Vojtek et al. (2025) Dataset of physical-geographical predictors

Identification

• Journal: Mendeley Data
• Year: 2025
• Date: 2025-11-03
• Authors: Vojtek, Matej, Benko, Ľubomír, Kapusta, Jozef, Držík, Dávid, munk, michal, Munkova, Dasa, Drlik, Martin, Vojteková, Jana
• DOI: 10.17632/bprmy76fdv.1

Research Groups

• Univerzita Konstantina Filozofa v Nitre (Constantine the Philosopher University in Nitra)

Short Summary

This dataset provides a collection of seven high-resolution physical-geographical predictors derived from LiDAR DEM and orthophotos for a critical section of the Gidra River in Slovakia, intended for fluvial flood extent and flow depth modeling.

Objective

• To create and provide a dataset of seven physical-geographical raster predictors at 1 meter resolution, derived from high-resolution LiDAR Digital Elevation Model (DEM) and orthophotos, for a section of the Gidra River, to support fluvial flood extent and flow depth modeling.

Study Configuration

• Spatial Scale: A critical section of the Gidra River in western Slovakia; raster predictors at 1 meter resolution.
• Temporal Scale: Orthophotos from 2023.

Methodology and Data

• Models used:
  • Derivation of Height Above Nearest Drainage (HAND)
  • Calculation of Euclidean Distance to Drainage
  • Computation of Surface roughness (Manning’s n)
  • Calculation of Stream Power Index (SPI)
  • Calculation of Topographic Wetness Index (TWI)
  • Calculation of Normalized Difference Vegetation Index (NDVI)
  • Derivation of Slope
• Data sources:
  • Airborne laser scanned LiDAR Digital Elevation Model (DMR 5.0) with 1 meter resolution (provided by Geodetic and Cartographic Institute, Bratislava).
  • Orthophotos from 2023 with 15 centimeter pixel resolution.
  • Shapefile (LineString geometry) representing drainage.

Main Results

The study resulted in the creation and provision of a comprehensive dataset comprising raw orthophoto map, LiDAR DEM, a shapefile, and seven derived physical-geographical raster predictors for a section of the Gidra River. These predictors include Height Above Nearest Drainage, Euclidean Distance to Drainage, Surface roughness, Stream Power Index, Topographic Wetness Index, Normalized Difference Vegetation Index, and Slope, all at 1 meter resolution, packaged for use in fluvial flood modeling.

Contributions

This dataset offers high-resolution (1 meter) physical-geographical predictors specifically tailored for fluvial flood modeling in a critical river section, derived from state-of-the-art LiDAR and orthophoto data. It provides a valuable resource for training flood scenarios and improving the accuracy of flood extent and depth predictions, contributing to better flood risk management in the region.

Funding

• EU NextGenerationEU through the Recovery and Resilience Plan for Slovakia (Grant ID: 09I03-03-V03-00085)

Citation

@article{Vojtek2025Dataset,
  author = {Vojtek, Matej and Benko, Ľubomír and Kapusta, Jozef and Držík, Dávid and munk, michal and Munkova, Dasa and Drlik, Martin and Vojteková, Jana},
  title = {Dataset of physical-geographical predictors},
  journal = {Mendeley Data},
  year = {2025},
  doi = {10.17632/bprmy76fdv.1},
  url = {https://doi.org/10.17632/bprmy76fdv.1}
}