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

<p>This is a research compendium (RC) for the publication</p> <blockquote> <p>Monitoring forest health using hyperspectral imagery: Does feature selection improve the performance of machine-learning techniques?</p> </blockquote> <p>Code, figures, appendices and the manuscript can be found in the corresponding <a href="https://github.com/pat-s/2019-feature-selection">GitHub repository</a>.</p> <p>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.</p> <p><strong>Data sources and description</strong></p> <ul> <li><em>aoi.gpkg</em><strong>:</strong> Area of interest for downloading Sentinel-2 images. <em>Not used in the publication<strong>. </strong></em>Source:<em><strong> </strong></em>Custom.</li> <li><em>forest_mask.gpkg</em>: A forest/non-forest mask of the Basque Country. <em>Not used in the publication</em>. Source:<em><strong> </strong></em>Custom.</li> <li><em>hyperspectral.zip: </em>Hyperspectral remote sensing data used to extract reflectance values on the tree level. Source:<em><strong> </strong></em>Custom.</li> <li><em>plot-locations.gpkg: </em>Spatial location of the plots used in the study. Source:<em><strong> </strong></em>Custom.</li> <li><em>tree-in-situ-data-corrected.zip: </em>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:<em><strong> </strong></em>Custom.</li> <li><em>tree-in-situ-data.zip: </em>First version of in-situ data containing defoliation information on the tree level.<em> Not used in the publication. </em>Source:<em><strong> </strong></em>Custom.</li> </ul> <p><strong>Licenses</strong></p> <p>All files are licensed under CC BY 4.0.</p>