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Living Earth:Implementing national standardised land cover classification systems for Earth Observation in support of sustainable development

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Big Earth Data 2021年第3期5卷 368-390页

作者： Christopher J.Owers Richard M.Lucas Daniel Clewley Carole Planque Suvarna Punalekar Belle Tissott Sean M.T.Chua Pete Bunting norman mueller Graciela Metternicht Department of Geography and Earth Sciences Aberystwyth UniversityAberystwythCeredigionWalesUK Centre for Geospatial Applications Plymouth Marine LaboratoryPlymouthDevonEnglandUK Geoscience Australia SymonstonACTAustralia Earth and Sustainability Science Research Centre School of BiologicalEarth and Environmental SciencesUniversity of New South WalesSydneyAustralia

Earth Observation(EO)has been recognised as a key data source for supporting the United Nations Sustainable Development Goals(SDGs).Advances in data availability and analytical capabilities have provided a wide range of users access to global coverage analysis-ready data(ARD).However,ARD does not provide the information required by national agencies tasked with coordinating the implementation of ***,standardised,scalable mapping of land cover and its change over time and space facilitates informed deci-sion making,providing cohesive methods for target setting and reporting of *** aim of this study was to implement a global framework for classifying land *** Food and Agriculture Organisation’s Land Cover Classification System(FAO LCCS)provides a global land cover taxonomy suitable to comprehensively support SDG target setting and *** present a fully implemented FAO LCCS optimised for EO data;Living Earth,an open-source software package that can be readily applied using existing national EO infrastructure and satellite *** resolve several semantic challenges of LCCS for consistent EO implementation,including modifications to environmental descriptors,inter-dependency within the mod-ular-hierarchical framework,and increased flexibility associated with limited data *** ensure easy adoption of Living Earth for SDG reporting,we identified key environmental descriptors to provide resource allocation recommendations for generating routinely retrieved input *** Earth provides an optimal platform for global adoption of EO4SDGs ensuring a transparent methodology that allows monitoring to be standardised for all countries.

关键词： Land cover FAO LCCS open data cube sustainable development goals environmental descriptors

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Digital earth Australia-unlocking new value from earth observation data

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Big Earth Data 2017年第1期1卷 64-74页

作者： Trevor Dhu Bex Dunn Ben Lewis Leo Lymburner norman mueller Erin Telfer Adam Lewis Alexis McIntyre Stuart Minchin Claire Phillips Geoscience australia Canberraaustralia

Petascale archives of Earth observations from space(EOS)have the potential to characterise water resources at continental *** this data to be useful,it needs to be organised,converted from individual scenes as acquired by multiple sensors,converted into“analysis ready data”,and made available through high performance computing ***,converting this data into insights requires integration of non-EOS data-sets that can provide biophysical and climatic context for *** Earth Australia has demonstrated its ability to link EOS to rainfall and stream gauge data to provide insight into surface water dynamics during the hydrological extremes of flood and *** information is supporting the characterisation of groundwater resources across Australia’s north and could potentially be used to gain an understanding of the vulnerability of transport infrastructure to floods in remote,sparsely gauged regions of northern and central Australia.

关键词： Big data earth observations from space water resources analysis ready data

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Operational continental-scale land cover mapping of Australia using the Open Data Cube

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International Journal of Digital Earth 2022年第1期15卷 1715-1737页

作者： Christopher J.Owers Richard M.Lucas Daniel Clewley Belle Tissott Sean M.T.Chua Gabrielle Hunt norman mueller Carole Planque Suvarna M.Punalekar Pete Bunting Peter Tan Graciela Metternicht School of Environmental and Life Sciences University of NewcastleCallaghanAustralia Department of Geography and Earth Sciences Aberystwyth UniversityAberystwythUK Commonwealth Scientific and Industrial Research Organisation Land and WaterCanberraAustralia Centre for Geospatial Applications Plymouth Marine LaboratoryPlymouthUK Geoscience Australia SymonstonAustralia Earth and Sustainability Science Research Centre University of New South WalesSydneyAustralia School of Science Western Sydney UniversityRichmondAustralia

To comprehensively support national and international initiatives for sustainable development,land cover products need to be reliably and routinely generated within operational *** with consistent semantics and taxonomies,ensuring confidence in mapping land cover for multiple time periods,facilitates informed decision-making at scales appropriate to multiple policy *** United Nations Food and Agriculture Organisation(FAO)Land Cover Classification System(LCCS)provides a taxonomy that comparable at different scales,level of detail and geographic *** Open Data Cube(ODC)initiative offers a framework for operational continental-scale land cover mapping using analysis-ready Earth Observation *** study utilised the FAO LCCS framework and the Landsat sensor data through Digital Earth Australia(DEA;Australia’s ODC instance)to generate consistent and continent-wide land cover mapping(DEA Land Cover)of the Australian *** Land Cover provides annual maps from 1988 to 2020 at 25 m *** maps were validated with∼12,000 independent validation points,giving an overall map accuracy of 80%.DEA Land Cover provides Australia with a nationally consistent picture of land cover,with an open-source software package using readily available global coverage data and demonstrates a pathway of adoption for national implementations across the world.

关键词： DEA land cover digital earth Australia FAO LCCS landsat sustainable development goals

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

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International Journal of Digital Earth 2016年第1期9卷 106-111页

作者： Adam Lewis Leo Lymburner Matthew B.J.Purss Brendan Brooke Ben Evans Alex Ip Arnold G.Dekker James R.Irons Stuart Minchin norman mueller Simon Oliver Dale Roberts Barbara Ryan Medhavy Thankappan Rob Woodcock Lesley Wyborn Geoscience Australia CanberraAustralia National Computational Infrastructure Australian National UniversityCanberraAustralia Commonwealth Scientific and Industrial Research Organisation CanberraAustralia NASA Goddard Space Flight Center GreenbeltMDUSA Research School of Finance Actuarial Studies and Applied StatisticsANUCanberraAustralia The Group on Earth Observations Secretariate GenevaSwitzerland

The effort and cost required to convert satellite Earth Observation(EO)data into meaningful geophysical variables has prevented the systematic analysis of all available *** overcome these problems,we utilise an integrated High Performance Computing and Data environment to rapidly process,restructure and analyse the Australian Landsat data *** 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 *** approach is pixel-based and incorporates geometric and spectral calibration and quality assurance of each Earth surface reflectance *** 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 *** 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.

关键词： Earth Observation Data Cube HPD HPC surface water Landsat Australia

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Performance and calibration of quark/gluon-jet taggers using 140 fb^(-1) of pp collisions at √s=13 TeV with the ATLAS detector

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Chinese Physics C 2024年第2期48卷 1-25页

作者： G. Aad B. Abbott K. Abeling N.J. Abicht S.H. Abidi A. Aboulhorma H. Abramowicz H. Abreu Y. Abulaiti B.S. Acharya C. Adam Bourdarios L. Adamczyk S.V. Addepalli M.J. Addison J. Adelman A. Adiguzel T. Adye A.A. Affolder Y. Afik M.N. Agaras J. Agarwala A. Aggarwal C. Agheorghiesei A. Ahmad F. Ahmadov W.S. Ahmed S. Ahuja X. Ai G. Aielli A. Aikot M. Ait Tamlihat B. Aitbenchikh I. Aizenberg M. Akbiyik T.P.A. Åkesson A.V. Akimov D. Akiyama N.N. Akolkar K. Al Khoury G.L. Alberghi J. Albert P. Albicocco G.L. Albouy S. Alderweireldt M. Aleksa I.N. Aleksandrov C. Alexa T. Alexopoulos F. Alfonsi M. Algren M. Alhroob B. Ali H.M.J. Ali S. Ali S.W. Alibocus M. Aliev G. Alimonti W. Alkakhi C. Allaire B.M.M. Allbrooke J.F. Allen C.A. Allendes Flores P.P. Allport A. Aloisio F. Alonso C. Alpigiani M. Alvarez Estevez A. Alvarez Fernandez M. Alves Cardoso M.G. Alviggi M. Aly Y. Amaral Coutinho A. Ambler C. Amelung M. Amerl C.G. Ames D. Amidei S.P. Amor Dos Santos K.R. Amos V. Ananiev C. Anastopoulos T. Andeen J.K. Anders S.Y. Andrean A. Andreazza S. Angelidakis A. Angerami A.V. Anisenkov A. Annovi C. Antel M.T. Anthony E. Antipov M. Antonelli F. Anulli M. Aoki T. Aoki J.A. Aparisi Pozo M.A. Aparo L. Aperio Bella C. Appelt A. Apyan N. Aranzabal S.J. Arbiol Val C. Arcangeletti A.T.H. Arce E. Arena J-F. Arguin S. Argyropoulos J.-H. Arling O. Arnaez H. Arnold G. Artoni H. Asada K. Asai S. Asai N.A. Asbah J. Assahsah K. Assamagan R. Astalos S. Atashi R.J. Atkin M. Atkinson H. Atmani P.A. Atmasiddha K. Augsten S. Auricchio A.D. Auriol V.A. Austrup G. Avolio K. Axiotis G. Azuelos D. Babal H. Bachacou K. Bachas A. Bachiu F. Backman A. Badea P. Bagnaia M. Bahmani A.J. Bailey V.R. Bailey J.T. Baines L. Baines O.K. Baker E. Bakos D. Bakshi Gupta V. Balakrishnan R. Balasubramanian E.M. Baldin P. Balek E. Ballabene F. Balli L.M. Baltes W.K. Balunas J. Balz E. Banas M. Bandieramonte A. Bandyopadhyay S. Bansal L. Barak M. Barakat E.L. Barberio D. Barberis M. Barbero M.Z. Barel K.N. Barends T. Barillari M-S. Barisits T. Barklow P. Baron D.A. Baron Mo The ATLAS Collaboration

The identification of jets originating from quarks and gluons,often referred to as quark/gluon tagging,plays an important role in various analyses performed at the Large Hadron Collider,as Standard Model measurements and searches for new particles decaying to quarks often rely on suppressing a large gluon-induced *** paper describes the measurement of the efficiencies of quark/gluon taggers developed within the ATLAS Collaboration,using √s=13 TeV proton–proton collision data with an integrated luminosity of 140 fb^(-1) collected by the ATLAS *** taggers with high performances in rejecting jets from gluon over jets from quarks are studied:one tagger is based on requirements on the number of inner-detector tracks associated with the jet,and the other combines several jet substructure observables using a boosted decision tree.A method is established to determine the quark/gluon fraction in data,by using quark/gluon-enriched subsamples defined by the jet *** in tagging efficiency between data and simulation are provided for jets with transverse momentum between 500 GeV and 2 TeV and for multiple tagger working points.

关键词： ATLAS JET QUARK GLUON TAGGING

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