DOI: 10.5281/zenodo.15490677
title: 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"

description: 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).