SiDroForest: a comprehensive forest inventory of Siberian boreal forest investigations including drone-based point clouds, individually labeled trees, synthetically generated tree crowns, and Sentinel-2 labeled image patches

• Published in: Earth System Science Data
• Main Authors: van Geffen, Femke, Heim, Birgit, Brieger, Frederic, Geng, Rongwei, Shevtsova, Iuliia A., Schulte, Luise, Stuenzi, Simone M., Bernhardt, Nadine, Troeva, Elena I., Pestryakova, Luidmila A., Zakharov, Evgenii S., Pflug, Bringfried, Herzschuh, Ulrike, Kruse, Stefan
• Format: Article in Journal/Newspaper
• Language: English
• Published: Copernicus Publications 2022
• Subjects: article; Verlagsveröffentlichung; Chukotka; taiga; Tundra; Yakutia; Siberia
• Online Access: https://doi.org/10.5194/essd-14-4967-2022; https://noa.gwlb.de/receive/cop_mods_00063423; https://noa.gwlb.de/servlets/MCRFileNodeServlet/cop_derivate_00062477/essd-14-4967-2022.pdf; https://essd.copernicus.org/articles/14/4967/2022/essd-14-4967-2022.pdf

The SiDroForest (Siberian drone-mapped forest inventory) data collection is an attempt to remedy the scarcity of forest structure data in the circumboreal region by providing adjusted and labeled tree-level and vegetation plot-level data for machine learning and upscaling purposes. We present datasets of vegetation composition and tree and plot level forest structure for two important vegetation transition zones in Siberia, Russia; the summergreen–evergreen transition zone in Central Yakutia and the tundra–taiga transition zone in Chukotka (NE Siberia). The SiDroForest data collection consists of four datasets that contain different complementary data types that together support in-depth analyses from different perspectives of Siberian Forest plot data for multi-purpose applications. i. Dataset 1 provides unmanned aerial vehicle (UAV)-borne data products covering the vegetation plots surveyed during fieldwork (Kruse et al., 2021, https://doi.org/10.1594/PANGAEA.933263). The dataset includes structure-from-motion (SfM) point clouds and red–green–blue (RGB) and red–green–near-infrared (RGN) orthomosaics. From the orthomosaics, point-cloud products were created such as the digital elevation model (DEM), canopy height model (CHM), digital surface model (DSM) and the digital terrain model (DTM). The point-cloud products provide information on the three-dimensional (3D) structure of the forest at each plot. ii. Dataset 2 contains spatial data in the form of point and polygon shapefiles of 872 individually labeled trees and shrubs that were recorded during fieldwork at the same vegetation plots (van Geffen et al., 2021c, https://doi.org/10.1594/PANGAEA.932821). The dataset contains information on tree height, crown diameter, and species type. These tree and shrub individually labeled point and polygon shapefiles were generated on top of the RGB UVA orthoimages. The individual tree information collected during the expedition such as tree height, crown diameter, and vitality are provided in table format. This dataset can ...