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

Forecasting forest dynamics with the individual-based model LAVESI across the Siberian treeline: from UAV surveys to simulations

Boreal forests in Siberia store huge amounts of aboveground carbon. Global warming potentially threatens this carbon storage due to more frequent droughts or other disturbances such as fires. These disturbances can change recruitment patterns, and thus may have long-lasting impacts on population dynamics. Assessing high-resolution forest stand structures and forecasting their response for the upcoming decades with detailed models is needed to understand the involved key processes and consequences of global change. We present forest stand inventories derived from UAV imagery and a developed processing chain including Individual Tree Detection (ITD) and species determination for 56 sites on a bioclimatic gradient at the Tundra-Taiga-Ecotone in Northeastern Siberia. We will use these and further 58 traditional count and measurement data as starting points for the detailed individual-based spatially explicit forest model LAVESI to predict future forest dynamics covering multiple sites across the Siberian treeline. In our analyses, we will focus on assessing future structural changes of the forests and their aboveground biomass dynamics. For our discussion, we will evaluate the reliability of UAV-derived forest inventories by measuring the impact strength of error sources introduced in the methodology on the forecasts

Tundra conservation challenged by forest expansion in a complex mountainous treeline ecotone as revealed by spatially explicit tree aboveground biomass modeling

The subarctic forest tundra transition zone is one of the most vulnerable ecological regions worldwide and susceptible to climate change. Forest changes could lead to biodiversity losses when tundra areas become colonized. However, the impact of complex landscapes with barriers and channels for seed dispersal is highly understudied. Hence, we investigated potential tree aboveground biomass (AGB) change in mountainous central Chukotka (Siberia) with the individual-based spatially explicit vegetation model Larix vegetation simulator (LAVESI). In a climate sensitivity study, we simulate forest dynamics until 3000 CE for Representative Concentration Pathways (RCPs) with and without hypothetical cooling after 2300 CE to twentieth-century levels. The current state and spatiotemporal dynamics of tree AGB are validated against field and satellite-derived data. Our results suggest densification of existing tree stands and a lagged forest expansion depending on the distance to the current tree line (~39 percent of the total study area, RCP 8.5) under all considered climate scenarios. In scenarios with cooling after 2300 CE, forests stopped expanding and then gradually retreated to their pre-twenty-first-century position (~10 percent, RCP 8.5). However, forest remnants remain in the colonized area, leaving an imprint of forests in former tundra areas, which will likely have an adverse impact on tundra biodiversity

Recent above-ground biomass changes in central Chukotka (Russian Far East) using field sampling and Landsat satellite data

Upscaling plant biomass distribution and dynamics is essential for estimating carbon stocks and carbon balance. In this respect, the Russian Far East is among the least investigated sub-Arctic regions despite its known vegetation sensitivity to ongoing warming. We representatively harvested above-ground biomass (AGB; separated by dominant taxa) at 40 sampling plots in central Chukotka. We used ordination to relate field-based taxa projective cover and Landsat-derived vegetation indices. A general additive model was used to link the ordination scores to AGB. We then mapped AGB for paired Landsat-derived time slices (i.e. 2000/2001/2002 and 2016/2017), in four study regions covering a wide vegetation gradient from closed-canopy larch forests to barren alpine tundra. We provide AGB estimates and changes in AGB that were previously lacking for central Chukotka at a high spatial resolution and a detailed description of taxonomical contributions. Generally, AGB in the study region ranges from 0 to 16 kg m−2, with Cajander larch providing the highest contribution. Comparison of changes in AGB within the investigated period shows that the greatest changes (up to 1.25 kg m−2 yr−1) occurred in the northern taiga and in areas where land cover changed to larch closed-canopy forest. As well as the notable changes, increases in AGB also occur within the land-cover classes. Our estimations indicate a general increase in total AGB throughout the investigated tundra–taiga and northern taiga, whereas the tundra showed no evidence of change in AGB

Total above-ground biomass of 39 vegetation sites of central Chukotka from 2018

Field investigations were performed in four areas: a treeless mountainous tundra (16-KP-04; Lake Rauchuagytgyn area), tundra-taiga transition zone (16-KP-01, Lake Ilirney area) and a northern taiga (18-BIL-01, 18-BIL-02). In total, 39 sites were investigated. The sites were placed to cover different vegetation communities that characterise central Chukotka. Fifteen-meter radius sample plots (sites) were demarcated in the most homogeneous locations. Heterogeneity was accommodated by roughly assorting vegetation into two to three vegetation types per sampling plot. Within each area of roughly estimated vegetation types we selected one 0.5 x 0.5 m subplot for representative ground-layer above-ground biomass (ABG) harvesting (major taxa and other). For moss and lichen AGB harvesting inside 0.5 x 0.5 m subplots representative 0.1 x 0.1 m subplots were chosen. All ground-layer vegetation AGB assessments were calculated for the fifteen-meter radius plot in g m^2 for each sample plot. Tree (Larix cajanderi) AGB was assessed using partial harvesting of three representative individual trees per sample plot, specifically developed for the study area allometric equations and measurements of all trees' heights on the fifteen-meter radius plot. AGB of tall shrubs (Alnus fruticosa, Pinus pumila and Salix spp. (non-creeping)) was assessed from harvested subsamples and projective cover on the fifteen-meter radius sample plot. All harvested AGB samples were weighed fresh in the field. In general, AGB samples with a weight of more than 15 g were subsampled. All samples were oven dried (60 °C, 24 h for ground-layer and moss and lichen samples, 48 h for shrub and tree branch samples, up to one week for tree stem discs) and weighed again. All data was collected by scientists from Alfred Wegener Institute (AWI), Helmholtz Centre for Polar and Marine Research and University of Potsdam, Germany, The Institute for Biological problems of the Cryolithozone, Russian Academy of Sciences, Siberian branch, and The Institute of Natural Sciences, North-Eastern Federal University of Yakutsk, Yakutsk, Russia

Foliage projective cover of 57 vegetation sites of central Chukotka from 2016

Field investigations were performed in four areas forming a vegetation gradient from a treeless mountainous tundra (16-KP-04; Lake Rauchuagytgyn area) via tundra-taiga transition zone (16-KP-01, Lake Ilirney area; 16-KP-03, Nutenvut lakes area) to a northern taiga (16-KP-02, Bolshoy Anyuy river area). In total, 57 sites were investigated. The sites were chosen to cover a large NDVI gradient (0.3 - 0.8). The sites differ in elevation (100-900 m a. s. l.), slope angle (0-54°) and aspect (overall south aspects prevail). For every field site a detailed description of the vegetation was made. Foliage projective cover for all major taxa estimated as percent and averaged from five representative 2 x 2 m plots within circular sites with a diameter of 30 m: one at the centre and four 7.5 m away in each of the cardinal directions (south, north, west, east). Presence of deadwood and open soil were also recorded. All data was collected by scientists from Alfred Wegener Institute (AWI), Helmholtz Centre for Polar and Marine Research and University of Potsdam, Germany, The Institute for Biological problems of the Cryolithozone, Russian Academy of Sciences, Siberian branch, and The Institute of Natural Sciences, North-Eastern Federal University of Yakutsk, Yakutsk, Russia

Ground layer above-ground biomass of 39 sites in central Chukotka from 2018 - Raw data of dry weight for each sub-ground vegetation type sampling plot

Field investigations were performed in four areas: a treeless mountainous tundra (16-KP-04; Lake Rauchuagytgyn area), tundra-taiga transition zone (16-KP-01, Lake Ilirney area) and a northern taiga (18-BIL-01, 18-BIL-02). In total, 39 sites were investigated. The sites were placed to cover different vegetation communities that characterise central Chukotka. Fifteen-meter radius sample plots (sites) were demarcated in the most homogeneous locations. Heterogeneity was accommodated by roughly assorting vegetation into two to three vegetation types per sampling plot. Within each area of roughly estimated vegetation types we selected one 0.5 x 0.5 m subplot for representative ground-layer above-ground biomass (ABG) harvesting (major taxa and other). For moss and lichen AGB harvesting inside 0.5 x 0.5 m subplots representative 0.1 x 0.1 m subplots were chosen. All harvested AGB samples were weighed fresh in the field. In general, AGB samples with a weight of more than 15 g were subsampled. All samples were oven dried (60 °C, 24 h for ground-layer and moss and lichen samples, 48 h for shrub and tree branch samples) and weighed again. This dataset contains the raw data of dry weight for each sub-ground vegetation type sampling plot. All data was collected by scientists from Alfred Wegener Institute (AWI), Helmholtz Centre for Polar and Marine Research and University of Potsdam, Germany, The Institute for Biological problems of the Cryolithozone, Russian Academy of Sciences, Siberian branch, and The Institute of Natural Sciences, North-Eastern Federal University of Yakutsk, Yakutsk, Russia. The AGB data calculations for the plot area including tree and tall shrubs can be found at https://doi.org/10.1594/PANGAEA.923719

Strong shrub expansion in tundra-taiga, tree infilling in taiga and stable tundra in central Chukotka (north-eastern Siberia) between 2000 and 2017

Vegetation is responding to climate change, which is especially prominent in the Arctic. Vegetation change is manifest in different ways and varies regionally, depending on the characteristics of the investigated area. Although vegetation in some Arctic areas has been thoroughly investigated, central Chukotka (NE Siberia) with its highly diverse vegetation, mountainous landscape and deciduous needle-leaf treeline remains poorly explored, despite showing strong greening in remote-sensing products. Here we quantify recent vegetation compositional changes in central Chukotka over 15 years between 2000/2001/2002 and 2016/2017. We numerically related field-derived information on foliage projective cover (percentage cover) of different plant taxa from 52 vegetation plots to remote-sensing derived (Landsat) spectral indices (Normalised Difference Vegetation Index (NDVI), Normalised Difference Water Index (NDWI) and Normalised Difference Snow Index (NDSI)) using constrained ordination. Clustering of ordination scores resulted in four land-cover classes: (1) larch closed-canopy forest, (2) forest tundra and shrub tundra, (3) graminoid tundra and (4) prostrate herb tundra and barren areas. We produced land-cover maps for early (2000, 2001 or 2002) and recent (2016 or 2017) time-slices for four focus regions along the tundra-taiga vegetation gradient. Transition from graminoid tundra to forest tundra and shrub tundra is interpreted as shrubification and amounts to 20% area increase in the tundra-taiga zone and 40% area increase in the northern taiga. Major contributors of shrubification are alder, dwarf birch and some species of the heather family. Land-cover change from the forest tundra and shrub tundra class to the larch closed-canopy forest class is interpreted as tree infilling and is notable in the northern taiga. We find almost no land-cover changes in the present treeless tundra

Individual tree and tall shrub partial above-ground biomass of central Chukotka in 2018

Tree and tall shrub above ground biomass (AGB) samples were taken in five areas: a treeless mountainous tundra (16-KP-04; Lake Rauchuagytgyn area), tundra-taiga transition zone (16-KP-01, Lake Ilirney area; 18-BIL-00) and a northern taiga (18-BIL-01, 18-BIL-02). In total, 31 sample plots with 15-m radius were investigated for tree and tall shrub AGB. The only present tree species there is Larix cajanderi Mayr. By tall shrubs we mean Pinus pumila (Pall.) Regel, Alnus viridis ssp. fruticosa (Rupr.) Nyman and Salix spp. L. Three living trees (the lowest, a tree with the average height and the highest) per each site were cut down. From each individual tree certain representative samples were taken: samples of branches, needles, cones and tree stem discs. Sampled branches were divided into four categories: 1) big (first order, connected to the stem), 2) medium (second order, connected to the big branches), 3) small (third order, connected to the medium branches), 4) dead (including dead cones). Needles are typically found on the third order branches. Cones were divided by colour (red, brown and grey). Tree stem discs were taken at the base of a tree (0 cm, disc A), breast height (130 cm, disc B) and top/close to the top of a tree (260 cm, disc C). To estimate each tree's stem biomass, the stem was assumed to have a cone shape. Dead trees were also sampled, but irregularly (not at every sample plot). In most cases, they did not have branch and needle material, so the samples of dead trees mostly consist of tree discs' samples. Tall shrubs were representatively sampled similarly to trees – three individuals per site. Samples included branch, leaves/needles and cones/catkin biomass. All harvested AGB samples were weighed fresh in the field and subsampled. All subsamples were oven dried (60 °C, 48 h for shrub and tree branch samples, up to one week for tree stem discs) and weighed again. Protocol for total tree and shrub AGB estimation can be found enclosed as a separate file. All data were collected by scientists from Alfred Wegener Institute (AWI), Helmholtz Centre for Polar and Marine Research and University of Potsdam, Germany, The Institute for Biological problems of the Cryolithozone, Russian Academy of Sciences, Siberian branch, and The Institute of Natural Sciences, North-Eastern Federal University of Yakutsk, Yakutsk, Russia.” All data were collected during the “Chukotka 2018” expedition, that has been supported by the German Federal Ministry of Education and Research (BMBF), which enabled the Russian-German research programme “Kohlenstoff im Permafrost KoPf” (grant no. 03F0764A) and by the Initiative and Networking Fund of the Helmholtz Association and by the ERC consolidator grant Glacial Legacy of Ulrike Herzschuh (grant no. 772852)

Ground layer above-ground biomass of 20 sites of Yakutia from 2018 - Accumulated data for 15m²-radius plots

Field investigations were performed selecting locations to include the variety of present boreal forest stands between 113-130 °E, eastwards of the city Yaktusk until westernmost sites close to Lake Khamra. To provide a full assessment of the current state of aboveground biomass the ground vegetation layer was additionally sampled. Fifteen-meter radius sample plots (sites) were demarcated in the most homogeneous locations. Heterogeneity was accommodated by roughly assorting vegetation into one to two vegetation types per sampling plot. Within each area of roughly estimated vegetation types we selected one 0.5 x 0.5 m subplot for representative ground-layer above-ground biomass (ABG) harvesting (major taxa and other). For moss and lichen AGB harvesting inside 0.5 x 0.5 m subplots representative 0.1 x 0.1 m subplots were chosen. All ground-layer vegetation AGB assessments were calculated for the fifteen-meter radius plot in g/m² for each sample plot. The present trees and tree-shape growing shrub species were included only up to heights of <40 cm. This dataset contains the accumulated data for the complete 15 m²-radius plot