Digital earth Australia – unlocking new value from earth observation data

Dhu, Trevor; Dunn, Bex; Lewis, Ben; Lymburner, Leo; Mueller, Norman; Telfer, Erin; Lewis, Adam; McIntyre, A.; Minchin, Stuart; Phillips, Claire

doi:10.1080/20964471.2017.1402490

Cited by 76 publications

(54 citation statements)

References 18 publications

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“…To assess subpixel waterline extraction performance across a range of common land-water boundaries, we extracted a set of typical spectra from freely available Landsat 8 OLI imagery available within the 30 year Digital Earth Australia archive produced by Geoscience Australia [3,4]. This data is available as atmospherically and terrain corrected 'analysis-ready' data processed to surface reflectance, allowing reliable spectra to be extracted with no additional processing or calibration required [44].…”

Section: Sample Spectra and Index Calculationmentioning

confidence: 99%

“…Paired samples of land and neighbouring water surface reflectance spectra from five contrasting environments along the Australian coastline (a-e). Spectra were extracted from cloud-free Landsat 8 OLI imagery from the Digital Earth Australia archive [3,4].…”

Section: Sample Spectra and Index Calculationmentioning

confidence: 99%

“…Historically, the use of satellite data for operational environmental monitoring has been limited by data access restrictions, inconsistent data structures and formats, a requirement for time and resource-intensive pre-processing before analysis could be conducted, and rapidly increasing data volumes which made desktop-based analyses of large extents or long time-series impractical [2]. In recent years, the development of high-performance earth observation 'data cubes' such as the Open Data Cube [3,4] and Google Earth Engine [5] have revolutionised the analysis of extremely large and complex remote sensing datasets [2]. By organising freely available medium resolution satellite imagery such as Landsat or Sentinel-2 into spectrally and geometrically calibrated stacks of analysis-ready data, these platforms support the development of new automated and cost effective workflows for consistent, rapid and repeatable operational monitoring of environmental change across time and space [4].…”

Section: Introductionmentioning

confidence: 99%

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Sub-Pixel Waterline Extraction: Characterising Accuracy and Sensitivity to Indices and Spectra

et al. 2019

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Accurately mapping the boundary between land and water (the 'waterline') is critical for tracking change in vulnerable coastal zones, and managing increasingly threatened water resources. Previous studies have largely relied on mapping waterlines at the pixel scale, or employed computationally intensive sub-pixel waterline extraction methods that are impractical to implement at scale. There is a pressing need for operational methods for extracting information from freely available medium resolution satellite imagery at spatial scales relevant to coastal and environmental management. In this study, we present a comprehensive evaluation of a promising method for mapping waterlines at sub-pixel accuracy from satellite remote sensing data. By combining a synthetic landscape approach with high resolution WorldView-2 satellite imagery, it was possible to rapidly assess the performance of the method across multiple coastal environments with contrasting spectral characteristics (sandy beaches, artificial shorelines, rocky shorelines, wetland vegetation and tidal mudflats), and under a range of water indices (Normalised Difference Water Index, Modified Normalised Difference Water Index, and the Automated Water Extraction Index) and thresholding approaches (optimal, zero and automated Otsu's method). The sub-pixel extraction method shows a strong ability to reproduce both absolute waterline positions and relative shape at a resolution that far exceeds that of traditional whole-pixel methods, particularly in environments without extreme contrast between the water and land (e.g., accuracies of up to 1.50-3.28 m at 30 m Landsat resolution using optimal water index thresholds). We discuss key challenges and limitations associated with selecting appropriate water indices and thresholds for sub-pixel waterline extraction, and suggest future directions for improving the accuracy and reliability of extracted waterlines. The sub-pixel waterline extraction method has a low computational overhead and is made available as an open-source tool, making it suitable for operational continental-scale or full time-depth analyses aimed at accurately mapping and monitoring dynamic waterlines through time and space.

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Section: Sample Spectra and Index Calculationmentioning

confidence: 99%

Section: Sample Spectra and Index Calculationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Sub-Pixel Waterline Extraction: Characterising Accuracy and Sensitivity to Indices and Spectra

et al. 2019

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“…The DEA is an instance of the Open Data Cube (https://www.opendatacube.org/) and has been developed from an earlier prototype referred to as the Australian Geoscience Data Cube (AGDC) [5,25].…”

Section: Application Of the Framework-temporal And Tidalmentioning

confidence: 99%

Generating Continental Scale Pixel-Based Surface Reflectance Composites in Coastal Regions with the Use of a Multi-Resolution Tidal Model

et al. 2018

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Generating continental-scale pixel composites in dynamic coastal and estuarine environments presents a unique challenge, as the application of a temporal or seasonal approach to composite generation is confounded by tidal influences. We demonstrate how this can be resolved using an approach to compositing that provides robust composites of multi-type environments. In addition to the visual aesthetics of the images created, we demonstrate the utility of these composites for further interpretation and analysis. This is enabled by the manner in which our approach captures the spatial variation in tidal dynamics through the use of a Voronoi mesh, and preserves the band relationships within the modelled spectra at each pixel. Case studies are presented which include continental-scale mosaics of the Australian coastline at high and low tide, and tailored examples demonstrating the potential of the tidally constrained composites to address a range of coastal change detection and monitoring applications. We conclude with a discussion on the potential applications of the composite products and method in the coastal and marine environment, as well as further development directions for our tidal modelling framework.

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“…While there is still an ongoing discussion about how ARD are defined and specified, it is usually understood as calibrated data, and in the case of CARD4L (Committee on EO Satellites ARD for Land), even contains masks as a target requirement specification, such as for cloud and water [17,18]. The intention is to shift the burden of pre-processing from users to data providers, who are often better equipped to consistently and reliably process large volumes of high-velocity data [6,17,19]. Processing steps with a high potential level of automation can be conducted centrally where they only must be conducted once and are then available to all users.…”

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confidence: 99%

Semantic Earth Observation Data Cubes

Augustin

Sudmanns

Tiede

et al. 2019

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There is an increasing amount of free and open Earth observation (EO) data, yet more information is not necessarily being generated from them at the same rate despite high information potential. The main challenge in the big EO analysis domain is producing information from EO data, because numerical, sensory data have no semantic meaning; they lack semantics. We are introducing the concept of a semantic EO data cube as an advancement of state-of-the-art EO data cubes. We define a semantic EO data cube as a spatio-temporal data cube containing EO data, where for each observation at least one nominal (i.e., categorical) interpretation is available and can be queried in the same instance. Here we clarify and share our definition of semantic EO data cubes, demonstrating how they enable different possibilities for data retrieval, semantic queries based on EO data content and semantically enabled analysis. Semantic EO data cubes are the foundation for EO data expert systems, where new information can be inferred automatically in a machine-based way using semantic queries that humans understand. We argue that semantic EO data cubes are better positioned to handle current and upcoming big EO data challenges than non-semantic EO data cubes, while facilitating an ever-diversifying user-base to produce their own information and harness the immense potential of big EO data.names. The main advantage of ingesting data is that the data can be stored in a query-optimised way, and specific access patterns can be realised more efficiently, such as time series analysis or spatial analysis.Various technical solutions to create these logical views on EO data have rapidly gained traction over the past few years. The first national scale EO data cube was established in Australia [5], whose technology is now the basis of Digital Earth Australia [6] and the Open Data Cube (ODC) [7].The free and open source ODC technology is also behind other operational EO data cubes, such as in Switzerland [8], Colombia [9], Vietnam [10], the Africa Regional Data Cube [11] and at least nine other national or regional initiatives under development [7]. Rasdaman [12], an array database system that has been around since the mid-1990s, is another leading technology behind initiatives such as EarthServer [13] and the Copernicus Data and Exploitation platform for Germany [14]). Other software implementations exist, such as the Earth System Data Cube from the European Space Agency [15] and SciDB [16].State-of-the-art EO data cubes simplify data provision to users by facilitating data uptake and aiming to provide analysis-ready data (ARD) [4]. While there is still an ongoing discussion about how ARD are defined and specified, it is usually understood as calibrated data, and in the case of CARD4L (Committee on EO Satellites ARD for Land), even contains masks as a target requirement specification, such as for cloud and water [17,18]. The intention is to shift the burden of pre-processing from users to data providers, who are often better equipped to consistently...

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Digital earth Australia – unlocking new value from earth observation data

Cited by 76 publications

References 18 publications

Sub-Pixel Waterline Extraction: Characterising Accuracy and Sensitivity to Indices and Spectra

Sub-Pixel Waterline Extraction: Characterising Accuracy and Sensitivity to Indices and Spectra

Generating Continental Scale Pixel-Based Surface Reflectance Composites in Coastal Regions with the Use of a Multi-Resolution Tidal Model

Semantic Earth Observation Data Cubes

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