TY - JOUR A1 - Geng, Rongwei A1 - Andreev, Andrei A1 - Kruse, Stefan A1 - Heim, Birgit A1 - van Geffen, Femke A1 - Pestryakova, Luidmila A1 - Zakharov, Evgenii A1 - Troeva, Elena I. A1 - Shevtsova, Iuliia A1 - Li, Furong A1 - Zhao, Yan A1 - Herzschuh, Ulrike T1 - Modern pollen assemblages from lake sediments and soil in East Siberia and relative pollen productivity estimates for Major Taxa JF - Frontiers in Ecology and Evolution N2 - Modern pollen-vegetation-climate relationships underpin palaeovegetation and palaeoclimate reconstructions from fossil pollen records. East Siberia is an ideal area for investigating the relationships between modern pollen assemblages and near natural vegetation under cold continental climate conditions. Reliable pollen-based quantitative vegetation and climate reconstructions are still scarce due to the limited number of modern pollen datasets. Furthermore, differences in pollen representation of samples from lake sediments and soils are not well understood. Here, we present a new pollen dataset of 48 moss/soil and 24 lake surface-sediment samples collected in Chukotka and central Yakutia in East Siberia. The pollen-vegetation-climate relationships were investigated by ordination analyses. Generally, tundra and taiga vegetation types can be well distinguished in the surface pollen assemblages. Moss/soil and lake samples contain generally similar pollen assemblages as revealed by a Procrustes comparison with some exceptions. Overall, modern pollen assemblages reflect the temperature and precipitation gradients in the study areas as revealed by constrained ordination analysis. We estimate the relative pollen productivity (RPP) of major taxa and the relevant source area of pollen (RSAP) for moss/soil samples from Chukotka and central Yakutia using Extended R-Value (ERV) analysis. The RSAP of the tundra-forest transition area in Chukotka and taiga area in central Yakutia are ca. 1300 and 360 m, respectively. For Chukotka, RPPs relative to both Poaceae and Ericaceae were estimated while RPPs for central Yakutia were relative only to Ericaceae. Relative to Ericaceae (reference taxon, RPP = 1), Larix, Betula, Picea, and Pinus are overrepresented while Alnus, Cyperaceae, Poaceae, and Salix are underrepresented in the pollen spectra. Our estimates are in general agreement with previously published values and provide the basis for reliable quantitative reconstructions of East Siberian vegetation. KW - modern pollen assemblages KW - pollen-vegetation-climate relationships KW - East Siberia KW - tundra KW - taiga KW - relative pollen productivity KW - quantitative vegetation reconstruction Y1 - 2022 U6 - https://doi.org/10.3389/fevo.2022.837857 SN - 2296-701X VL - 10 PB - Frontiers Media CY - Lausanne ER - TY - JOUR A1 - van Geffen, Femke A1 - Heim, Birgit A1 - Brieger, Frederic A1 - Geng, Rongwei A1 - Shevtsova, Iuliia A1 - Schulte, Luise A1 - Stuenzi, Simone M. A1 - Bernhardt, Nadine A1 - Troeva, Elena I. A1 - Pestryakova, Luidmila Agafyevna A1 - Zakharov, Evgenii S. A1 - Pflug, Bringfried A1 - Herzschuh, Ulrike A1 - Kruse, Stefan T1 - 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 JF - Earth system science data N2 - 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.
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. Y1 - 2022 U6 - https://doi.org/10.5194/essd-14-4967-2022 SN - 1866-3508 SN - 1866-3516 VL - 14 IS - 11 SP - 4967 EP - 4994 PB - Copernicus CY - Göttingen ER - TY - JOUR A1 - Glückler, Ramesh A1 - Geng, Rongwei A1 - Grimm, Lennart A1 - Baisheva, Izabella A1 - Herzschuh, Ulrike A1 - Stoof-Leichsenring, Kathleen R. A1 - Kruse, Stefan A1 - Andreev, Andrej Aleksandrovic A1 - Pestryakova, Luidmila A1 - Dietze, Elisabeth T1 - Holocene wildfire and vegetation dynamics in Central Yakutia, Siberia, reconstructed from lake-sediment proxies JF - Frontiers in Ecology and Evolution N2 - Wildfires play an essential role in the ecology of boreal forests. In eastern Siberia, fire activity has been increasing in recent years, challenging the livelihoods of local communities. Intensifying fire regimes also increase disturbance pressure on the boreal forests, which currently protect the permafrost beneath from accelerated degradation. However, long-term relationships between changes in fire regime and forest structure remain largely unknown. We assess past fire-vegetation feedbacks using sedimentary proxy records from Lake Satagay, Central Yakutia, Siberia, covering the past c. 10,800 years. Results from macroscopic and microscopic charcoal analyses indicate high amounts of burnt biomass during the Early Holocene, and that the present-day, low-severity surface fire regime has been in place since c. 4,500 years before present. A pollen-based quantitative reconstruction of vegetation cover and a terrestrial plant record based on sedimentary ancient DNA metabarcoding suggest a pronounced shift in forest structure toward the Late Holocene. Whereas the Early Holocene was characterized by postglacial open larch-birch woodlands, forest structure changed toward the modern, mixed larch-dominated closed-canopy forest during the Mid-Holocene. We propose a potential relationship between open woodlands and high amounts of burnt biomass, as well as a mediating effect of dense larch forest on the climate-driven intensification of fire regimes. Considering the anticipated increase in forest disturbances (droughts, insect invasions, and wildfires), higher tree mortality may force the modern state of the forest to shift toward an open woodland state comparable to the Early Holocene. Such a shift in forest structure may result in a positive feedback on currently intensifying wildfires. These new long-term data improve our understanding of millennial-scale fire regime changes and their relationships to changes of vegetation in Central Yakutia, where the local population is already being confronted with intensifying wildfire seasons. KW - fire KW - larch KW - boreal KW - forest KW - Russia KW - charcoal KW - pollen KW - ancient DNA Y1 - 2022 U6 - https://doi.org/10.3389/fevo.2022.962906 SN - 2296-701X VL - 10 PB - Frontiers Media CY - Lausanne ER -