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

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

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

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

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

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