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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, 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.

Remotely sensed point clouds provide detailed structural data of landscapes and ecosystem characteristics. Especially in the analysis of forests and topography, this data type has proven its ability to derive relevant quantitative parameters such as biomass or subsidence rates. Arctic and boreal permafrost ecosystems are severely affected by climate change and resulting vegetation shifts, environmental disturbances, and permafrost thaw which lead to rapid changes in these northern environments that can be detected and characterized with point cloud datasets. In recent decades, the amount of point clouds acquired and generated in high-latitude regions by terrestrial (TLS), mobile (MLS), unmanned aerial system (UAS) based (ULS), up to airborne-based (ALS) LiDAR (Light detection and ranging) and Structure from Motion (SfM) has steadily increased. Multi-temporal datasets are available for a wide range of observation targets.The characteristics of the point clouds such as the extent of the area covered as well as the point density and thus the level of detail differ depending on the sensor, method, and the acquisition specifications. To use point cloud data for topographic, morphological, and forestry analysis, segmentation and classification of the point cloud into specific components such as individual trees, stems, foliage, or terrain features is essential. This is a time-consuming manual process and not feasible when addressing large datasets. Several previous analyses showed the potential for machine learning-based semantic segmentation of a single point cloud type, e.g., terrestrial LiDAR (TLS) with identical acquisition mode and sensor. We aim at an automated segmentation of different point cloud types generated by i) TLS, MLS, ULS and ALS as well as ii) SfM using (multi)spectral UAS and airborne image data to enable an analysis of Arctic and boreal permafrost ecosystems. Thereby, we will focus on the following questions:1) How can we reduce the time consuming process of labeling the point clouds?2) Can we train a model for segmentation using all point clouds or does transfer learning lead to better results?3) To what level of detail can we accurately segment and classify the different point cloud types?With this automated segmentation and classification, we aim to open up the possibility of exploiting the information contained in the multitude of point cloud data for a variety of ecological research applications.

Circumboreal forests represent close to 30% of all forested areas and are changing in response to climate, with potentially important feedback mechanisms to regional and global climate through altered carbon cycles and albedo dynamics (e.g., Loranty et al., 2018). A large portion of these boreal forests are located in Siberia, Russia. Here the forests are made up of mainly two types: evergreen (coniferous i.e., Pine, Picea) and summergreen (deciduous i.e., Larix) needle-leaf. The evergreen–summergreen forest zone stretching across Western Central Yakutia is a dynamic vegetation transition zone with high disturbance due to forest fire and potential invasion of evergreen forest taxa to the east into the summergreen dominated forest zone that needs mapping and monitoring.Sentinel-2 based Remote Sensing offers the opportunity to obtain forest type maps on a 10-20 m spatial scale. We provide in the SiDroForest (Siberian drone-mapped forest inventory) data collection (https://doi.org/10.1594/PANGAEA.933268), a Sentinel-2 data set containing Level-2 Bottom of Atmosphere labelled image patches for the early (April-May), peak (June-July) and late (August-September) summer seasons (van Geffen et al., 2022). This dataset contains 63 30 by 30-meter labelled patches with vegetation labels assigned derived from fieldwork measurements taken by the Alfred Wegener Institute in Siberia, Russia in 2018.Building on the SiDroForest dataset, we used K-means clustering to perform an unsupervised classification of Sentinel-2 for five locations from the SiDroForest set. We then assigned two broad forest classes in the Sentinel-2 images, summergreen and evergreen. We used the SiDroForest Sentinel-2 patches as validation data for the K-means generated classes in addition to the fieldwork and expert knowledge.The new dataset contains 100,000 labelled pixels, distributed over the two classes. We created the dataset for three time stamps to include different forest phenophases in the classification. The phenophases make it easier to distinguish between the two types of forests as summergreen’s spectral signal changes significantly over the seasons.We trained a Gaussian Naïve Bayes (GNB), a Random Forest (RF) and a Decision Tree (DT) classifier on three-time stamps separately. A combination of Sentinel-2 bands and the NDVI were evaluated with the different classifiers. The highest average accuracy score was achieved with a DT classifier and a balanced set for the two classes and the early summer time stamp and the NDVI band (82%). The peak summer also performed decently with 74%, but the accuracy dropped to 60% for the late summer time stamp. We used the trained DT to classify Sentinel-2 data at two locations in Siberia ; Lake Khamra and Nyurba. We masked out all non-forest data and created a forest map to measure the distribution of evergreen and summergreen over the larger areas. With our analyses we will improve the understanding of satellite data for monitoring remote places. The insights from the Siberian boreal forests are valuable in analyses of the boreal forests located in other parts of the world as well in these times or rapidly changing climate.

Boreal forests, which represent roughly one-third of the world’s total forested area, provide critical ecosystem services including carbon stocks, climate feedback, permafrost stability, biodiversity, and economic benefits. Located in the northern latitude, they are mainly dominated by evergreen needle-leaf tree taxa (Pinus, Picea, Abies) in North America, Northern Europe, and Western Siberia, and by deciduous needle-leaf tree taxa (Larix) in Eastern Siberia. Remote sensing applications in high latitudes are possible but remain challenging for optical satellite sensors due to frequent cloud coverage, forest fires, and low illumination. Additionally, there is little data available prepared as multi-label datasets for remote sensing applications focusing on the structure of boreal forests, specifically on Larix deciduous trees. Furthermore, labeled datasets of summer green and evergreen forest types for specific satellite sensors would enable remote sensing and deep learning applications such as classification, and ultimately improve our understanding of evergreen and summer green tree dynamics. An example of such a dataset is the TreeSatAI multi-sensor Artificial Intelligence Benchmark Archive (doi.org/10.5281/zenodo.6780578), which provides labels on species and forest composition in Europe. Another one is the SiDroForest data collection, consisting of a synthetic Unmanned Aerial Vehicle (UAV) Siberian Larch Dataset (doi.org/10.1594/PANGAEA.932795) and Sentinel-2 image patches (doi.org/10.1594/PANGAEA.933268) of 54 forest plots in Eastern Siberia. Here we are building up an extensive multi-labeled training dataset based on optical Sentinel-2 image patches (60 x 60 m image patch of the 10 m and 20 m S2-bands), including meta-data information on summer green and evergreen tree species and forest structure from vegetation plots. Over 250 vegetation plots were collected since 2011 from nine field expeditions of the Alfred Wegener Institute in Eastern Siberia (doi.org/10.5194/essd-14-5695-2022) and Western Canada, where vegetation was sampled and described, and UAV images were taken (UAV solely in 2021 and 2022). In addition to in-situ plots, we gathered all cloud-free Sentinel-2 data from late spring to early fall (May to October) that geographically coincides with the vegetation plots. Therefore, the dataset contains different phenophases of evergreen and summer green forests and provides detailed label information on forest structure – such as tree species and density. The multi-labeling will include broader and more detailed forest-type classes. Some examples of higher-level labels are “Sparse larch forest” or “Dense evergreen forest’’. The poster will demonstrate how we defined forest labels from in-situ data, UAV, Sentinel-2, and their corresponding spectral signatures.We anticipate our dataset to be a starting point for a significantly more extensive one with the addition of radar satellite sensors such as Sentinel-1 and TanDEM-X, and other ground vegetation plots (new expedition expected in Alaska and Canada in summer 2023), data search in literature and repositories– e.g. NASA Arctic Boreal Vulnerability Experiment. Our dataset will be publicly available and can be used as a training dataset for deep learning algorithms to identify and characterize evergreen and summer green needle-leaf trees in boreal forest regions.

We present a long-year data collection of freshwater chemistry from 590 high latitude lakes based on water sampling during our past expeditions in the Central and Eastern Siberian continuous permafrost regions. We compiled and standardised all acquired seasonal limnological data on major ions, alkalinity, pH and Electrical Conductivity including time series of data from lakes that we sampled repeatedly. The data collection encompasses diverse Siberian regions with numerous lakes along climate and vegetation gradients in the Khatanga and central Yakutian lowlands, to the glacial lakes of the Chukotka mountain region in Eastern Siberia and the central Lena Delta in the Arctic zone. The data collection is rich in standardised metadata following the metadata schema of the International Generic Sample Number (IGSN), which provides a unique and persistent identifier for physical objects (samples). Within the project FAIR Workflows to establish IGSN for Samples in the Helmholtz Association (FAIR WISH), funded by the Helmholtz Metadata Collaboration Platform (HMC), we customised the GFZ-specific IGSN schema to better describe lakes and water samples in a hierarchical (parent and child) scheme with standardised metadata on lake and sample characteristics, ecoregions, principal investigators and many more.This standardised data collection will be made available in the PANGAEA data repository (www.pangaea.de) to enable analyses of land-to-lake geochemical fluxes and will support biodiversity, biogeochemical, bioindicator and many more analyses. In this presentation, we visualize the data in form of Piper and Schoeller plots in order to categorize the water bodies and investigate their geographic distribution and links to catchment vegetation, active layer depth, permafrost disturbances and lithology.

The Lena Delta is the largest river delta in the Arctic (about 30 000 km2) and prone to rapid changes due to climate warming, associated cryosphere loss and ecological shifts. The delta is characterized by ice-rich permafrost landscapes and consists of geologically and geomorphologically diverse terraces covered with tundra vegetation and of active floodplains, featuring approximately 6 500 km of channels and over 30 000 lakes. Because of its broad landscape and habitat diversity the delta is a biodiversity hotspot with high numbers of nesting and breeding migratory birds, fish, caribou and other mammals and was designated a State Nature Reserve in 1995. Characterizing plant composition, above ground biomass and application of field spectroscopy was a major focus of a 2018 expedition to the delta. These field data collections were linked to Sentinel-2 satellite data to upscale local patterns in land cover and associated habitats to the entire delta. Here, we describe multiple field datasets collected in the Lena Delta during summer 2018 including foliage projective cover (Shevtsova et al., 2021a), above ground biomass (Shevtsova et al., 2021b), and hyperspectral field measurements (Runge et al., 2022, https://doi.pangaea.de/10.1594/PANGAEA.945982). We further describe a detailed Sentinel-2 satellite image-based classification of habitat types for the central Lena Delta (Landgraf et al., 2022), an upscaled classification for the entire Lena Delta (Lisovski et al., 2022), as well as a synthesis product for disturbance regimes (Heim and Lisovski, 2023, https://doi.org/10.5281/zenodo.7575691) in the delta that is based on the classification, the described datasets, and field expertise. We present context and detailed methods of these openly available datasets and show how they can improve our understanding of the rapidly changing Arctic tundra system. The new Lena Delta habitat distribution dataset represents a first baseline against which future observations can be compared. With the link between such detailed habitat type classifications and disturbance regimes future upscaling efforts may provide a better understanding of how Arctic lowland landscapes will respond to climate change and how this will impact land surface processes.

The International Generic Sample Number (IGSN) is a unique and persistent identifier for physical objects that was originally developed in the Geosciences. In 2022, after 10 years of service operation and more than 10 million registered samples worldwide, IGSN e.V. and DataCite have agreed on a strategic partnership. As a result, all IGSNs are now registered as DataCite DOIs and the IGSN metadata schema will be mapped to the DataCite Metadata Schema according to agreed guidelines. This will, on the one hand, enrich the very limited mandatory information shared by IGSN allocating agents so far. On the other hand, the DataCite metadata schema is not designed for the comprehensive description of physical objects and their provenance.The IGSN Metadata Schema is modular: the mandatory Registration Schema only included information on the IGSN identifier, the minting agent and a date - complemented by the IGSN Description Schema (for data discovery) and additional extensions by the allocating agents to customise the sample description according to their sample’s subdomain.Within the project “FAIR Workflows to establish IGSN for Samples in the Helmholtz Association (FAIR WISH)”, funded by the Helmholtz Metadata Collaboration Platform (HMC), we(1) customised the GFZ-specific schema to describe water, soil and vegetation samples and(2) support the metadata collection by the individual researcher with a user-friendly, easy-to-use batch registration template in MS Excel.The information collected with the template can directly be converted to XML files (or JSON in the future) following the IGSN Metadata schema that is required to generate IGSN landing pages. The template is also the source for the generation of DataCite metadata.The integration of linked data vocabularies (RDF, SKOS) in the metadata is an essential step in harmonising information across different research groups and institutions and important for the implementation of the FAIR Principles (Findable, Accessible, Interoperable, Reusable) for sample descriptions. More information on these controlled vocabularies can be found in the FAIR WISH D1 List of identified linked open data vocabularies to be included in IGSN metadata (https://doi.org/10.5281/zenodo.6787200).The template to register IGSNs for samples should ideally fit to various sample types. In a first step, we created templates for samples from surface water and vegetation from AWI polar expeditions on land (AWI Use Case) and incorporated the two other FAIR WISH use cases with core material from the Ketzin coring site (Ketzin Use Case) and for a wide range of marine biogeochemical samples (Hereon Use Case). The template comprises few mandatory and many optional variables to describe a sample, the sampling activity, location and so on. Users can easily create their Excel-template, including only the variables needed to describe a sample. A tutorial on how to use the FAIR WISH: Sample description template (https://doi.org/10.5281/zenodo.7520016) can be found in the FAIR WISH D3 Video Tutorial for the FAIR SAMPLES Template (https://doi.org/10.5281/zenodo.7381390). As our registration template is still a work in progress, we are furthermore happy for user feedback (https://doi.org/10.5281/zenodo.7377904).Here we will present the template and discuss its applicability for sample registration.

Here we describe the LegacyPollen 1.0, a dataset of 2831 fossil pollen records with metadata, harmonized taxonomy, and standardized chronologies. A total of 1032 records originate from North America, 1075 from Europe, 488 from Asia, 150 from Latin America, 54 from Africa, and 32 from the Indo-Pacific. The pollen data cover the Late Quaternary (mostly the Holocene). The original 10,110 pollen taxa names (including variations in the notations) were harmonized to 1002 taxa, with woody taxa and major herbaceous taxa to genus level and other herbaceous taxa to family level. The dataset is valuable for synthesis studies such as taxa areal changes, vegetation dynamics, human impact (e.g., deforestation), and climate change at global or continental scales. The harmonized pollen and metadata as well as the harmonization table are available from PANGAEA (https://doi.pangaea.de/10.1594/PANGAEA.929773; Herzschuh et al., 2021a). R code for the harmonization is provided at Zenodo (https://doi.org/10.5281/zenodo.5910972; Herzschuh et al., 2022) so that datasets at a customized harmonization level can be easily established.

The International Geo Sample Number (IGSN) is a globally unique and persistent identifier (PID) for physical samples and collections with discovery function on the Internet. IGSNs enable to directly link data and publications with samples they originate from and thus close the last gap in the full provenance of research results. The modular IGSN metadata schema has a small number of mandatory and recommended metadata elements that can be individually extended with discipline-specific elements.Based on three use cases that represent all states of digitisation - from individual scientists, collecting sample descriptions in their field books to digital sample management systems fed by an app that is used in the field - FAIR WISH will (1) develop standardised and discipline specific IGSN metadata schemes for different sample types from the Earth and Environment Sciences, (2) develop workflows to generate machine-readable IGSN metadata from different states of digitisation, (3) develop workflows to automatically register IGSNs and (4) prepare the resulting workflows for further use in the Earth Science community.

This data collection is an attempt to remedy the scarcity of tree level forest structure data in the circum-boreal region, whilst providing, as part of the data collection, adjusted and labelled tree level and vegetation plot level data for machine learning and upscaling practices. Publicly available comprehensive datasets on tree level forest structure are rare, due to the involvement of governmental agencies, public sectors, and private actors that all influence the availability of these datasets. 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 collection contains a variety of data mainly based on unmanned aerial vehicle (UAV) and field data collected from 64 vegetation plots during fieldwork jointly performed by the Alfred Wegener Institute for Polar and Marine Research (AWI) and the North-Eastern Federal University of Yakutsk (NEFU) during the Chukotka 2018 expedition to Siberia. The data collection consists of four separate datasets. The fieldwork locations are the anchors that bind the data types together based on the location of the vegetation plot. i) The first dataset (Kruse et al., 2021, https://doi.pangaea.de/10.1594/PANGAEA.933263) provides UAV-borne data products covering the 64 vegetation plots surveyed during fieldwork: including structure from motion (SfM) point clouds, point-cloud products such as Digital Elevation Model (DEM), Canopy Height Model (CHM), Digital Surface Model (DSM) and Digital Terrain Model (DTM) constructed from Red Green Blue (RGB) and Red Green Near Infrared (RGN) orthomosaics. Forest structure and vegetation composition data are crucial in the assessment of whether a forest is to act as a carbon sink under changing climate conditions. Fieldwork and UAV-products can provide such data in depth. ii) The second dataset contains spatial data in the form of points and polygon shape files of 872 labelled individual trees and shrubs that were recorded during fieldwork at the same vegetation plots with information on tree height, crown diameter, and species (van Geffen et al., 2021c, https://doi.pangaea.de/10.1594/PANGAEA.932821). These tree- and shrub-individual labelled point and polygon shape files were generated and are located on the UAV RGB orthoimages. The individual number links to the information collected during the expedition such as tree height, crown diameter and vitality provided in table format. This dataset can be used to link individual trees in the SfM point clouds, providing unique insights into the vegetation composition and also allows future monitoring of the individual trees and the contents of the recorded vegetation plots at large. iii) The third dataset 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.pangaea.de/10.1594/PANGAEA.932795). The synthetic dataset was created specifically to detect Siberian larch species. iv) If publicly available forest-structure datasets at tree level are rarely available for Siberia, even fewer ready-to-use tree and plot level data are available for machine learning approaches, for example optimised data formats containing annotated vegetation categories. The fourth set contains Sentinel-2 Level-2 bottom of atmosphere labelled image patches with seasonal information and annotated vegetation categories covering the vegetation plots (van Geffen et al., 2021b, https://doi.pangaea.de/10.1594/PANGAEA.933268). The dataset is created with the aim of providing a small ready-to use validation and training data set to be used in various vegetation-related machine-learning tasks. The SidroForest data collection serves a variety of user communities. First of all, the UAV-derived top of canopy structure information, orthomosaics and the detailed vegetation information in the labelled data set provide detailed information on forest type, structure and composition for scientific communities with ecological and biological applications. The detailed Land Cover and Vegetation structure information in the first two data sets are of use for 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, parts of the SiDroForest dataset are prepared to be used 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 data set contains standardized Sentinel-2 labelled image patches that provide training data on vegetation class categories for machine learning classification with JSON labels provided. The SiDroForst data collective serves as a basis to add future data collected during expeditions performed by the Alfred Wegener Institute, creating a larger dataset in the upcoming years that can provide unique insights into remote hard to reach boreal regions of Siberia.

The Arctic is directly impacted by climate change. The increase in air temperature drives the thawing of permafrost and an increase in coastal erosion and river discharge. This leads to a greater input of sediment and organic matter into coastal waters, which substantially impacts the ecosystems, the subsistence economy of the local population, and the climate because of the transformation of organic matter into greenhouse gases. Yet, the patterns of sediment dispersal in Arctic nearshore zones and their role in the Carbon cycle are not well known due to difficult accessibility and challenging weather conditions. In this study we present the first multi-sensor turbidtiy- reflectance relationship that was specifically calibrated for Arctic nearshore environments. Field data was collected during summer seasons 2018 and 2019 in the inner shelf waters of the Canadian Beaufort Sea close to Herschel Island Qikiqtaruk. The turbidity-reflectance relationship was calibrated to mid to high spatial resolution sensors which are used in ocean color remote sensing, including Landsat 8, Sentinel 2, and Sentinel 3, using the relative spectral response functions. The results for Landsat 8 and Sentinel 2 are very promising and showcase the possibility to resolve sediment accumulations, sediment pathways and filaments at higher detail than before. Both sensors are able to resolve high turbidity close to the coast with values comparable to our field measurements. Sentinel 3, on the other hand, is too coarse to resolve these features but provides great applicability due to its high temporal resolution. The transferability of these relationships to nearshore environments outside the Canadian Beaufort Sea has to be tested in the future with the potential to map the sediment dispersal in nearshore environments at a circum- Arctic scale.

Vegetation biomass is a globally important climate-relevant terrestrial carbon pool. Landsat, Sentinel-2 and Sentinel-1 satellite missions provide a landscape-level opportunity to upscale tundra vegetation communities and biomass in high latitude terrestrial environments. We assessed the applicability of landscape-level remote sensing for the low Arctic Lena Delta region in Northern Yakutia, Siberia, Russia. The Lena Delta is the largest delta in the Arctic and is located North of the treeline and the 10 °C July isotherm at 72° Northern Latitude in the Laptev Sea region. We evaluated circum-Arctic harmonized ESA GlobPermafrost land cover and vegetation height remote sensing products covering subarctic to Arctic land cover types for the central Lena Delta. The products are freely available and published in the PANGAEA data repository under https://doi.org/10.1594/PANGAEA.897916, and https://doi.org/10.1594/PANGAEA.897045.Vegetation and biomass field data (30 m x 30 m plot size) and shrub samples for dendrology were collected during a Russian-German expedition in summer 2018 in the central Lena Delta. We also produced a regionally optimized land cover classification for the central Lena Delta based on the in-situ vegetation data and a summer 2018 Sentinel-2 acquisition that we optimized on the biomass and wetness regimes. We also produced biomass maps derived from Sentinel-2 at a pixel size of 20 m investigating several techniques. The final biomass product for the central Lena Delta shows realistic spatial patterns of biomass distribution, and also showing smaller scale patterns. However, patches of high shrubs in the tundra landscape could not spatially be resolved by all of the landscape-level land cover and biomass remote sensing products.Biomass is providing the magnitude of the carbon flux, whereas stand age is irreplaceable to provide the cycle rate. We found that high disturbance regimes such as floodplains, valleys, and other areas of thermo-erosion are linked to high and rapid above ground carbon fluxes compared to low disturbance on Yedoma upland tundra and Holocene terraces with decades slower and in magnitude smaller above ground carbon fluxes.

Changes in future above-ground biomass (AGB) of terrestrial ecosystems is one of the current interests in the light of climate change and essential to forecast for predicting potential climate feedbacks such as influence on the carbon balance. The tundra-taiga ecotone is a region that is prone to notable above-ground biomass changes, in the first instance due to the treeline advance. Forest is expected to occupy non-polygonal tundra. Our study region in central Chukotka (Northeastern Siberia) is a mountainous area on the northern border of the tundra-taiga ecotone that covers a wide range of vegetation types on a density gradient starting with lichen communities via open graminoid tundra to forest tundra. There is only one tree species – a deciduous conifer Larix cajanderi. We applied the individual-based spatially explicit model LAVESI to simulate larch AGB change from nowadays to 3000 AD under different climate scenarios, depending on Representative Concentration Pathways (RCPs) RCP 2.6, RCP 4.5 and RCP 8.5. We implemented in the model topographical parameters, as well as region-specific individual larch AGB equations, biological parameters of the tree growth and climate variables. We validated the new version of the model against field and Landsat satellite-based data, as well as a high spatial resolution image with distinctive trees visible, provided by ESRI (ArcGIS/World_imagery). Our first results are indicating mostly densification of existing tree stands before 2200 AD and forest expansion in the study region after 2200 AD even under the mildest RCP 2.6 scenario. First evaluations of the average tree AGB increase rates from present to 2200 AD are ranges from 0.007 (RCP 2.6) to 0.01 (RCP 8.5) kg*m-2*yr-1. Obtained rates of tree AGB change and its future distribution on the landscape can be particularly useful for conservation measures and modelling of future above-ground carbon stock dynamics.

To gain a better understanding of global carbon storage and albedo feedback mechanisms it is important to have insights into high latitude vegetation change. Boreal forest compositions are changing in response to changes in climate, which in turn can lead to feedbacks in regional and global climate through altered carbon cycles and albedo dynamics. Circumpolar boreal forests represent close to 30% of all forested area on the planet, between 900 and 1,200 million ha. These forests are located primarily in Alaska, Canada, and Russia. Due to the remote location of these forests and the short seasons without snow, data collected on the boreal vegetation is limited. The proposed dataset is an attempt to remedy data scarcity whilst providing adjusted data for machine learning practices.We present a dataset containing diverse formats of forest structure information that covers two important vegetation transition zones in Siberia: the Evergreen - Summergreen transition zone in Central Yakutia and the northern treeline in Chukotka (NE Siberia).This dataset contains data from the locations covered by fieldwork was performed by the Alfred Wegener Institute for Polar and Marine research, (AWI) and the North-Eastern Federal University of Yakutsk​ (NEFU). The fieldwork upscaled through the addition of Red Green Blue(RGB) UAV (Unmanned Aerial Vehicle) camera data and Sentinel-2 satellite data cropped to a 5 km radius around the fieldwork sites. The dataset is created with the aim of providing ground truth validation and training data to be used in various vegetation related machine learning tasks .The dataset contains:1.Labelled individual trees per 30x30 m plot assigned in field work with additional data on species, height, crown width, and biomass.2.Structure from Motion (SfM)point clouds that provide 3D information about the forest structure, included generated Canopy Height Model (CHM), Digital Elevation Model (DEM) and a Digital Surface Model (DSM) per 50x50 m.3.Multispectral Sentinel-2 satellite data (10 m ) cropped to a 5km radius with generated a NDVI(normalized difference vegetation index), available in three seasons: Early Summer, Peak Summer and Late Summer.4.Extracted tree crowns with species information and a synthetically generated large (10.000 samples) dataset for training machine leaning algorithms.The dataset will be made publicly available on the data repository PANGAEA.

Remobilization of soil carbon as a result of permafrost degradation in the drainage basin of the major Siberian rivers combined with higher precipitation in a warming climate potentially increase the flux of terrestrial derived dissolved organic matter (tDOM) into the Arctic Ocean. The Laptev (LS) and East Siberian Seas (ESS) receive enormous amounts of tDOM-rich river water, which undergoes at least one freeze-melt cycle in the Siberian Arctic shelf seas. To better understand how freezing and melting affect the tDOM dynamics in the LS and ESS, we sampled sea ice, river and seawater for their dissolved organic carbon (DOC) concentration and the colored fraction of dissolved organic matter. The sampling took place in different seasons over a period of 9 years (2010–2019). Our results suggest that the main factor regulating the tDOM distribution in the LS and ESS is the mixing of marine waters with freshwater sources carrying different tDOM concentrations. Of particular importance in this context are the 211 km3 of meltwater from land-fast ice from the LS, containing ~ 0.3 Tg DOC, which in spring mixes with 245 km3 of river water from the peak spring discharge of the Lena River, carrying ~ 2.4 Tg DOC into the LS. During the ice-free season, tDOM transport on the shelves takes place in the surface mixed layer, with the direction of transport depending on the prevailing wind direction. In winter, about 1.2 Tg of brine-related DOC, which was expelled from the growing land-fast ice in the LS, is transported in the near-surface water layer into the Transpolar Drift Stream that flows from the Siberian Shelf toward Greenland. The actual water depth in which the tDOM-rich brines are transported, depends mainly on the density stratification of the LS and ESS in the preceding summer and the amount of ice produced in winter. We suspect that climate change in the Arctic will fundamentally alter the dynamics of tDOM transport in the Arctic marginal seas, which will also have consequences for the Arctic carbon cycle.

Lakes in permafrost regions are dynamic landscape components and play an important role for climate change feedbacks. Lake processes such as mineralization and flocculation of dissolved organic carbon (DOC), one of the main carbon fractions in lakes, contribute to the greenhouse effect and are part of the global carbon cycle. These processes are in focus of climate research but studies so far are limited to specific study regions. In our synthesis, we analysed 2,167 water samples from 1,833 lakes across the Arctic in permafrost regions of Alaska, Canada, Greenland, and Siberia to provide first pan-Arctic insights for linkages between DOC concentrations and the environment. Using published data and unpublished datasets from the author team we report regional DOC differences linked to latitude, permafrost zones, ecoregions, geology, near-surface soil organic carbon contents, and ground ice classification of each lake region. The lake DOC concentrations in our dataset range from 0 mg L−1 to 1,130 mg L−1 (10.8 mg L−1 median DOC concentration). Regarding the permafrost regions of our synthesis, we found median lake DOC concentrations of 12.4 mg L−1 (Siberia), 12.3 mg L−1 (Alaska), 10.3 mg L−1 (Greenland), and 4.5 mg L−1 (Canada). Our synthesis shows a significant relationship of lake DOC concentration and ecoregion of the lake. We found higher lake DOC concentrations in boreal permafrost sites compared to tundra sites. About 22 % of the lakes in our extensive dataset are located in regions with ice-rich syngenetic permafrost deposits (yedoma). Yedoma contains large amounts of easily erodible organic carbon and we found significantly higher DOC concentrations in yedoma lakes compared to non-yedoma lakes. Compared to previous studies we found a weak significant relationship of soil organic carbon content and lake DOC concentration as well as between ground-ice content and lake DOC. Our pan-Arctic dataset shows that the DOC concentration of a lake strongly depends on its environmental properties, especially on permafrost extent and ecoregion, as well as vegetation, which is the most important driver of lake DOC in this study. This new dataset will be fundamental to quantify a pan-Arctic lake DOC pool for estimations of the impact of lake DOC on the global carbon cycle and climate change.

Permafrost-region lakes are dynamic landscape systems and play an important role for climate change feedbacks. Lake processes such as mineralization and flocculation of DOC, one of the main carbon fraction in lakes, contribute to the global carbon cycle. These processes are in focus of climate research but studies have been limited in geographic extent. We synthesized published datasets and unpublished datasets from the author team totaling 1,691 water samples from 1,387 lakes across the Subarctic and Arctic in permafrost regions of Alaska, Canada, Siberia, and Greenland to provide first insights for linkages between DOC concentration to the basin. In our synthesis, we find regional differences in DOC concentration of permafrost-region lakes. We focussed on relations between lake DOC concentration and latitude, permafrost zones, ecoregions, lake surrounding deposit type, and ground ice classification of each lake basin. Additionally, we analysed the lake surrounding soil organic carbon content from 0-100 cm depth and 0-300 cm depth. Individual lake DOC concentrations of our dataset range from below detection limit assigned to 0 mg L-1 (North Slope, Alaska) to 1,130 mg L-1 (Yukon Flats, Alaska). We found regional median lake DOC concentrations of 18.8 mg L-1 (Greenland, n=25), 12.2 mg L-1 (Alaska, n= 1,135), 9.6 mg L-1 (Siberia, n=252), and 7.2 mg L-1 (Canada, n=279). Lakes in the isolated permafrost zone had the highest median DOC concentration compared to lakes in the sporadic, discontinuous, and continuous permafrost zones. Our synthesis shows increasing lake DOC concentration with decreasing latitude and, due to a larger availability of biomass and organic carbon, a significant relationship of lake DOC concentration and ecoregion of the lake. We found higher lake DOC concentrations in boreal permafrost sites compared to tundra sites. About 22 % of lakes in our dataset are located in regions with ice-rich syngenetic permafrost deposits (yedoma). Because yedoma contains large amounts of organic carbon, we assumed to find higher DOC concentrations in yedoma lakes compared to non-yedoma lakes. Our analysis shows a significant relationship of lake DOC concentration and surrounding deposit type but not a higher DOC concentration in yedoma lakes compared to non-yedoma lakes. Finally, we found a relationship of soil organic carbon content from 0-100 cm depth and lake DOC concentration. In contrast, a comparison of soil organic carbon content from 0-300 cm depth and lake DOC concentration shows no significant correlation. This was also found for ground-ice content and lake DOC concentration. Our dataset of lakes across the Arctic shows that the DOC concentration of a lake strongly depends on its environmental properties. This dataset will be fundamental to establish a pan-Arctic lake DOC pool for estimations of the impact of lake DOC on the global carbon cycle and further on climate change.

Climate change is especially prominent in Arctic and sub-Arctic regions. Therefore, it is of great importance to investigate ecosystem processes and the dynamics of its components in the Siberian high latitudes. Plant biomass assessment is essential for estimating carbon stocks and further carbon balance and wildlife habitat modeling. Our study region in central Chukotka (north-eastern Siberia) is one of the least investigated sub-Arctic regions in terms of vegetation dynamics. We quantified changes in four vegetation classes: (1) larch closed-canopy forest, (2) forest tundra and shrub tundra, (3) graminoid tundra, and, (4) prostrate herb tundra and barren areas. We used Landsat spectral indices (Normalised Difference Vegetation Index (NDVI), Normalised Difference Water Index (NDWI), Normalised Difference Snow Index (NDSI)) to map the vegetation classes in four focus areas in 2000/2001 and 2016/2017. In 2016, we collected field data on foliage projective cover (percentage cover) of dominant taxa from 52 sites along the tundra–taiga gradient. We applied constrained ordination for coupling projective cover with corresponding Landsat spectral indices from 2016/2017. Ordination scores were used in a k-means classification. We inferred significant shrubification in the tundra–taiga zone (20%) and in the northern taiga (40%), as well as notable tree infilling in the northern taiga (9%), and, no significant changes in the treeless tundra area. To estimate carbon stocks and its changes within and between differentiated vegetation classes, we aim to upscale above ground biomass across tundra, tundra-taiga and northern taiga zones and derive above ground biomass change for the 15 investigated years. In 2018, another expedition took part to this region with new 38 sites at witch we described projective cover and representatively harvested total above ground biomass (dominant taxa and rest). This data can be projected in the created previously ordination space with the use of projective cover similarities. Using interpolation, we plan to predict above ground biomass in the whole ordination space. We will further use Landsat data and interpolation results to produce above ground biomass maps for the four focus areas in central Chukotka and derive difference maps from 2000/2001 to 2016/2017.