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Time-first approach for land cover mapping using big Earth observation data time-series in a data cube-a case study from the Lake Geneva region(Switzerland)

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Big Earth Data 2024年第3期8卷 435-466页

作者： gregory giuliani Institute for Environmental Sciences EnviroSPACE labUniversity of GenevaGenevaSwitzerland United Nations Environment Programme Division of Early Warning and AssessmentGenevaSwitzerland

Accurate,consistent,and high-resolution Land Use&Cover(LUC)information is fundamental for effectively monitoring landscape dynamics and better apprehending drivers,pressures,state,and impacts on land ***,the availability of such national products with high thematic accuracy is still limited and consequently researchers and policymakers are constrained to work with data that do not necessarily reflect on-the-ground realities impending to correctly capture details of landscape features as well as limiting the identification and quantification of drivers and rate of ***,we took advantage of the Switzerland’s official LUC statistical sampling survey and dense time-series of Sentinel-2 data,combining them with Machine and Deep Learning methods to produce an accurate and high spatial resolution land cover map over the Lake Geneva *** suggest that time-first approach is a valuable alternative to space-first approaches,accounting for the intra-annual variability of classes,hence improving classification *** demonstrate that Deep Learning methods outperform more traditional Machine Learning ones such as Random Forest,providing more accurate predictions with lower *** produced land cover map has a high accuracy,an improved spatial resolution,while at the same time preserving the statistical significance(*** proportion)of the official national *** work paves the way towards the objective to produce a yearly high resolution land cover map of Switzerland and potentially implement a continuous land change monitoring *** further work is required to properly address challenges such as the need for increased temporal resolution for LUC information or the quality of training samples.

关键词： Land cover Sentinel-2 timeseries SITS Arealstatistik

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Change in observed long-term greening across Switzerlandevidence from a three decades NDVI time-series and its relationship with climate and land cover factors

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Big Earth Data 2024年第1期8卷 1-32页

作者： Claire Obuchowicz Charlotte Poussin gregory giuliani Institute for Environmental Sciences EnviroSPACE labUniversity of GenevaGenevaSwitzerland Institute for Environmental Sciences University of GenevaGenevaSwitzerland

Environmental changes are significantly modifying terrestrial vegetation dynamics,with serious consequences on Earth system functioning and provision of ecosystem *** conditions are an essential element underpinning global sustainability frameworks,such as the Sustainable Development Goals(SDGs),requiring effective solutions to assess the impacts of changing land conditions induced by various driving *** the global scale,long-term increase of vegetation greening has been widely reported notably in seasonally snow-covered ecosystems as a response to warming ***,greening trends at the national scale have received less attention,although countries like Switzerland are prone to important changing climate ***,we used a 35-year satellite-derived annual and seasonal time-series of Normalized Difference Vegetation Index(NDVI)to assess vegetation greenness evolution at different spatial and temporal scales across Switzerland and related them to temperature,precipitation,and land cover to investigate possible responses of changing climatic *** indicate that there is a statistically significant greening trend at the national scale with an NDVI mean increasing slope of 0.6%/year for the 61%significant pixels across *** particular,the seasonal mean NDVI shows an important break for winter,autumn and spring seasons starting from 2010,potentially indicating a critical point of changing land *** biogeographical scale,we observed an apparent clustering(Jura-Plateau;Northern-Southern Alps;Eastern-Western Alps)related to landscape characteristics,while forested land cover classes are more responsive to NDVI ***,the NDVI values are mostly a function of temperature at elevations below the tree line rather than *** findings suggest that multi-annual and seasonal NDVI can be a valuable indicator to monitor vegetation conditions at different scales and can provide complementary observati

关键词： Landsat climate land cover NDVI anomaly

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Beyond the SDG 15.3.1 Good Practice Guidance 1.0 using the Google Earth Engine platform: developing a self-adjusting algorithm to detect significant changes in water use efficiency and net primary production

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Big Earth Data 2023年第1期7卷 59-80页

作者： Andrea Markos Neil Sims gregory giuliani Foundation for the Conservation of the Chiquitano Forest Santa CruzBolivia Bolivian National Academy of Sciences La PazBolivia Centre for Earth Observation Space&AstronomyCSIROClaytonAustralia GEO LDN Initiative Institute for Environmental Sciences EnviroSPACE LabUniversity of GenevaGenevaSwitzerland Institute for Environmental Sciences GRID-GenevaUniversity of GenevaGenevaSwitzerland

Monitoring changes in Annual Net Primary Productivity(ANPP)is required for reporting on UN Sustainable Development Goal(SDG)Indicator 15.3.1:the proportion of land that is degraded over the total land *** time-series observations of ANPP to derive Water Use Efficiency(WUE;a measure of ANPP per unit of evapotranspiration)can minimize the influence of climate factors on ANPP observations and highlight the influence of non-climatic drivers of degradation such as land use *** the ANPP and WUE time series may be useful for identifying the primary drivers of land degradation,which could be used to support the Land Degradation Neutrality objectives of the UN Convention to Combat Desertification(UNCCD).This paper presents an algorithm for the Google Earth Engine(freely and openly available upon request-http://***/10.5281/zenodo.4429773)to calculate and compare ANPP and WUE time series for Santa Cruz,Bolivia,which has recently experienced an intensification in its land *** code builds on the Good Practice Guidance document(ver-sion 1)for monitoring SDG Indicator *** use the MODIS 16-day average,250 m resolution to demonstrate that the Enhanced Vegetation Index(EVI)responds faster to changes in water avail-ability than the Normalized Difference Vegetation Index(NDVI).We also consider the relationships between ANPP and *** and concordant trends may highlight good agricultural practices or increased resilience in ecosystem structure and productivity when they are positive or reducing resilience and functional integrity if *** sign and significance of the correlation between ANPP and WUE may also diverge over *** further analysis,it may be possible to interpret this relationship in terms of the drivers of change in plant productivity and ecosystem resilience.

关键词： Land degradation land productivity water use efficiency Google Earth Engine MODIS Good Practice Guidance SDG 15.3.1

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Towards a knowledge base to support global change policy goals

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International Journal of Digital Earth 2020年第2期13卷 188-216页

作者： Stefano Nativi Mattia Santoro gregory giuliani Paolo Mazzetti European Commission–DG Joint Research Centre Ispra(VA)Italy National research Council of Italy–Institute on Atmospheric research pollution Sesto Fiorentino(FI)Italy University of Geneva Institute for Environmental SciencesGenevaSwitzerland UN Environment Science DivisionGRID-GenevaGenevaSwitzerland

In 2015,it was adopted the 2030 Agenda for Sustainable Development to end poverty,protect the planet and ensure that all people enjoy peace and *** year after,17 Sustainable Development Goals(SDGs)officially came into *** 2015,GEO(Group on Earth Observation)declared to support the implementation of *** GEO Global Earth Observation System of Systems(GEOSS)required a change of paradigm,moving from a data-centric approach to a more knowledge-driven *** this end,the GEO System-of-Systems(SoS)framework may refer to the well-known Data-Information-Knowledge-Wisdom(DIKW)*** the context of an Earth Observation(EO)SoS,a set of main elements are recognized as connecting links for generating knowledge from EO and non-EO data–*** and economic *** elements are:Essential Variables(EVs),Indicators and Indexes,Goals and *** generation and use requires the development of a SoS KB whose management process has evolved the GEOSS Software Ecosystem into a GEOSS Social *** includes:collect,formalize,publish,access,use,and update *** project analysed the knowledge necessary to recognize,formalize,access,and use *** analysis recognized GEOSS gaps providing recommendations on supporting global decision-making within and across different domains.

关键词： Knowledge base from data to knowledge essential variables SDGs GEOSS interoperability science big earth data

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Reviewing the discoverability and accessibility to data and information products linked to Essential Climate Variables

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International Journal of Digital Earth 2020年第2期13卷 236-252页

作者： Maria Teresa Miranda Espinosa gregory giuliani Nicolas Ray Institute for Environmental Sciences enviroSPACE labUniversity of GenevaGenevaSwitzerland Institute for Environmental Sciences GRID-GenevaUniversity of GenevaGenevaSwitzerland GeoHealth Group Institute of Global HealthFaculty of MedicineUniversity of GenevaGenevaSwitzerland'

Essential Climate Variables(ECVs)are geophysical records generated from systematic Earth Observations associated with climate variations,changes,and *** products support the data and information needs of international frameworks and policies such as the work of the United Nations Framework Convention on Climate Change(UNFCCC)and the Intergovernmental Panel on Climate Change(IPCC).We map the main networks and initiatives publishing ECVs,by presenting an overview of existing satellite-based ECVs,their general data creation characteristics,discoverability and accessibility methods from an end-user *** investigate key initiatives providing or coordinating access to ECV data records,such as the Global Climate Observing System(GCOS),the Committee on Earth Observation Satellites(CEOS),the Coordination Group for Meteorological Satellites(CGMS),Joint Working Group on Climate(WGClimate),the Remote Sensing Systems(REMSS),and the European Space Agency Climate Change Initiative(ESA CCI).We find that ECV data discovery and access is difficult and time consuming due to the lack of common data and metadata *** addition,the selection of fit-for-purpose data records by end-users requires the implementation of interoperable standards and scalable data infrastructures to allow the generation of tailored applications and datadriven information products in support of decision-making processes.

关键词： Essential Climate Variables Climate services discoverability accessibility usability

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Towards integrated essential variables for sustainability

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International Journal of Digital Earth 2020年第2期13卷 158-165页

作者： Anthony Lehmann Joan Masò Stefano Nativi gregory giuliani enviroSPACE Institute for Environmental SciencesUniversity of GenevaGenevaSwitzerland Department F.-A.Forel for Environment and Water Sciences Faculty of SciencesUniversity of GenevaGenevaSwitzerland CREAF Grumets Research Group CREAFEdifici C.Universitat Autònoma de BarcelonaBellaterraSpain National Research Council of Italy–Institute of Atmospheric Pollution Research Sesto FiorentinoItaly GRID-Geneva Institute for Environmental SciencesUniversity of GenevaGenevaSwitzerland

Measuring the achievement of a sustainable development requires the integration of various data sets and disciplines describing bio-physical and socio-economic *** data allow characterizing any location on Earth,assessing the status of the environment at various scales(***,regional,global),understanding interactions between different systems(***,hydrosphere,biosphere,geosphere),and modeling future *** Group on Earth Observations(GEO)was established in 2005 in response to the need for coordinated,comprehensive,and sustained observations related to the state of the ***’s global engagement priorities include supporting the UN 2030 Agenda for Sustainable Development,the Paris Agreement on Climate,and the Sendai Framework for Disaster Risk Reduction.A proposition is made for generalizing and integrating the concept of EVs across the Societal Benefit Areas of GEO and across the border between SocioEconomic and Earth systems *** contributions of the European Union projects ConnectinGEO and GEOEssential in the evaluation of existing EV classes are ***,the main aim of the 10 papers of the special issue is shortly presented and mapped according to the proposed typology of SBA-related EV classes.

关键词： Earth observation GEO/GEOSS environment policy indicators knowledge implementation SDGs

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Emerging trends in big Earth data management and analysis

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Big Earth Data 2023年第3期7卷 451-454页

作者： Martin Sudmanns gregory giuliani Dirk Tiede Hannah Augustin Department of Geoinformatics Paris Lodron University SalzburgSalzburgAustria Institute for Environmental Sciences University of GenevaGenevaSwitzerland

Big Earth data are increasingly used in a variety of *** the same time,technological developments happen rapidly and include Earth observation data cubes,analysis-ready data(ARD),the need to access distributed systems and data to avoid replicating datasets,searching and finding datasets,or visualization of data and information in a comprehensive way.

关键词： Earth searching analysis

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One decade(2011–2020)of European agricultural water stress monitoring by MSG-SEVIRI:workflow implementation on the Virtual Earth Laboratory(VLab)platform

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International Journal of Digital Earth 2022年第1期15卷 730-747页

作者： Bagher Bayat Carsten Montzka Alexander Graf gregory giuliani Mattia Santoro Harry Vereecken Institute of Bio-and Geosciences:Agrosphere (IBG-3) Forschungszentrum Jülich GmbHJülichGermany Institute for Environmental Sciences EnviroSPACE labUniversity of GenevaGenevaSwitzerland Institute of Atmospheric Pollution Research National Research Council of Italy (CNR)Sesto FiorentinoItaly

Cloud computing facilities can provide crucial computing support for processing the time series of satellite data and exploiting their spatio-temporal information ***,dedicated efforts are still required to develop workflows,executable on cloud-based platforms,for ingesting the satellite data,performing the targeted processes,and generating the desired *** this study,an operational workflow is proposed,based on monthly Evaporative Stress Index(ESI)anomaly,and implemented in cloud-based online Virtual Earth Laboratory(VLab)platform,as a demonstration,to monitor European agricultural water *** this end,daily time-series of actual and reference evapotranspiration(ETa and ET0),from the Spinning Enhanced Visible and Infrared Imager(SEVIRI)sensor,were used to execute the proposed workflow successfully on *** execution of the workflow resulted in obtaining one decade(2011–2020)of European monthly agricultural water stress maps at 0.04˚spatial resolution and corresponding stress reports for each *** support open science,all the workflow outputs are stored in GeoServer,documented in GeoNetwork,and made available through *** enables creating a dashboard for better visualization of the results for *** results from this study demonstrate the capability of VLab platform for water stress detection from time series of SEVIRI-ET data.

关键词： ET SEVIRI ESI water stress workflow Europe VLab demonstration

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Monitoring land degradation at national level using satellite Earth Observation time-series data to support SDG15-exploring the potential of data cube

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Big Earth Data 2020年第1期4卷 3-22页

作者： gregory giuliani Bruno Chatenoux Antonio Benvenuti Pierre Lacroix Mattia Santoro Paolo Mazzetti Institute for Environmental Sciences EnviroSPACE lab.University of GenevaGenevaSwitzerland Institute for Environmental Sciences GRID-GenevaUniversity of GenevaGenevaSwitzerland National Research Council of Italy(CNR) Institute of Atmospheric Pollution ResearchSesto FiorentinoItaly

Avoiding,reducing,and reversing land degradation and restoring degraded land is an urgent priority to protect the biodiversity and ecosystem services that are vital to life on *** halt and reverse the current trends in land degradation,there is an immediate need to enhance national capacities to undertake quantitative assessments and mapping of their degraded lands,as required by the Sustainable Development Goals(SDGs),in particular,the SDG indicator 15.3.1(“proportion of land that is degraded over total land area”).Earth Observations(EO)can play an important role both for generating this indicator as well as complementing or enhancing national official data *** like *** to monitor land degradation in accordance with the SDG15.3.1 rely on default datasets of coarse spatial resolution provided by MODIS or ***,there is a need to develop methodologies to benefit from medium to high-resolution satellite EO data(*** or Sentinels).In response to this issue,this paper presents an initial overview of an innovative approach to monitor land degradation at the national scale in compliance with the SDG15.3.1 indicator using Landsat observations using a data cube but further work is required to improve the calculation of the three sub-indicators.

关键词： Land degradation Sustainable Development Goals Open Data Cube Landsat Sentinel-2 SDG15.3.1

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Think global,cube local:an Earth Observation Data Cube’s contribution to the Digital Earth vision

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Big Earth Data 2023年第3期7卷 831-859页

作者： Martin Sudmanns Hannah Augustin Brian Killough gregory giuliani Dirk Tiede Alex Leith Fang Yuan Adam Lewis Department of Geoinformatics Paris Lodron University SalzburgSalzburgAustria NASA Langley ResearchCenter HamptonVAUSA Institute for Environmental Sciences University of GenevaenviroSPACEGenevaSwitzerland Institute for Environmental Sciences University of GenevaGRID-GenevaGenevaSwitzerland Geoscience Australia CanberraACTAustralia Group on Earth Observations(GEO)DigitalEarth Africa GenevaSwitzerland

The technological landscape for managing big Earth observation(EO)data ranges from global solutions on large cloud infrastructures with web-based access to self-hosted *** data cubes are a leading technology for facilitating big EO data analysis and can be deployed on different spatial scales:local,national,regional,or *** EO data cubes with a geographic focus(“local EO data cubes”)have been ***,their alignment with the Digital Earth(DE)vision and the benefits and trade-offs in creating and maintaining them ought to be further *** investigate local EO data cubes from five perspectives(science,business and industry,government and policy,education,communities and citizens)and illustrate four examples covering three continents at different geographic scales(Swiss Data Cube,semantic EO data cube for Austria,DE Africa,Virginia Data Cube).A local EO data cube can benefit many stakeholders and players but requires several technical *** developments include enabling local EO data cubes based on public,global,and cloud-native EO data streaming and interoperability between local EO data *** argue that blurring the dichotomy between global and local aligns with the DE vision to access the world’s knowledge and explore information about the planet.

关键词： Earth Observation data cube Digital Earth interoperability workflows open data cube

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