随着地球观测进入大数据时代,传统的数据管理技术已经难以适应大数据需求,Open Data Cube(ODC)作为新型的开源的地球观测数据管理与分析平台,适合进行时间序列数据的高性能计算和探索性数据分析,是亚大区域综合地球观测系统AOGEOSS的重...随着地球观测进入大数据时代,传统的数据管理技术已经难以适应大数据需求,Open Data Cube(ODC)作为新型的开源的地球观测数据管理与分析平台,适合进行时间序列数据的高性能计算和探索性数据分析,是亚大区域综合地球观测系统AOGEOSS的重要技术支撑平台。但当前ODC对国产卫星影像支持不友好,缺乏自动化管理和数据组织工具,使用ODC进行国产卫星影像数据管理的技术不成熟。因此,本文以高分一号卫星影像为例,通过开发ODC_GFTool中间件和自定义高分卫星影像产品格式探索ODC框架下国产影像数据自动化管理流程,最后以鄱阳湖为试验区,进行了基于ODC框架的水体提取应用实验,论证了ODC框架下国产卫星数据存取的可行性,研究结果表明相较于传统的数据处理工具ODC具有明显的效率优势,能够为AOGEOSS基础设施建设和国产卫星影像数据管理提供一定的参考。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
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
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 ...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 SDGs.Reliable,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 SDGs.The aim of this study was to implement a global framework for classifying land cover.The 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 reporting.We 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 data.We 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 availability.To ensure easy adoption of Living Earth for SDG reporting,we identified key environmental descriptors to provide resource allocation recommendations for generating routinely retrieved input parameters.Living Earth provides an optimal platform for global adoption of EO4SDGs ensuring a transparent methodology that allows monitoring to be standardised for all countries.展开更多
文摘随着地球观测进入大数据时代,传统的数据管理技术已经难以适应大数据需求,Open Data Cube(ODC)作为新型的开源的地球观测数据管理与分析平台,适合进行时间序列数据的高性能计算和探索性数据分析,是亚大区域综合地球观测系统AOGEOSS的重要技术支撑平台。但当前ODC对国产卫星影像支持不友好,缺乏自动化管理和数据组织工具,使用ODC进行国产卫星影像数据管理的技术不成熟。因此,本文以高分一号卫星影像为例,通过开发ODC_GFTool中间件和自定义高分卫星影像产品格式探索ODC框架下国产影像数据自动化管理流程,最后以鄱阳湖为试验区,进行了基于ODC框架的水体提取应用实验,论证了ODC框架下国产卫星数据存取的可行性,研究结果表明相较于传统的数据处理工具ODC具有明显的效率优势,能够为AOGEOSS基础设施建设和国产卫星影像数据管理提供一定的参考。
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.
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.
基金This research has been conducted with the support of Geoscience Australia,through the DEA Land Cover project,and the European Research Development Fund(ERDF)Sêr Cymru II program award(80761-AU-108,Living Wales).
文摘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 SDGs.Reliable,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 SDGs.The aim of this study was to implement a global framework for classifying land cover.The 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 reporting.We 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 data.We 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 availability.To ensure easy adoption of Living Earth for SDG reporting,we identified key environmental descriptors to provide resource allocation recommendations for generating routinely retrieved input parameters.Living Earth provides an optimal platform for global adoption of EO4SDGs ensuring a transparent methodology that allows monitoring to be standardised for all countries.
