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  1. Improving Forest Loss Mapping In Nepal Using Landtrendr Time-Series And Machine Learning
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    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

  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

  3. 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

  4. 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

  5. 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..."
    ,

  6. Morning walk in the park....monsoon effects plain to see. The Bagmati river in this area remains polluted from upstream but huge strides are evident thanks to the sewers put in place here. Lots more to do. #nepal #bagmati #lalitpur #monsoon

  7. Morning walk in the park....monsoon effects plain to see. The Bagmati river in this area remains polluted from upstream but huge strides are evident thanks to the sewers put in place here. Lots more to do. #nepal #bagmati #lalitpur #monsoon