Modern Pollen Assemblages From Lake Sediments and Soil in East Siberia and Relative Pollen Productivity Estimates for Major Taxa

Modern pollen–vegetation–climate relationships underpin palaeovegetation and palaeoclimate reconstructions from fossil pollen records. East Siberia is an ideal area for investigating the relationships between modern pollen assemblages and near natural vegetation under cold continental climate conditions. Reliable pollen-based quantitative vegetation and climate reconstructions are still scarce due to the limited number of modern pollen datasets. Furthermore, differences in pollen representation of samples from lake sediments and soils are not well understood. Here, we present a new pollen dataset of 48 moss/soil and 24 lake surface-sediment samples collected in Chukotka and central Yakutia in East Siberia. The pollen–vegetation–climate relationships were investigated by ordination analyses. Generally, tundra and taiga vegetation types can be well distinguished in the surface pollen assemblages. Moss/soil and lake samples contain generally similar pollen assemblages as revealed by a Procrustes comparison with some exceptions. Overall, modern pollen assemblages reflect the temperature and precipitation gradients in the study areas as revealed by constrained ordination analysis. We estimate the relative pollen productivity (RPP) of major taxa and the relevant source area of pollen (RSAP) for moss/soil samples from Chukotka and central Yakutia using Extended R-Value (ERV) analysis. The RSAP of the tundra-forest transition area in Chukotka and taiga area in central Yakutia are ca. 1300 and 360 m, respectively. For Chukotka, RPPs relative to both Poaceae and Ericaceae were estimated while RPPs for central Yakutia were relative only to Ericaceae. Relative to Ericaceae (reference taxon, RPP = 1), Larix, Betula, Picea, and Pinus are overrepresented while Alnus, Cyperaceae, Poaceae, and Salix are underrepresented in the pollen spectra. Our estimates are in general agreement with previously published values and provide the basis for reliable quantitative reconstructions of East Siberian vegetation.</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

Prognostic Implications of Lateral Lymph Nodes in Rectal Cancer:A Population-Based Cross-sectional Study with Standardized Radiological Evaluation after Dedicated Training

BACKGROUND: There is an ongoing discussion regarding the prognostic implications of the presence, short-axis diameter, and location of lateral lymph nodes. OBJECTIVE: To analyze lateral lymph node characteristics, the role of downsizing on restaging MRI, and associated local recurrence rates for patients with cT3-4 rectal cancer after MRI re-review and training. DESIGN: Retrospective population-based cross-sectional study. SETTINGS: This collaborative project was led by local investigators from surgery and radiology departments in 60 Dutch hospitals. PATIENTS: A total of 3057 patients underwent rectal cancer surgery in 2016: 1109 had a cT3-4 tumor located ≤8 cm from the anorectal junction, of whom 891 received neoadjuvant therapy. MAIN OUTCOME MEASURES: Local recurrence and (ipsi) lateral local recurrence rates. RESULTS: Re-review identified 314 patients (35%) with visible lateral lymph nodes. Of these, 30 patients had either only long-stretched obturator (n = 13) or external iliac (n = 17) nodes, and both did not lead to any lateral local recurrences. The presence of internal iliac/obturator lateral lymph nodes (n = 284) resulted in 4-year local recurrence and lateral local recurrence rates of 16.4% and 8.8%, respectively. Enlarged (≥7 mm) lateral lymph nodes (n = 122) resulted in higher 4-year local recurrence (20.8%, 13.1%, 0%; p &lt;.001) and lateral local recurrence (14.7%, 4.4%, 0%; p &lt; 0.001) rates compared to smaller and no lateral lymph nodes, respectively. Visible lateral lymph nodes (HR 1.8 [1.1-2.8]) and enlarged lateral lymph nodes (HR 1.9 [1.1-3.5]) were independently associated with local recurrence in multivariable analysis. Enlarged lateral lymph nodes with malignant features had higher 4-year lateral local recurrence rates of 17.0%. Downsizing had no impact on lateral local recurrence rates. Enlarged lateral lymph nodes were found to be associated with higher univariate 4-year distant metastasis rates (36.4% vs 24.4%; p = 0.021), but this was not significant in multivariable analyses (HR 1.3 [0.9-1.]) and did not worsen overall survival. LIMITATIONS: This study was limited by the retrospective design and total number of patients with lateral lymph nodes. CONCLUSIONS: The risk of lateral local recurrence due to (enlarged) lateral lymph nodes was confirmed, but without the prognostic impact of downsizing after neoadjuvant therapy. These results point toward the incorporation of primary lateral lymph node size into treatment planning. See Video Abstract.</p

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

SiDroForest: Sentinel-2 Level-2 Bottom of Atmosphere labelled image patches with seasonal information for Central Yakutia and Chukotka vegetation plots (Siberia, Russia)

This Sentinel-2 Level-2 (Bottom-of-Atmosphere) image patches with seasonal information for Central Yakutia and Chukotka vegetation plots dataset in Siberia, Russia, is a part of the SiDroForest data collection. The aim of SiDroForest is to map current vegetation dynamics in the boreal to sub arctic region of Siberia. Sentinel-2 is an ESA optical satellite mission providing satellite imagery globally- and freely-available, which facilitates low-cost large-scale analyses of circumpolar boreal forests. The Sentinel-2 mission is composed of two identical satellites that were launched in 2015 and 2017. Though freely available, Sentinel-2 data often contains clouds and finding a cloud and haze-free acquisition can take time. Therefore, this Data Collection provides cloud free atmospherically corrected image patches of the vegetation plots in three different seasons of the vegetation period in Siberia: early summer, peak summer and summer. The atmospherically corrected Sentinel-2 data were optimized prior to vegetation related analyses: we resampled the spectral bands to 10 m spatial resolution to make them comparable in the same spatial pixel resolution, removing the 60m bands that support atmospheric correction but are not optimal for land surface classification. In addition, for vegetation monitoring it is common to apply the Normalized Difference Vegetation Index (NDVI) that we provide as an additional band. The dataset presented here contains subsets of Sentinel-2 acquisitions that cover all the 54 locations where fieldwork was performed in Siberia during a 2-month fieldwork expedition in 2018 by the Alfred Wegener Institute Helmholtz Centre for Polar and Marine Research in Germany (Kruse et al., 2019). 30 by 30m image patches were cropped and given a vegetation label (classes 1-11)

Tree height and crown diameter during fieldwork expeditions that took place in 2018 in Central Yakutia and Chukotka, Siberia

This Individual-labelled trees dataset is a part of the SiDroForest data collection (https://www.pangaea.de/?q=keyword%3A%22SiDroForest%22) and contains spatial data in the form of points and polygons of 872 trees and shrubs that were recorded in Siberia during a 2-month fieldwork expedition in 2018 by the Alfred Wegener Institute Helmholtz Centre for Polar and Marine Research in Germany (Kruse et al., 2019). During the fieldwork, 15 m radius vegetation surveys were performed during which information such as height, species, crown diameter were recorded for individual trees. In addition, the vegetation plots were covered by 50*50-meter Unmanned Aerial Vehicle (UAV) flights and Red Green Blue (RGB) photographs were taken with a consumer grade DJI Phantom 4 quadcopter (Brieger et al. 2019). From these UAV images, orthoimages were constructed with Agisoft PhotoScan Professional (Agisoft, 2018) using using Structure-from- Motion/Multi-view Stereo (SfM-MVS) techniques. (Agisoft manual 2019). Orthophotos are geometrically corrected images that are georeferenced. When the individual is located in the orthoimage we also know the location of said individual in real life. The construction techniques are explained in detail in Brieger et al (2019). In these orthoimages, individual trees and shrubs recorded during the expeditions were mapped (Fig 1 in README). The trees recorded during fieldwork that could be located in the orthoimages were marked in a point shapefile and polygon shapefile that outlines the tree crown of the individual tree, each element contains the individual number of the tree, the species and the type (Tree or Shrub). In addition, to clarify between Tree and Shrub, the type attribute was added because in the Chukotka sites there are Pinus species that are shrubs which can be misleading. The Point shapefiles also include the x and y coordinates of the point. The individual number can be used to link the tree or shrub to the rest of the information collected during the expedition such as tree height, crown diameter and vitality

Shapefiles of individual-labelled trees acquired during fieldwork expeditions that took place in 2018 in Central Yakutia and Chukotka, Siberia

This Individual-labelled trees dataset is a part of the SiDroForest data collection (https://www.pangaea.de/?q=keyword%3A%22SiDroForest%22) and contains spatial data in the form of points and polygons of 872 trees and shrubs that were recorded in Siberia during a 2-month fieldwork expedition in 2018 by the Alfred Wegener Institute Helmholtz Centre for Polar and Marine Research in Germany (Kruse et al., 2019). During the fieldwork, 15 m radius vegetation surveys were performed during which information such as height, species, crown diameter were recorded for individual trees. In addition, the vegetation plots were covered by 50*50-meter Unmanned Aerial Vehicle (UAV) flights and Red Green Blue (RGB) photographs were taken with a consumer grade DJI Phantom 4 quadcopter (Brieger et al. 2019). From these UAV images, orthoimages were constructed with Agisoft PhotoScan Professional (Agisoft, 2018) using using Structure-from- Motion/Multi-view Stereo (SfM-MVS) techniques. (Agisoft manual 2019). Orthophotos are geometrically corrected images that are georeferenced. When the individual is located in the orthoimage we also know the location of said individual in real life. The construction techniques are explained in detail in Brieger et al (2019). In these orthoimages, individual trees and shrubs recorded during the expeditions were mapped (Fig 1). The trees recorded during fieldwork that could be located in the orthoimages were marked in a point shapefile and polygon shapefile that outlines the tree crown of the individual tree, each element contains the individual number of the tree, the species and the type (Tree or Shrub). In addition, to clarify between Tree and Shrub, the type attribute was added because in the Chukotka sites there are Pinus species that are shrubs which can be misleading. The Point shapefiles also include the x and y coordinates of the point. The individual number can be used to link the tree or shrub to the rest of the information collected during the expedition such as tree height, crown diameter and vitality

SiDroForest: Individual-labelled trees acquired during the fieldwork expeditions that took place in 2018 in Central Yakutia and Chukotka, Siberia

This Individual-labelled trees dataset is a part of the SiDroForest data collection (https://www.pangaea.de/?q=keyword%3A%22SiDroForest%22) and contains spatial data in the form of points and polygons of 872 trees and shrubs that were recorded in Siberia during a 2-month fieldwork expedition in 2018 by the Alfred Wegener Institute Helmholtz Centre for Polar and Marine Research in Germany (Kruse et al., 2019). During the fieldwork, 15 m radius vegetation surveys were performed during which information such as height, species, crown diameter were recorded for individual trees. In addition, the vegetation plots were covered by 50*50-meter Unmanned Aerial Vehicle (UAV) flights and Red Green Blue (RGB) photographs were taken with a consumer grade DJI Phantom 4 quadcopter (Brieger et al. 2019). From these UAV images, orthoimages were constructed with Agisoft PhotoScan Professional (Agisoft, 2018) using using Structure-from- Motion/Multi-view Stereo (SfM-MVS) techniques. (Agisoft manual 2019). Orthophotos are geometrically corrected images that are georeferenced. When the individual is located in the orthoimage we also know the location of said individual in real life. The construction techniques are explained in detail in Brieger et al (2019). In these orthoimages, individual trees and shrubs recorded during the expeditions were mapped (Fig 1 in README). The trees recorded during fieldwork that could be located in the orthoimages were marked in a point shapefile and polygon shapefile that outlines the tree crown of the individual tree, each element contains the individual number of the tree, the species and the type (Tree or Shrub). In addition, to clarify between Tree and Shrub, the type attribute was added because in the Chukotka sites there are Pinus species that are shrubs which can be misleading. The Point shapefiles also include the x and y coordinates of the point. The individual number can be used to link the tree or shrub to the rest of the information collected during the expedition such as tree height, crown diameter and vitality. This information is included in the dataset doi:10.1594/PANGAEA.932817