How much data is enough? Multi‑sensor DTMs and terrain metrics for steep mountain forests (Cesuna, Italy)

Abstract

This dataset contains multi‑sensor digital terrain models (DTMs), ground‑classified point clouds, and analysis tables used in the study “How much data is enough? Sensor choice, scale, and point‑density effects on terrain metrics in steep mountain forests” (Cesuna, Asiago Plateau, NE Italy). It supports an assessment of how sensor type (ALS, UAV LiDAR, handheld LiDAR, UAV‑SfM), LiDAR ground‑point density, and grid resolution influence elevation accuracy, slope‑based terrain class aggregation, and terrain‑ruggedness metrics in steep, closed‑canopy forests. The repository provides: (i) ground‑classified point clouds for UAV LiDAR, handheld LiDAR, and UAV‑SfM (and ALS where licensing permits); (ii) aligned DTMs for all sensors across multiple grid sizes (0.5–30 m); and (iii) tabular outputs used to compute elevation accuracy statistics, slope‑class agreement metrics, and relationships between slope and terrain ruggedness index (TRI). These products allow users to reproduce the main analyses in the manuscript and to explore alternative processing or classification schemes. All rasters are georeferenced in EPSG:32632 (WGS 84 / UTM zone 32N), with elevation in metres. Details of acquisition, preprocessing, and metric computation (LAStools, FUSION/LDV, GDAL, SAGA GIS, QGIS, and R/Python workflows) are described in the associated article and summarized in the included README.