Advances in the Derivation of Northeast Siberian Forest Metrics Using High-Resolution UAV-Based Photogrammetric Point Clouds

Forest structure is a crucial component in the assessment of whether a forest is likely to act as a carbon sink under changing climate. Detailed 3D structural information about the tundra–taiga ecotone of Siberia is mostly missing and still underrepresented in current research due to the remoteness and restricted accessibility. Field based, high-resolution remote sensing can provide important knowledge for the understanding of vegetation properties and dynamics. In this study, we test the applicability of consumer-grade Unmanned Aerial Vehicles (UAVs) for rapid calculation of stand metrics in treeline forests. We reconstructed high-resolution photogrammetric point clouds and derived canopy height models for 10 study sites from NE Chukotka and SW Yakutia. Subsequently, we detected individual tree tops using a variable-window size local maximum filter and applied a marker-controlled watershed segmentation for the delineation of tree crowns. With this, we successfully detected 67.1% of the validation individuals. Simple linear regressions of observed and detected metrics show a better correlation (R2) and lower relative root mean square percentage error (RMSE%) for tree heights (mean R2 = 0.77, mean RMSE% = 18.46%) than for crown diameters (mean R2 = 0.46, mean RMSE% = 24.9%). The comparison between detected and observed tree height distributions revealed that our tree detection method was unable to representatively identify trees 15–20 m to capture homogeneous and representative forest stands. Additionally, we identify sources of omission and commission errors and give recommendations for their mitigation. In summary, the efficiency of the used method depends on the complexity of the forest’s stand structure

Selective ethylene trimerization by titanium complexes bearing phenoxy-imine ligands: NMR and EPR Spectroscopic studies of the reaction intermediates

The catalyst systems (FI)TiCl₃/MAO (FI = phenoxyimine ligand with an additional aryl–O–CH₃ donor) display exceptionally high activity in selective ethylene trimerization. By means of NMR and EPR spectroscopy, the nature of the Ti species formed in the catalyst systems (FI)TiCl₃/MAO, (FI)TiCl₃/MMAO, and (FI)TiCl₃/AlR₃/[Ph₃C]⁺[B(C₆F₅)₄]⁻ (R = Me, Et, ⁱBu) has been studied. It was shown that outer-sphere ion pairs of the type [(FI)TiIVMe₂]⁺[A]⁻ ([A]− = [MeMAO]⁻, [MeMMAO]⁻, [B(C₆F₅)₄]⁻) are formed at the initial stage of the reaction of (FI)TiCl₃ with MAO, MMAO, and AlMe₃/[Ph₃C]⁺[B(C₆F₅)₄]⁻. These ion pairs further partially convert into TiIII and TiII species. In the systems (FI)TiCl₃/MAO and (FI)TiCl₃/AlMe₃/[Ph₃C]⁺[B(C₆F5)₄]⁻, complexes with the proposed structures (FI)TiIIIMe₂, (FI)TiIICl, and [(FI)TiII(S)]⁺[A]⁻ ([A]− = [MeMAO]⁻, [B(C₆F₅)4)]⁻, S = solvent, vacancy) were observed (concentrations of TiIII species was lower than those of the TiII congeners). In contrast, in the system (FI)TiCl₃/MMAO, the concentrations of TiIII species (ion pairs of the type [(FI)TiIII(μ-H)(μ-Cl)AlⁱBu₂]⁺[MeMMAO]⁻) were higher than those of the TiII counterparts (ion pairs [(FI)TiII(S)]⁺[MeMMAO]⁻). The system (FI)TiCl₃/MMAO displays lower activity and selectivity in 1-hexene formation, in comparison to (FI)TiCl₃/MAO, due to undesirable PE generation. Probably, TiII and TiIV ion pairs are those participating in ethylene trimerization

Forest structure and individual tree inventories of northeastern Siberia along climatic gradients

We compile a data set of forest surveys from expeditions to the northeast of the Russian Federation, in Krasnoyarsk Krai, the Republic of Sakha (Yakutia), and the Chukotka Autonomous Okrug (59–73∘ N, 97–169∘ E), performed between the years 2011 and 2021. The region is characterized by permafrost soils and forests dominated by larch (Larix gmelinii Rupr. and Larix cajanderi Mayr). Our data set consists of a plot database describing 226 georeferenced vegetation survey plots and a tree database with information about all the trees on these plots. The tree database, consisting of two tables with the same column names, contains information on the height, species, and vitality of 40 289 trees. A subset of the trees was subject to a more detailed inventory, which recorded the stem diameter at base and at breast height, crown diameter, and height of the beginning of the crown. We recorded heights up to 28.5 m (median 2.5 m) and stand densities up to 120 000 trees per hectare (median 1197 ha−1), with both values tending to be higher in the more southerly areas. Observed taxa include Larix Mill., Pinus L., Picea A. Dietr., Abies Mill., Salix L., Betula L., Populus L., Alnus Mill., and Ulmus L. In this study, we present the forest inventory data aggregated per plot. Additionally, we connect the data with different remote sensing data products to find out how accurately forest structure can be predicted from such products. Allometries were calculated to obtain the diameter from height measurements for every species group. For Larix, the most frequent of 10 species groups, allometries depended also on the stand density, as denser stands are characterized by thinner trees, relative to height. The remote sensing products used to compare against the inventory data include climate, forest biomass, canopy height, and forest loss or disturbance. We find that the forest metrics measured in the field can only be reconstructed from the remote sensing data to a limited extent, as they depend on local properties. This illustrates the need for ground inventories like those data we present here. The data can be used for studying the forest structure of northeastern Siberia and for the calibration and validation of remotely sensed data. They are available at https://doi.org/10.1594/PANGAEA.943547 (Miesner et al., 2022).</p

Modern Pollen Assemblages From Lake Sediments and Soil in East Siberia and Relative Pollen Productivity Estimates for Major Taxa

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.</jats:p

Late Glacial and Holocene vegetation and lake changes in SW Yakutia, Siberia, inferred from sedaDNA, pollen, and XRF data

Only a few palaeo-records extend beyond the Holocene in Yakutia, eastern Siberia, since most of the lakes in the region are of Holocene thermokarst origin. Thus, we have a poor understanding of the long-term interactions between terrestrial and aquatic ecosystems and their response to climate change. The Lake Khamra region in southwestern Yakutia is of particular interest because it is in the transition zones from discontinuous to sporadic permafrost and from summergreen to evergreen boreal forests. Our multiproxy study of Lake Khamra sediments reaching back to the Last Glacial Maximum 21 cal ka BP, includes analyses of organic carbon, nitrogen, XRF-derived elements, sedimentary ancient DNA amplicon sequencing of aquatic and terrestrial plants and diatoms, as well as classical counting of pollen and non-pollen palynomorphs (NPP). The palaeogenetic approach revealed 45 diatom, 191 terrestrial plant, and 65 aquatic macrophyte taxa. Pollen analyses identified 34 pollen taxa and 28 NPP taxa. The inferred terrestrial ecosystem of the Last Glacial comprises tundra vegetation dominated by forbs and grasses, likely inhabited by megaherbivores. By 18.4 cal ka BP a lake had developed with a high abundance of macrophytes and dominant fragilarioid diatoms, while shrubs expanded around the lake. In the Bølling-Allerød at 14.7 cal ka BP both the terrestrial and aquatic systems reflect climate amelioration, alongside lake water-level rise and woodland establishment, which was curbed by the Younger Dryas cooling. In the Early Holocene warmer and wetter climate led to taiga development and lake water-level rise, reflected by diatom composition turnover from only epiphytic to planktonic diatoms. In the Mid-Holocene the lake water level decreased at ca. 8.2 cal ka BP and increased again at ca. 6.5 cal ka BP. At the same time mixed evergreen-summergreen forest expanded. In the Late Holocene, at ca. 4 cal ka BP, vegetation cover similar to modern conditions established. This study reveals the long-term shifts in aquatic and terrestrial ecosystems and a comprehensive understanding of lake development and catchment history of the Lake Khamra region.</jats:p

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

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 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.iii. Dataset 3 contains a synthesis of 10 000 generated images and masks that have the tree crowns of two species of larch (Larix gmelinii and Larix cajanderi) automatically extracted from the RGB UAV images in the common objects in context (COCO) format (van Geffen et al., 2021a, https://doi.org/10.1594/PANGAEA.932795). 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.iv. Dataset 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, https://doi.org/10.1594/PANGAEA.933268). 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.</p

Formation of Trivalent Zirconocene Complexes from ansa-Zirconocene-Based Olefin-Polymerization Precatalysts: An EPR- and NMR-Spectroscopic Study

Reduction of Zr(IV) metallocenium cations with sodium amalgam (NaHg) produces EPR signals assignable to Zr(III) metallocene complexes. The chloro-bridged heterodinuclear ansa-zirconocenium cation [(SBI)Zr(μ-Cl)_2AlMe_2]^+ (SBI = rac-dimethylsilylbis(1-indenyl)), present in toluene solution as its B(C_6F_5)_4^– salt, thus gives rise to an EPR signal assignable to the complex (SBI)Zr^(III)(μ-Cl)_2AlMe_2, while (SBI)ZrIII-Me and (SBI)Zr^(III)(μ-H)_2Al^(i)Bu_2 are formed by reduction of [(SBI)Zr(μ-Me)_2AlMe_2]^+ B(C_6F_5)_4– and [(SBI)Zr(μ-H)_3(AliBu_2)_2]^+ B(C_6F_5)_4^–, respectively. These products can also be accessed, along with (SBI)ZrIII-iBu and [(SBI)ZrIII]^+ AlR_4^–, when (SBI)ZrMe_2 is allowed to react with HAl^(i)Bu_2, eliminating isobutane en route to the Zr(III) complex. Further studies concern interconversion reactions between these and other (SBI)Zr(III) complexes and reaction mechanisms involved in their formation

Phenological shifts of abiotic events, producers and consumers across a continent

Ongoing climate change can shift organism phenology in ways that vary depending on species, habitats and climate factors studied. To probe for large-scale patterns in associated phenological change, we use 70,709 observations from six decades of systematic monitoring across the former Union of Soviet Socialist Republics. Among 110 phenological events related to plants, birds, insects, amphibians and fungi, we find a mosaic of change, defying simple predictions of earlier springs, later autumns and stronger changes at higher latitudes and elevations. Site mean temperature emerged as a strong predictor of local phenology, but the magnitude and direction of change varied with trophic level and the relative timing of an event. Beyond temperature-associated variation, we uncover high variation among both sites and years, with some sites being characterized by disproportionately long seasons and others by short ones. Our findings emphasize concerns regarding ecosystem integrity and highlight the difficulty of predicting climate change outcomes. The authors use systematic monitoring across the former USSR to investigate phenological changes across taxa. The long-term mean temperature of a site emerged as a strong predictor of phenological change, with further imprints of trophic level, event timing, site, year and biotic interactions.Peer reviewe

Chronicles of nature calendar, a long-term and large-scale multitaxon database on phenology

We present an extensive, large-scale, long-term and multitaxon database on phenological and climatic variation, involving 506,186 observation dates acquired in 471 localities in Russian Federation, Ukraine, Uzbekistan, Belarus and Kyrgyzstan. The data cover the period 1890-2018, with 96% of the data being from 1960 onwards. The database is rich in plants, birds and climatic events, but also includes insects, amphibians, reptiles and fungi. The database includes multiple events per species, such as the onset days of leaf unfolding and leaf fall for plants, and the days for first spring and last autumn occurrences for birds. The data were acquired using standardized methods by permanent staff of national parks and nature reserves (87% of the data) and members of a phenological observation network (13% of the data). The database is valuable for exploring how species respond in their phenology to climate change. Large-scale analyses of spatial variation in phenological response can help to better predict the consequences of species and community responses to climate change.Peer reviewe