Pressures on natural resources are increasing and a number of challenges need to be overcome to meet the needs of a growing population in a period of environmental variability.Some of these environmental issues can be...Pressures on natural resources are increasing and a number of challenges need to be overcome to meet the needs of a growing population in a period of environmental variability.Some of these environmental issues can be monitored using remotely sensed Earth Observations(EO)data that are increasingly available from a number of freely and openly accessible repositories.However,the full information potential of EO data has not been yet realized.They remain still underutilized mainly because of their complexity,increasing volume,and the lack of efficient processing capabilities.EO Data Cubes(DC)are a new paradigm aiming to realize the full potential of EO data by lowering the barriers caused by these Big data challenges and providing access to large spatio-temporal data in an analysis ready form.Systematic and regular provision of Analysis Ready Data(ARD)will significantly reduce the burden on EO data users.Nevertheless,ARD are not commonly produced by data providers and therefore getting uniform and consistent ARD remains a challenging task.This paper presents an approach to enable rapid data access and pre-processing to generate ARD using interoperable services chains.The approach has been tested and validated generating Landsat ARD while building the Swiss Data Cube.展开更多
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 Earth.To halt and reverse the current tre...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 Earth.To 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 sources.Implementations like Trends.Earth to monitor land degradation in accordance with the SDG15.3.1 rely on default datasets of coarse spatial resolution provided by MODIS or AVHRR.Consequently,there is a need to develop methodologies to benefit from medium to high-resolution satellite EO data(e.g.Landsat 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.展开更多
Environmental changes are significantly modifying terrestrial vegetation dynamics,with serious consequences on Earth system functioning and provision of ecosystem services.Land conditions are an essential element unde...Environmental changes are significantly modifying terrestrial vegetation dynamics,with serious consequences on Earth system functioning and provision of ecosystem services.Land 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 forces.At the global scale,long-term increase of vegetation greening has been widely reported notably in seasonally snow-covered ecosystems as a response to warming climate.However,greening trends at the national scale have received less attention,although countries like Switzerland are prone to important changing climate conditions.Hereby,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 conditions.Results 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 Switzerland.In 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 conditions.At 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 changes.Finally,the NDVI values are mostly a function of temperature at elevations below the tree line rather than precipitation.The findings suggest that multi-annual and seasonal NDVI can be a valuable indicator to monitor vegetation conditions at different scales and can provide complementary observations for national statistics on the ecological state of vegetation to monitor land affected by changing environmental conditions.This work is aiming at strengthening the insights into the driving factors of vegetation change and supporting monitoring changing land conditions to provide guidance for effective and efficient environmental management and sustainable development policy advice at the national scale.展开更多
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 area.Calibrating...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 area.Calibrating 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 changes.Comparing 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://doi.org/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 use.This code builds on the Good Practice Guidance document(ver-sion 1)for monitoring SDG Indicator 15.3.1.We 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 WUE.Significant 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 negative.The sign and significance of the correlation between ANPP and WUE may also diverge over time.With further analysis,it may be possible to interpret this relationship in terms of the drivers of change in plant productivity and ecosystem resilience.展开更多
Measuring the achievement of a sustainable development requires the integration of various data sets and disciplines describing bio-physical and socio-economic conditions.These data allow characterizing any location o...Measuring the achievement of a sustainable development requires the integration of various data sets and disciplines describing bio-physical and socio-economic conditions.These data allow characterizing any location on Earth,assessing the status of the environment at various scales(e.g.national,regional,global),understanding interactions between different systems(e.g.atmosphere,hydrosphere,biosphere,geosphere),and modeling future changes.The 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 Earth.GEO’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 EVs.The contributions of the European Union projects ConnectinGEO and GEOEssential in the evaluation of existing EV classes are introduced.Finally,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.展开更多
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 implementations.EO data cubes are a leading techn...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 implementations.EO data cubes are a leading technology for facilitating big EO data analysis and can be deployed on different spatial scales:local,national,regional,or global.Several EO data cubes with a geographic focus(“local EO data cubes”)have been implemented.However,their alignment with the Digital Earth(DE)vision and the benefits and trade-offs in creating and maintaining them ought to be further examined.We 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.These developments include enabling local EO data cubes based on public,global,and cloud-native EO data streaming and interoperability between local EO data cubes.We 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.展开更多
The concept of Digital Earth(DE)was formalized by Al Gore in 1998.At that time the technologies needed for its implementation were in an embryonic stage and the concept was quite visionary.Since then digital technolog...The concept of Digital Earth(DE)was formalized by Al Gore in 1998.At that time the technologies needed for its implementation were in an embryonic stage and the concept was quite visionary.Since then digital technologies have progressed significantly and their speed and pervasiveness have generated and are still causing the digital transformation of our society.This creates new opportunities and challenges for the realization of DE.‘What is DE today?’,‘What could DE be in the future?’,and‘What is needed to make DE a reality?’.To answer these questions it is necessary to examine DE considering all the technological,scientific,social,and economic aspects,but also bearing in mind the principles that inspired its formulation.By understanding the lessons learned from the past,it becomes possible to identify the remaining scientific and technological challenges,and the actions needed to achieve the ultimate goal of a‘Digital Earth for all’.This article reviews the evolution of the DE vision and its multiple definitions,illustrates what has been achieved so far,explains the impact of digital transformation,illustrates the new vision,and concludes with possible future scenarios and recommended actions to facilitate full DE implementation.展开更多
When defining indicators on the environment,the use of existing initiatives should be a priority rather than redefining indicators each time.From an Information,Communication and Technology perspective,data interopera...When defining indicators on the environment,the use of existing initiatives should be a priority rather than redefining indicators each time.From an Information,Communication and Technology perspective,data interoperability and standardization are critical to improve data access and exchange as promoted by the Group on Earth Observations.GEOEssential is following an end-user driven approach by defining Essential Variables(EVs),as an intermediate value between environmental policy indicators and their appropriate data sources.From international to local scales,environmental policies and indicators are increasingly percolating down from the global to the local agendas.The scientific business processes for the generation of EVs and related indicators can be formalized in workflows specifying the necessary logical steps.To this aim,GEOEssential is developing a Virtual Laboratory the main objective of which is to instantiate conceptual workflows,which are stored in a dedicated knowledge base,generating executable workflows.To interpret and present the relevant outputs/results carried out by the different thematic workflows considered in GEOEssential(i.e.biodiversity,ecosystems,extractives,night light,and food-water-energy nexus),a Dashboard is built as a visual front-end.This is a valuable instrument to track progresses towards environmental policies.展开更多
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 systems.Never...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 systems.Nevertheless,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 change.Hereafter,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 region.Findings suggest that time-first approach is a valuable alternative to space-first approaches,accounting for the intra-annual variability of classes,hence improving classification performances.Results demonstrate that Deep Learning methods outperform more traditional Machine Learning ones such as Random Forest,providing more accurate predictions with lower uncertainty.The produced land cover map has a high accuracy,an improved spatial resolution,while at the same time preserving the statistical significance(i.e.class proportion)of the official national dataset.This 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 capability.However 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.展开更多
Cloud computing facilities can provide crucial computing support for processing the time series of satellite data and exploiting their spatio-temporal information content.However,dedicated efforts are still required t...Cloud computing facilities can provide crucial computing support for processing the time series of satellite data and exploiting their spatio-temporal information content.However,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 products.In 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 stress.To 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 VLab.The 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 country.To support open science,all the workflow outputs are stored in GeoServer,documented in GeoNetwork,and made available through MapStore.This enables creating a dashboard for better visualization of the results for end-users.The results from this study demonstrate the capability of VLab platform for water stress detection from time series of SEVIRI-ET data.展开更多
Essential Climate Variables(ECVs)are geophysical records generated from systematic Earth Observations associated with climate variations,changes,and impacts.ECVs products support the data and information needs of inte...Essential Climate Variables(ECVs)are geophysical records generated from systematic Earth Observations associated with climate variations,changes,and impacts.ECVs 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 perspective.We 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 catalogues.In 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.展开更多
基金The authors would like to thank the Swiss Federal Office for the Environment(FOEN)for their financial support to the Swiss Data Cube.
文摘Pressures on natural resources are increasing and a number of challenges need to be overcome to meet the needs of a growing population in a period of environmental variability.Some of these environmental issues can be monitored using remotely sensed Earth Observations(EO)data that are increasingly available from a number of freely and openly accessible repositories.However,the full information potential of EO data has not been yet realized.They remain still underutilized mainly because of their complexity,increasing volume,and the lack of efficient processing capabilities.EO Data Cubes(DC)are a new paradigm aiming to realize the full potential of EO data by lowering the barriers caused by these Big data challenges and providing access to large spatio-temporal data in an analysis ready form.Systematic and regular provision of Analysis Ready Data(ARD)will significantly reduce the burden on EO data users.Nevertheless,ARD are not commonly produced by data providers and therefore getting uniform and consistent ARD remains a challenging task.This paper presents an approach to enable rapid data access and pre-processing to generate ARD using interoperable services chains.The approach has been tested and validated generating Landsat ARD while building the Swiss Data Cube.
基金This research was funded by the European Commission“Horizon 2020 Program”ERA-PLANET/GEOEssential project,grant number 689443.
文摘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 Earth.To 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 sources.Implementations like Trends.Earth to monitor land degradation in accordance with the SDG15.3.1 rely on default datasets of coarse spatial resolution provided by MODIS or AVHRR.Consequently,there is a need to develop methodologies to benefit from medium to high-resolution satellite EO data(e.g.Landsat 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.
文摘Environmental changes are significantly modifying terrestrial vegetation dynamics,with serious consequences on Earth system functioning and provision of ecosystem services.Land 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 forces.At the global scale,long-term increase of vegetation greening has been widely reported notably in seasonally snow-covered ecosystems as a response to warming climate.However,greening trends at the national scale have received less attention,although countries like Switzerland are prone to important changing climate conditions.Hereby,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 conditions.Results 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 Switzerland.In 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 conditions.At 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 changes.Finally,the NDVI values are mostly a function of temperature at elevations below the tree line rather than precipitation.The findings suggest that multi-annual and seasonal NDVI can be a valuable indicator to monitor vegetation conditions at different scales and can provide complementary observations for national statistics on the ecological state of vegetation to monitor land affected by changing environmental conditions.This work is aiming at strengthening the insights into the driving factors of vegetation change and supporting monitoring changing land conditions to provide guidance for effective and efficient environmental management and sustainable development policy advice at the national scale.
基金This study was partially funded by UNDP Grant:BOL/118208(“Laboratorios de Recuperación Temprana”),a study led by Fundación para Conservación del Bosque Chiquitano(www.fcbc.org.bo)to determine forest patches requiring post-fire assisted recovery in the aftermath of 2019 wildfires in Santa Cruz,Bolivia:“Plan Estratégico para la Restauración de lasÁreas Afectadas por los Incendios en el 2019”.
文摘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 area.Calibrating 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 changes.Comparing 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://doi.org/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 use.This code builds on the Good Practice Guidance document(ver-sion 1)for monitoring SDG Indicator 15.3.1.We 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 WUE.Significant 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 negative.The sign and significance of the correlation between ANPP and WUE may also diverge over time.With further analysis,it may be possible to interpret this relationship in terms of the drivers of change in plant productivity and ecosystem resilience.
文摘Measuring the achievement of a sustainable development requires the integration of various data sets and disciplines describing bio-physical and socio-economic conditions.These data allow characterizing any location on Earth,assessing the status of the environment at various scales(e.g.national,regional,global),understanding interactions between different systems(e.g.atmosphere,hydrosphere,biosphere,geosphere),and modeling future changes.The 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 Earth.GEO’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 EVs.The contributions of the European Union projects ConnectinGEO and GEOEssential in the evaluation of existing EV classes are introduced.Finally,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.
基金the Austrian Research Promotion Agency(FFG)under the Austrian Space Application Programme(ASAP)within the projects Sen2Cube.at(project no.:866016)SemantiX(project no.:878939)SIMS(project no.:885365).
文摘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 implementations.EO data cubes are a leading technology for facilitating big EO data analysis and can be deployed on different spatial scales:local,national,regional,or global.Several EO data cubes with a geographic focus(“local EO data cubes”)have been implemented.However,their alignment with the Digital Earth(DE)vision and the benefits and trade-offs in creating and maintaining them ought to be further examined.We 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.These developments include enabling local EO data cubes based on public,global,and cloud-native EO data streaming and interoperability between local EO data cubes.We 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.
文摘The concept of Digital Earth(DE)was formalized by Al Gore in 1998.At that time the technologies needed for its implementation were in an embryonic stage and the concept was quite visionary.Since then digital technologies have progressed significantly and their speed and pervasiveness have generated and are still causing the digital transformation of our society.This creates new opportunities and challenges for the realization of DE.‘What is DE today?’,‘What could DE be in the future?’,and‘What is needed to make DE a reality?’.To answer these questions it is necessary to examine DE considering all the technological,scientific,social,and economic aspects,but also bearing in mind the principles that inspired its formulation.By understanding the lessons learned from the past,it becomes possible to identify the remaining scientific and technological challenges,and the actions needed to achieve the ultimate goal of a‘Digital Earth for all’.This article reviews the evolution of the DE vision and its multiple definitions,illustrates what has been achieved so far,explains the impact of digital transformation,illustrates the new vision,and concludes with possible future scenarios and recommended actions to facilitate full DE implementation.
基金This work was supported by European Commission[grant number H2020 ERA-PLANET project No.689443].
文摘When defining indicators on the environment,the use of existing initiatives should be a priority rather than redefining indicators each time.From an Information,Communication and Technology perspective,data interoperability and standardization are critical to improve data access and exchange as promoted by the Group on Earth Observations.GEOEssential is following an end-user driven approach by defining Essential Variables(EVs),as an intermediate value between environmental policy indicators and their appropriate data sources.From international to local scales,environmental policies and indicators are increasingly percolating down from the global to the local agendas.The scientific business processes for the generation of EVs and related indicators can be formalized in workflows specifying the necessary logical steps.To this aim,GEOEssential is developing a Virtual Laboratory the main objective of which is to instantiate conceptual workflows,which are stored in a dedicated knowledge base,generating executable workflows.To interpret and present the relevant outputs/results carried out by the different thematic workflows considered in GEOEssential(i.e.biodiversity,ecosystems,extractives,night light,and food-water-energy nexus),a Dashboard is built as a visual front-end.This is a valuable instrument to track progresses towards environmental policies.
基金funded by the Data Science Impulse grant of the University of Geneva.
文摘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 systems.Nevertheless,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 change.Hereafter,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 region.Findings suggest that time-first approach is a valuable alternative to space-first approaches,accounting for the intra-annual variability of classes,hence improving classification performances.Results demonstrate that Deep Learning methods outperform more traditional Machine Learning ones such as Random Forest,providing more accurate predictions with lower uncertainty.The produced land cover map has a high accuracy,an improved spatial resolution,while at the same time preserving the statistical significance(i.e.class proportion)of the official national dataset.This 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 capability.However 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.
基金supported by The European Commission HORIZON 2020 Program ERA-PLANET/GEOEssential project[grant number 689443].
文摘Cloud computing facilities can provide crucial computing support for processing the time series of satellite data and exploiting their spatio-temporal information content.However,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 products.In 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 stress.To 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 VLab.The 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 country.To support open science,all the workflow outputs are stored in GeoServer,documented in GeoNetwork,and made available through MapStore.This enables creating a dashboard for better visualization of the results for end-users.The results from this study demonstrate the capability of VLab platform for water stress detection from time series of SEVIRI-ET data.
基金This work was supported by Horizon 2020 Framework Programme[grant number 689443].
文摘Essential Climate Variables(ECVs)are geophysical records generated from systematic Earth Observations associated with climate variations,changes,and impacts.ECVs 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 perspective.We 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 catalogues.In 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.
