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

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

Biogeography of larches in eastern Siberia – using single nucleotide polymorphisms derived by genotyping by sequencing

The present distribution of Siberian boreal forests that are dominated by larches (Larix spp.) is influenced, to an unknown extent, by glacial history. Knowing the past treeline dynamics can improve our understanding of future treeline shifts under changing climate. Here, we study patterns in the genetic variability of Siberian Larix to help unravel biogeographic migration routes since the Last Glacial Maximum (LGM). We infer the spatial distribution and the postglacial demographic history of Larix using genome-wide single nucleotide polymorphisms (SNPs) derived through genotyping by sequencing (GBS) from 130 individuals sampled across eastern Siberia. Our analysis gives statistical support for two or three clusters, spanning from western to eastern Siberia. These clusters reveal a genetic structure influenced by isolation resulting from geographical distance, barriers imposed by geographic features, and distinct glacial histories. Assuming three clusters, our demographic inference indicates that the common ancestor of the current Larix populations existed in northeast Siberia well before the LGM. This suggests that Larix persisted in the northern region throughout previous glacials. Our genetic studies suggest that Larix likely survived the cold LGM in northern refugia, enabling a fast colonization of Siberia. Instead of complete repopulation from southern areas postglacially, the northernmost Larix expansion during the Holocene seems to have benefitted from refugial populations ahead of the treeline. Present-day migration is expected to be slow initially, due to the absence of current refugial populations in the far north, in contrast to the early-Holocene situation

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

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

Pollen data for 72 sites in East Siberia

A total of 72 surface samples were collected from Chukotka and Yakutia in East Siberia in the Russian-German Cooperation Expeditions to Siberia in 2016 and 2018: 10 lake surface samples were collected in July 2016, 48 moss/soil polsters and 14 lake sediment samples in July and August 2018. Coordinates of the sampling sites were obtained by hand-held Global Positioning System (GPS). The altitudes of the sampling sites range from 94 to 843 m above sea level. At least 300 terrestrial pollen grains were counted and identified in each sample under a microscope at 400X using published pollen atlases and identification keys. Pollen percentages were calculated based on the total number of terrestrial pollen grains. Ecoregions, including Floodplain and Anthropogenic Meadows, Mountain Tundra, Open Woodlands and Middle Taiga, of each sampling site were extracted from a 1:4 million scale vegetation map for the land area of the former Soviet Union

High-resolution photogrammetric point clouds from northeast Siberian forest stands. Alfred-Wegener-Institute research expedition "Chukotka 2018"

This dataset features ten ultra-high resolution photogrammetric point clouds from northeast Siberian forest stands. The data has been acquired on the joint research expedition "Chukotka 2018" led by Alfred-Wegener-Institute, Helmholtz Centre for Polar and Marine Research Potsdam, Germany and the Northeastern Federal University of Yakutsk, Russia. The field sites have an approximate size of 50*50 m and are located in different locations accross Chukotka (~67.36° N 168.32° E) and Yakutia (~59.99° N 112.98° E). The forest stands are diverse in tree density, species composition, crown structure, height distribution, and crown cover. The point clouds have been reconstructed from close range UAV-based RGB imagery. The data has been cleaned. Details on the dataset, processing steps and study areas can be found in Brieger et al. (2019)

SiDroForest: Synthetic Siberian Larch Tree Crown Dataset of 10.000 instances in the Microsoft's Common Objects in Context dataset (coco) format

This synthetic Siberian Larch tree crown dataset was created for upscaling and machine learning purposes as a part of the SiDroForest (Siberia Drone Forest Inventory) project. The SiDroForest data collection (https://www.pangaea.de/?q=keyword%3A%22SiDroForest%22) consists of vegetation plots covered in Siberia during a 2-month fieldwork expedition in 2018 by the Alfred Wegener Institute Helmholtz Centre for Polar and Marine Research in Germany. During fieldwork fifty-six, 50*50-meter vegetation plots were covered by Unmanned Aerial Vehicle (UAV) flights and Red Green Blue (RGB) and Red Green Near Infrared (RGNIR) photographs were taken with a consumer grade DJI Phantom 4 quadcopter. The synthetic dataset provided here contains Larch (Larix gmelinii (Rupr.) Rupr. and Larix cajanderi Mayr.) tree crowns extracted from the onboard camera RGB UAV images of five selected vegetation plots from this expedition, placed on top of full-resized images from the same RGB flights. The extracted tree crowns have been rotated, rescaled and repositioned across the images with the result of a diverse synthetic dataset that contains 10.000 images for training purposes and 2000 images for validation purposes for complex machine learning neural networks. In addition, the data is saved in the Microsoft's Common Objects in Context dataset (COCO) format (Lin et al.,2013) and can be easily loaded as a dataset for networks such as the Mask R-CNN, U-Nets or the Faster R-NN. These are neural networks for instance segmentation tasks that have become more frequently used over the years for forest monitoring purposes. The images included in this dataset are from the field plots: EN18062 (62.17° N 127.81° E), EN18068 (63.07° N 117.98° E), EN18074 (62.22° N 117.02° E), EN18078 (61.57° N 114.29° E), EN18083 (59.97° N 113° E), located in Central Yakutia, Siberia. These sites were selected based on their vegetation content, their spectral differences in color as well as UAV flight angles and the clarity of the UAV images that were taken with automatic shutter and white balancing (Brieger et al. 2019). From each site 35 images were selected in order of acquisition, starting at the fifteenth image in the flight to make up the backgrounds for the dataset. The first fifteen images were excluded because they often contain a visual representation of the research team. The 117 tree crowns were manually cut out in Gimp software to ensure that they were all Larix trees.Of the tree crowns,15% were included that are at the margin of the image to make sure that the algorithm does not rely on a full tree crown in order to detect a tree. As a background image for the extracted tree crowns, 35 raw UAV images for each of the five sites were selected were included. The images were selected based on their content. In some of the UAV images, the research teams are visible and those have been excluded from this dataset. The five sites were selected based on their spectral diversity, and their vegetation content. The raw UAV images were cropped to 640 by 480 pixels at a resolution of 72 dpi. These are later rescaled to 448 by 448 pixels in the process of the dataset creation. In total there were 175 cropped backgrounds. The synthetic images and their corresponding annotations and masks were created using the cocosynth python software provided by Adam Kelly (2019). The software is open source and available on GitHub: https://github.com/akTwelve/cocosynth. The software takes the tree crowns and rescales and transform them before placing up to three tree crowns on the backgrounds that were provided. The software also creates matching masks that are used by instance segmentation and object detection algorithms to learn the shapes and location of the synthetic crown. COCO annotation files with information about the crowns name and label are also generated. This format can be loaded into a variety of neural networks for training purposes

Forest inventories on circular plots on the expedition Chukotka 2018, NE Russia

We undertook a large-scale forest inventory in central Chukotka in summer 2018 on a joint Russian-German expedition by the AWI (Potsdam) and the NEFU (Yakutsk). We covered different densities of larch forest plots accross the tundra-taiga gradient. Tree height estitmations were conducted after training with a clinometer (SUUNTO, Finland) for trees present on 22 sites inventoriziong all trees (N=2624) on 15 m radial plots