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58 results for “tomhengl”
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Excited to join @opengeohub summer school with @nowosad, @tomhengl, @robinlovelace et al. for this week after an impressively smooth trip to #Poznan via beautiful Wroclaw
#OpenGeoHub -
To extend our OpenLandMap-soildb (https://doi.org/10.5194/essd-18-989-2026), we are adding global predictions of organic soils extent and peat depth at 30 m resolution. We have fitted models and generated predictions of (1) organic soils based on cca 470k training points with USDA soil classification system, (2) peat depth based on cca 350k training points published in Peat-DBase v.1 and multiple national data sources.
Data: https://doi.org/10.5281/zenodo.20121127
Post: https://medium.com/@opengeohub/global-organic-soils-extent-and-peat-depth-at-30-m-spatial-resolution-based-on-multisource-eo-data-c6e00f390069
@ai4soilhealth @landcarbonlab -
Compare with e.g. the Netherlands
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USA and Chine are apparently the world's no.1 and no.2. China, the world's factory, generates roughly 16% of all global merchandise exports. USA is the world's biggest economy & the biggest military. But how do China and USA rank on important issues such as Democracy (https://ourworldindata.org/grapher/democracy-index-eiu), Freedom & Prosperity (https://www.atlanticcouncil.org/programs/freedom-and-prosperity-center/2026-atlas-freedom-and-prosperity-around-the-world/), Human Rights, Corruption perception, World Giving Index (https://www.cafonline.org/insights/research/world-giving-index), or Children well-being (https://www.unicef.org/innocenti/reports/child-well-being-unpredictable-world)? Not that well.
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We are hosting 2 science webinars in May: "Global 30-m resolution ensemble DTM and (median) vegetation height data (2000-2022)"
https://www.eventbrite.com/e/1334229118209
"ML methods for census data: lessons learned from mapping global livestocks"
https://www.eventbrite.com/e/1334292999279
to celebrate #EarthDay2025 and under the auspices of the @earthmonitororg and Land Carbon Lab / Global Pasture Watch (https://landcarbonlab.org/about-global-pasture-watch/) projects
Reserve your place before it is too late! -
#SoilOrganicCarbon (organic matter / plant and animal residues at various stages of decomposition) is one of the key indicators of hashtag#soilhealth / soil ecosystem services. But how do you measure and report SOC? We advocate that SOC density [kg/m3] is the central variable to monitor SOC dynamics. It is derived by measuring SOC content and Bulk density (separately) and then by a simple formula:
SOC [kg/m3] = SOC [dg/kg]/100 * BD [kg/m3] * (1-CF)
*CF is the coarse fragments fraction (0-1).
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What exactly is a "digital twin"? Paul Clarke (CTO at Ocado) in this interesting podcast (https://www.buzzsprout.com/1154870/episodes/16540872-351-paul-clarke-ai-digital-twins-modeling-engineering-our-way-out) tries to explain the difference between:
1. #Simulation - any mathematical realization of some model using different scenarios,
2. #Emulation - a simulation that tries to copy actual physical world,
3. #Visualization ,
4. #DigitalShadow - digital copy of physical twin with data flow 1 way only,
5. #DigitalTwin - physical and dig twin and data flows connected in 2 directions, -
What exactly is a "digital twin"? Paul Clarke (CTO at Ocado) in this interesting podcast (https://www.buzzsprout.com/1154870/episodes/16540872-351-paul-clarke-ai-digital-twins-modeling-engineering-our-way-out) tries to explain the difference between:
1. #Simulation - any mathematical realization of some model using different scenarios,
2. #Emulation - a simulation that tries to copy actual physical world,
3. #Visualization ,
4. #DigitalShadow - digital copy of physical twin with data flow 1 way only,
5. #DigitalTwin - physical and dig twin and data flows connected in 2 directions, -
What exactly is a "digital twin"? Paul Clarke (CTO at Ocado) in this interesting podcast (https://www.buzzsprout.com/1154870/episodes/16540872-351-paul-clarke-ai-digital-twins-modeling-engineering-our-way-out) tries to explain the difference between:
1. #Simulation - any mathematical realization of some model using different scenarios,
2. #Emulation - a simulation that tries to copy actual physical world,
3. #Visualization ,
4. #DigitalShadow - digital copy of physical twin with data flow 1 way only,
5. #DigitalTwin - physical and dig twin and data flows connected in 2 directions, -
What exactly is a "digital twin"? Paul Clarke (CTO at Ocado) in this interesting podcast (https://www.buzzsprout.com/1154870/episodes/16540872-351-paul-clarke-ai-digital-twins-modeling-engineering-our-way-out) tries to explain the difference between:
1. #Simulation - any mathematical realization of some model using different scenarios,
2. #Emulation - a simulation that tries to copy actual physical world,
3. #Visualization ,
4. #DigitalShadow - digital copy of physical twin with data flow 1 way only,
5. #DigitalTwin - physical and dig twin and data flows connected in 2 directions, -
What exactly is a "digital twin"? Paul Clarke (CTO at Ocado) in this interesting podcast (https://www.buzzsprout.com/1154870/episodes/16540872-351-paul-clarke-ai-digital-twins-modeling-engineering-our-way-out) tries to explain the difference between:
1. #Simulation - any mathematical realization of some model using different scenarios,
2. #Emulation - a simulation that tries to copy actual physical world,
3. #Visualization ,
4. #DigitalShadow - digital copy of physical twin with data flow 1 way only,
5. #DigitalTwin - physical and dig twin and data flows connected in 2 directions, -
I made long-term estimates of soil moisture at 1km for EU (quarterly 2014-2024) based on 3728 daily rasters from Copernicus Land Monitoring Service (Surface Soil Moisture 2014-present Europe, daily – version 1). I tried also producing monthly/bimonthly aggregates, but these have too many artifacts. It is still not ARD data because there are still gaps and issues, but if you test it and give me some feedback we can look how to improve this data together: https://doi.org/10.5281/zenodo.14833052
#AI4SoilHealth -
... and this is one of thousands things that make Canada (and similar allies) different from USA (and not the #51st state). Besides, TikTok is largely a troian, but so are Meta's/X.com apps (https://mastodon.social/@eff/113844921122999712)? Instead of totally banning social media apps, what about taxing them higher+educating people of the risks and how to defend themselves? Not to mention - hey, we have Mastodon which is decentralized, community driven, not-for-profit... and does not scans your mobile phone/sells data.
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Last 12+ months we've put an effort to process GLAD Landsat (1.4PB) to try to make complete consistent global cloud-free mosaics (as open data) / part of the #LandCarbonLab project. So proud of Davide Consoli and the team for getting this publication and the data out. Stay tuned as we plan to release 10× more data in 2025. Many thanks to our colleagues from University of Maryland / WRI, LAPIG UFG for helping with processing and quality control. https://peerj.com/articles/18585
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I made a time-series of global annual cropland fractions (0-100%) for 2000 to 2022 based on the GLAD cropland product (https://glad.umd.edu/dataset/croplands). You can download the data from here: https://zenodo.org/doi/10.5281/zenodo.12527545
I prepared data in 4 spatial resolutions: 30-m, 100-m, 250-m and 1km (the 100-m and 30-m resolution data does not fit Zenodo, so you need to used links provided). Total size of this data is about 120GB. I've run all processing using GDAL and terra pkg in R. #OpenEarthMonitor #OpenData
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I made a time-series of global annual cropland fractions (0-100%) for 2000 to 2022 based on the GLAD cropland product (https://glad.umd.edu/dataset/croplands). You can download the data from here: https://zenodo.org/doi/10.5281/zenodo.12527545
I prepared data in 4 spatial resolutions: 30-m, 100-m, 250-m and 1km (the 100-m and 30-m resolution data does not fit Zenodo, so you need to used links provided). Total size of this data is about 120GB. I've run all processing using GDAL and terra pkg in R. #OpenEarthMonitor #OpenData
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I made a time-series of global annual cropland fractions (0-100%) for 2000 to 2022 based on the GLAD cropland product (https://glad.umd.edu/dataset/croplands). You can download the data from here: https://zenodo.org/doi/10.5281/zenodo.12527545
I prepared data in 4 spatial resolutions: 30-m, 100-m, 250-m and 1km (the 100-m and 30-m resolution data does not fit Zenodo, so you need to used links provided). Total size of this data is about 120GB. I've run all processing using GDAL and terra pkg in R. #OpenEarthMonitor #OpenData
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I made a time-series of global annual cropland fractions (0-100%) for 2000 to 2022 based on the GLAD cropland product (https://glad.umd.edu/dataset/croplands). You can download the data from here: https://zenodo.org/doi/10.5281/zenodo.12527545
I prepared data in 4 spatial resolutions: 30-m, 100-m, 250-m and 1km (the 100-m and 30-m resolution data does not fit Zenodo, so you need to used links provided). Total size of this data is about 120GB. I've run all processing using GDAL and terra pkg in R. #OpenEarthMonitor #OpenData
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I made a time-series of global annual cropland fractions (0-100%) for 2000 to 2022 based on the GLAD cropland product (https://glad.umd.edu/dataset/croplands). You can download the data from here: https://zenodo.org/doi/10.5281/zenodo.12527545
I prepared data in 4 spatial resolutions: 30-m, 100-m, 250-m and 1km (the 100-m and 30-m resolution data does not fit Zenodo, so you need to used links provided). Total size of this data is about 120GB. I've run all processing using GDAL and terra pkg in R. #OpenEarthMonitor #OpenData
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I've created the annual mean, max and standard deviation for (1) bare soil fraction, and (2) photosynthetic and (3) non-photosynthetic vegetation annual at 500 m resolution for 2001–2023 (the original data source is explained in Hill and Guerschman, 2022 / MCD43A4 product). You can access the data from: https://zenodo.org/doi/10.5281/zenodo.11961219
If you spot an issue or bug, please post here.
#OpenData #AI4SoilHealth #OpenEarthMonitor -
I've created the annual mean, max and standard deviation for (1) bare soil fraction, and (2) photosynthetic and (3) non-photosynthetic vegetation annual at 500 m resolution for 2001–2023 (the original data source is explained in Hill and Guerschman, 2022 / MCD43A4 product). You can access the data from: https://zenodo.org/doi/10.5281/zenodo.11961219
If you spot an issue or bug, please post here.
#OpenData #AI4SoilHealth #OpenEarthMonitor -
I've created the annual mean, max and standard deviation for (1) bare soil fraction, and (2) photosynthetic and (3) non-photosynthetic vegetation annual at 500 m resolution for 2001–2023 (the original data source is explained in Hill and Guerschman, 2022 / MCD43A4 product). You can access the data from: https://zenodo.org/doi/10.5281/zenodo.11961219
If you spot an issue or bug, please post here.
#OpenData #AI4SoilHealth #OpenEarthMonitor -
I've created the annual mean, max and standard deviation for (1) bare soil fraction, and (2) photosynthetic and (3) non-photosynthetic vegetation annual at 500 m resolution for 2001–2023 (the original data source is explained in Hill and Guerschman, 2022 / MCD43A4 product). You can access the data from: https://zenodo.org/doi/10.5281/zenodo.11961219
If you spot an issue or bug, please post here.
#OpenData #AI4SoilHealth #OpenEarthMonitor -
I've created the annual mean, max and standard deviation for (1) bare soil fraction, and (2) photosynthetic and (3) non-photosynthetic vegetation annual at 500 m resolution for 2001–2023 (the original data source is explained in Hill and Guerschman, 2022 / MCD43A4 product). You can access the data from: https://zenodo.org/doi/10.5281/zenodo.11961219
If you spot an issue or bug, please post here.
#OpenData #AI4SoilHealth #OpenEarthMonitor -
Are you looking for #OpenScience global environmental data sets to use for modeling or decision making? We are putting terrabytes of global COGs on https://OpenLandMap.org, part of our Horizon Europe #OpenEarthMonitor project and with many thanks to @gilabrs and colleagues from the OEMC project. To download data or analyze smaller parts in #qgis please use the oemc QGIS plugin (all explained in the GIF below). Let us know if you have problems accessing the data or ideas what we could add next!
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Are you looking for #OpenScience global environmental data sets to use for modeling or decision making? We are putting terrabytes of global COGs on https://OpenLandMap.org, part of our Horizon Europe #OpenEarthMonitor project and with many thanks to @gilabrs and colleagues from the OEMC project. To download data or analyze smaller parts in #qgis please use the oemc QGIS plugin (all explained in the GIF below). Let us know if you have problems accessing the data or ideas what we could add next!
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Are you looking for #OpenScience global environmental data sets to use for modeling or decision making? We are putting terrabytes of global COGs on https://OpenLandMap.org, part of our Horizon Europe #OpenEarthMonitor project and with many thanks to @gilabrs and colleagues from the OEMC project. To download data or analyze smaller parts in #qgis please use the oemc QGIS plugin (all explained in the GIF below). Let us know if you have problems accessing the data or ideas what we could add next!
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Are you looking for #OpenScience global environmental data sets to use for modeling or decision making? We are putting terrabytes of global COGs on https://OpenLandMap.org, part of our Horizon Europe #OpenEarthMonitor project and with many thanks to @gilabrs and colleagues from the OEMC project. To download data or analyze smaller parts in #qgis please use the oemc QGIS plugin (all explained in the GIF below). Let us know if you have problems accessing the data or ideas what we could add next!
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Are you looking for #OpenScience global environmental data sets to use for modeling or decision making? We are putting terrabytes of global COGs on https://OpenLandMap.org, part of our Horizon Europe #OpenEarthMonitor project and with many thanks to @gilabrs and colleagues from the OEMC project. To download data or analyze smaller parts in #qgis please use the oemc QGIS plugin (all explained in the GIF below). Let us know if you have problems accessing the data or ideas what we could add next!
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So proud of Julia @opengeohub and the #OpenEarthMonitor project in general for delivering cca. 1.4TB ARCO (analysis-ready cloud-optimized) #OpenData + models and trend-analysis all explained in @PeerJ paper:
"FAPAR monthly time-series at 250 m spatial resolution for 2000-2021"
https://peerj.com/articles/16972/
We specifically looked at differences between potential FAPAR and trends in FAPAR over the last 22 years. The gaps we estimated could help environmental agencies assess land degradation.