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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, ). 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. 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, ). 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 be used to link individual information on trees to the location of the specific tree in the SfM point clouds, providing for example, opportunity to validate the extracted tree height from the first dataset. The dataset provides unique insights into the current state of individual trees and shrubs and allows for monitoring the effects of climate change on these individuals in the future. Dataset 3 contains a synthesis of 10 000 generated images and masks that have the tree crowns of two species of larch ( and ) automatically extracted from the RGB UAV images in the common objects in context (COCO) format (van Geffen et al., 2021a, ). As machine-learning algorithms need a large dataset to train on, the synthetic dataset was specifically created to be used for machine-learning algorithms to detect Siberian larch species. Larix gmeliniiLarix cajanderiDataset 4 contains Sentinel-2 (S-2) Level-2 bottom-of-atmosphere processed labeled image patches with seasonal information and annotated vegetation categories covering the vegetation plots (van Geffen et al., 2021b, ). The dataset is created with the aim of providing a small ready-to-use validation and training dataset to be used in various vegetation-related machine-learning tasks. It enhances the data collection as it allows classification of a larger area with the provided vegetation classes. The SiDroForest data collection serves a variety of user communities. <br /> The detailed vegetation cover and structure information in the first two datasets are of use for ecological applications, on one hand for summergreen and evergreen needle-leaf forests and also for tundra-taiga ecotones. Datasets 1 and 2 further support the generation and validation of land cover remote-sensing products in radar and optical remote sensing. In addition to providing information on forest structure and vegetation composition of the vegetation plots, the third and fourth datasets are prepared as training and validation data for machine-learning purposes. For example, the synthetic tree-crown dataset is generated from the raw UAV images and optimized to be used in neural networks. Furthermore, the fourth SiDroForest dataset contains S-2 labeled image patches processed to a high standard that provide training data on vegetation class categories for machine-learning classification with JavaScript Object Notation (JSON) labels provided. The SiDroForest data collection adds unique insights into remote hard-to-reach circumboreal forest regions.
Woodlands and steppes
(2018)
Based on fossil organism remains including plant macrofossils, charcoal, pollen, and invertebrates preserved in syngenetic deposits of the Batagay permafrost sequence in the Siberian Yana Highlands, we reconstructed the environmental history during marine isotope stages (MIS) 6 to 2. Two fossil assemblages, exceptionally rich in plant remains, allowed for a detailed description of the palaeo-vegetation during two climate extremes of the Late Pleistocene, the onset of the last glacial maximum (LGM) and the last interglacial. In addition, altogether 41 assemblages were used to outline the vegetation history since the penultimate cold stage of MIS 6. Accordingly, meadow steppes analogue to modern communities of the phytosociological order Festucetalia lenensis formed the primary vegetation during the Saalian and Weichselian cold stages. Cold-resistant tundra-steppe communities (Carici rupestris-Kobresietea bellardii) as they occur above the treeline today were, in contrast to more northern locations, mostly lacking. During the last interglacial, open coniferous woodland similar to modern larch taiga was the primary vegetation at the site. Abundant charcoal indicates wildfire events during the last interglacial. Zoogenic disturbances of the local vegetation were indicated by the presence of ruderal plants, especially by abundant Urtica dioica, suggesting that the area was an interglacial refugium for large herbivores. Meadow steppes, which formed the primary vegetation during cold stages and provided potentially suitable pastures for herbivores, were a significant constituent of the plant cover in the Yana Highlands also under the full warm stage conditions of the last interglacial. Consequently, meadow steppes occurred in the Yana Highlands during the entire investigated timespan from MIS 6 to MIS 2 documenting a remarkable environmental stability. Thus, the proportion of meadow steppe vegetation merely shifted in response to the respectively prevailing climatic conditions. Their persistence indicates low precipitation and a relatively warm growing season throughout and beyond the late Pleistocene. The studied fossil record also proves that modern steppe occurrences in the Yana Highlands did not establish as late as in the Holocene but instead are relicts of a formerly continuous steppe belt extending from Central Siberia to Northeast Yakutia during the Pleistocene. The persistence of plants and invertebrates characteristic of meadow steppe vegetation in interior Yakutia throughout the late Quaternary indicates climatic continuity and documents the suitability of this region as a refugium also for other organisms of the Pleistocene mammoth steppe including the iconic large herbivores. (C)2018 Elsevier Ltd. All rights reserved.