics over Australia (Tulbure, Broich, Stehman, & Kommareddy, 2016), or extracting the intertidal extent and topography of the Australian coastline (Sagar, Roberts, Bala, & Lymburner, 2017).…”

“…They remain still underutilized and stored in electronic silos of data (Gore, 1998;Lewis et al, 2016). This is due to several reasons: (1) increasing volumes of data generated by EO satellites; (2) lack of expertise, infrastructure, or internet bandwidth to efficiently and effectively access, process, and utilize EO data; (3) the particular type of highly structured data that EO data represent introducing challenges when trying to integrate or analyze them; (4) and the substantial effort and cost required to store and process data limits the efficient use of these data (CEOS, 2017;Lewis et al, 2016;Purss et al, 2015). Therefore, EO data can be considered as Big Data, data that are too large, fast-lived, heterogeneous, or complex to get understood and exploited (Baumann, Rossi, et al, 2016).…”

The Australian approach to geospatial capabilities; positioning, earth observation, infrastructure and analytics: issues, trends and perspectives

“…• Continental scale long-term data bases of essential environmental variables, such as surface water cover, vegetation dynamics, and inter-tidal areas (Lewis et al 2016); ) through the Australian geoscience data cube (AGDC); (Dhanjal et al 2016). …”

A New Fully Gap-Free Time Series of Land Surface Temperature from MODIS LST Data

“…Remote sensing based time series are becoming increasingly available, and this tendency will continue to grow not only because of new Earth observation satellites being launched, but because of the availability of new methods to harmonize their data [1,2] and reconstruct incomplete records [3][4][5][6][7] along with the growing demand of different sectors for the monitoring of environment, analysis of trends and patterns, and forecasting. In this scenario, air temperature is as an essential climatic and ecological driver, one of the most important variables in climate research and global change.…”