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
To comprehensively support national and international initiatives for sustainable development,land cover products need to be reliably and routinely generated within operational frameworks.Coupled with consistent seman...To comprehensively support national and international initiatives for sustainable development,land cover products need to be reliably and routinely generated within operational frameworks.Coupled 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 domains.The 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 location.The Open Data Cube(ODC)initiative offers a framework for operational continental-scale land cover mapping using analysis-ready Earth Observation data.This 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 continent.DEA Land Cover provides annual maps from 1988 to 2020 at 25 m resolution.Output 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.展开更多
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.
基金support of Geoscience Australia,through the DEA Land Cover project,and the European Regional Development Fund(ERDF)Sêr Cymru II programme award[grant number 80761-AU-108,Living Wales].
文摘To comprehensively support national and international initiatives for sustainable development,land cover products need to be reliably and routinely generated within operational frameworks.Coupled 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 domains.The 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 location.The Open Data Cube(ODC)initiative offers a framework for operational continental-scale land cover mapping using analysis-ready Earth Observation data.This 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 continent.DEA Land Cover provides annual maps from 1988 to 2020 at 25 m resolution.Output 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.
