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#himalayas — Public Fediverse posts

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

  1. Chinese national rescued from Nepal tunnel

    A Chinese national was pulled alive from a tunnel in Nepal ten ‌days after a wall of ice, rock and mud tore through a Himalayan valley, killing more than 1,300 people. #nepal #environment #himalayas #mudslides #china #tibet #climatechange #glacier #asia #News #Reuters #Newsfeed Read the story here: 👉 Subscribe: Keep up with the latest news from around the world: Follow Reuters on Facebook: Follow Reuters on X: Follow Reuters…

    fllics.com/en/video/chinese-na

  2. Chinese national rescued from Nepal tunnel

    A Chinese national was pulled alive from a tunnel in Nepal ten ‌days after a wall of ice, rock and mud tore through a Himalayan valley, killing more than 1,300 people. #nepal #environment #himalayas #mudslides #china #tibet #climatechange #glacier #asia #News #Reuters #Newsfeed Read the story here: 👉 Subscribe: Keep up with the latest news from around the world: Follow Reuters on Facebook: Follow Reuters on X: Follow Reuters…

    fllics.com/en/video/chinese-na

  3. Chinese national rescued from Nepal tunnel

    A Chinese national was pulled alive from a tunnel in Nepal ten ‌days after a wall of ice, rock and mud tore through a Himalayan valley, killing more than 1,300 people. #nepal #environment #himalayas #mudslides #china #tibet #climatechange #glacier #asia #News #Reuters #Newsfeed Read the story here: 👉 Subscribe: Keep up with the latest news from around the world: Follow Reuters on Facebook: Follow Reuters on X: Follow Reuters…

    fllics.com/en/video/chinese-na

  4. Chinese national rescued from Nepal tunnel

    A Chinese national was pulled alive from a tunnel in Nepal ten ‌days after a wall of ice, rock and mud tore through a Himalayan valley, killing more than 1,300 people. #nepal #environment #himalayas #mudslides #china #tibet #climatechange #glacier #asia #News #Reuters #Newsfeed Read the story here: 👉 Subscribe: Keep up with the latest news from around the world: Follow Reuters on Facebook: Follow Reuters on X: Follow Reuters…

    fllics.com/en/video/chinese-na

  5. Chinese national rescued from Nepal tunnel

    A Chinese national was pulled alive from a tunnel in Nepal ten ‌days after a wall of ice, rock and mud tore through a Himalayan valley, killing more than 1,300 people. #nepal #environment #himalayas #mudslides #china #tibet #climatechange #glacier #asia #News #Reuters #Newsfeed Read the story here: 👉 Subscribe: Keep up with the latest news from around the world: Follow Reuters on Facebook: Follow Reuters on X: Follow Reuters…

    fllics.com/en/video/chinese-na

  6. In 2022, DJI was able to fly one of their drones over Mt. Everest.

    Here's the video recorded by the drone around the highest point on Earth!
    youtu.be/Zz9oI3B6v4c

    In 2024 and 2025, DJI drones, specifically the Mavic 3 Pro and Mavic 4 Pro, achieved record-setting, high-altitude flights over Mount Everest, capturing 8K footage of the summit at 8,848 meters. These drones, often operated with 8KRAW, showcased remarkable stability in high winds (up to 50 km/h) and thin air.

    DJI Mavic 3 Pro: youtu.be/A-iVxaFhr7s
    DJI Mavic 4 Pro: youtu.be/csDriucITDE

    Here are some of the physics challenges involved 👇

    #Drone #DronePhotography #MtEverest #Everest #Aerodynamics #Physics #DJIdroneshots #HighAltitude #ThinAir #PhysicsChallenge #Nepal #Himalayas #Drones #DroneShots

  7. Permafrost Distribution, Degradation, And Potential Mass Movement Cascades In The Western Himalaya Using Machine Learning And Numerical Models
    --
    doi.org/10.1038/s44304-026-002 <-- shared paper
    --
    doi.org/10.1038/s41598-025-220 <-- shared (earlier) paper
    --
    doi.org/10.1080/2150704X.2025. <-- shared (earlier) paper
    --
    H/T @abhinav Alangadan
    “Can we develop a first-order understanding of permafrost degradation and glacial lakes exposed to degradation-induced mass movements in the Himalaya?
    [The authors] tried to address this question. The study [first link above] integrates machine learning, statistical modeling, and numerical modeling to investigate high-resolution permafrost distribution, potential degradation, and associated mass-movement hazards in the Kinnaur district of Himachal Pradesh, India.
    Using rock glaciers as proxies, [they] generated a high-resolution permafrost distribution using machine learning, while potential degradation zones were delineated using the 0°C isotherm as a first-order indicator. [They] further identified glacial lakes located near potentially degrading permafrost zones and reconstructed their bathymetry. A detailed scenario-based GLOF process-chain simulation was then carried out for Kashang Lake using r.avaflow and HEC-RAS.
    [Their] results indicate that seven glacial lakes in #Kinnaur are located close to potentially degrading permafrost zones. The simulations further show that a potential GLOF from Kashang Lake could inundate critical downstream infrastructure, including nearly 11 km of National Highway 5…”
    #permafrost #distribution #GIS #spatial #mapping #Himalayas #India #Kinnaur #HimachalPradesh #KashangLake #massmovement #engineeringgeology #machinelearning #AI #model #modeling #numericalmodel #glaciallakes #glaciet #glacial #glaciallakeoutburstflood #GLOF #cryosphere #geostatistics #rockglaciers #GeoAI #bathymetry #processchainsimulation #HECRAS #avaflow #risk #hazard #mitigation #riskassessment #infrastructure #HEP #publicsafety #downstream #avalanche

  8. Permafrost Distribution, Degradation, And Potential Mass Movement Cascades In The Western Himalaya Using Machine Learning And Numerical Models
    --
    doi.org/10.1038/s44304-026-002 <-- shared paper
    --
    doi.org/10.1038/s41598-025-220 <-- shared (earlier) paper
    --
    doi.org/10.1080/2150704X.2025. <-- shared (earlier) paper
    --
    H/T @abhinav Alangadan
    “Can we develop a first-order understanding of permafrost degradation and glacial lakes exposed to degradation-induced mass movements in the Himalaya?
    [The authors] tried to address this question. The study [first link above] integrates machine learning, statistical modeling, and numerical modeling to investigate high-resolution permafrost distribution, potential degradation, and associated mass-movement hazards in the Kinnaur district of Himachal Pradesh, India.
    Using rock glaciers as proxies, [they] generated a high-resolution permafrost distribution using machine learning, while potential degradation zones were delineated using the 0°C isotherm as a first-order indicator. [They] further identified glacial lakes located near potentially degrading permafrost zones and reconstructed their bathymetry. A detailed scenario-based GLOF process-chain simulation was then carried out for Kashang Lake using r.avaflow and HEC-RAS.
    [Their] results indicate that seven glacial lakes in #Kinnaur are located close to potentially degrading permafrost zones. The simulations further show that a potential GLOF from Kashang Lake could inundate critical downstream infrastructure, including nearly 11 km of National Highway 5…”
    #permafrost #distribution #GIS #spatial #mapping #Himalayas #India #Kinnaur #HimachalPradesh #KashangLake #massmovement #engineeringgeology #machinelearning #AI #model #modeling #numericalmodel #glaciallakes #glaciet #glacial #glaciallakeoutburstflood #GLOF #cryosphere #geostatistics #rockglaciers #GeoAI #bathymetry #processchainsimulation #HECRAS #avaflow #risk #hazard #mitigation #riskassessment #infrastructure #HEP #publicsafety #downstream #avalanche

  9. Permafrost Distribution, Degradation, And Potential Mass Movement Cascades In The Western Himalaya Using Machine Learning And Numerical Models
    --
    doi.org/10.1038/s44304-026-002 <-- shared paper
    --
    doi.org/10.1038/s41598-025-220 <-- shared (earlier) paper
    --
    doi.org/10.1080/2150704X.2025. <-- shared (earlier) paper
    --
    H/T @abhinav Alangadan
    “Can we develop a first-order understanding of permafrost degradation and glacial lakes exposed to degradation-induced mass movements in the Himalaya?
    [The authors] tried to address this question. The study [first link above] integrates machine learning, statistical modeling, and numerical modeling to investigate high-resolution permafrost distribution, potential degradation, and associated mass-movement hazards in the Kinnaur district of Himachal Pradesh, India.
    Using rock glaciers as proxies, [they] generated a high-resolution permafrost distribution using machine learning, while potential degradation zones were delineated using the 0°C isotherm as a first-order indicator. [They] further identified glacial lakes located near potentially degrading permafrost zones and reconstructed their bathymetry. A detailed scenario-based GLOF process-chain simulation was then carried out for Kashang Lake using r.avaflow and HEC-RAS.
    [Their] results indicate that seven glacial lakes in #Kinnaur are located close to potentially degrading permafrost zones. The simulations further show that a potential GLOF from Kashang Lake could inundate critical downstream infrastructure, including nearly 11 km of National Highway 5…”
    #permafrost #distribution #GIS #spatial #mapping #Himalayas #India #Kinnaur #HimachalPradesh #KashangLake #massmovement #engineeringgeology #machinelearning #AI #model #modeling #numericalmodel #glaciallakes #glaciet #glacial #glaciallakeoutburstflood #GLOF #cryosphere #geostatistics #rockglaciers #GeoAI #bathymetry #processchainsimulation #HECRAS #avaflow #risk #hazard #mitigation #riskassessment #infrastructure #HEP #publicsafety #downstream #avalanche

  10. Permafrost Distribution, Degradation, And Potential Mass Movement Cascades In The Western Himalaya Using Machine Learning And Numerical Models
    --
    doi.org/10.1038/s44304-026-002 <-- shared paper
    --
    doi.org/10.1038/s41598-025-220 <-- shared (earlier) paper
    --
    doi.org/10.1080/2150704X.2025. <-- shared (earlier) paper
    --
    H/T @abhinav Alangadan
    “Can we develop a first-order understanding of permafrost degradation and glacial lakes exposed to degradation-induced mass movements in the Himalaya?
    [The authors] tried to address this question. The study [first link above] integrates machine learning, statistical modeling, and numerical modeling to investigate high-resolution permafrost distribution, potential degradation, and associated mass-movement hazards in the Kinnaur district of Himachal Pradesh, India.
    Using rock glaciers as proxies, [they] generated a high-resolution permafrost distribution using machine learning, while potential degradation zones were delineated using the 0°C isotherm as a first-order indicator. [They] further identified glacial lakes located near potentially degrading permafrost zones and reconstructed their bathymetry. A detailed scenario-based GLOF process-chain simulation was then carried out for Kashang Lake using r.avaflow and HEC-RAS.
    [Their] results indicate that seven glacial lakes in #Kinnaur are located close to potentially degrading permafrost zones. The simulations further show that a potential GLOF from Kashang Lake could inundate critical downstream infrastructure, including nearly 11 km of National Highway 5…”
    #permafrost #distribution #GIS #spatial #mapping #Himalayas #India #Kinnaur #HimachalPradesh #KashangLake #massmovement #engineeringgeology #machinelearning #AI #model #modeling #numericalmodel #glaciallakes #glaciet #glacial #glaciallakeoutburstflood #GLOF #cryosphere #geostatistics #rockglaciers #GeoAI #bathymetry #processchainsimulation #HECRAS #avaflow #risk #hazard #mitigation #riskassessment #infrastructure #HEP #publicsafety #downstream #avalanche

  11. Permafrost Distribution, Degradation, And Potential Mass Movement Cascades In The Western Himalaya Using Machine Learning And Numerical Models
    --
    doi.org/10.1038/s44304-026-002 <-- shared paper
    --
    doi.org/10.1038/s41598-025-220 <-- shared (earlier) paper
    --
    doi.org/10.1080/2150704X.2025. <-- shared (earlier) paper
    --
    H/T @abhinav Alangadan
    “Can we develop a first-order understanding of permafrost degradation and glacial lakes exposed to degradation-induced mass movements in the Himalaya?
    [The authors] tried to address this question. The study [first link above] integrates machine learning, statistical modeling, and numerical modeling to investigate high-resolution permafrost distribution, potential degradation, and associated mass-movement hazards in the Kinnaur district of Himachal Pradesh, India.
    Using rock glaciers as proxies, [they] generated a high-resolution permafrost distribution using machine learning, while potential degradation zones were delineated using the 0°C isotherm as a first-order indicator. [They] further identified glacial lakes located near potentially degrading permafrost zones and reconstructed their bathymetry. A detailed scenario-based GLOF process-chain simulation was then carried out for Kashang Lake using r.avaflow and HEC-RAS.
    [Their] results indicate that seven glacial lakes in are located close to potentially degrading permafrost zones. The simulations further show that a potential GLOF from Kashang Lake could inundate critical downstream infrastructure, including nearly 11 km of National Highway 5…”

  12. In 2022, DJI was able to fly one of their drones over Mt. Everest.

    Here are some of the physics challenges involved (see comments) 👇

    Here's the drone video (2022) recorded at the highest point on Earth!
    youtu.be/Zz9oI3B6v4c

    In 2024 and 2025, DJI drones, specifically the Mavic 3 Pro and Mavic 4 Pro, achieved record-setting, high-altitude flights over Mount Everest, capturing 8K footage of the summit at 8,848 meters. These drones, often operated with 8KRAW, showcased remarkable stability in high winds (up to 50 km/h) and thin air.

    DJI Mavic 3 Pro: youtu.be/A-iVxaFhr7s
    DJI Mavic 4 Pro: youtu.be/csDriucITDE

    #Drone #DronePhotography #MtEverest #Everest #Aerodynamics #Physics #DJIdroneshots #HighAltitude #ThinAir #PhysicsChallenge #Nepal #Himalayas #Drones #DroneShots

  13. Improving Forest Loss Mapping In Nepal Using Landtrendr Time-Series And Machine Learning
    --
    doi.org/10.1016/j.rsase.2025.1 <-- share paper
    --
    “HIGHLIGHTS:
    • ViT-based forest mask, multispectral ensemble LandTrendr and terrain shadow mask.
    • District-level RF/XGBoost model training with expert-weighted validation.
    • Outperformed GFC and REDD + AI benchmarks in accuracy and F1 performance.
    • RF excelled in High Mountains/Himalayas; XGBoost in the lower Mountain regions.
    • NBR contributed the most; snow-impacted forest loss uncertainty was observed..."
    #Forestdisturbance #forest #disturbance #remotesensing #LandTrendr #workflow #timeseries #ViT #RF #XGBoost #GEE #Nepal #ForestNepal #spatial #GIS #mapping #earthobservation #landsat #Himalayas #mountains #alpine #vegetation #AI #multispectral #monitoring #spatialanalysis #spatiotemporal #loss #change #machinelearning #NDR #conservation #planning #policy #mitagion #ecology #Karnali #Bagmati, #Darchula #Siwalik #GlobalForestChange #Degradation

  14. Dust plumes today including the one from Gala Lake #Tibet posted earlier in southerly flow over the #Himalayas and spreading north. Seen by #himawari 9 🛰️

  15. #15August #HappyIndependenceDay #RepublicOfIndia

    As we #Celebrate our 76th #IndependenceDay today here is the symbol of our #Democracy the #Buddhist Dharam Chakra (Wheel of Law) which is the centre of the #IndianFlag

    It has 24 spokes representing the 24 Rishis of #Himalayas. It also represents Time (24 hours of the day) to signify that #India is constantly changing & marching ahead.

    This 2nd century #Budhism Dharam Chakra comes from Amaravati in #AndharaPradesh & was on a Christies sale.