An Operational Analysis Ready Radar Backscatter Dataset for the African Continent

Digital Earth Africa is now providing an operational Sentinel-1 normalized radar backscatter dataset for Africa. This is the first free and open continental scale analysis ready data of this kind that has been developed to be compliant with the CEOS Analysis Ready Data for Land (CARD4L) specification for normalized radar backscatter (NRB) products. Partnership with Sinergise, a European geospatial company and Earth observation data provider, has ensured this dataset is produced efficiently in the cloud infrastructure and can be sustained in the long term. The workflow applies radiometric terrain correction (RTC) to the Sentinel-1 ground range detected (GRD) product, using the Copernicus 30 m digital elevation model (DEM). The method is used to generate data for a range of sites around the world and has been validated as producing good results. This dataset over Africa is made available publicly as a AWS public dataset and can be accessed through the Digital Earth Africa platform and its Open Data Cube API. We expect this dataset to support a wide range of applications, including natural resource monitoring, agriculture, and land cover mapping across Africa.

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Land Cover Mapping using Digital Earth Australia

Data ◽

10.3390/data4040143 ◽

2019 ◽

Vol 4 (4) ◽

pp. 143 ◽

Cited By ~ 3

Author(s):

Richard Lucas ◽

Norman Mueller ◽

Anders Siggins ◽

Christopher Owers ◽

Daniel Clewley ◽

...

Keyword(s):

Land Cover ◽

Open Data ◽

Google Earth ◽

Water Levels ◽

Data Cube ◽

Observation Data ◽

Time Step ◽

Ecosystem Monitoring ◽

Digital Earth ◽

Fractional Cover

This study establishes the use of the Earth Observation Data for Ecosystem Monitoring (EODESM) to generate land cover and change classifications based on the United Nations Food and Agriculture Organisation (FAO) Land Cover Classification System (LCCS) and environmental variables (EVs) available within, or accessible from, Geoscience Australia’s (GA) Digital Earth Australia (DEA). Classifications representing the LCCS Level 3 taxonomy (8 categories representing semi-(natural) and/or cultivated/managed vegetation or natural or artificial bare or water bodies) were generated for two time periods and across four test sites located in the Australian states of Queensland and New South Wales. This was achieved by progressively and hierarchically combining existing time-static layers relating to (a) the extent of artificial surfaces (urban, water) and agriculture and (b) annual summaries of EVs relating to the extent of vegetation (fractional cover) and water (hydroperiod, intertidal area, mangroves) generated through DEA. More detailed classifications that integrated information on, for example, forest structure (based on vegetation cover (%) and height (m); time-static for 2009) and hydroperiod (months), were subsequently produced for each time-step. The overall accuracies of the land cover classifications were dependent upon those reported for the individual input layers, with these ranging from 80% (for cultivated, urban and artificial water) to over 95% (for hydroperiod and fractional cover). The changes identified include mangrove dieback in the southeastern Gulf of Carpentaria and reduced dam water levels and an associated expansion of vegetation in Lake Ross, Burdekin. The extent of detected changes corresponded with those observed using time-series of RapidEye data (2014 to 2016; for the Gulf of Carpentaria) and Google Earth imagery (2009–2016 for Lake Ross). This use case demonstrates the capacity and a conceptual framework to implement EODESM within DEA and provides countries using the Open Data Cube (ODC) environment with the opportunity to routinely generate land cover maps from Landsat or Sentinel-1/2 data, at least annually, using a consistent and internationally recognised taxonomy.

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Analyzing large-scale Earth Observation data repositories made simple with OpenEO Platform

10.5194/egusphere-egu21-9602 ◽

2021 ◽

Author(s):

Edzer Pebesma ◽

Patrick Griffiths ◽

Christian Briese ◽

Alexander Jacob ◽

Anze Skerlevaj ◽

...

Keyword(s):

Large Scale ◽

Virtual Machines ◽

Google Earth ◽

Earth Observation ◽

Data Cube ◽

Observation Data ◽

Data Repositories ◽

Software Ecosystem ◽

Wide Range ◽

Earth Observation Data

The OpenEO API allows the analysis of large amounts of Earth Observation data using a high-level abstraction of data and processes. Rather than focusing on the management of virtual machines and millions of imagery files, it allows to create jobs that take a spatio-temporal section of an image collection (such as Sentinel L2A), and treat it as a data cube. Processes iterate or aggregate over pixels, spatial areas, spectral bands, or time series, while working at arbitrary spatial resolution. This pattern, pioneered by Google Earth Engine&#8482; (GEE), lets the user focus on the science rather than on data management.The openEO H2020 project (2017-2020) has developed the API as well as an ecosystem of software around it, including clients (JavaScript, Python, R, QGIS, browser-based), back-ends that translate API calls into existing image analysis or GIS software or services (for Sentinel Hub, WCPS, Open Data Cube, GRASS GIS, GeoTrellis/GeoPySpark, and GEE) as well as a hub that allows querying and searching openEO providers for their capabilities and datasets. The project demonstrated this software in a number of use cases, where identical processing instructions were sent to different implementations, allowing comparison of returned results.A follow-up, ESA-funded project &#8220;openEO Platform&#8221; realizes the API and progresses the software ecosystem into operational services and applications that are accessible to everyone, that involve federated deployment (using the clouds managed by EODC, Terrascope, CreoDIAS and EuroDataCube), that will provide payment models (&#8220;pay per compute job&#8221;) conceived and implemented following the user community needs and that will use the EOSC (European Open Science Cloud) marketplace for dissemination and authentication. A wide range of large-scale cases studies will demonstrate the ability of the openEO Platform to scale to large data volumes.&#160; The case studies to be addressed include on-demand ARD generation for SAR and multi-spectral data, agricultural demonstrators like crop type and condition monitoring, forestry services like near real time forest damage assessment as well as canopy cover mapping, environmental hazard monitoring of floods and air pollution as well as security applications in terms of vessel detection in the mediterranean sea.While the landscape of cloud-based EO platforms and services has matured and diversified over the past decade, we believe there are strong advantages for scientists and government agencies to adopt the openEO approach. Beyond the absence of vendor/platform lock-in or EULA&#8217;s we mention the abilities to (i) run arbitrary user code (e.g. written in R or Python) close to the data, (ii) carry out scientific computations on an entirely open source software stack, (iii) integrate different platforms (e.g., different cloud providers offering different datasets), and (iv) help create and extend this software ecosystem. openEO uses the OpenAPI standard, aligns with modern OGC API standards, and uses the STAC (SpatioTemporal Asset Catalog) to describe image collections and image tiles.

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National Open Data Cubes and Their Contribution to Country-Level Development Policies and Practices

Data ◽

10.3390/data4040144 ◽

2019 ◽

Vol 4 (4) ◽

pp. 144 ◽

Cited By ~ 4

Author(s):

Trevor Dhu ◽

Gregory Giuliani ◽

Jimena Juárez ◽

Argyro Kavvada ◽

Brian Killough ◽

...

Keyword(s):

International Development ◽

Water Resource Management ◽

Open Data ◽

Data Cube ◽

Observation Data ◽

Country Level ◽

Global Trend ◽

Development Policies ◽

Earth Observation Data ◽

Development Agendas

The emerging global trend of satellite operators producing analysis-ready data combined with open source tools for managing and exploiting these data are leading to more and more countries using Earth observation data to drive progress against key national and international development agendas. This paper provides examples from Australia, Mexico, Switzerland, and Tanzania on how the Open Data Cube technology has been combined with analysis-ready data to provide new insights and support better policy making across issues as diverse as water resource management through to urbanization and environmental–economic accounting.

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Earth Observation Open Science: Enhancing Reproducible Science Using Data Cubes

Data ◽

10.3390/data4040147 ◽

2019 ◽

Vol 4 (4) ◽

pp. 147 ◽

Cited By ~ 9

Author(s):

Gregory Giuliani ◽

Gilberto Camara ◽

Brian Killough ◽

Stuart Minchin

Keyword(s):

Open Science ◽

Earth Observation ◽

Data Cube ◽

Observation Data ◽

Special Issue ◽

Data Repositories ◽

Data Cubes ◽

Information Power ◽

Wide Range ◽

Earth Observation Data

Earth Observation Data Cubes (EODC) have emerged as a promising solution to efficiently and effectively handle Big Earth Observation (EO) Data generated by satellites and made freely and openly available from different data repositories. The aim of this Special Issue, “Earth Observation Data Cube”, in Data, is to present the latest advances in EODC development and implementation, including innovative approaches for the exploitation of satellite EO data using multi-dimensional (e.g., spatial, temporal, spectral) approaches. This Special Issue contains 14 articles covering a wide range of topics such as Synthetic Aperture Radar (SAR), Analysis Ready Data (ARD), interoperability, thematic applications (e.g., land cover, snow cover mapping), capacity development, semantics, processing techniques, as well as national implementations and best practices. These papers made significant contributions to the advancement of a more Open and Reproducible Earth Observation Science, reducing the gap between users’ expectations for decision-ready products and current Big Data analytical capabilities, and ultimately unlocking the information power of EO data by transforming them into actionable knowledge.

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Open Data Cube for Natural Resources Mapping in Mexico

10.29007/d19p ◽

2019 ◽

Author(s):

José Luis Ornelas De Anda ◽

Juan Carlos Camacho Pérez ◽

Hugo Alfredo Sánchez Miranda

Keyword(s):

Natural Resources ◽

Spatial Data ◽

Landsat Imagery ◽

Open Data ◽

Temporal Analysis ◽

Data Cube ◽

Full Potential ◽

Observation Data ◽

Visual Interpretation ◽

Earth Observation Data

In recent years, the efforts to enhance the analysis of Earth’s surface with satellite imagery have forced the scientific community to develop different techniques and methodologies. The Open Data Cube aims to provide tools to execute multi-temporal analysis and get accurate products, excluding low-quality pixels in small or large areas of study with an accuracy subject to the resolution of the data used for the analysis. This means that we can make use of the full potential of Earth observation data available from satellite data providers, in this document we take a closer look at Landsat Imagery and its applications. The beginning of the implementation of the Open Data Cube platform began in 2018, positioning itself as a valuable source of spatial data for Natural Resources projects in INEGI and seeks to support the decision-making process based on territorial analyzes with great certainty. The use of this technological solution represents a great leap between the traditional visual interpretation of raster data and the automated processing of data in time series.

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Multidimensional enrichment of spatial RDF data for SOLAP

Semantic Web ◽

10.3233/sw-210423 ◽

2021 ◽

pp. 1-35

Author(s):

Nurefşan Gür ◽

Torben Bach Pedersen ◽

Katja Hose ◽

Mikael Midtgaard

Keyword(s):

Semantic Web ◽

Spatial Data ◽

Spatial Information ◽

Complex Geometry ◽

Open Data ◽

Data Cube ◽

Multidimensional Data ◽

Wide Range ◽

Advanced Analysis ◽

Rdf Data

Large volumes of spatial data and multidimensional data are being published on the Semantic Web, which has led to new opportunities for advanced analysis, such as Spatial Online Analytical Processing (SOLAP). The RDF Data Cube (QB) and QB4OLAP vocabularies have been widely used for annotating and publishing statistical and multidimensional RDF data. Although such statistical data sets might have spatial information, such as coordinates, the lack of spatial semantics and spatial multidimensional concepts in QB4OLAP and QB prevents users from employing SOLAP queries over spatial data using SPARQL. The QB4SOLAP vocabulary, on the other hand, fully supports annotating spatial and multidimensional data on the Semantic Web and enables users to query endpoints with SOLAP operators in SPARQL. To bridge the gap between QB/QB4OLAP and QB4SOLAP, we propose an RDF2SOLAP enrichment model that automatically annotates spatial multidimensional concepts with QB4SOLAP and in doing so enables SOLAP on existing QB and QB4OLAP data on the Semantic Web. Furthermore, we present and evaluate a wide range of enrichment algorithms and apply them on a non-trivial real-world use case involving governmental open data with complex geometry types.

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Digital Earth in Australia

Manual of Digital Earth ◽

10.1007/978-981-32-9915-3_21 ◽

2019 ◽

pp. 683-711

Author(s):

Zaffar Sadiq Mohamed-Ghouse ◽

Cheryl Desha ◽

Luis Perez-Mora

Keyword(s):

Data Use ◽

Open Data ◽

Data Access ◽

Lessons Learned ◽

Data Cube ◽

Digital Earth ◽

National Initiatives ◽

Processing And Storage ◽

Access To Data ◽

And Storage

Abstract Australia must overcome a number of challenges to meet the needs of our growing population in a time of increased climate variability. Fortunately, we have unprecedented access to data about our land and the built environment that is internationally regarded for its quality. Over the last two decades Australia has risen to the forefront in developing and implementing Digital Earth concepts, with several key national initiatives formalising our digital geospatial journey in digital globes, open data access and ensuring data quality. In particular and in part driven by a lack of substantial resources in space, we have directed efforts towards world-leading innovation in big data processing and storage. This chapter highlights these geospatial initiatives, including case-uses, lessons learned, and next steps for Australia. Initiatives addressed include the National Data Grid (NDG), the Queensland Globe, G20 Globe, NSW Live (formerly NSW Globe), Geoscape, the National Map, the Australian Geoscience Data Cube and Digital Earth Australia. We explore several use cases and conclude by considering lessons learned that are transferrable for our colleagues internationally. This includes challenges in: 1) Creating an active context for data use, 2) Capacity building beyond ‘show-and-tell’, and 3) Defining the job market and demand for the market.

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EVALUATING THE IMPACT OF LASRC AND SEN2COR ATMOSPHERIC CORRECTION ALGORITHMS ON LANDSAT-8/OLI AND SENTINEL-2/MSI DATA OVER AERONET STATIONS IN BRAZILIAN TERRITORY

ISPRS Annals of Photogrammetry Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-annals-v-3-2021-271-2021 ◽

2021 ◽

Vol V-3-2021 ◽

pp. 271-277

Author(s):

R. F. B. Marujo ◽

J. G. Fronza ◽

A. R. Soares ◽

G. R. Queiroz ◽

K. R. Ferreira

Keyword(s):

Atmospheric Correction ◽

Open Data ◽

Image Data ◽

Data Cube ◽

Landsat 8 ◽

Surface Reflectance ◽

Landsat 8 Oli ◽

Continental Scale ◽

The Impact ◽

Sentinel 2

Abstract. Accurate and consistent Surface Reflectance estimation from optical remote sensor observations is directly dependant on the used atmospheric correction processor and the differences caused by it may have implications on further processes, e.g. classification. Brazil is a continental scale country with different biomes. Recently, new initiatives, as the Brazil Data Cube Project, are emerging and using free and open data policy data, more specifically medium spatial resolution sensor images, to create image data cubes and classify the Brazilian territory crops. For this reason, the purpose of this study is to verify, on Landsat-8 and Sentinel-2 images for the Brazilian territory, the suitability of the atmospheric correction processors maintained by their image providers, LaSRC from USGS and Sen2cor from ESA, respectively. To achieve this, we tested the surface reflectance products from Landsat-8 processed through LaSRC and Sentinel-2 processed through LaSRC and Sen2cor comparing to a reference dataset computed by ARCSI and AERONET. The obtained results point that Landsat-8/OLI images atmospherically corrected using the LaSRC corrector are consistent to the surface reflectance reference and other atmospheric correction processors studies, while for Sentinel-2/MSI images, Sen2cor performed best. Although corrections over Sentinel-2/MSI data weren’t as consistent as in Landsat-8/OLI corrections, in comparison to the surface reflectance references, most of the spectral bands achieved acceptable APU results.

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Evidence-Based Transit and Land Use Sketch Planning Using Interactive Accessibility Methods on Combined Schedule and Headway-Based Networks

Transportation Research Record Journal of the Transportation Research Board ◽

10.3141/2653-06 ◽

2017 ◽

Vol 2653 (1) ◽

pp. 45-53 ◽

Cited By ~ 10

Author(s):

Matthew Wigginton Conway ◽

Andrew Byrd ◽

Marco van der Linden

Keyword(s):

Land Use ◽

Public Transportation ◽

Planning Process ◽

Early Stage ◽

Open Data ◽

Time Of Day ◽

System Quality ◽

Cloud Infrastructure ◽

Immediate Feedback ◽

Wide Range

There is a need for indicators of transportation–land use system quality that are understandable to a wide range of stakeholders and that can provide immediate feedback on the quality of interactively designed scenarios. Location-based accessibility indicators are promising candidates, but indicator values can vary strongly depending on time of day and transfer wait times. Capturing this variation increases complexity, slowing calculations. This paper presents new methods for rapid yet rigorous computation of accessibility metrics, allowing immediate feedback during early-stage transit planning while being rigorous enough for final analyses. The approach is statistical, characterizing the uncertainty and variability in accessibility metrics related to differences in departure time and headway-based scenario specification. The analysis was carried out on a detailed multimodal network model including both public transportation and streets. Land use data were represented at high resolution. These methods were implemented as open-source software running on a commodity cloud infrastructure. Networks were constructed from standard open data sources, and scenarios were built in a map-based web interface. A case study is presented, describing how these methods were applied in a long-term transportation planning process for an urbanized, polycentric Randstad region in the Netherlands.

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The Drought & Flood Mitigation Service in Uganda – First Results

10.5194/egusphere-egu2020-17108 ◽

2020 ◽

Author(s):

Hermen Westerbeeke ◽

Deus Bamanya ◽

George Gibson

Keyword(s):

Open Data ◽

Smart Phone ◽

Remote Sensing Data ◽

Capacity Development ◽

Earth Observation ◽

Data Cube ◽

Flood Mitigation ◽

Observation Data ◽

Service Project ◽

Information Products

Since 2017, the governments of Uganda and the United Kingdom have been taking an innovative approach to mitigating the impacts of drought and floods on Ugandan society in the DFMS Project. Recognising both that the only sustainable solution to this issue is the continued capacity development in Uganda&#8217;s National Meteorological and Hydrological Services, and that it will take time for this capacity development to deliver results, the Drought & Flood Mitigation Service Project developed DFMS, bringing together meteorological, hydrological, and Earth observation information products and making these available to decision-makers in Uganda.After the DFMS Platform was designed and developed in cooperation between a group of UK organisations that includes the Met Office and is led by the REA Group and five Ugandan government agencies including UNMA, led by the Ministry of Water and Environment (MWE), 2020 saw the start of a 2.5-year Demonstration Phase in which UNMA, MWE, and the other agencies will trial DFMS and DFMS will be fine-tuned to their needs. We will be presenting the first experiences with DFMS, including how it is being used related to SDG monitoring, and will showcase the platform itself in what we hope will be a very interactive session.DFMS is a suite of information products and access only requires an Internet-connected device (e.g. PC, laptop, tablet, smart phone). Data and information are provided as maps or in graphs and tables, and several analysis tools allow for bespoke data processing and visualisation. Alarms can be tailored to indicate when observed or forecast parameters exceed user-defined thresholds. DFMS also comes with automatic programmable interfaces allowing it to be integrated with other automatic systems. The DFMS Platform is built using Open Source software, including Open Data Cube technology for storing and analysing Earth Observation data. It extensively uses (free) satellite remote sensing data, but also takes in data gathered in situ. By making the platform scalable and replicable, DFMS can be extended to contain additional features (e.g. related to landslides or crop diseases) or be rolled out in other countries in the region and beyond.

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