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.

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.

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.

With this paper we continue a new annual series, the main purpose of which is to make significant floristic findings from Russia and neighboring countries more vi sible in Russia and abroad. In total, this paper presents new records for 24 vascular plant species from 4 Eurasian countries, obtained during field explorations, as well as during taxonomic revisions of herbarium materials. For the first time, new locali ty of Ageratum conyzoides, Salvia hispanica, Thymus rasitatus, Tulipa mongolica is recorded for Russia, Sparganium glomeratum for North Korea, Alyssum armenum for Georgia, Thymus pseudopannonicus for Kazakhstan, Cymbalaria muralis for the Asian part of Russia, Anthemis ruthenica for Siberia, Capsella orientalis, Echinops sphaerocephalus, Hera cleum sosnowskyi, Thymus elegans for Eastern Siberia, Persicaria orientalis for Western Si beria, Galatella crinitoides for the Black Soil Region, Centaurea orientalis for Zavolzhye, Silene dichotoma for the Altai Republic, Onobrychis arenaria, Symphyotrichum squamatum, Verbesina encelioides for the Republic of Dagestan, Geranium dahuricum for the Re public of Sakha (Yakutia), Koeleria spryginii for the Republic of Tatarstan, Phacelia tanacetifolia for Sakhalin, Adonis wolgensis for Novosibirsk Region. For each species, the general distribution, habitat, and taxonomy, indicating differences from related species and location are presented.

On the southern periphery of Badzhal Mountain Range on, an area of about 25 sq. km, we found 313 species of mosses, listed here with distribution along with the altitudinal belts, ecotopes, and substrates. Among them, 73 species are newly recorded for the middle part of Khabarovsk Territory, and 39 of them are new to the whole Khabarovsk Territory, including rare species with no or few previously known records in Russia such as Campylopus gracilis, Sematophyllum substrumulosum, Anomodon solovjovii, Bryoerythrophyllum chenii, Dicranum setifolium, Ditrichum macrorhynchum, Haplohymenium longinerve, Okamurea hakkoniensis, Orthotrichum rogeri, Sphagnum miyabeanum, S. subnitens; identity of several species needs in specially focused taxonomic studies. Studied flora is compared with ones of Zeysky State Nature Reserve, Upper Bureya, and a combined list of mosses of Botchinsky Nature Reserve and Tordoki-Yani Mt. Among all these floras, the proportion of Eastern species in Badzhal is the highest. Main types of vegetation and bryophyte ecotopes are briefly characterized; distribution of species along an altitudinal gradient among considered types of habitats and substrates, is discussed.