(🛰〰️🌳, previously 📡〰️🌧️),
randonnée 🥾, vélo 🚲, et théâtre 🎭 :)
📍 LIF - Géodata Paris / French Mapping Agency (@ignfrance.bsky.social)
+ info ➡️ https://nbesic.github.io
4/4
4/4
- Height time series captures local forest growth, validated with ALS.
- Species-specific height–age curves consistent with field data.
3/4
- Height time series captures local forest growth, validated with ALS.
- Species-specific height–age curves consistent with field data.
3/4
- Yearly forest height, AGB, and GSV maps (2018–2024) at 10–30 m resolution in France.
- Canopy height validated with NFI data (MAE = 3.07 m).
2/4
- Yearly forest height, AGB, and GSV maps (2018–2024) at 10–30 m resolution in France.
- Canopy height validated with NFI data (MAE = 3.07 m).
2/4
Congrats again to @gbrldst.bsky.social for leading this important step toward more accurate and flexible flux measurements!
#EnvironmentalMonitoring #CarbonCycle #EcosystemFluxes #Wavelets
(5/5)
Congrats again to @gbrldst.bsky.social for leading this important step toward more accurate and flexible flux measurements!
#EnvironmentalMonitoring #CarbonCycle #EcosystemFluxes #Wavelets
(5/5)
– higher summer carbon uptake (+2 ± 1 µmol/m²/s)
– better nighttime respiration estimates
– strong agreement with standard EC
Implemented in TurbulenceFlux.jl (Julia)
(4/5)
– higher summer carbon uptake (+2 ± 1 µmol/m²/s)
– better nighttime respiration estimates
– strong agreement with standard EC
Implemented in TurbulenceFlux.jl (Julia)
(4/5)
This helps separate turbulence from advection.
(3/5)
This helps separate turbulence from advection.
(3/5)
But traditional 30-min eddy-covariance averaging often misses rapid responses to environmental change.
(2/5)
But traditional 30-min eddy-covariance averaging often misses rapid responses to environmental change.
(2/5)