Relevance and aim. Korean pine-broadleaf forests of the southern Sikhote-Alin are unique forest ecosystems with high biological value and complex structure. Under ongoing forest exploitation and degradation of old-growth stands, the evaluation of long-term logging consequences is of special importance. This study aims to assess the state of forest stands 60–70 years after single selective logging conducted in the 1960s, and to compare them with preserved old-growth forests. Materials and methods. The data were collected within the Verkhneussuriysky biogeocenotic station, where 346 temporary sample plots were established. For analysis, 127 plots of the K4 forest type (multi-shrub Korean pine forest with yellow birch) were selected, including 69 old-growth and 58 post-logging sites. Tree diameter, height, basal area, and stem volume were measured. Calculations were performed in Python using nonparametric statistical methods. Results. The obtained results show that even after 60–70 years, post-logging forests still differ significantly from old-growth stands: they have lower timber stock, smaller average diameters, and lack large trees. Fast-growing pioneer deciduous species predominate, while the proportion of Korean pine is significantly reduced. Conclusion. The restoration of the original structure of Korean pine-broadleaf forests is extremely slow and remains incomplete even decades after logging. This highlights the need to reconsider forestry practices aimed at conserving and restoring these ecosystems.

The assessment of the carbon pool in representative forest stands of the northern, middle, and southern taiga subzones of Central Siberia, located in the territory of the Krasnoyarsk Kraihas, has been conducted. The area of these taiga regions accounts for 87.5% of the total territory of Central Siberia, and they make the main contribution to carbon deposition in this area. The total mass of deposited carbon in the representative stands of the northern taiga is 73970 thousand tons, in the stands of the middle taiga this value is 1257101 thousand tons, and for the southern taiga, it is 2766554 thousand tons. The average mass of deposited carbon for the northern taiga subzone is 13.2 tons per hectare, for the middle taiga it is 44.6 tons per hectare, and for the southern taiga, it is 64.5 tons per hectare. Such differences are due to the zonal characteristics of the natural and climatic conditions in these areas and, consequently, the varying productivity of the forest stands formed in these taiga subzones. The fractional composition of the carbon pool depends on many indicators, primarily on the bonitet (site quality), density, and fullness of the forest stand. For all the considered representative forest stands, the main contribution to carbon deposition comes from the trunks and roots of trees. In the northern taiga, the share of trunks accounts for 49.9% to 66.7% of the deposited carbon, while roots account for 18.1% to 34.8%. For the middle taiga, these values range from 53.8% to 70.4% for trunks and from 13.2% to 33.4% for roots. For the southern taiga, the share of deposited carbon in trunks is from 53.4% to 69.6%, and in roots, it is from 17.7% to 31.9%. The obtained data on the carbon pool of forest stands in the taiga zone of Central Siberia are important for understanding carbon exchange processes in forest ecosystems, as well as for developing effective strategies for the conservation and management of forest areas in the context of climate change.

This article presents the results of a study examining the potential of threshold segmentation of intercrown areas of forest canopy images using domestic ultra-high-resolution satellite images obtained from the Resurs-P1 (Geoton-L) satellite to identify the relationship between segmentation parameters and biometric characteristics of pine stands, using the forests of the Curonian Spit National Park as an example. The proposed method is based on identifying shaded segments of the intercrown space within forest stand boundaries, taking into account a specified brightness range, and then merging adjacent pixels based on spectral proximity at a new specified brightness threshold. For each specified threshold, the areas and average brightness values ​​of shadow segments within the stand boundaries, standard deviations, and median values ​​are determined. Based on these values, a threshold canopy closure is calculated for each stand, taking into account only shaded intercrown spaces. Statistical characteristics of average brightness and canopy closure threshold serve as variables for regression modeling of biometric characteristics (height, diameter, and stand age) of pine forests. The regression analysis was conducted using an ensemble method with Random Forest (RF) decision tree construction. The R² coefficient of determination for pine forest characteristics ranges from 0.29 to 0.37. The results of the validation model for the test set are virtually identical to those for the training set, demonstrating the robustness of the RF model. Regression modeling of pine stand characteristics using the RF algorithm (using pure pine stands in the Curonian Spit National Park as an example), using predictors derived from threshold segmentation of forest canopy images on Geoton-L panchromatic images, yields stable results with a root-mean-square error of approximately 4 m for average height, 6 cm for diameter, and 20 years for age. Threshold segmentation of tree canopy images is useful for preliminary assessment of stand characteristics in cases where radiometric correction of spectral data is insufficient for calculating standard textural characteristics.

A concept of soil organopedogenesis has been proposed as an unheeded active process of general pedogenesis reflecting the predominant significance of the biological factor in soil formation according to Vernadsky’s theory on organisms’ leading role in all natural processes in the continental part of the biosphere. A methodology is presented to reflect the role of organopedogenesis in the existing soil classifications by including in their taxonomy the types of accumulation and transformation of soil organic matter and the types of organoprofiles. The inclusion of these components will make the classification dynamic and increase its practical importance. That is especially significant for decision making at the time being given the fast negative global environmental changes.

This article presents the results and summary of the most important and interesting reports from the IX All-Russian (with international participation) scientific conference “Aerospace Methods and Geoinformation Technologies in Forest Science, Forestry, and Ecology”, held April 15-17, 2025, in Moscow at the Isaev Centre for Forest Ecology and Productivity of the Russian Academy of Sciences (CEPF RAS). Over the three days of the conference, 60 reports were presented on promising areas of using remote sensing methods and GIS technologies in various aspects of forest ecosystem studies. The 130 participants represented research and educational organizations, as well as commercial companies from Russia, Belarus, and Azerbaijan. The plenary sessions covered important topics related to the assessment of large-scale changes in Russian forests using space monitoring data: the dynamics of species and age structure, forest damage from fires, and pyrogenic carbon emissions from forests. Considerable attention was also paid to the potential for predicting forest insect outbreaks using satellite data and the need for remote monitoring of forest reforestation on abandoned agricultural lands in Russia. A significant number of sectional presentations were devoted to the challenges and prospects of using aerial imagery from unmanned aerial vehicles (UAVs), airborne and terrestrial laser scanning, web application development, information and analytical systems, and automated services for monitoring forest vegetation changes to assess forest characteristics. Conference participants proposed recommendations for improving remote monitoring systems and noted significant progress in the development and use of artificial intelligence for recognizing tree crowns, clear-cut areas, forest infrastructure facilities, and other features using remote sensing data. A collection of abstracts from the conference was prepared electronically and posted on the website of the scientific electronic library Elibrary. Information about the IX All-Russian Scientific Conference, including the program, collection of materials, video broadcasts of plenary and sectional sessions, and presentations of papers, is available at https://cepl.rssi.ru/confs/ASGIS2025/.

Oil field development in forested areas has a significant impact on forests. Field development involves clearing the area for oil production sites, creating facilities for primary oil processing, and pipelines for its transportation. Oil production development at an already developed field is associated with oil losses during accidents at production sites and oil pipelines and vegetation degradation at oil spill sites. Both aspects — forest area reduction and vegetation degradation in oil production areas — determine the relevance of their research using space images. The work is based on the example of a site at the Samotlor field in Western Siberia. The research material is modern space images from the WorldView-3 satellite in 2019 and 2022, and a 1980 general topographic map. Research methods include determining forest areas using the 1980 map and 2019 images; interpreting oil spills using space images. obtaining spectral radiance graphs for areas with different types of degraded vegetation within spills and for similar types of vegetation outside spills, in natural conditions, using images obtained in the year of the spill and three years later. As a result, 3-fold reduction in the forest area of the study area since 1980 was revealed. The spectral radiance curves of degraded vegetation obtained from WorldView-3 images, compared to natural vegetation, are characterized by a decrease in the NIR1 maximum of up to 60% for shrub vegetation and up to 80% for forest vegetation. A repeat survey three years later showed a decrease in these differences, recording partial natural restoration of vegetation.

Forest fires remain a major problem for the country’s forestry sector, requiring an analysis and rethinking of the fire safety system in the Russian Federation’s forests. This article examines the early detection and suppression of forest fires. It identifies a number of measures necessary to improve the effectiveness of forest fire protection, and provides recommendations for emergency response to forest fires and the use of federal reserve funds, the formation of which is provided for in Article 53.9 of the Forest Code of the Russian Federation.

Paper presents the development of a plugin for Open Source QGIS for automated assessment of data quality of road network when solving the logistics and transport problems of the forestry complex and infrastructure projects in a Russian region (Novosibirsk region). Automation of data quality assessment is especially relevant in the context of Open Data use, diversity and an increase in the number of sources. National standards, industry documents, cases of Russian companies were studied. The developed plug-in “Compare_road” is used to analyze and verify data on roads in the format of vector linear files. During the analysis, the following metrics are calculated: accuracy of compliance, completeness and data relevance. Plug-in is published on GitHub under the MIT license. The target audience of the plug-in is researchers, students and amateur-users. To test the work of the plug-in, an analysis of data on the road network of the Novosibirsk region of the Natural Earth, VMAP, Digital Chart of the World projects was carried out using OSM as a reference. The best accuracy indicators were shown by the Digital Chart of the World – 270.7 m, the best VMAP data completeness indicator – 22.6%. The analysis of roads data quality for the forestries of Novosibirsk region was carried out. Best quality resullts have Tatarskoye (127.6 m, 35.9 %) and Maslyaninskoye (154 m, 35.9 %), worst – Kushtovskoye (378.5 m, 0.7 %) and Suzunskoye (418 m, 1.5 %).