Dataset used for the manuscript "The potential of geometric and radiometric Airborne LiDAR features in determining when the standing trees died – Case study Bohemian Switzerland National Park, Czech Republic"

The raw ALS (Airborne Laser Scanner) data from Bohemian Switzerland National Park is very large. Since the area is a national park protected under federal laws of the Czech Republic, these data can only be provided upon reasonable request to the authors. The dataset available here consists of the code used for processing the point clouds and the rasters generated from the LiDAR data, such as the CHM (Canopy Height Model) and pixel-based LiDAR metrics. Due to the large file sizes, all rasters have been saved in .tif format using LZW compression. Also included are the vector files resulting from the segmentation using superpixels, as well as the classification results for each scenario, along with their respective probabilities. All these data support the results and discussion of the manuscript. The Coordinate Reference System (CRS) used was S-JTSK Krovak EastNorth, EPSG:5514. FOLDERS 1_Code The provided script contains the full workflow used in this study, including LiDAR data processing (noise removal, point classification, normalization of intensities and heights, generation of digital models, and extraction of pixel-metrics related to height distribution, returns, and intensities), segmentation using the superpixel method, attribute extraction from rasters, and classification of the different scenarios regarding when the standing trees died in the park. The script was written using the R programming language (R Core Team, 2020), version 4.4.1 for Windows (https://cran.r-project.org/bin/windows/base/). The code was developed and executed in the RStudio integrated development environment, version 2024.04.2+764 for Windows 10/11 64-bit (https://posit.co/download/rstudio-desktop/). 2_Rasters This folder contains the rasters required for the extraction of object-based LiDAR metrics (i.e., superpixels). All rasters have a Ground Sample Distance (GSD) of 1 meter. CHM_Park_Clip.tif – Canopy Height Model used to generate the superpixels.Stdmetrics_filtered.tif – A total of 56 metrics were extracted using the default settings of the lidR package. These metrics were saved as a multiband image, where each band corresponds to one LiDAR metric. The order of the metrics is as follows: Band n. Metric Band n. Metric Band n. Metric Band n. Metric 1 zmax 15 zq35 29 zpcum2 43 ipground 2 zmean 16 zq40 30 zpcum3 44 ipcumzq10 3 zsd 17 zq45 31 zpcum4 45 ipcumzq30 4 zskew 18 zq50 32 zpcum5 46 ipcumzq50 5 zkurt 19 zq55 33 zpcum6 47 ipcumzq70 6 zentropy 20 zq60 34 zpcum7 48 ipcumzq90 7 pzabovezmean 21 zq65 35 zpcum8 49 p1th 8 pzabove2 22 zq70 36 zpcum9 50 p2th 9 zq5 23 zq75 37 itot 51 p3th 10 zq10 24 zq80 38 imax 52 p4th 11 zq15 25 zq85 39 imean 53 p5th 12 zq20 26 zq90 40 isd 54 pground 13 zq25 27 zq95 41 iskew 55 n 14 zq30 28 zpcum1 42 ikurt 56 area Percentis_Int_filtered.tif - Fifteen percentiles of LiDAR return intensity were calculated per pixel. These percentiles were saved in a multiband image, where each band corresponds to a specific percentile. The order of the bands and their respective percentiles is as follows: Band n. Metric Band n. Metric 1 iq1 9 iq60 2 iq5 10 iq70 3 iq10 11 iq75 4 iq20 12 iq80 5 iq25 13 iq90 6 iq30 14 iq95 7 iq40 15 iq99 8 iq50 3_Vectors This folder contains the generated vector files: Superpixels.gpkg – Contains the segments generated from the CHM, clipped to the area where standing dead trees were mapped, along with the respective year of death for each cell (column "Rok" in the attribute table).Classification_Scenario(x).gpkg – Contains the classification results for each of the three study scenarios (column "final_predictions" in the attribute table), as well as the probability that each segment was correctly classified (column "Max_Prob" in the attribute table).