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#bagmati — Public Fediverse posts

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  1. Nearly 160,000 women, girls affected by Nepal floods

    In Brief:
    157,050 women and girls affected in #Rasuwa and #Nuwakot

    Disaster toll: 500+ killed, nearly 1,000 missing, 10,000 homes destroyed

    Survivors face shortages of #food, #water, #health and #protection services

    Patricia Fernandez-Pacheco: "Existing risks skyrocket when disasters hit"

    One in three #women in affected districts is illiterate, limiting access to relief

    One-third of households in #Bagmati headed by women, many reliant on remittances

    #UN Women mobilising Community Kitchen initiative for food and livelihoods

    Agency has appealed for US$2 million to support women and girls at risk

    thehimalayantimes.com/nepal/ne

    #Nepal
    #Tibet
    #BhotekoshiFlood
    #NepaliFlood
    #FlashFlood
    #EndFossilFuels

  2. 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