Monitoring forest health using hyperspectral imagery: Does feature selection improve the performance of machine-learning techniques? (Q6494)

This is a research compendium (RC) for the publication Monitoring forest health using hyperspectral imagery: Does feature selection improve the performance of machine-learning techniques? Code, figures, appendices and the manuscript can be found in the corresponding GitHub repository. This RC is a static snapshot at the time of submission. The GitHub repository holds the latest version and may see changes after the publication was accepted. Data sources and description aoi.gpkg: Area of interest for downloading Sentinel-2 images. Not used in the publication. Source: Custom. forest_mask.gpkg: A forest/non-forest mask of the Basque Country. Not used in the publication. Source: Custom. hyperspectral.zip: Hyperspectral remote sensing data used to extract reflectance values on the tree level. Source: Custom. plot-locations.gpkg: Spatial location of the plots used in the study. Source: Custom. tree-in-situ-data-corrected.zip: Corrected in-situ data containing defoliation information on the tree level. A correction of the spatial location was applied by the creators of the data. Source: Custom. tree-in-situ-data.zip: First version of in-situ data containing defoliation information on the tree level. Not used in the publication. Source: Custom. Licenses All files are licensed under CC BY 4.0.