Terrestrial, UAV-borne, and airborne laser scanning point clouds of central European forest plots, Germany, with extracted individual trees and manual forest inventory measurements

DOI

Laser scanning point clouds of forest stands were acquired in southwest Germany in 2019 and 2020 from different platforms: an aircraft, an uncrewed aerial vehicle (UAV) and a ground-based tripod. The UAV-borne and airborne laser scanning campaigns cover twelve forest plots of approximately 1 ha. The plots are located in mixed central European forests close to Bretten and Karlsruhe, in the federal state of Baden-Württemberg, Germany. Terrestrial laser scanning was performed in selected locations within the twelve forest plots. Airborne and terrestrial laser scanning point clouds were acquired under leaf-on conditions, UAV-borne laser scans were acquired both under leaf-on and later under leaf-off conditions. In addition to the laser scanning campaigns, forest inventory tree properties (species, height, diameter at breast height, crown base height, crown diameter) were measured in-situ during summer 2019 in six of the twelve 1-ha plots. Single tree point clouds were extracted from the different laser scanning datasets and matched to the field measurements. For each tree entry, point clouds, tree species, position, and field-measured and point cloud-derived tree metrics are provided. For 249 trees, point clouds from all three platforms are available. The tree models form the basis of a single tree database covering a range of species typical for central European forests which is currently being established in the framework of the SYSSIFOSS project.

Version 2, 2022-03-31: Previous version doi:10.1594/PANGAEA.933426 contains errors due to data processing.

Identifier
DOI https://doi.org/10.1594/PANGAEA.942856
Metadata Access https://ws.pangaea.de/oai/provider?verb=GetRecord&metadataPrefix=datacite4&identifier=oai:pangaea.de:doi:10.1594/PANGAEA.942856
Provenance
Creator Weiser, Hannah ORCID logo; Schäfer, Jannika; Winiwarter, Lukas ORCID logo; Krašovec, Nina; Seitz, Christian; Schimka, Marian; Anders, Katharina ORCID logo; Baete, Daria; Braz, Andressa Soarez; Brand, Johannes; Debroize, Denis; Kuss, Paula; Martin, Lioba Lucia; Mayer, Angelo; Schrempp, Torben; Schwarz, Lisa-Maricia; Ulrich, Veit; Fassnacht, Fabian E; Höfle, Bernhard ORCID logo
Publisher PANGAEA
Publication Year 2022
Funding Reference German Research Foundation https://doi.org/10.13039/501100001659 Crossref Funder ID 411263134 Synthetic structural remote sensing data for improved forest inventory models
Rights Creative Commons Attribution-ShareAlike 4.0 International; https://creativecommons.org/licenses/by-sa/4.0/
OpenAccess true
Representation
Resource Type Dataset
Format text/tab-separated-values
Size 12 data points
Discipline Biospheric Sciences; Ecology; Geosciences; Natural Sciences
Spatial Coverage (8.416W, 48.996S, 8.717E, 49.041N); Germany, Baden-Wuerttemberg