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Home Page: https://pat-s.github.io/2019-feature-selection/
License: Other
Research project
Home Page: https://pat-s.github.io/2019-feature-selection/
License: Other
Finish models for
Laukiz 1 (1)
Laukiz 2 (1)
Luiando (1)
Oiartzun (1)
Supermodel (2)
(1) 5-fold 5 repeated CV
(2) Block CV (4 folds) on the plot level
After feature selection and hyperparameter tuning.
Maybe outside the CV with the models and indices from the CV. Advantage: We can parallelize it then and have a break (in case sth goes wrong in the CV).
Why use remote sensing data for forest health analysis?
Show other studies using remote sensing on forest health
Show all single plots and complete overview
Describe the hyperspectral data
Explain derivation of indices already here?
Initial issue: Some intermediate steps try to use some Sentinel scenes from a flight path which was not yet downloaded.
The reason for this is unclear.
Possible reasons:
Downloading these new scenes errors at the moment due to an issue with the latest version of {getSpatialData}.
Updating the package was required because the version used until now did not work anymore due to changes in the Sentinel API.
To see potential patterns
Point density
Coefficient of variation (COV) of defoliation
more?
Die prediction ist rein fürs Projekt, nicht fürs paper.
Prediction auf plot Ebene mit den hyperspectral Daten hab ich bereits gemacht: /mnt/mccoy/home/patrick/PhD/papers/03_hyperspectral/02_scripts/08_prediction.Rmd
.
Wir nehmen xgboost
mit 7 vars als Modell: /mnt/lossa/data_mccoy_kirk_scotty/patrick/mod/hyperspectral/prediction/xgboost_trained_tuned_7vars.rda
Sentinel-Daten: /mnt/lossa/data_mccoy_kirk_scotty/daphne/mod/Sentinel_clip
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