#globalpasturewatch — Public Fediverse posts
Live and recent posts from across the Fediverse tagged #globalpasturewatch, aggregated by home.social.
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Annual 30 Metre Maps Of Global Grassland Class And Extent (2000–2022) Based On Spatiotemporal Machine Learning
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https://doi.org/10.1038/s41597-024-04139-6 <-- shared paper
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https://landcarbonlab.org/insights/first-global-annual-cultivated-natural-grassland-data/ <-- blog post
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https://developers.google.com/earth-engine/datasets/publisher/global-pasture-watch <-- Google EarthEngine
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https://github.com/wri/global-pasture-watch <-- GitHub Repository
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#GIS #spatial #mapping #workflow #global #grassland #shrubland #vegetation #spatialanalysis #AI #machinelearning #spatiotemporal #remotesensing #earthobservation #GLAD #landsat #imagery #opendata #landcover #agriculture #change #natural #model #modeling #pasture #cultivated #GlobalPastureWatch #grazing #livestock -
Annual 30 Metre Maps Of Global Grassland Class And Extent (2000–2022) Based On Spatiotemporal Machine Learning
--
https://doi.org/10.1038/s41597-024-04139-6 <-- shared paper
--
https://landcarbonlab.org/insights/first-global-annual-cultivated-natural-grassland-data/ <-- blog post
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https://developers.google.com/earth-engine/datasets/publisher/global-pasture-watch <-- Google EarthEngine
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https://github.com/wri/global-pasture-watch <-- GitHub Repository
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#GIS #spatial #mapping #workflow #global #grassland #shrubland #vegetation #spatialanalysis #AI #machinelearning #spatiotemporal #remotesensing #earthobservation #GLAD #landsat #imagery #opendata #landcover #agriculture #change #natural #model #modeling #pasture #cultivated #GlobalPastureWatch #grazing #livestock -
Annual 30 Metre Maps Of Global Grassland Class And Extent (2000–2022) Based On Spatiotemporal Machine Learning
--
https://doi.org/10.1038/s41597-024-04139-6 <-- shared paper
--
https://landcarbonlab.org/insights/first-global-annual-cultivated-natural-grassland-data/ <-- blog post
--
https://developers.google.com/earth-engine/datasets/publisher/global-pasture-watch <-- Google EarthEngine
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https://github.com/wri/global-pasture-watch <-- GitHub Repository
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#GIS #spatial #mapping #workflow #global #grassland #shrubland #vegetation #spatialanalysis #AI #machinelearning #spatiotemporal #remotesensing #earthobservation #GLAD #landsat #imagery #opendata #landcover #agriculture #change #natural #model #modeling #pasture #cultivated #GlobalPastureWatch #grazing #livestock -
Annual 30 Metre Maps Of Global Grassland Class And Extent (2000–2022) Based On Spatiotemporal Machine Learning
--
https://doi.org/10.1038/s41597-024-04139-6 <-- shared paper
--
https://landcarbonlab.org/insights/first-global-annual-cultivated-natural-grassland-data/ <-- blog post
--
https://developers.google.com/earth-engine/datasets/publisher/global-pasture-watch <-- Google EarthEngine
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https://github.com/wri/global-pasture-watch <-- GitHub Repository
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#GIS #spatial #mapping #workflow #global #grassland #shrubland #vegetation #spatialanalysis #AI #machinelearning #spatiotemporal #remotesensing #earthobservation #GLAD #landsat #imagery #opendata #landcover #agriculture #change #natural #model #modeling #pasture #cultivated #GlobalPastureWatch #grazing #livestock -
Annual 30 Metre Maps Of Global Grassland Class And Extent (2000–2022) Based On Spatiotemporal Machine Learning
--
https://doi.org/10.1038/s41597-024-04139-6 <-- shared paper
--
https://landcarbonlab.org/insights/first-global-annual-cultivated-natural-grassland-data/ <-- blog post
--
https://developers.google.com/earth-engine/datasets/publisher/global-pasture-watch <-- Google EarthEngine
--
https://github.com/wri/global-pasture-watch <-- GitHub Repository
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#GIS #spatial #mapping #workflow #global #grassland #shrubland #vegetation #spatialanalysis #AI #machinelearning #spatiotemporal #remotesensing #earthobservation #GLAD #landsat #imagery #opendata #landcover #agriculture #change #natural #model #modeling #pasture #cultivated #GlobalPastureWatch #grazing #livestock -
Within the #GlobalPastureWatch (https://LandCarbonLab.org/data) project we are building global open data sets at 30-m resolution to help World Resources Institute produce most accurate and most up-to-date maps of pastures and grasslands including changes in canopy height and monthly GPP. The LandCarbonLab.org project specifically aims at deploying solutions for sustainable landscapes on the ground across boundaries.
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We are processing global 1997-2022+ Landsat images to produce complete, consistent, current & correct (cloud-free) mosaics per continent & globally. Our interest is primarily in monthly / bimonthly values, then we plan to use these to quantify vegetation, map pasture types, crops / land cover, tillage intensity, dynamic soil properties and similar. Read more in: https://opengeohub.org/article/analysis-ready-and-cloud-optimized-arco-landsat-data-for-all/ #OpenEarthMonitor #AI4soilhealth #globalpasturewatch