DOI: 10.5061/dryad.jq2bvq8js
title: Data from: Hyperspectral imaging has a limited ability to remotely sense the onset of beech bark disease

description: This dataset includes hyperspectral and beech bark disease assessment data
 from our study area, Mont-Saint-Bruno National Park in
 Saint-Bruno-de-Montarville, Quebec, Canada. Here, we tested whether
 airborne hyperspectral imagery - involving data from 344 wavelengths in
 the visible, near infrared (NIR), and shortwave infrared (SWIR) - can be
 used to assess the severity and progression of beech bark disease in
 southern Quebec, in the hope of developing new methods for effective
 remote sensing of this fungal infection that is widespread in eastern
 North America. Field data on disease severity were linked to airborne
 hyperspectral data using georeferenced red-green-blue (RGB) drone imagery
 to delineate beech crowns of interest. We also looked for a relationship
 between this same disease severity variable and hyperspectral data at leaf
 level (with canopy leaf samples). This dataset therefore comprises four
 sections: 1. Canopy-level hyperspectral data (n=126); 2. Crown geometries
 for the 126 beech trees with aerial hyperspectral imaging; 3. Leaf-level
 hyperspectral data on selected beech trees (n=37) and 4. Beech bark
 disease assessment data (n=160).
description: Data for this project were collected in three different contexts.
 First, airborne hyperspectral data from Mont-Saint-Bruno National Park in
 Saint-Bruno-de-Montarville, Quebec, Canada, were collected for the CABO project in
 September 2018 with a resolution of 2 meters per pixel. Secondly, we
 assessed the progression of beech bark disease in the same study area on
 160 beech trees across a broad disease gradient, using bark and canopy
 condition indices as predictors of disease progression. In the end, only
 126 of these were linked to aerial hyperspectral imagery, as the spatial
 error on the geometries of the remaining 34 beech crowns in the flight
 line was too high. Finally, we measured the reflectance of the fresh
 leaves of 37 beech trees using a portable hyperspectral sensor.
 For hyperspectral data processing, we have combined several
 techniques for reducing atmospheric noise in spectral data, including
 masking of atmospheric absorption regions (350-400 nm and 920-957 nm)
 followed by reflectance interpolation in the masked regions, and
 application of a smoothing filter (Savitzky-Golay) to the raw data.
 Downloaded datasets have already been processed using the above methods.
 Complete CABO raw hyperspectral datasets for the whole park are available
 on request. For further details on data collection and processing, please
 refer to the following preprint: https://doi.org/10.1101/2024.09.20.614150.
description: # Data from: Hyperspectral imaging has a limited ability to remotely sense
 the onset of beech bark disease
 [https://doi.org/10.5061/dryad.jq2bvq8js](https://doi.org/10.5061/dryad.jq2bvq8js) ## Description of the data and file structure Insect and pathogen outbreaks significantly affect northern forest ecosystems. Even for long-established pathogens, such as beech bark disease (BBD), new waves of host population mortality are anticipated. This creates a need for innovative, real-time monitoring approaches. In this study, we explore whether airborne hyperspectral imaging—using data from 344 wavelengths across the visible, near-infrared (NIR), and short-wave infrared (SWIR) spectra—can effectively assess the severity of beech bark disease in southern Quebec, Canada. For this study, we linked field data on disease severity with airborne hyperspectral data for individual beech crowns and with leaf-level hyperspectral data extracted from canopy leaves of sampled beech trees. ### Files and variables #### File: beech\_rawdata.csv **Description:** Field data related to beech bark disease assessment on 161 sampled beech trees (see metadata file for more information). NA values mean that a measure was not applicable in the given case. ##### Variables * ID: Tree ID. * Date_ID: Date of assessment. * DBH: Diameter at breast height (cm). * Crown_Stat: Beech crown status index (1-5). * Bark_state_N: Beech bark status index (1-5). Northern side of the tree. * Bark_state_E: Beech bark status index (1-5). Eastern side of the tree. * Bark_state_S: Beech bark status index (1-5). Southern side of the tree. * Bark_state_W: Beech bark status index (1-5). Western side of the tree. * Comments: Commentaries. * P_length: Width of canopy, perpendicular to maximum length (m). * Max_length: Maximum canopy length (m). * Angle_canopy: Angle at which the canopy is largest (0-180 degrees) * Proximity: Distance to the closest beech tree (m). * Snap_snag: Presence of beech snap and/or beech snag events. * Notes: Notes. * Species: Dominant species in stand. * Density_beech: Beech basal area measured with factor 2 prism. * Scale_ab: Beech scale abundance index (1-5). * Perithecia_ab: Perithecia abundance index (1-5). * Height: Tree height (m). #### File: airborne\_rawdata\_bands.csv **Description:** Hyperspectral raw data for selected beech trees at the canopy level (airborne; see metadata file for more information). A value of 0 for reflectance means that it was not measured for these particular tree and wavelength. ##### Variables * ID: Tree ID. * X"wavelength_number": Average reflectance of pixels from corresponding tree for this wavelength. #### File: leaflevel\_rawdata\_bands.csv **Description:** Hyperspectral raw data for sampled beech trees at the leaf level (see metadata file for more information). ##### Variables * ID: Tree ID. * X"wavelength_number": Average reflectance of pixels from corresponding tree for this wavelength. #### File: beech\_crown\_geometries.gpkg **Description:** Geometries of beech trees linked to airborne hyperspectral imaging (see metadata file for more information). ##### Variables * ID: Tree ID. * Geometry: Coordinates of beech crown polygons (sf object). CRS is EPSG=32618 (WGS84 / UTM 18N). ## Code/software #### **Software recommended for data analysis :** **R, version 4.2**: R is a powerful open-source programming language and software environment for statistical computing and data analysis, offering extensive libraries and tools that make it well-suited for handling hyperspectral data due to its ability to efficiently process large datasets, perform advanced statistical analyses, and support specialized packages like **`raster`**, **`sf`**, and **`hsdar`** for geospatial and spectral analysis. #### Packages used : * **`sf`** : This package allows you to visualize the beech_geometry dataset (.gpkg) with its function "st_read".  * **`ggplot2`**: For data visualization and plotting. * **`raster`**: Provides tools for reading, writing, manipulating, and analyzing geospatial raster data, allowing efficient handling of large datasets and facilitating spatial modeling and analysis. * **`hsdar`**: Designed for handling and analyzing hyperspectral data, offering tools for data preprocessing, spectral analysis, and integration with remote sensing data to facilitate environmental and ecological research. * **`exactextractr`** **:** Gives efficient tools for extracting values from raster datasets based on vector geometries, enabling precise statistical summaries and analyses at specified locations or within defined polygons. More information about code and software used in this study can be found in the attached preprint: [https://doi.org/10.1101/2024.09.20.614150](https://doi.org/10.1101/2024.09.20.614150)