Wind disturbances are significant phenomena in forest spatial structure and succession dynamics. They cause changes in biodiversity, impact on forest ecosystems at different spatial scales, and have a strong influence on economics and human beings. The reliable recognition and mapping of windthrow areas are of high importance from the perspective of forest management and nature conservation. Recent research in artificial intelligence and computer vision has demonstrated the incredible potential of neural networks in addressing image classification problems. The most efficient algorithms are based on artificial neural networks of nested and complex architecture (e.g., convolutional neural networks (CNNs)), which are usually referred to by a common term—deep learning. Deep learning provides powerful algorithms for the precise segmentation of remote sensing data. We developed an algorithm based on a U-Net-like CNN, which was trained to recognize windthrow areas in Kunashir Island, Russia. We used satellite imagery of very-high spatial resolution (0.5 m/pixel) as source data. We performed a grid search among 216 parameter combinations defining different U-Net-like architectures. The best parameter combination allowed us to achieve an overall accuracy for recognition of windthrow sites of up to 94% for forested landscapes by coniferous and mixed coniferous forests. We found that the false-positive decisions of our algorithm correspond to either seashore logs, which may look similar to fallen tree trunks, or leafless forest stands. While the former can be rectified by applying a forest mask, the latter requires the usage of additional information, which is not always provided by satellite imagery.

The paper describes the structure and functional aspects of the electronic herbarium system with a web interface developed at the Botanical Garden-Institute FEB RAS (BGI) in 2016-2017. The main purpose of the system is to provide online access to the herbarium data, including online search operations and the facilities to enter new records into the herbarium database and to generate labels for specimens. The system is therefore multipurpose. It is primarily written in the Python programming language and has several key features: a two step validation process of digitized herbarium records, multi-user and multi-acronym support, semi-automatic herbarium sheet labelling based on entered data, handling of multispecies herbarium records (e.g. cryptogams), flexible taxon-level search and filtering within geographical areas via a web interface or automated search engine relying on HTTP API. The current system is actively used to manage a digital herbarium at the BGI, including its departments in Sakhalin and Amur Branches. The system can be used as well to integrate herbarium information from many other collections. Наз на-чение сис те мы -обеспечить онлайн доступ к гербарным данным, вклю чая базовые опе рации поиска и внесения записей в гербарную базу, а также соз дание макетов этикеток для гербарных образцов. Таким образом, систе-ма управления электронным гербарием представляет собой многоцелевой про грам мный комплекс. Она написана преимущественно на языке про-граммирования Python и обладает следующими возможностями: двухэтап-ным контролем оцифрованных гербарных образцов, поддержкой одновре-мен ной ра боты нескольких пользователей и управления несколькими гер ба рия ми с различными акронимами, полуавтоматической подсистемой эти ке тиро ва ния образцов, а также возможностью введения информации о мно гови до вых сборах (например, споровых), гибким поиском и фильтра-ци ей его ре зультатов, в том числе по географическим областям, с исполь-зо ванием как web-интерфейса, так и поисковых возможностей на основе HTTP API. Дан ная система используется для управления электронным гер-ба рием в БСИ, включая его Сахалинский и Амурский филиалы. Система так же может использоваться для интеграции гербарной информации кол-лек ций других учреждений.

Very high resolution satellite imageries provide an excellent foundation for precise mapping of plant communities and even single plants. We aim to perform individual tree recognition on the basis of very high resolution RGB (red, green, blue) satellite images using deep learning approaches for northern temperate mixed forests in the Primorsky Region of the Russian Far East. We used a pansharpened satellite RGB image by GeoEye-1 with a spatial resolution of 0.46 m/pixel, obtained in late April 2019. We parametrized the standard U-Net convolutional neural network (CNN) and trained it in manually delineated satellite images to solve the satellite image segmentation problem. For comparison purposes, we also applied standard pixel-based classification algorithms, such as random forest, k-nearest neighbor classifier, naive Bayes classifier, and quadratic discrimination. Pattern-specific features based on grey level co-occurrence matrices (GLCM) were computed to improve the recognition ability of standard machine learning methods. The U-Net-like CNN allowed us to obtain precise recognition of Mongolian poplar (Populus suaveolens Fisch. ex Loudon s.l.) and evergreen coniferous trees (Abies holophylla Maxim., Pinus koraiensis Siebold & Zucc.). We were able to distinguish species belonging to either poplar or coniferous groups but were unable to separate species within the same group (i.e. A. holophylla and P. koraiensis were not distinguishable). The accuracy of recognition was estimated by several metrics and exceeded values obtained for standard machine learning approaches. In contrast to pixel-based recognition algorithms, the U-Net-like CNN does not lead to an increase in false-positive decisions when facing green-colored objects that are similar to trees. By means of U-Net-like CNN, we obtained a mean accuracy score of up to 0.96 in our computational experiments. The U-Net-like CNN recognizes tree crowns not as a set of pixels with known RGB intensities but as spatial objects with a specific geometry and pattern. This CNN’s specific feature excludes misclassifications related to objects of similar colors as objects of interest. We highlight that utilization of satellite images obtained within the suitable phenological season is of high importance for successful tree recognition. The suitability of the phenological season is conceptualized as a group of conditions providing highlighting objects of interest over other components of vegetation cover. In our case, the use of satellite images captured in mid-spring allowed us to recognize evergreen fir and pine trees as the first class of objects (“conifers”) and poplars as the second class, which were in a leafless state among other deciduous tree species.

The genus Hydrocharis L. includes three geographically isolated species. Analysis of the actual data (32 thousand geographical locations and 1946 herbarium sheets) covering the period 1765–2019 made it possible to clarify the nature of the distri- bution of these species and its changes. Hydrocharis morsus-ranae L. has disjunctive Eurasian – North American temperate range, with a massive North American en- clave, the formation of which began in the 1930–1940s. The range of Hydrocharis dubia (Blume) Backer is disjunctive Southeast Asian – Australian subboreal-tropical, Australia enclave began forming in the 1850–1870s. Hydrocharis chevalieri (De Wild.) Dandy is a macrothermal Central African equatorial endemic. Current threat status of all species may be estimated as Least Concern. The mean annual temperature is the most contrasting feature of the distribution areas of the species, water depth, bottom soil type and hydrochemical composition have lower impact. Prognostic models of the potential distribution of the tagged species have been made.

Accurate remote detection of various forest disturbances is a challenge in global environmental monitoring. Addressing this issue is crucial for forest health assessment, planning salvage logging operations, modeling stand dynamics, and estimating forest carbon stocks and uptake. Substantial progress on this problem has been achieved owing to the rapid development of remote sensing devices that provide very high-resolution images. Concurrently, image processing algorithms have witnessed rapid development owing to the extensive use of artificial neural networks with complex architectures and deep learning approaches. This opens new opportunities and perspectives for applying deep learning methods to solving various problems in environmental sciences. In this study, we used deep convolutional neural networks (DCNNs) to recognize forest damage induced by windthrows and bark beetles. We used satellite imagery of very high resolution in visual spectra represented as pansharpened images (RGB channels). When predicting forest damage, we obtained accuracies higher than 90% on test data for recognition of both windthrow areas and damaged trees impacted by bark beetles. A comparative analysis indicated that the DCNN-based approach outperforms traditional pixel-based classification methods (AdaBoost, random forest, support vector machine, quadratic discrimination) by at least several percentage points. DCNNs can learn a specific pattern of the area of interest and thus yield fewer false positive decisions than pixel-based algorithms. The ability of DCNNs to generalize makes them a good tool for delineating smooth and illdefined boundaries of damaged forest areas, such as windthrow patches.

Tropical cyclones (hurricanes and typhoons) cause large-scale disturbances in forest ecosystems all over the world. In the summer of 2016, a strong tropical cyclone named Lionrock created windthrow patches in the area of more than 400 km 2 on the forested eastern slopes of the Sikhote-Alin Range, in the Russian Far East. Such large-scale forest destruction by wind had never been recorded in the area prior to this event. We examined the tropical cyclone impact upon the forest composition, structure and tree mortality rates on two study sites (1 ha and 0.5 ha in size)-a contiguous windthrow patch site, and a site with partial canopy damage. Korean pine (Pinus koraiensis Siebold and Zucc.), Manchurian fir (Abies nephrolepis Trautv.) and Dahurian larch (Larix cajanderi Mayr.) were the primary tree species represented in the affected forest communities. Combined with the partial canopy damage, 7.7% of trees were blown down by the disturbance event. We determined that this one event mortality rate nearly equaled the average mortality rate for a ten year period for these forests (8.5 ± 4.0%) under normal conditions (no large-scale disturbances). Within a contiguous windthrow patch, tree mortality was determined to be 52.6%, which is significantly higher than the cumulative tree loss for the previous 50 years (42.4%). A substantial portion of thinner-stemmed trees (DBH (diameter measured at breast height) < 30 cm) were wind snapped, and those with larger diameters (DBH > 60 cm) were uprooted. Our results indicate that the probability of tree loss due to catastrophic wind loads increases as a result of the decrease in local density. We believe that tree loss estimates should include the impacts within contiguous patches of windthrows, as well as the patches with only partial tree canopy damage. Strong wind impact forecasting is possible with accounting for species composition within the stand sites and their spatial structure.

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