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#earthobservation — Public Fediverse posts

Live and recent posts from across the Fediverse tagged #earthobservation, aggregated by home.social.

  1. In 2025, an area equivalent to **eight Norways** burned across the planet.

    That sounds like a catastrophe.
    - It isn't quite that simple.

    Not every fire is a disaster. Some are natural. Some are deliberate. Some are tiny. Some are enormous.

    So what actually happened?

    Our latest feature digs into the satellite data behind **265 million hectares of recorded burned area** — and asks a more interesting question than simply *how many fires were there?*

    **What the hell was actually going on?**

    🔥 **EIGHT NORWAYS BURNED IN ONE YEAR**
    A new feature from RABAGAS Magazine.
    rabagas.ghost.io/eight-norways

    #Rabagasmagazine #Wildfires #ClimateScience #EarthObservation #ClimateCrisis #SatelliteData #DataJournalism #Climate

  2. In 2025, an area equivalent to **eight Norways** burned across the planet.

    That sounds like a catastrophe.
    - It isn't quite that simple.

    Not every fire is a disaster. Some are natural. Some are deliberate. Some are tiny. Some are enormous.

    So what actually happened?

    Our latest feature digs into the satellite data behind **265 million hectares of recorded burned area** — and asks a more interesting question than simply *how many fires were there?*

    **What the hell was actually going on?**

    🔥 **EIGHT NORWAYS BURNED IN ONE YEAR**
    A new feature from RABAGAS Magazine.
    rabagas.ghost.io/eight-norways

    #Rabagasmagazine #Wildfires #ClimateScience #EarthObservation #ClimateCrisis #SatelliteData #DataJournalism #Climate

  3. In 2025, an area equivalent to **eight Norways** burned across the planet.

    That sounds like a catastrophe.
    - It isn't quite that simple.

    Not every fire is a disaster. Some are natural. Some are deliberate. Some are tiny. Some are enormous.

    So what actually happened?

    Our latest feature digs into the satellite data behind **265 million hectares of recorded burned area** — and asks a more interesting question than simply *how many fires were there?*

    **What the hell was actually going on?**

    🔥 **EIGHT NORWAYS BURNED IN ONE YEAR**
    A new feature from RABAGAS Magazine.
    rabagas.ghost.io/eight-norways

    #Rabagasmagazine #Wildfires #ClimateScience #EarthObservation #ClimateCrisis #SatelliteData #DataJournalism #Climate

  4. In 2025, an area equivalent to **eight Norways** burned across the planet.

    That sounds like a catastrophe.
    - It isn't quite that simple.

    Not every fire is a disaster. Some are natural. Some are deliberate. Some are tiny. Some are enormous.

    So what actually happened?

    Our latest feature digs into the satellite data behind **265 million hectares of recorded burned area** — and asks a more interesting question than simply *how many fires were there?*

    **What the hell was actually going on?**

    🔥 **EIGHT NORWAYS BURNED IN ONE YEAR**
    A new feature from RABAGAS Magazine.
    rabagas.ghost.io/eight-norways

    #Rabagasmagazine #Wildfires #ClimateScience #EarthObservation #ClimateCrisis #SatelliteData #DataJournalism #Climate

  5. In 2025, an area equivalent to **eight Norways** burned across the planet.

    That sounds like a catastrophe.
    - It isn't quite that simple.

    Not every fire is a disaster. Some are natural. Some are deliberate. Some are tiny. Some are enormous.

    So what actually happened?

    Our latest feature digs into the satellite data behind **265 million hectares of recorded burned area** — and asks a more interesting question than simply *how many fires were there?*

    **What the hell was actually going on?**

    🔥 **EIGHT NORWAYS BURNED IN ONE YEAR**
    A new feature from RABAGAS Magazine.
    rabagas.ghost.io/eight-norways

    #Rabagasmagazine #Wildfires #ClimateScience #EarthObservation #ClimateCrisis #SatelliteData #DataJournalism #Climate

  6. To Predict Tree Death, Scientists Tapped Gamma Rays To Peer Underground
    (Airborne radiation sensors could help forecast and prevent drought-driven tree mortality_
    --
    science.org/content/article/pr <-- shared technical article
    --
    doi.org/10.1029/2026GL122182 <-- shared paper
    --
    H/T @hannah Richter
    “Over an 18-month period starting in 2023, the dense forests of Western Australia [WA] experienced a record-setting drought. Jarrah trees towering 35 metres high died off in patchy brown splotches, turning 400 square kilometres - 3% of the forest - into brittle, fire-prone stands. The event led researchers to wonder whether there was a better way to predict where such die-offs might occur both there and in other forests, a problem that has long been tricky to solve because important factors such as soil depth are hidden underground…
    Now, those same researchers have unveiled a surprising new tool for predicting tree mortality: gamma rays [link above.] Resulting from the natural decay of the potassium-40 isotope from granite-rich bedrock, the radiation acts as a proxy for soil depth, which in turn signals how much water a tree can access during drought. The new method could be applied to other highly weathered soils, which cover one-third of Earth’s ice-free land...”
    --
    "... PLAIN LANGUAGE SUMMARY: During a record-breaking drought and heat event in 2023–2024, forests in southwestern Australia experienced widespread, patchy die-off. While we know that extreme weather triggers these events, it is often a hidden factor, the thickness of soil and the depth to underlying bedrock, that determines which trees live or die. Trees growing in shallow soil over solid rock are highly vulnerable due to limited water storage. Here, [they] show how to map these hidden zones from the air using gamma rays that are naturally emitted by potassium in the ground. Like southwestern Australia, many parts of the world have highly weathered soils where potassium has been washed out of the upper layers of soil. However, [they] showed that higher potassium areas signal that potassium-rich bedrock is closer to the surface and this is sensitive for tens of meters. By comparing gamma ray maps with ground-based geophysical surveys and satellite data, [they] showed that these potassium hotspots accurately predict where forests are most likely to experience die-off during a drought. These types of soils cover about one-third of the Earth's land, so the method provides a powerful new tool for managers to identify and protect vulnerable forests from future, hotter droughts…”
    #GIS #spatial #mapping #spatialanalysis #spatiotemporal #Australia #WesternAustralia #WA #forests #vegetation #bush #jarrah #karri #drought #heat #extremedrought #extremeweather #climatechange #water #waterresources #dieoff #soil #weathering #erosion #moisture #nutrients #airborne #gammarays #GRS #granite #gneiss #bedrock #geology #potassium40 #potassium #K #remotesensing #earthobservation #groundwater #interstitial #subsurface #waterstorage #electricalresistivitytomography

  7. To Predict Tree Death, Scientists Tapped Gamma Rays To Peer Underground
    (Airborne radiation sensors could help forecast and prevent drought-driven tree mortality_
    --
    science.org/content/article/pr <-- shared technical article
    --
    doi.org/10.1029/2026GL122182 <-- shared paper
    --
    H/T @hannah Richter
    “Over an 18-month period starting in 2023, the dense forests of Western Australia [WA] experienced a record-setting drought. Jarrah trees towering 35 metres high died off in patchy brown splotches, turning 400 square kilometres - 3% of the forest - into brittle, fire-prone stands. The event led researchers to wonder whether there was a better way to predict where such die-offs might occur both there and in other forests, a problem that has long been tricky to solve because important factors such as soil depth are hidden underground…
    Now, those same researchers have unveiled a surprising new tool for predicting tree mortality: gamma rays [link above.] Resulting from the natural decay of the potassium-40 isotope from granite-rich bedrock, the radiation acts as a proxy for soil depth, which in turn signals how much water a tree can access during drought. The new method could be applied to other highly weathered soils, which cover one-third of Earth’s ice-free land...”
    --
    "... PLAIN LANGUAGE SUMMARY: During a record-breaking drought and heat event in 2023–2024, forests in southwestern Australia experienced widespread, patchy die-off. While we know that extreme weather triggers these events, it is often a hidden factor, the thickness of soil and the depth to underlying bedrock, that determines which trees live or die. Trees growing in shallow soil over solid rock are highly vulnerable due to limited water storage. Here, [they] show how to map these hidden zones from the air using gamma rays that are naturally emitted by potassium in the ground. Like southwestern Australia, many parts of the world have highly weathered soils where potassium has been washed out of the upper layers of soil. However, [they] showed that higher potassium areas signal that potassium-rich bedrock is closer to the surface and this is sensitive for tens of meters. By comparing gamma ray maps with ground-based geophysical surveys and satellite data, [they] showed that these potassium hotspots accurately predict where forests are most likely to experience die-off during a drought. These types of soils cover about one-third of the Earth's land, so the method provides a powerful new tool for managers to identify and protect vulnerable forests from future, hotter droughts…”
    #GIS #spatial #mapping #spatialanalysis #spatiotemporal #Australia #WesternAustralia #WA #forests #vegetation #bush #jarrah #karri #drought #heat #extremedrought #extremeweather #climatechange #water #waterresources #dieoff #soil #weathering #erosion #moisture #nutrients #airborne #gammarays #GRS #granite #gneiss #bedrock #geology #potassium40 #potassium #K #remotesensing #earthobservation #groundwater #interstitial #subsurface #waterstorage #electricalresistivitytomography

  8. To Predict Tree Death, Scientists Tapped Gamma Rays To Peer Underground
    (Airborne radiation sensors could help forecast and prevent drought-driven tree mortality_
    --
    science.org/content/article/pr <-- shared technical article
    --
    doi.org/10.1029/2026GL122182 <-- shared paper
    --
    H/T @hannah Richter
    “Over an 18-month period starting in 2023, the dense forests of Western Australia [WA] experienced a record-setting drought. Jarrah trees towering 35 metres high died off in patchy brown splotches, turning 400 square kilometres - 3% of the forest - into brittle, fire-prone stands. The event led researchers to wonder whether there was a better way to predict where such die-offs might occur both there and in other forests, a problem that has long been tricky to solve because important factors such as soil depth are hidden underground…
    Now, those same researchers have unveiled a surprising new tool for predicting tree mortality: gamma rays [link above.] Resulting from the natural decay of the potassium-40 isotope from granite-rich bedrock, the radiation acts as a proxy for soil depth, which in turn signals how much water a tree can access during drought. The new method could be applied to other highly weathered soils, which cover one-third of Earth’s ice-free land...”
    --
    "... PLAIN LANGUAGE SUMMARY: During a record-breaking drought and heat event in 2023–2024, forests in southwestern Australia experienced widespread, patchy die-off. While we know that extreme weather triggers these events, it is often a hidden factor, the thickness of soil and the depth to underlying bedrock, that determines which trees live or die. Trees growing in shallow soil over solid rock are highly vulnerable due to limited water storage. Here, [they] show how to map these hidden zones from the air using gamma rays that are naturally emitted by potassium in the ground. Like southwestern Australia, many parts of the world have highly weathered soils where potassium has been washed out of the upper layers of soil. However, [they] showed that higher potassium areas signal that potassium-rich bedrock is closer to the surface and this is sensitive for tens of meters. By comparing gamma ray maps with ground-based geophysical surveys and satellite data, [they] showed that these potassium hotspots accurately predict where forests are most likely to experience die-off during a drought. These types of soils cover about one-third of the Earth's land, so the method provides a powerful new tool for managers to identify and protect vulnerable forests from future, hotter droughts…”
    #GIS #spatial #mapping #spatialanalysis #spatiotemporal #Australia #WesternAustralia #WA #forests #vegetation #bush #jarrah #karri #drought #heat #extremedrought #extremeweather #climatechange #water #waterresources #dieoff #soil #weathering #erosion #moisture #nutrients #airborne #gammarays #GRS #granite #gneiss #bedrock #geology #potassium40 #potassium #K #remotesensing #earthobservation #groundwater #interstitial #subsurface #waterstorage #electricalresistivitytomography

  9. To Predict Tree Death, Scientists Tapped Gamma Rays To Peer Underground
    (Airborne radiation sensors could help forecast and prevent drought-driven tree mortality_
    --
    science.org/content/article/pr <-- shared technical article
    --
    doi.org/10.1029/2026GL122182 <-- shared paper
    --
    H/T @hannah Richter
    “Over an 18-month period starting in 2023, the dense forests of Western Australia [WA] experienced a record-setting drought. Jarrah trees towering 35 metres high died off in patchy brown splotches, turning 400 square kilometres - 3% of the forest - into brittle, fire-prone stands. The event led researchers to wonder whether there was a better way to predict where such die-offs might occur both there and in other forests, a problem that has long been tricky to solve because important factors such as soil depth are hidden underground…
    Now, those same researchers have unveiled a surprising new tool for predicting tree mortality: gamma rays [link above.] Resulting from the natural decay of the potassium-40 isotope from granite-rich bedrock, the radiation acts as a proxy for soil depth, which in turn signals how much water a tree can access during drought. The new method could be applied to other highly weathered soils, which cover one-third of Earth’s ice-free land...”
    --
    "... PLAIN LANGUAGE SUMMARY: During a record-breaking drought and heat event in 2023–2024, forests in southwestern Australia experienced widespread, patchy die-off. While we know that extreme weather triggers these events, it is often a hidden factor, the thickness of soil and the depth to underlying bedrock, that determines which trees live or die. Trees growing in shallow soil over solid rock are highly vulnerable due to limited water storage. Here, [they] show how to map these hidden zones from the air using gamma rays that are naturally emitted by potassium in the ground. Like southwestern Australia, many parts of the world have highly weathered soils where potassium has been washed out of the upper layers of soil. However, [they] showed that higher potassium areas signal that potassium-rich bedrock is closer to the surface and this is sensitive for tens of meters. By comparing gamma ray maps with ground-based geophysical surveys and satellite data, [they] showed that these potassium hotspots accurately predict where forests are most likely to experience die-off during a drought. These types of soils cover about one-third of the Earth's land, so the method provides a powerful new tool for managers to identify and protect vulnerable forests from future, hotter droughts…”

  10. To Predict Tree Death, Scientists Tapped Gamma Rays To Peer Underground
    (Airborne radiation sensors could help forecast and prevent drought-driven tree mortality_
    --
    science.org/content/article/pr <-- shared technical article
    --
    doi.org/10.1029/2026GL122182 <-- shared paper
    --
    H/T @hannah Richter
    “Over an 18-month period starting in 2023, the dense forests of Western Australia [WA] experienced a record-setting drought. Jarrah trees towering 35 metres high died off in patchy brown splotches, turning 400 square kilometres - 3% of the forest - into brittle, fire-prone stands. The event led researchers to wonder whether there was a better way to predict where such die-offs might occur both there and in other forests, a problem that has long been tricky to solve because important factors such as soil depth are hidden underground…
    Now, those same researchers have unveiled a surprising new tool for predicting tree mortality: gamma rays [link above.] Resulting from the natural decay of the potassium-40 isotope from granite-rich bedrock, the radiation acts as a proxy for soil depth, which in turn signals how much water a tree can access during drought. The new method could be applied to other highly weathered soils, which cover one-third of Earth’s ice-free land...”
    --
    "... PLAIN LANGUAGE SUMMARY: During a record-breaking drought and heat event in 2023–2024, forests in southwestern Australia experienced widespread, patchy die-off. While we know that extreme weather triggers these events, it is often a hidden factor, the thickness of soil and the depth to underlying bedrock, that determines which trees live or die. Trees growing in shallow soil over solid rock are highly vulnerable due to limited water storage. Here, [they] show how to map these hidden zones from the air using gamma rays that are naturally emitted by potassium in the ground. Like southwestern Australia, many parts of the world have highly weathered soils where potassium has been washed out of the upper layers of soil. However, [they] showed that higher potassium areas signal that potassium-rich bedrock is closer to the surface and this is sensitive for tens of meters. By comparing gamma ray maps with ground-based geophysical surveys and satellite data, [they] showed that these potassium hotspots accurately predict where forests are most likely to experience die-off during a drought. These types of soils cover about one-third of the Earth's land, so the method provides a powerful new tool for managers to identify and protect vulnerable forests from future, hotter droughts…”
    #GIS #spatial #mapping #spatialanalysis #spatiotemporal #Australia #WesternAustralia #WA #forests #vegetation #bush #jarrah #karri #drought #heat #extremedrought #extremeweather #climatechange #water #waterresources #dieoff #soil #weathering #erosion #moisture #nutrients #airborne #gammarays #GRS #granite #gneiss #bedrock #geology #potassium40 #potassium #K #remotesensing #earthobservation #groundwater #interstitial #subsurface #waterstorage #electricalresistivitytomography

  11. On-Demand Global Landsat Evapotranspiration Product - Development, Evaluation, And Dissemination
    --
    doi.org/10.1016/j.rse.2026.115 <-- shared paper
    --
    espa.cr.usgs.gov <-- shared (open data) USGS EROS Science Processing Architecture (ESPA) platform
    --
    etdata.org/ <-- OpenET SSEBop platform implementation (water management)
    --
    usgs.gov/landsat-missions/land <-- shared USGS Landsat Collection 2 Provisional Actual Evapotranspiration Science Product
    --
    H/T @mac Friedrichs | Remote Sensing Scientist, KBR | USGS EROS
    “This paper summarizes new achievements in developing and distributing the Global Landsat Level-3 Evapotranspiration (ET) product. It is freely available through the USGS EROS Science Processing Architecture (ESPA) platform (2nd link above). 🛰️ …
    Since the product launch in June 2020, there have been over 1.2 million Landsat-based ET orders around the world. This indicates increasing awareness and application of the ET data to help understand and manage the relationships among food, energy, and water resources. 🌽💧 …
    It features the ESPA workflow and evaluation of the upgraded SSEBop model using a variety of observational datasets and hydrologic regions, and examination against OpenET SSEBop platform implementation (3rd link above.)…”
    --
    “HIGHLIGHTS
    • ESPA platform allows access to on-demand, global, Landsat-based, ET products.
    • SSEBop model has been used to create ET data since 1982 through ESPA.
    • A quick estimation of field-scale crop consumptive water use can be achieved.
    • Numerous orders reflect worldwide extensive interest and utilization of the data.
    • Method, workflow, and performance of the actual ET data are presented in the study..."
    #EROSScienceProcessingArchitecture #climate #global #Evapotranspiration #ET #GIS #spatial #mapping #remotesensing #earthobservation #water #hydrology #opendata #Landsat #OpenET #SSEBop #WaterManagement #Agriculture #USGS #EROS #datadelivery #food #foodsecurity #energy #water #watersecurity #model #modeling #ESPA #farming #cropland #wateruse #waterresources #workflow
    @USGS @EROS

  12. On-Demand Global Landsat Evapotranspiration Product - Development, Evaluation, And Dissemination
    --
    doi.org/10.1016/j.rse.2026.115 <-- shared paper
    --
    espa.cr.usgs.gov <-- shared (open data) USGS EROS Science Processing Architecture (ESPA) platform
    --
    etdata.org/ <-- OpenET SSEBop platform implementation (water management)
    --
    usgs.gov/landsat-missions/land <-- shared USGS Landsat Collection 2 Provisional Actual Evapotranspiration Science Product
    --
    H/T @mac Friedrichs | Remote Sensing Scientist, KBR | USGS EROS
    “This paper summarizes new achievements in developing and distributing the Global Landsat Level-3 Evapotranspiration (ET) product. It is freely available through the USGS EROS Science Processing Architecture (ESPA) platform (2nd link above). 🛰️ …
    Since the product launch in June 2020, there have been over 1.2 million Landsat-based ET orders around the world. This indicates increasing awareness and application of the ET data to help understand and manage the relationships among food, energy, and water resources. 🌽💧 …
    It features the ESPA workflow and evaluation of the upgraded SSEBop model using a variety of observational datasets and hydrologic regions, and examination against OpenET SSEBop platform implementation (3rd link above.)…”
    --
    “HIGHLIGHTS
    • ESPA platform allows access to on-demand, global, Landsat-based, ET products.
    • SSEBop model has been used to create ET data since 1982 through ESPA.
    • A quick estimation of field-scale crop consumptive water use can be achieved.
    • Numerous orders reflect worldwide extensive interest and utilization of the data.
    • Method, workflow, and performance of the actual ET data are presented in the study..."
    #EROSScienceProcessingArchitecture #climate #global #Evapotranspiration #ET #GIS #spatial #mapping #remotesensing #earthobservation #water #hydrology #opendata #Landsat #OpenET #SSEBop #WaterManagement #Agriculture #USGS #EROS #datadelivery #food #foodsecurity #energy #water #watersecurity #model #modeling #ESPA #farming #cropland #wateruse #waterresources #workflow
    @USGS @EROS

  13. On-Demand Global Landsat Evapotranspiration Product - Development, Evaluation, And Dissemination
    --
    doi.org/10.1016/j.rse.2026.115 <-- shared paper
    --
    espa.cr.usgs.gov <-- shared (open data) USGS EROS Science Processing Architecture (ESPA) platform
    --
    etdata.org/ <-- OpenET SSEBop platform implementation (water management)
    --
    usgs.gov/landsat-missions/land <-- shared USGS Landsat Collection 2 Provisional Actual Evapotranspiration Science Product
    --
    H/T @mac Friedrichs | Remote Sensing Scientist, KBR | USGS EROS
    “This paper summarizes new achievements in developing and distributing the Global Landsat Level-3 Evapotranspiration (ET) product. It is freely available through the USGS EROS Science Processing Architecture (ESPA) platform (2nd link above). 🛰️ …
    Since the product launch in June 2020, there have been over 1.2 million Landsat-based ET orders around the world. This indicates increasing awareness and application of the ET data to help understand and manage the relationships among food, energy, and water resources. 🌽💧 …
    It features the ESPA workflow and evaluation of the upgraded SSEBop model using a variety of observational datasets and hydrologic regions, and examination against OpenET SSEBop platform implementation (3rd link above.)…”
    --
    “HIGHLIGHTS
    • ESPA platform allows access to on-demand, global, Landsat-based, ET products.
    • SSEBop model has been used to create ET data since 1982 through ESPA.
    • A quick estimation of field-scale crop consumptive water use can be achieved.
    • Numerous orders reflect worldwide extensive interest and utilization of the data.
    • Method, workflow, and performance of the actual ET data are presented in the study..."
    #EROSScienceProcessingArchitecture #climate #global #Evapotranspiration #ET #GIS #spatial #mapping #remotesensing #earthobservation #water #hydrology #opendata #Landsat #OpenET #SSEBop #WaterManagement #Agriculture #USGS #EROS #datadelivery #food #foodsecurity #energy #water #watersecurity #model #modeling #ESPA #farming #cropland #wateruse #waterresources #workflow
    @USGS @EROS

  14. On-Demand Global Landsat Evapotranspiration Product - Development, Evaluation, And Dissemination
    --
    doi.org/10.1016/j.rse.2026.115 <-- shared paper
    --
    espa.cr.usgs.gov <-- shared (open data) USGS EROS Science Processing Architecture (ESPA) platform
    --
    etdata.org/ <-- OpenET SSEBop platform implementation (water management)
    --
    usgs.gov/landsat-missions/land <-- shared USGS Landsat Collection 2 Provisional Actual Evapotranspiration Science Product
    --
    H/T @mac Friedrichs | Remote Sensing Scientist, KBR | USGS EROS
    “This paper summarizes new achievements in developing and distributing the Global Landsat Level-3 Evapotranspiration (ET) product. It is freely available through the USGS EROS Science Processing Architecture (ESPA) platform (2nd link above). 🛰️ …
    Since the product launch in June 2020, there have been over 1.2 million Landsat-based ET orders around the world. This indicates increasing awareness and application of the ET data to help understand and manage the relationships among food, energy, and water resources. 🌽💧 …
    It features the ESPA workflow and evaluation of the upgraded SSEBop model using a variety of observational datasets and hydrologic regions, and examination against OpenET SSEBop platform implementation (3rd link above.)…”
    --
    “HIGHLIGHTS
    • ESPA platform allows access to on-demand, global, Landsat-based, ET products.
    • SSEBop model has been used to create ET data since 1982 through ESPA.
    • A quick estimation of field-scale crop consumptive water use can be achieved.
    • Numerous orders reflect worldwide extensive interest and utilization of the data.
    • Method, workflow, and performance of the actual ET data are presented in the study..."
    #EROSScienceProcessingArchitecture #climate #global #Evapotranspiration #ET #GIS #spatial #mapping #remotesensing #earthobservation #water #hydrology #opendata #Landsat #OpenET #SSEBop #WaterManagement #Agriculture #USGS #EROS #datadelivery #food #foodsecurity #energy #water #watersecurity #model #modeling #ESPA #farming #cropland #wateruse #waterresources #workflow
    @USGS @EROS

  15. On-Demand Global Landsat Evapotranspiration Product - Development, Evaluation, And Dissemination
    --
    doi.org/10.1016/j.rse.2026.115 <-- shared paper
    --
    espa.cr.usgs.gov <-- shared (open data) USGS EROS Science Processing Architecture (ESPA) platform
    --
    etdata.org/ <-- OpenET SSEBop platform implementation (water management)
    --
    usgs.gov/landsat-missions/land <-- shared USGS Landsat Collection 2 Provisional Actual Evapotranspiration Science Product
    --
    H/T @mac Friedrichs | Remote Sensing Scientist, KBR | USGS EROS
    “This paper summarizes new achievements in developing and distributing the Global Landsat Level-3 Evapotranspiration (ET) product. It is freely available through the USGS EROS Science Processing Architecture (ESPA) platform (2nd link above). 🛰️ …
    Since the product launch in June 2020, there have been over 1.2 million Landsat-based ET orders around the world. This indicates increasing awareness and application of the ET data to help understand and manage the relationships among food, energy, and water resources. 🌽💧 …
    It features the ESPA workflow and evaluation of the upgraded SSEBop model using a variety of observational datasets and hydrologic regions, and examination against OpenET SSEBop platform implementation (3rd link above.)…”
    --
    “HIGHLIGHTS
    • ESPA platform allows access to on-demand, global, Landsat-based, ET products.
    • SSEBop model has been used to create ET data since 1982 through ESPA.
    • A quick estimation of field-scale crop consumptive water use can be achieved.
    • Numerous orders reflect worldwide extensive interest and utilization of the data.
    • Method, workflow, and performance of the actual ET data are presented in the study..."

    @USGS @EROS

  16. An MTG-I2 satellite launches on Ariane 6, completing Europe’s new weather satellite constellation.

    The launch of MTG-I2 from French Guiana completes the first trio of Meteosat Third Generation satellites, delivering high-resolution data and images every 2.5 minutes to transform weather forecasting and early warnings of extreme events.

    mediafaro.org/article/20260828

    #Satellite #Weather #ESA #Space #Science #Tech #Aerospace #Ariane6 #EarthObservation

  17. Details on the #flood catastrophe in #Nepal -
    you may also read this as a story of why #technology fails, and will always fail to prevent suffering like this.
    Countries that don't coordinate,
    commercial #satellite #earthObservation companies that have the #data but do not act,
    #sensor stations that get washed away.

    you may hope that #technology is the solution .. maybe ... (?)

    1/2

    kathmandupost.com/national/202

  18. Essential Earth Observation Variables For High-Level Multi-Scale Indicators And Policies
    --
    doi.org/10.1016/j.envsci.2021. <-- shared paper
    --
    earthobservations.org/about-us <-- shared author memorial
    --
    H/T @Paolo Mazzetti | Senior Researcher at the Institute of Technologies and Environmental Intelligence (ITIAm) of CNR
    “HIGHLIGHTS:
    • Defining Essential Variables (EVs) to describe the global socio-ecological system.
    • Setting the objectives of a new Group on Earth Observation (GEO) community activity on EVs.
    • Defining criteria for selecting EVs.
    • Demonstrating a fully functional workflow on land degradation.
    • Presenting a new vision for the mainstreaming of EVs in science-policy interfaces..."
    #Earthobservations #Essentialvariables #Indicators #Workflows #Sustainabledevelopmentgoals #Policy #earthobservation #essentialvariables #Indicators #Workflow #sustainabledevelopmentgoals #SDGs #policy #spatial #mapping #remotesensing #EVs #GroupOnEarthObservation #GEO #socioecological #sustainability #earthsystems #Drivers #Pressures #States #Impacts #Responses #sustainability #monitoring #observations #organisation #GlobalEarthObservationSystemofSystems #overview #review #literaturereview #environmental #Essentiality #Evolvability #Unambiguity #Feasibility #ecologicalfootprinting #viability

  19. Challenges In The Use Of Local Data For Regional Scale Mapping Of C And N Stocks In The Continuous Permafrost Zone At The Yukon Coastal Plain | Heatwave Risks To Tipping Point Of Permafrost
    --
    doi.org/10.5194/soil-12-113-20 <-- shared paper
    --
    doi.org/10.1038/s41558-026-026 <-- shared paper
    --
    theguardian.com/environment/20 <-- shared media article
    --
    cbc.ca/news/canada/north/perma <-- shared media article
    --
    [putting together two different ‘sorts’/focuses of research/reporting, but…]
    H/T @gustaf Hugelius | Professor at Stockholm University
    --
    “Permafrost soils are particularly vulnerable to climate change. To assess and improve estimations of carbon (C) and nitrogen (N) budgets it is necessary to accurately map soil carbon and nitrogen in the permafrost region. In particular, soil organic carbon (SOC) stocks have been predicted and mapped by many studies from local to pan-Arctic scales. Several studies have been carried out at the Canadian Beaufort Sea coast, though no regional maps of terrestrial carbon stocks based on spatial modelling has been conducted yet. This study combines available field data from the Canadian Yukon coastal plain and uses it to map regional SOC and N stocks using the machine learning algorithm random forest and environmental variables based on remote sensing data. [The authors] developed models using the data for the entire region and separate models for the coastal mainland area and Qikiqtaruk Herschel Island. Each model was used to map SOC and N stocks for its respective area. [They] assessed the performance of the different random forest models by using crossvalidation. [They] further assessed model results using the Area of Applicability (AOA) method and the quantile regression forest approach, comparing the results and discussing their implications within the context of both methods. [They] explore[d] local differences in soil properties and how soil data distribution across the region affects the accuracy of the predictions of SOC and N stocks..."
    #permafrost #soils #geology #climatechange #temperature #thawing #melting #emissions #CO2 #methane #carbon #nitrogen #GIS #spatial #mapping #Qikiqtaruk #HerschelIsland #Yukon #Canada #soilorganiccarbon #SOC #arctic #cryosphere #BeaufortSea #coast #coastal #machinelearning #model #modeling #remotesensing #earthobservation #carbonstocks #island #mainland #spatialanalysis #scale

  20. Comparing DESIS Hyperspectral and Landsat 10 Simulated Superspectral Data for Crop Type Classification in California’s Central Valley
    --
    doi.org/10.3390/rs18142282 <-- shared paper
    --
    usgs.gov/publications/comparin <-- shared USGs publication page
    --
    H/T @USGS
    “How can we get better at classifying crops from space? 🛰️🌽
    Some types of satellite imagery are better at distinguishing crops than others. A USGS study compared two approaches and found one produced more accurate crop maps.
    Here's what the researchers found:
    • Hyperspectral imagery achieved 86% accuracy, compared to 75% for simulated superspectral imagery.
    • Using just 14 carefully selected hyperspectral bands produced nearly the same results as using all 60 DLR Earth Sensing Imaging Spectrometer (DESIS) bands, showing those wavelengths contain much of the information needed to distinguish crop types.
    • Using superspectral imagery on the upcoming Landsat 10 mission will allow for routine tracking of crops and enhance finer crop mapping.
    • The findings help scientists identify which wavelengths provide the most useful information for crop mapping and future remote sensing applications.
    Better crop maps will help governments and scientists track global agriculture, monitor the current crop season, and study agricultural trends…”
    --
    “HIGHLIGHTS:
    • What are the main findings?
    - The 14 DESIS hyperspectral narrowbands (10 nm) aligned with the Landsat 10 (formerly Landsat Next) spectral dataset produced similar accuracy results to the full 60-band DESIS hyperspectral dataset for classifying crop types. These 14 DESIS narrowbands resulted in higher accuracy than the 14 simulated Landsat 10 superspectral broadbands.
    - When using DESIS narrowbands, Support Vector Machine (SVM) resulted in higher accuracy than Random Forest (RF).
    • What are the implications of the main findings?
    - A carefully selected set of 14 DESIS hyperspectral narrowbands (10 nm) can achieve classification accuracy comparable to those obtained using all 60 DESIS narrowbands across the 400–1000 nm range. These 14 strategically positioned narrowbands classified crop types with higher classification accuracy than the corresponding 14 Landsat 10 superspectral broadbands within the same spectral range.
    - This study underscores the importance of multi-temporal imagery across the full crop-growing season for achieving more detailed and accurate crop type classifications. Such temporal coverage is more feasible with the planned Landsat 10 routine acquisition of broadband imagery than with task-based hyperspectral collections…”
    #hyperspectral #superspectral #optimalbands #randomforest #supportvectormachine #agriculture #crops #croptype #classifaction #croplands #California #CentralValley #GIS #spatial #mapping #remotesensing #earthobservation #imagery #DESIS #Landsat #Landsat10 #satellite #spatialanalysis #spatiotemporal #global #AI #machinelearning #model #modeling #SupportVectorMachine #SVM #RandomForest #RF #GoogleEarthEngine
    @USGS

  21. Mapping Snow On Northern Winter Roads - A Dual-Frequency Polarimetric Radar Approach For Snow Characterization Over Land, Lake And Sea Ice
    --
    doi.org/10.5194/tc-20-4367-2026 <-- shared paper
    --
    H/T @Monojit Saha | Geospatial Analysis | Remote Sensing | Satellite Altimetry | Cryosphere
    “Winter roads are essential transportation links for many remote northern communities, but their safety and reliability depend strongly on snow conditions and ice growth. In this study [link above], [the authors] evaluated a fully polarimetric, dual-frequency Ku- and Ka-band radar approach for retrieving snow depth across landfast sea ice, lake ice, and tundra.
    Using field measurements near Churchill, Manitoba, and Resolute Bay, Nunavut [Canada], [they] found that the approach produced snow-depth retrieval bias and error within 3 cm over landfast ice, with encouraging Ku-band performance over frozen ground as well. [They] also developed an interface-detection approach for lake ice that can retrieve both snow depth and ice thickness - a promising direction for characterizing conditions relevant to winter-road planning and safety…”
    --
    “Winter roads are lifelines for remote northern communities. Built over land, lakes, rivers, and sea ice, these travel routes are increasingly vulnerable to warming temperatures and variable precipitation. To ensure safety and adapt to these changes, operators require high-resolution monitoring of snow depth across these diverse surfaces, as natural snow accumulation dictates ice growth rates, route viability and road stability. This study extends our polarimetric radar method, previously demonstrated on pack ice, to landfast sea ice, tundra, and frozen lakes and assesses how well we can retrieve snow depth over these surfaces. Results indicate consistency with earlier sea ice analyses, maintaining a mean snow depth retrieval bias and error within 3 cm over the landfast ice. Promising performance is also found over frozen ground using Ku-band (mean biases less than 6 cm). To address the specific challenge of lake ice, which includes strong returns from the ice/water interface, we present a new interface-detection technique that simultaneously retrieves snow depth and ice thickness. While current validation focuses on undisturbed snow, this approach could provide a path forward for characterizing the cryospheric environment in a way that can directly support the optimization of winter roads…”
    #Cryosphere #RemoteSensing #Snow #SeaIce #LakeIce #WinterRoads #characterisation #ArcticResearch #EarthObservation #PolarScience #maintainence #ploughing #winter #roads #transportation #northern #communities #mines #FirstNation #canada #remotesensing #polarimetric #radar #snowdepth #ice #landfastice #iceroad #tundra #Churchill #Manitoba #ResoluteBay #Nunavut #monitoring #planning #safety #trucking #freight

  22. Impact Of Urbanization Driven Land Use And Land Cover Change On Ecological Environmental Quality In Rupandehi Nepal Assessed Using The Remote Sensing Ecological Index
    --
    doi.org/10.1007/s44288-026-006 <-- shared paper
    --
    kathmandupost.com/money/2026/0 <-- shared media article
    --
    H/T@ Gaurav Parajulim
    “[The authors] studied how the ecological quality of Nepal's Rupandehi District has changed over three decades (1993–2023), using satellite imagery and the Remote Sensing Ecological Index (RSEI) to track the health of the landscape year by year and to understand how urbanization-driven land use change has reshaped it.
    What [they] found tells a nuanced story: as Butwal and Bhairahawa grew and built-up land expanded, ecological quality shifted in ways that a single number can't capture, some areas recovered, others declined, and the patterns rarely moved in a straight line…”
    --
    “Rapid urbanization and population growth are major drivers of land use and land cover (LULC) change and can substantially alter ecological environmental quality (EEQ). This study assessed the spatiotemporal dynamics of LULC and their effect on EEQ in Rupandehi District, Nepal, over a 30-year period (1993–2023). Four ecological indicators representing greenness, wetness, dryness, and heat were derived from Landsat imagery in Google Earth Engine (GEE), and LULC was classified using a Support Vector Machine (SVM). The Remote Sensing Ecological Index (RSEI) was then constructed from these indicators using Principal Component Analysis (PCA) in ArcGIS Pro, and its spatial structure was examined using global and local spatial autocorrelation. The mean RSEI followed a non-linear trajectory, rising from 0.59 in 1993 to 0.635 in 2004, declining to 0.55 in 2013, and recovering to 0.67 in 2023, indicating an overall improvement in EEQ with a temporary mid-period decline. Over the same period, built-up areas expanded substantially and agricultural land declined, whereas forest cover fluctuated but showed a slight net increase by 2023, and barren land decreased markedly. Higher EEQ was concentrated in the forested northern hills, while lower values occurred in the urban centers of Butwal and Bhairahawa, closely matching the spatial pattern of LULC change. The results indicate that ecological quality reflects the combined influence of all land cover classes rather than any single class. This study provides a transferable and reproducible workflow for long-term ecological assessment based on openly available Landsat data, with the analysis code shared in a public repository, offering practical guidance for sustainable land management and environmentally responsible urban development...”
    #GIS #spatial #mapping #RemoteSensing #GIS #RSEI #EnvironmentalMonitoring #Nepal #Research #GoogleEarthEngine #ArcGIS #EcologicalQuality #spatialautocorrelation #ecology #environment #earthobservation #RemoteSensingEcologicalIndex #landscape #urbanisation #urban #development #landuse #change #spatialanalysis #spatiotemporal

  23. Flood And Landslide Susceptibility Assessment And Multi Hazard Interaction Mapping Using Machine Learning And GIS For Sustainable Settlement Planning In Nepal
    --
    doi.org/10.1007/s44288-026-006 <-- shared paper
    --
    H/T @Narayan Thapa | Earth Data Modeling
    “Nepal lies within an active seismic zone and is influenced by most dynamic climatic systems in the world. It faces compounding floods and landslide threats. Impacts are worst where multi-hazard interactions create spatially linked corridors. Despite frequent co-occurrence, national-scale assessments remain limited. This study presents machine learning and GIS-based approach to map nationwide susceptibility to floods, landslides, and identify their potential interaction zones, and delineate critical multi-hazard flow zones through spatial adjacency analysis. Using Google Earth Engine, the Random Forest model integrates topographic, climatic, environmental, and hydrological datasets to overcome subjective expert-driven methods. The model achieved strong predictive accuracy (AUC: 0.84 for floods, 0.85 for landslides). The results showed 19% of Nepal’s lowlands are medium to very highly susceptible to inundation, threatening approximately 900,000 people and over 3.4 million buildings; whilst in the hilly terrains, 40% is susceptible to slope-failure endangering 200,000 people and about 0.6 million buildings. K-means clustering followed by spatial adjacency analysis identified four spatial zonation: 81% of national area as low-hazard zone, 9% as flood-only zone, 5% as landslide-only zone, and 5% as interaction zones. Critical multi-hazard flow zone covering 7,588 km² represents spatially connected corridors linking interaction zones to downstream flood-prone populated areas, affecting 88 km² built-up land and 1,722 km² cropland. These zones represent susceptibility-based spatial connectivity rather than physically simulated cascading processes. These findings support recommendations for risk-informed land-use planning, resilient infrastructure development and climate adaptation aligned to sustainable development and investment risk screening…”
    #GIS #spatial #mapping #GoogleEarthEngine #MachineLearning #RemoteSensing #GeospatialAI #DisasterRiskReduction #MultiHazard #ClimateAdaptation #climatechange #extremeweather #LandUsePlanning #SustainableDevelopment #InfrastructurePlanning #RiskAssessment #NaturalHazards #Nepal #EarthObservation #HinduKushHimalaya #HKH #HinduKush #Himalayas #risk #hazard #assessment #national #regional #spatialanalysis #spatiotemporal #massmovement #landslide #assessment #mitigation #water #hydrology #flood #flooding #sustainability

  24. Identifying Agricultural Consumptive-Use Patterns To Support Adaptive Water Management In California’s Santa Clara Valley Via Remote Sensing And Machine Learning
    --
    doi.org/10.1371/journal.pwat.0 <-- shared paper
    --
    H/T @Guillaume Wright | Executive Editor, PLOS
    “💧 With drought [and high temperatures] gripping many areas of the world right now... [the H/T] wanted to highlight a new paper in PLOS Water this week with a very timely focus on hydroclimatic stresses and what can be done to mitigate this through water management practices when it comes to agriculture.
    [The authors] investigate[d] adaptive water management practices in California’s Santa Clara Valley via remote sensing and machine learning techniques. They [found] good evidence for use of customized agricultural water-management plans for irrigation monitoring, conservation planning, and adaptive water management in groundwater-dependent regions such as is found in California…”
    #GIS #spatial #mapping #California #SantaClara #SantaClaraValley #custom #watermanagement #practices #waterresources #agriculture #remotesensing #spatialanalysis #machinelearning #earthobservation #AI #planning #wateruse #efficiency #water #hydrology #irrigation #conservation #adaptivewatermanagement #model #modeling #drought #extremeweather #hydroclimate #stress #crop #cropland #evapotranspiration #ET #NDVI #PRISM #precipitation #rainfall #watermanagementplan #groundwater

  25. Global Performance of #RemoteSensing Based and Reanalysis-Driven Models to Estimate Open Water Evaporation
    --
    doi.org/10.1029/2025WR042363
    --
    “ABSTRACT: Evaporation plays an essential role in the water cycle, influencing local and regional climates while directly impacting water availability in lakes. However, directly measuring evaporation over water bodies remains challenging due to the high costs of installing and maintaining the required in situ instrumentation. Although several remote sensing algorithms have been providing evaporation estimates, the lack of a global validation hinders our understanding of their relative uncertainties and performances across different regions. Here, [they] analyze[d] the performance of a suite of models that leverage satellite data and meteorological reanalysis to estimate evaporation over lakes worldwide. [They] compare[d] three remote sensing-based models, one reanalysis-driven model and one ensemble approach, using in situ observations from 27 lakes representing a diverse range of geographic and climatic regions. [Their] results demonstrate that, overall, the ensemble outperformed any individual model in terms of accuracy, with a RMSE and a bias of 1.3 and 0.3 mm/day, respectively. These findings highlight the benefits of using an ensemble approach to estimate open water evaporation with satellite-based models at the global scale, leveraging the unique strengths of each model. For the individual models, differences in the representation of heat storage changes and advection effects led to lower values of RMSE and bias, depending on the location and depth of the lakes. This study sets the path for future improvement of open water evaporation algorithms globally, while remote sensing techniques are proven satisfactory to monitoring of water loss in lakes globally, an essential step toward effective large-scale water resources management.
    PLAIN LANGUAGE SUMMARY: Water loss through evaporation in lakes and reservoirs directly affects water availability, which highlights the need to monitor these losses. However, measuring evaporation in situ is challenging and expensive. An alternative is to estimate evaporation using remote-sensing models and compare these estimates with in-situ data to verify their accuracy. Here, [they] evaluated four models and their ensemble (the models' mean value) using measurements from 27 lakes and reservoirs worldwide. [They] found that the ensemble presented higher accuracy and consistency than any individual model because it benefits from the strengths of each model. This approach can guide future improvements in estimating open-water evaporation, which is essential for large-scale water-resource management…”
    #global #mapping #earthobservation #GIS #spatial #spatialanalysis #spatiotemporal #model #modeling #water #hydrology #surfacewater #waterbody #lake #reservoir #evaporation #evapotranspiration #watercycle #weather #meteorology #usecase #waterresources #watermanagement #waterloss #regional #estimate #policy #planning #instrumentation #comparasion

  26. Refined Modeling of Arctic Circumpolar Building Stock Increases Estimated Mid-Century Permafrost Degradation Damages
    --
    doi.org/10.1029/2026EF008578 <-- shared paper
    --
    thearcticinstitute.org/climate | thearcticinstitute.org/dwindli <-- shared technical articles
    --
    theguardian.com/cities/2016/oc <-- shared media article
    --
    news.grida.no/new-map-shows-ex <-- shared technical article
    --
    H/T @elias Manos
    “Why the increase?
    Our understanding of climate risk is only as good as our understanding of our exposure to hazards. The better we can account for what is at risk, the better we can measure risk in a changing world.
    In this new study [link above], [they] investigate[d] how damage to the building stock across the Arctic, a key impact of permafrost degradation, is underestimated because of underdeveloped exposure information. With National Science Foundation (NSF) supercomputers and 400 TB of Vantor satellite imagery, [they] detected building footprints across the Arctic and classified their use types using deep learning models. Then, using Polar Geospatial Center's ArcticDEM digital surface model, [they] estimated the total floor space of each residential building. This move from 2D to 3D representation of the building stock was the largest contributor to increased building damage.
    Properly estimating this consequence is necessary for understanding the near future of the Arctic economy. Knowing the magnitude of damages is critical for sustaining the communities and livelihoods of more than 5 million people that call the Arctic home. There are also much broader implications. With the Arctic continuing to emerge as a strategic centerpiece in global affairs and the global economy, accurately quantifying the physical shocks to its built environment will allow researchers to more effectively represent the Arctic in global climate economic models. More precise international policymaking will also be enabled by these improvements.
    Ultimately, this research highlights a similar challenge in completely different regions of the world (e.g., Southeast Asia, Sub-Saharan Africa) where exposure is constantly evolving alongside rapid population growth and urbanization. Building stock information can quickly become outdated as these changes occur; satellite remote sensing and AI are key players in keeping up with these changes and supporting data-driven disaster risk management…”
    #arctic #circumpolar #permafrost #model #modeling #spatialanalysis #spatiotemporal #GIS #spatial #mapping #melting #degradation #damage #cost #economics #risk #hazard #climaterisk #climatechange #remotesensing #HPC #earthobservation #ArcticDEM #buildingfootprint #LLM #AI #machinelearning #engineering #economy #buildingstock #community #policy #planning #geopolitics #risk #management @UConn Research

  27. Refined Modeling of Arctic Circumpolar Building Stock Increases Estimated Mid-Century Permafrost Degradation Damages
    --
    doi.org/10.1029/2026EF008578 <-- shared paper
    --
    thearcticinstitute.org/climate | thearcticinstitute.org/dwindli <-- shared technical articles
    --
    theguardian.com/cities/2016/oc <-- shared media article
    --
    news.grida.no/new-map-shows-ex <-- shared technical article
    --
    H/T @elias Manos
    “Why the increase?
    Our understanding of climate risk is only as good as our understanding of our exposure to hazards. The better we can account for what is at risk, the better we can measure risk in a changing world.
    In this new study [link above], [they] investigate[d] how damage to the building stock across the Arctic, a key impact of permafrost degradation, is underestimated because of underdeveloped exposure information. With National Science Foundation (NSF) supercomputers and 400 TB of Vantor satellite imagery, [they] detected building footprints across the Arctic and classified their use types using deep learning models. Then, using Polar Geospatial Center's ArcticDEM digital surface model, [they] estimated the total floor space of each residential building. This move from 2D to 3D representation of the building stock was the largest contributor to increased building damage.
    Properly estimating this consequence is necessary for understanding the near future of the Arctic economy. Knowing the magnitude of damages is critical for sustaining the communities and livelihoods of more than 5 million people that call the Arctic home. There are also much broader implications. With the Arctic continuing to emerge as a strategic centerpiece in global affairs and the global economy, accurately quantifying the physical shocks to its built environment will allow researchers to more effectively represent the Arctic in global climate economic models. More precise international policymaking will also be enabled by these improvements.
    Ultimately, this research highlights a similar challenge in completely different regions of the world (e.g., Southeast Asia, Sub-Saharan Africa) where exposure is constantly evolving alongside rapid population growth and urbanization. Building stock information can quickly become outdated as these changes occur; satellite remote sensing and AI are key players in keeping up with these changes and supporting data-driven disaster risk management…”
    #arctic #circumpolar #permafrost #model #modeling #spatialanalysis #spatiotemporal #GIS #spatial #mapping #melting #degradation #damage #cost #economics #risk #hazard #climaterisk #climatechange #remotesensing #HPC #earthobservation #ArcticDEM #buildingfootprint #LLM #AI #machinelearning #engineering #economy #buildingstock #community #policy #planning #geopolitics #risk #management @UConn Research

  28. Refined Modeling of Arctic Circumpolar Building Stock Increases Estimated Mid-Century Permafrost Degradation Damages
    --
    doi.org/10.1029/2026EF008578 <-- shared paper
    --
    thearcticinstitute.org/climate | thearcticinstitute.org/dwindli <-- shared technical articles
    --
    theguardian.com/cities/2016/oc <-- shared media article
    --
    news.grida.no/new-map-shows-ex <-- shared technical article
    --
    H/T @elias Manos
    “Why the increase?
    Our understanding of climate risk is only as good as our understanding of our exposure to hazards. The better we can account for what is at risk, the better we can measure risk in a changing world.
    In this new study [link above], [they] investigate[d] how damage to the building stock across the Arctic, a key impact of permafrost degradation, is underestimated because of underdeveloped exposure information. With National Science Foundation (NSF) supercomputers and 400 TB of Vantor satellite imagery, [they] detected building footprints across the Arctic and classified their use types using deep learning models. Then, using Polar Geospatial Center's ArcticDEM digital surface model, [they] estimated the total floor space of each residential building. This move from 2D to 3D representation of the building stock was the largest contributor to increased building damage.
    Properly estimating this consequence is necessary for understanding the near future of the Arctic economy. Knowing the magnitude of damages is critical for sustaining the communities and livelihoods of more than 5 million people that call the Arctic home. There are also much broader implications. With the Arctic continuing to emerge as a strategic centerpiece in global affairs and the global economy, accurately quantifying the physical shocks to its built environment will allow researchers to more effectively represent the Arctic in global climate economic models. More precise international policymaking will also be enabled by these improvements.
    Ultimately, this research highlights a similar challenge in completely different regions of the world (e.g., Southeast Asia, Sub-Saharan Africa) where exposure is constantly evolving alongside rapid population growth and urbanization. Building stock information can quickly become outdated as these changes occur; satellite remote sensing and AI are key players in keeping up with these changes and supporting data-driven disaster risk management…”
    #arctic #circumpolar #permafrost #model #modeling #spatialanalysis #spatiotemporal #GIS #spatial #mapping #melting #degradation #damage #cost #economics #risk #hazard #climaterisk #climatechange #remotesensing #HPC #earthobservation #ArcticDEM #buildingfootprint #LLM #AI #machinelearning #engineering #economy #buildingstock #community #policy #planning #geopolitics #risk #management @UConn Research

  29. Refined Modeling of Arctic Circumpolar Building Stock Increases Estimated Mid-Century Permafrost Degradation Damages
    --
    doi.org/10.1029/2026EF008578 <-- shared paper
    --
    thearcticinstitute.org/climate | thearcticinstitute.org/dwindli <-- shared technical articles
    --
    theguardian.com/cities/2016/oc <-- shared media article
    --
    news.grida.no/new-map-shows-ex <-- shared technical article
    --
    H/T @elias Manos
    “Why the increase?
    Our understanding of climate risk is only as good as our understanding of our exposure to hazards. The better we can account for what is at risk, the better we can measure risk in a changing world.
    In this new study [link above], [they] investigate[d] how damage to the building stock across the Arctic, a key impact of permafrost degradation, is underestimated because of underdeveloped exposure information. With National Science Foundation (NSF) supercomputers and 400 TB of Vantor satellite imagery, [they] detected building footprints across the Arctic and classified their use types using deep learning models. Then, using Polar Geospatial Center's ArcticDEM digital surface model, [they] estimated the total floor space of each residential building. This move from 2D to 3D representation of the building stock was the largest contributor to increased building damage.
    Properly estimating this consequence is necessary for understanding the near future of the Arctic economy. Knowing the magnitude of damages is critical for sustaining the communities and livelihoods of more than 5 million people that call the Arctic home. There are also much broader implications. With the Arctic continuing to emerge as a strategic centerpiece in global affairs and the global economy, accurately quantifying the physical shocks to its built environment will allow researchers to more effectively represent the Arctic in global climate economic models. More precise international policymaking will also be enabled by these improvements.
    Ultimately, this research highlights a similar challenge in completely different regions of the world (e.g., Southeast Asia, Sub-Saharan Africa) where exposure is constantly evolving alongside rapid population growth and urbanization. Building stock information can quickly become outdated as these changes occur; satellite remote sensing and AI are key players in keeping up with these changes and supporting data-driven disaster risk management…”
    #arctic #circumpolar #permafrost #model #modeling #spatialanalysis #spatiotemporal #GIS #spatial #mapping #melting #degradation #damage #cost #economics #risk #hazard #climaterisk #climatechange #remotesensing #HPC #earthobservation #ArcticDEM #buildingfootprint #LLM #AI #machinelearning #engineering #economy #buildingstock #community #policy #planning #geopolitics #risk #management @UConn Research

  30. Refined Modeling of Arctic Circumpolar Building Stock Increases Estimated Mid-Century Permafrost Degradation Damages
    --
    doi.org/10.1029/2026EF008578 <-- shared paper
    --
    thearcticinstitute.org/climate | thearcticinstitute.org/dwindli <-- shared technical articles
    --
    theguardian.com/cities/2016/oc <-- shared media article
    --
    news.grida.no/new-map-shows-ex <-- shared technical article
    --
    H/T @elias Manos
    “Why the increase?
    Our understanding of climate risk is only as good as our understanding of our exposure to hazards. The better we can account for what is at risk, the better we can measure risk in a changing world.
    In this new study [link above], [they] investigate[d] how damage to the building stock across the Arctic, a key impact of permafrost degradation, is underestimated because of underdeveloped exposure information. With National Science Foundation (NSF) supercomputers and 400 TB of Vantor satellite imagery, [they] detected building footprints across the Arctic and classified their use types using deep learning models. Then, using Polar Geospatial Center's ArcticDEM digital surface model, [they] estimated the total floor space of each residential building. This move from 2D to 3D representation of the building stock was the largest contributor to increased building damage.
    Properly estimating this consequence is necessary for understanding the near future of the Arctic economy. Knowing the magnitude of damages is critical for sustaining the communities and livelihoods of more than 5 million people that call the Arctic home. There are also much broader implications. With the Arctic continuing to emerge as a strategic centerpiece in global affairs and the global economy, accurately quantifying the physical shocks to its built environment will allow researchers to more effectively represent the Arctic in global climate economic models. More precise international policymaking will also be enabled by these improvements.
    Ultimately, this research highlights a similar challenge in completely different regions of the world (e.g., Southeast Asia, Sub-Saharan Africa) where exposure is constantly evolving alongside rapid population growth and urbanization. Building stock information can quickly become outdated as these changes occur; satellite remote sensing and AI are key players in keeping up with these changes and supporting data-driven disaster risk management…”
    @UConn Research

  31. Mapping Multifunctionality In Remote Patagonian Forest Landscapes Reveals High-Value Ecosystems Beyond Protected Areas
    --
    doi.org/10.1038/s43247-026-035 <-- shared paper
    --
    H/T @Peter Potapov | Researcher at the World Resources Institute (WRI)
    “This paper is] a strong example of multifunctionality analysis applied to conservation planning. The study mapped six ecosystem functions, including carbon storage, nutrient availability, water regulation, erosion control, habitat quality, and ecological connectivity. [The author] combined satellite data, field soil sampling, and spatial modeling for this comprehensive analysis.
    Two findings stand out.
    1. Old-growth forests had the highest multifunctionality index of any land cover type.
    2. 78.5% of the top multifunctionality hotspots fall outside the region's protected areas, even though PAs already cover more than 54% of the territory.
    Together, these results make a clear case for expanding conservation of the remaining Intact Forest Landscapes and primary forests in Patagonia and elsewhere…”
    --
    “Remote forest landscapes provide critical references for understanding ecosystem functions (EFs) under low anthropogenic pressure, yet their capacity to sustain multiple EFs simultaneously remains poorly understood. [They] assessed landscape multifunctionality in western Patagonia by integrating satellite indicators, field data, and spatial modeling. Six EFs (carbon storage, nutrient availability, water regulation, erosion control, habitat quality, and ecological connectivity) were mapped, and their spatial relationships and hotspot distribution within and outside protected areas (PAs) were analyzed. Old-growth and secondary forests showed the highest functional performance. Strong synergies (ρ ≥ 0.6) between carbon storage and nutrient availability covered >50% of the landscape, whereas strong trade-offs (ρ ≤ –0.6) were spatially limited ( < 6%). Notably, 78% of multifunctionality hotspots occurred outside PAs, indicating that high-functional-value areas extend beyond formal conservation boundaries. These findings reveal spatial mismatches between multifunctionality and protection status and provide a replicable framework for integrating multifunctionality into conservation planning under global change…”
    #Patagonia #chile #aysen #coyhaique #landcover #mapping #spatial #spatialpatterns #spatiotemporal #spatialanalysis #forest #vegetation #oldgrowth #secondgrowth #shrubland #grassland #steppe #ecosystem #habitat #nutrients #water #hydrology #erosion #multifunctionality #multifunctionalityanalysis #protectedareas #landuse #conservationplanning #conservation #ecology #carbonstorage #nutrientavailability #waterregulation #erosioncontrol #habitatquality #ecologicalconnectivity #remotesensing #satellite #earthobservation #modeling

  32. ☀️ Good morning from #FOSS4GE2026 !

    A new day has started in Timișoara, with sessions on ☁️ Cloud GIS , #QGIS 🧭, #MapLibre 🗺️, #GeoServer 🛠️, 🛰️ Earth Observation, 🤖 AI, 🌆Digital Twins, and much more.

    We look forward to inspiring discussions, live demonstrations, and interesting conversations with the open geospatial community. See you around! 🌍

    #OpenSource #Geospatial #GIS #EarthObservation #digitaltwins

  33. Remote Sensing, Real Impact - Technology-Driven Conservation At The Jane Goodall Institute
    (How the Jane Goodall Institute [JGI] is using satellite imagery and spatial technology to help people and animals thrive.)
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    geoweeknews.com/news/conservat <-- shared technical article
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    iceye.com/blog/government/solu <-- shared technical article
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    [this post should not be seen as endorsement of a particular provider and/or approach]
    “Dr. Jane Goodall’s groundbreaking research at Gombe National Park in Tanzania began in 1960, and transformed our understanding of chimpanzees and redefined the relationship between humans and animals. Dr. Goodall dedicated her life to conservation and community empowerment. Her recent passing marks the end of an extraordinary career in ethology and conservation science. Yet her legacy lives on powerfully through the Jane Goodall Institute [JGI] and the countless individuals and communities she inspired worldwide. Her pioneering work revealed that chimpanzees make and use tools, have complex social structures, and possess individual personalities; discoveries that fundamentally challenged our understanding of what it means to be human, for the better…
    The institute's current work embodies Dr. Goodall's profound philosophy: that humanity can only reach its full potential "when our clever brains and our compassionate hearts are connected."
    This principle guides every aspect of JGI's conservation science strategy, from analyzing satellite data to providing local communities with the tools they need to strengthen their environment. Dr. Goodall's vision that conservation must be rooted in compassion, scientific rigor, and respect for local communities continues to shape the institute's innovative approach to using technology for conservation action.
    As time has gone on, geospatial technologies have become increasingly indispensable to this mission, enabling JGI to monitor forest health in real-time, map critical chimpanzee habitats and migration corridors, and measure the tangible impact of community-led conservation initiatives. But perhaps most importantly, these technologies help the communities understand that the lives of people, animals and their shared environment are all interconnected…”
    #ConservationTechnology #illegal #Deforestation #hunting #mining #GombeNationalPark #remotesensing #radar #earthobservation #chimpanzee #conservation #communityempowerment #JaneGoodallInstitute #JGI #satellite #spatialanalysis #spatiotemporal #GIS #spatial #mapping #habitat #destruction #migration #corridors #monitoring #conservationscience #strategy #resource #cloudcover #alerts #foresthealth #protection #SAR #vegetation #ecosystems #rainforest #greatape #disease #foodsecurity #DRC #Tanzania #CongoBasin #nationalpark #ranger #parkranger #security #safety #alerts #wildlifetrafficking
    @JaneGoodallInstitute @iceeye

  34. Scientists See More Vegetation In The Himalayas - But It Is Not Good News, Because That Extra “Green” Can Disrupt Water, Snow, And High-Mountain Biodiversity | Plants Growing Higher Across Himalaya As Climate Warms
    (Vegetation On The Move: Elevational Shifts And Greening Dynamics Across The Himalayan Alpine Zone)
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    ecoticias.com/en/scientists-se <-- shared technical article
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    news.exeter.ac.uk/faculty-of-e <-- shared technical newsitem
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    doi.org/10.1002/ecog.08259 <-- shared (2026) paper
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    doi.org/10.1111/gcb.14919 <-- shared (2020) paper
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    “For years, the biggest climate warning from the Himalaya was easy to picture because glaciers were shrinking on the roof of Asia. Now, researchers are pointing to a quieter signal, one that can look almost harmless from a distance. The mountains are getting greener.
    New research [link above] shows alpine vegetation moving higher across six Himalayan regions from 1999 to 2022, pushed in part by warming and reduced snow depth. That might sound like nature recovering, but in this fragile landscape, more plant cover at extreme heights may change how snow is stored, how water runs downhill, and how rivers behave for communities far below…”
    #GIS #spatial #mapping #remotesensing #earthobservation #satellite #landsat #landcover #NDVI #Himalaya #Nepal #India #Bhutan #climatechange #glacier #vegetation #alpine #level #greening #spatialanalysis #spatiotemporal #snow #water #ice #hydrography #hydrology #ecosystems #humaninpacts #phenology #model #modeling #HighMountainAsia #greenness #ERA5 #vegetationline #altitude #climatictrends #warming #precipitation #rainfall