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

Siberian treeline dynamics in a warming climate - results from larch population genetics and vegetation modelling

A vegetation change from open tundra to dense taiga will fuel the global warming by positive feedback caused by albedo decreases. Yet, it is unclear how fast the arctic treeline, formed of Larix species, will advance north in the next decades. The most determinant factor of tree migration is the ability to disperse seeds (and pollen). Hence, to realistically forecast the migration of tree species in a dynamic vegetation model, it is crucial to incorporate reliable estimates of dispersal. Classical methods, for example counting seeds in seed traps, have been used to describe local dispersal abilities but are not applicable to give precise estimates on rare long-distance dispersal events. In this study we overcome this with the help of modern molecular techniques. By using a set of 16 nuclear microsatellites we inferred the cryptic signal of heritage among larch individuals to study the migration history among well-established tree stands and for different time-cohorts. We analyzed the genetic structure of larch populations for several latitudinal transects spanning north-to-south from tundra to open taiga forests in Siberia and additionally of several age cohorts which established throughout the last century in prevailing cold and warm periods. Finally, we present the results of simulations with our individual-based model LAVESI which was developed by us originally to study population dynamics of larch forest stands. Using downscaled global climate models and 'representative carbon pathway' (RCP) scenarios it is feasible to project the future treeline in Siberia

Sedimentary DNA versus morphology in the analysis of diatom-environment relationships

The Arctic treeline ecotone is characterised by a steep vegetation gradient from arctic tundra to northern taiga forests, which is thought to influence the water chemistry of thermokarst lakes in this region. Environmentally sensitive diatoms respond to such ecological changes in terms of variation in diatom diversity and richness, which so far has only been documented by microscopic surveys. We applied next-generation sequencing to analyse the diatom composition of lake sediment DNA extracted from 32 lakes across the treeline in the Katanga region, Siberia, using a short fragment of the rbcL chloroplast gene as a genetic barcode. We compared diatom richness and diversity obtained from the genetic approach with diatom counts from traditional microscopic analysis. Both datasets were employed to investigate diversity and relationships with environmental variables, using ordination methods. Aftereffective filtering of the raw data, the two methods gave similar results for diatom richness and composition at the genus level (DNA 12 taxa; morphology 19 taxa), even though there was a much higher absolute number of sequences obtained per genetic sample (median 50,278), compared with microscopic counts (median 426). Dissolved organic carbon explained the highest percentage of variance in both datasets (14.2 % DNA; 18.7 % morphology), reflecting the compositional turnover of diatom assemblages along the tundra-taiga transition. Differences between the two approaches are mostly a consequence of the filtering process of genetic data and limitations of genetic references in the database, which restricted the determination of genetically identified sequence types to the genus level. The morphological approach, however, allowed identifications mostly to species level, which permits better ecological interpretation of the diatom data. Nevertheless, because of a rapidly increasing reference database, the genetic approach with sediment DNA will, in the future, enable reliable investigations of diatom composition from lake sediments that will have potential applications in both paleoecology and environmental monitoring

Sensitivity of ecosystem-protected permafrost under changing boreal forest structures

Boreal forests efficiently insulate underlying permafrost. The magnitude of this insulation effect is dependent on forest density and composition. A change therein modifies the energy and water fluxes within and below the canopy. The direct influence of climatic change on forests and the indirect effect through a change in permafrost dynamics lead to extensive ecosystem shifts such as a change in composition or density, which will, in turn, affect permafrost persistence. We derive future scenarios of forest density and plant functional type composition by analyzing future projections provided by the dynamic global vegetation model (LPJ-GUESS) under global warming scenarios. We apply a detailed permafrost-multilayer canopy model to study the spatial impact-variability of simulated future scenarios of forest densities and compositions for study sites throughout eastern Siberia. Our results show that a change in forest density has a clear effect on the ground surface temperatures (GST) and the maximum active layer thickness (ALT) at all sites, but the direction depends on local climate conditions. At two sites, higher forest density leads to a significant decrease in GSTs in the snow-free period, while leading to an increase at the warmest site. Complete forest loss leads to a deepening of the ALT up to 0.33 m and higher GSTs of over 8 ∘C independently of local climatic conditions. Forest loss can induce both, active layer wetting up to four times or drying by 50%, depending on precipitation and soil type. Deciduous-dominated canopies reveal lower GSTs compared to evergreen stands, which will play an important factor in the spreading of evergreen taxa and permafrost persistence under warming conditions. Our study highlights that changing density and composition will significantly modify the thermal and hydrological state of the underlying permafrost. The induced soil changes will likely affect key forest functions such as the carbon pools and related feedback mechanisms such as swamping, droughts, fires, or forest loss

Thermohydrological Impact of Forest Disturbances on Ecosystem‐Protected Permafrost

Boreal forests cover over half of the global permafrost area and protect underlying permafrost. Boreal forest development, therefore, has an impact on permafrost evolution, especially under a warming climate. Forest disturbances and changing climate conditions cause vegetation shifts and potentially destabilize the carbon stored within the vegetation and permafrost. Disturbed permafrost-forest ecosystems can develop into a dry or swampy bush- or grasslands, shift toward broadleaf- or evergreen needleleaf-dominated forests, or recover to the pre-disturbance state. An increase in the number and intensity of fires, as well as intensified logging activities, could lead to a partial or complete ecosystem and permafrost degradation. We study the impact of forest disturbances (logging, surface, and canopy fires) on the thermal and hydrological permafrost conditions and ecosystem resilience. We use a dynamic multilayer canopy-permafrost model to simulate different scenarios at a study site in eastern Siberia. We implement expected mortality, defoliation, and ground surface changes and analyze the interplay between forest recovery and permafrost. We find that forest loss induces soil drying of up to 44%, leading to lower active layer thicknesses and abrupt or steady decline of a larch forest, depending on disturbance intensity. Only after surface fires, the most common disturbances, inducing low mortality rates, forests can recover and overpass pre-disturbance leaf area index values. We find that the trajectory of larch forests after surface fires is dependent on the precipitation conditions in the years after the disturbance. Dryer years can drastically change the direction of the larch forest development within the studied period

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

Late Holocene ice-wedge polygon dynamics in northeastern Siberian coastal lowlands

Ice-wedge polygons are common features of northeastern Siberian lowland periglacial tundra landscapes. To deduce the formation and alternation of ice-wedge polygons in the Kolyma Delta and in the Indigirka Lowland, we studied shallow cores, up to 1.3 m deep, from polygon center and rim locations. The formation of well-developed low-center polygons with elevated rims and wet centers is shown by the beginning of peat accumulation, increased organic matter contents and changes in vegetation cover from Poaceae-, Alnus-, and Betula-dominated pollen spectra to dominating Cyperaceae and Botryoccocus presence, and Carex and Drepanocladus revolvens macro-fossils. Tecamoebae data support such a change from wetland to open-water conditions in polygon centers by changes from dominating eurybiontic and sphagnobiontic to hydrobiontic species assemblages. The peat accumulation indicates low-center polygon formation and started between 2380 ± 30 and 1676 ± 32 years before present (BP) in the Kolyma Delta. We recorded an opposite change from open-water to wetland conditions due to rim degradation and consecutive high-center polygon formation in the Indigirka Lowland between 2144 ± 33 and 1632 ± 32 yrs BP. The late Holocene records of polygon landscape development reveal changes in local hydrology and soil moisture

Dispersal distances and migration rates at the arctic treeline in Siberia – a genetic and simulation-based study

A strong temperature increase in the Arctic is expected to lead to latitudinal treeline shift. This tundra–taiga turnover would cause a positive vegetation–climate feedback due to albedo decrease. However, reliable estimates of tree migration rates are currently lacking due to the complex processes involved in forest establishment, which depend strongly on seed dispersal. We aim to fill this gap using LAVESI, an individual-based and spatially explicit Larix vegetation simulator. LAVESI was designed to simulate plots within homogeneous forests. Here, we improve the implementation of the seed dispersal function via field-based investigations. We inferred the effective seed dispersal distances of a typical open-forest stand on the southern Taymyr Peninsula (northern central Siberia) from genetic parentage analysis using eight nuclear microsatellite markers. The parentage analysis gives effective seed dispersal distances (median ∼10&thinsp;m) close to the seed parents. A comparison between simulated and observed effective seed dispersal distances reveals an overestimation of recruits close to the releasing tree and a shorter dispersal distance generally. We thus adapted our model and used the newly parameterised version to simulate south-to-north transects; a slow-moving treeline front was revealed. The colonisation of the tundra areas was assisted by occasional long-distance seed dispersal events beyond the treeline area. The treeline (∼1 tree/ha) advanced by ∼1.6 m/yr, whereas the forest line (∼100trees/ha) advanced by only ∼0.6m/yr. We conclude that the treeline in northern central Siberia currently lags behind the current strong warming and will continue to lag in the near future

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