Crown Density Research Articles

Automated tree crown labeling with 3D radiative transfer modelling achieves human comparable performances for tree segmentation in semi-arid landscapes

Mapping tree crowns in arid or semi-arid areas, which cover around one-third of the Earth’s land surface, is a key methodology towards sustainable management of trees. Recent advances in deep learning have shown promising results for tree crown segmentation. However, a large amount of manually labeled data is still required. We here propose a novel method to delineate tree crowns from high resolution satellite imagery using deep learning trained with automatically generated labels from 3D radiative transfer modeling, intending to reduce human annotation significantly. The methodological steps consist of 1) simulating images with a 3D radiative transfer model, 2) image style transfer learning based on generative adversarial network (GAN) and 3) tree crown segmentation using U-net segmentation model. The delineation performances of the proposed method have been evaluated on a manually annotated dataset consisting of more than 40,000 tree crowns. Our approach, which relies solely on synthetic images, demonstrates high segmentation accuracy, with an F1 score exceeding 0.77 and an Intersection over Union (IoU) above 0.64. Particularly, it achieves impressive accuracy in extracting crown areas (r2 greater than 0.87) and crown densities (r2 greater than 0.72), comparable to that of a trained dataset with human annotations only. In this study, we demonstrated that the integration of a 3D radiative transfer model and GANs for automatically generating training labels can achieve performances comparable to human labeling, and can significantly reduce the time needed for manual labeling in remote sensing segmentation applications.

Assessment of Fuel Wood Consumption and Its Effect on Forest Depletion in Dry Deciduous Forest of Haliyal Taluk, Uttar Kannada District, India

Forest plays a significant role in extending wood and non-wood resources around the world, Forests provide ecosystem services, such as timber, food, fuel, fodder, non-wood products and shelter, which are essential for human well-being. However the increasing demand and pressure on forest resources majorly like fuel wood lead to degradation of forests. Hence it is important to assess the fuel wood consumption pattern and its impact on the forest for sustainable management of resources. In the present study, Haliyal taluk was selected, the area which is under rapid urbanization, surrounded by dry deciduous forest and the people living in that area depend on forest for fuel wood consumption. The study examined fuel wood collection among different farmer categories: large, medium, small, and landless. Results from the semi structured questioner survey revealed that medium farmers collected the most fuel wood. The total fuel wood consumption in Haliyal taluk was 5232.2 tonnes, with an average household consumption of 2.60 quintals of fuel wood per year. Major preferred tree species for fuel wood were Xylia xylocarpa, Terminalia tomentosa and Lagerstroemia lanceolata, which are valued for their high energy content, favourable burning characteristics and availability. For estimation of forest degradation Randomized Block Design was used and the study classified the forest vegetation into four crown density classes: Very Dense Forest (T1), Moderately Dense Forest (T2), Open Forest (T3), and Scrub (T4). Data was collected from 20 plots (20 x 20 m each) across these classes to measure forest depletion. The results showed that statistically significant differences in degradation levels, with the highest depletion (31.56 m³/ha) in very dense forests followed by moderate depletion (6.75 m³/ha) in moderately dense forests and the lowest (2.4 m³/ha) in scrub forests. This pattern reflects greater anthropogenic pressures and higher fuel wood availability in more dense forest types.

Плотность всходов сосны обыкновенной в сосновых лесах под влиянием выбросов Карабашского медеплавильного комбината и лесных пожаров

We assessed the features of natural pine regeneration in forests with different ages of ground fires in the Southern Urals depending on the level of heavy metal pollution. Using the materials obtained from 81 sample plots laid in maturing, mature and over-mature pine forests of natural origin in three types of forest growth conditions (TFGC 1 – TFGC 3), the relationship between the density of pine seedlings and the state of forest stands (crown density, stand age, vital status index), with the characteristics of vegetation condition (total number of species, total projective cover of herb-shrub layer, projective cover of bryophytes layer), with external influences (pollution level, distance from emission source, duration of fire disturbance), and with the methodological features of the study (accounting year) was analyzed. The high variability of estimates of the density of pine seedlings did not allow us to identify the relationship between the density of seedlings and the level of pollution and/or fire disturbances. The density of seedlings is nonlinearly related to the protective cover of the herb-shrub layer. With increasing coverage of the herb-shrub layer, the density of seedlings increased in areas with high levels of pollution and decreased in areas with background pollution.

Comparison of Mineral Density of Dental Structures of Ancient Human Skeletons from the Roman Period with Modern-Day Human Dental Structures

Comparison of maxillary, mandibular, dental crown and root mineral density in human skeletons identified in historical excavations with today's human maxillary, mandibular dental crown and root. After the skull images were obtained, four groups were created: maxilla 1, mandible 1 from the old period, maxilla 2 from the images taken from today's patients, and mandible 2 from the images taken from today's patients. Seventeen skeletons were previously classified as young age, middle age, and older age. Among the archive tomography images, 17 images from young (15-35), middle (36-56) and older (57 and over) age images were included in the study. HU value of the desired region was calculated automatically by the device using Region of interest (ROI). In this study, 34 jaws and 68 teeth were evaluated, including 7 mandibles and 10 maxillae from the late Roman period, and 8 mandibles and 9 maxillae from today's people. The average HU value of the crowns of mandibular anterior teeth from the late Roman period was measured as 2406.0. The average HU value of the crowns of maxillary anterior teeth was found to be 3383.8. In the posterior aspect, the average crown HU value measured in the maxilla was found to be higher than that in the mandible. The finding showed that the density of dental tissues of ancient people, such as enamel and dentin forming the crown, and cementum and dentin forming the root, was significantly higher than today's people.

The influence of APOEε4 on the pTau interactome in sporadic Alzheimer’s disease

APOEε4 is the major genetic risk factor for sporadic Alzheimer’s disease (AD). Although APOEε4 is known to promote Aβ pathology, recent data also support an effect of APOE polymorphism on phosphorylated Tau (pTau) pathology. To elucidate these potential effects, the pTau interactome was analyzed across APOE genotypes in the frontal cortex of 10 advanced AD cases (n = 5 APOEε3/ε3 and n = 5 APOEε4/ε4), using a combination of anti-pTau pS396/pS404 (PHF1) immunoprecipitation (IP) and mass spectrometry (MS). This proteomic approach was complemented by an analysis of anti-pTau PHF1 and anti-Aβ 4G8 immunohistochemistry, performed in the frontal cortex of 21 advanced AD cases (n = 11 APOEε3/ε3 and n = 10 APOEε4/ε4). Our dataset includes 1130 and 1330 proteins enriched in IPPHF1 samples from APOEε3/ε3 and APOEε4/ε4 groups (fold change ≥ 1.50, IPPHF1vs IPIgG ctrl). We identified 80 and 68 proteins as probable pTau interactors in APOEε3/ε3 and APOEε4/ε4 groups, respectively (SAINT score ≥ 0.80; false discovery rate (FDR) ≤ 5%). A total of 47/80 proteins were identified as more likely to interact with pTau in APOEε3/ε3 vs APOEε4/ε4 cases. Functional enrichment analyses showed that they were significantly associated with the nucleoplasm compartment and involved in RNA processing. In contrast, 35/68 proteins were identified as more likely to interact with pTau in APOEε4/ε4 vs APOEε3/ε3 cases. They were significantly associated with the synaptic compartment and involved in cellular transport. A characterization of Tau pathology in the frontal cortex showed a higher density of plaque-associated neuritic crowns, made of dystrophic axons and synapses, in APOEε4 carriers. Cerebral amyloid angiopathy was more frequent and severe in APOEε4/ε4 cases. Our study supports an influence of APOE genotype on pTau-subcellular location in AD. These results suggest a facilitation of pTau progression to Aβ-affected brain regions in APOEε4 carriers, paving the way to the identification of new therapeutic targets.

Forest Management Type Identification Based on Stacking Ensemble Learning

Forest management is the fundamental approach to continuously improve forest quality and achieve the quadruple functions of forests. The identification of forest management types is the basis of forest management and a key technical link in the formulation of forest management plans. However, due to insufficient application of forestry informatization and digitization, there are problems in the organization and application of management types, such as inaccurate identification, diversified standards, long organizational cycles, and low decision-making efficiency. Typical technical models are difficult to widely promote and apply. To address these challenges, this study proposes the Stacking Ensemble Forest Management Type Identification (SEFMTI) method based on Stacking ensemble learning. Initially, four typical forest management types from the sustainable forest management pilot of the Yichun Forestry Group were selected as research subjects, and 19 stand parameters were chosen to form the research data, training various recognition models. Subsequently, the Least Absolute Shrinkage and Selection Operator (LASSO) regression and random forest (RF) methods were used to analyze key decision-making indicators for forest management type recognition and compare the performance of different models. The results show that (1) the SEFMTI model achieved an accuracy rate of 97.14%, effectively improving the accuracy of forest management type recognition while ensuring stability; (2) average age (AG), age group (AGG), crown density (CD), and stand origin (SO) are key decision-making indicators for recognizing forest management types; and (3) after feature selection, the SEFMTI model significantly enhanced the efficiency of model training while maintaining a high accuracy rate. The results validate the feasibility of the SEFMTI identification method, providing a basis for the gradual implementation of sustainable forest management pilots and aiding in the precise improvement of forest quality.

Optimizing crown density and volume estimation across two coniferous forest types in southern China via Boruta and Cubist methods

Abstract Quantifying forest stand parameters is crucial in forestry research and environmental monitoring because it provides important factors for analyzing forest structure and comprehending forest resources. And the estimation of crown density and volume has always been a prominent topic in forestry remote sensing. Based on GF-2 remote sensing data, sample plot survey data and forest resource survey data, this study used the Chinese fir (Cunninghamia lanceolata (Lamb.) Hook.) and Pinus massoniana Lamb. as research objects to tackle the key challenges in the use of remote sensing technology. The Boruta feature selection technique, together with multiple stepwise and Cubist regression models, was used to estimate crown density and volume in portions of the research area’s stands, introducing novel technological methods for estimating stand parameters. The results show that: (i) the Boruta algorithm is effective at selecting the feature set with the strongest correlation with the dependent variable, which solves the problem of data and the loss of original feature data after dimensionality reduction; (ii) using the Cubist method to build the model yields better results than using multiple stepwise regression. The Cubist regression model’s coefficient of determination (R2) is all more than 0.67 in the Chinese fir plots and 0.63 in the P. massoniana plots. As a result, combining the two methods can increase the estimation accuracy of stand parameters, providing a theoretical foundation and technical support for future studies.

Influence of tree planting pattern coupled with wall thermal effect on pollution dispersion within urban street canyon

Among the factors influencing the flow field and dispersion characteristics of pollutants in street canyons, trees not only act as obstacles that impede canyon ventilation but also contribute to non-uniform temperature distributions on its walls through transpiration and shading effects. The coupling effects, however, have not been given adequate attention. This study employed numerical simulations validated by wind-tunnel experiments to investigate the coupling effects of tree planting patterns (crown density Cd, planting density Pd) and wall heating conditions on airflow and pollutant diffusion within urban street canyons. Results show that the variations in Cd or Cd of the tree planting configurations studied have no significant impact on flow structure and pollutant dispersion in the isothermal street canyons. When Pd is 0.25, all three monitoring planes exhibit relatively high net escape velocity (NEV*) and notably reduced overall pollutant concentrations, indicating that this planting configuration is optimal for reducing pollution levels under leeward wall heating. When the windward wall is heated, the pollutant concentration is reduced more significantly under Pd of 0.25 and 0.75 while NEV* on the windward side is about 48. The average NEV* is minimized to approximately 21 at a Cd of 80 % when crown density or planting density is increased for all wall heating scenarios. Moreover, the other configurations of tree planting had little impact on ventilation and dispersion of pollutants in street canyons. The findings can offer technical guidance for the optimal design of urban green facilities to improve local microclimate environment and air quality.

산불의 초기 확산단계 전환에 영향을 미치는 인자 분석

Identifying the key factors that influence the transition from ignition to the early stage of spread is important for effective early response to forest fires. This study aimed to identify the factors influencing the stage transition of forest fires, focusing on those that occurred in five cities along the east coast of Gangwon State in South Korea from 2007 to 2019. The study analyzed the association of factors including terrain, forest characteristics, social and geographical factors of the burned areas with the transition from ignition to early stage of spread, using independence tests and logistic regression. The results showed that elevation, slope and the distance to the nearest initial firefighting resources were highly associated with the stage transition. Forest type, crown density and the number of fire monitoring resources observing burned area showed relatively low association with the stage transition. However, these factors had a relation with the frequency of forest fire occurrences. These findings are expected to be helpful in establishing strategies for early response to, and initial suppression of, forest fires.

Differences in Soundscape Perception of Plants Space in Urban Green Space and the Influence of Factors: The Case of Fuzhou, China

A soundscape can enhance the acoustic environment of urban areas and support the sustainable development of green spaces. This study is based on field research on plant information and a perception questionnaire conducted in 30 urban park sample sites in China. The study analysed the differences in soundscape perception among different plant spaces using three dimensions: sound source dominance degree (SDD), sound source harmony degree (SHD), and soundscape perception evaluation. Additionally, five physical indicators of plant space were selected to quantify the effects of physical characteristics of plant space on soundscape perception in urban green space (UGS). The results indicated that there were impacts on soundscape perception. The evaluation of soundscape perception between different types of plant spaces revealed notable differences, particularly in open and enclosed spaces. All eight indicators demonstrated significant differences, indicating that soundscape perception was influenced by the type of plant space. Additionally, there was a significant correlation between plant space and soundscape perception. The evaluation of soundscape perception in three dimensions was related to several factors, including the degree of spatial enclosure, crown density, average tree height, the proportion of trees and shrubs, and the number of tree species. The dominant factor affecting sound source dominance was found to be the degree of spatial enclosure, followed by the average height of trees. Additionally, the diversity of trees affected the overall soundscape perception.

Twenty years of irrigation acclimation is driven by denser canopies and not by plasticity in twig- and needle-level hydraulics in a Pinus sylvestris forest.

Climate change is predicted to increase atmospheric vapor pressure deficit, exacerbating soil drought, and thus enhancing tree evaporative demand and mortality. Yet, few studies have addressed the longer-term drought acclimation strategy of trees, particularly the importance of morphological versus hydraulic plasticity. Using a long-term (20 years) irrigation experiment in a natural forest, we investigated the acclimation of Scots pine (Pinus sylvestris) morpho-anatomical traits (stomatal anatomy and crown density) and hydraulic traits (leaf water potential, vulnerability to cavitation (Ψ50), specific hydraulic conductivity (Ks), and tree water deficit) to prolonged changes in soil moisture. We found that low water availability reduced twig water potential and increased tree water deficit during the growing season. Still, the trees showed limited adjustments in most branch-level hydraulic traits (Ψ50 and Ks) and needle anatomy. In contrast, trees acclimated to prolonged irrigation by increasing their crown density and hence the canopy water demand. This study demonstrates that despite substantial canopy adjustments, P. sylvestris may be vulnerable to extreme droughts because of limited adjustment potential in its hydraulic system. While sparser canopies reduce water demand, such shifts take decades to occur under chronic water deficits and might not mitigate short-term extreme drought events.

Predicting Individual Tree Mortality of Larix gmelinii var. Principis-rupprechtii in Temperate Forests Using Machine Learning Methods

Accurate prediction of individual tree mortality is essential for informed decision making in forestry. In this study, we proposed machine learning models to forecast individual tree mortality within the temperate Larix gmelinii var. principis-rupprechtii forests in Northern China. Eight distinct machine learning techniques including random forest, logistic regression, artificial neural network, generalized additive model, support vector machine, gradient boosting machine, k-nearest neighbors, and naive Bayes models were employed, to construct an ensemble learning model based on comprehensive dataset from this specific ecosystem. The random forest model emerged as the most accurate, demonstrating 92.9% accuracy and 92.8% sensitivity, making it the best model among those tested. We identified key variables impacting tree mortality, and the results showed that a basal area larger than the target trees (BAL), a diameter at 130 cm (DBH), a basal area (BA), an elevation, a slope, NH4-N, soil moisture, crown density, and the soil’s available phosphorus are important variables in the Larix Principis-rupprechtii individual mortality model. The variable importance calculation results showed that BAL is the most important variable with an importance value of 1.0 in a random forest individual tree mortality model. By analyzing the complex relationships of individual tree factors, stand factors, environmental, and soil factors, our model aids in decision making for temperate Larix gmelinii var. principis-rupprechtii forest conservation.

Zhodnocení růstu proveniencí borovice černé (Pinus nigra Arnold) ve středních Čechách ve věku 51 let

The paper contains the evaluation of 9 European black pine provenances on the research trial No. 41 – Roblín (Czech Republic) at the age of 51 years. The tested provenances originated from Bosnia and Herzegovina, Serbia, Croatia, Bulgaria, Greece, France and Slovakia. The evaluation is based on an assessment of following factors: total height, dbh, stem form, health state, defoliation, diameter of main branches, stem damage, crown density, twisted growth, stem nature pruning, cross-section, and taper. Obtained data sets were statistically analysed. The best growth both in quantitative and qualitative traits was recognised for the provenance 9 – Les Barres, Oise (France), which was excellent in height growth, health status and stem shape, and also for the Serbian provenance 2 – Crni Vrh, Titovo Užice, which excelled not only in growth rate, stem shape and health status, but also in diameter of main branches. Less suitable provenance seem to be 3 – Prušačka Rjeka, Banja Luka from Bosnia and Herzegovina (worse growth, stem nature pruning, cross-section and taper) and Croatian 5 – Južna Kandija, Osijek (worse growth, stem shape, and health). The most important for explaining the differences between provenances were the diameter at the breast height, crown density and defoliation.

Dynamics of the forest cover of raised bogs in southern taiga: Zapadnaya Dvina forest peatland station as an example (Tver region)

The article is devoted to testing the hypothesis about the current increase in the forest cover of raised bogs in the forest zone, depending on changes in climatic conditions. To analyze the dynamics of forest cover using the example of raised bogs in the Tver region, a methodology was developed based on the use of different generations of Landsat satellite data. The methodology was tested on ground data and applied to analyze changes in forest cover between 1976 and 2022 in several raised bog areas, including undisturbed areas and areas drained for forestry. The study tested approximately 20 vegetation indices across a variety of surveys, including summer and winter (snow) conditions. The classification results were verified using planting taxation data on circular plots and assessed using error matrices. Moderate-resolution Landsat satellite imagery, especially winter imagery, has been found to be suitable for long-term analysis of afforestation dynamics in raised bogs. An optimal vegetation index SWVI (Short wave vegetation index) and classification technique are proposed. The results of the study showed that afforestation increases in all undrained areas of swamps, regardless of their initial state. The smallest changes are observed for areas with low crown density (0-0.1), to a greater extent for density 0.2-0.3, and the greatest changes occur in areas with high crown density (0.6-0.7 and higher).

Impact of Climate Change on Livelihood of Poor and Marginalized Communities in Sudur Pashchim Nepal

The present world has brought out considerable attention on the issues of climate change and more so in the regions like Nepal where impacts are being directly and indirectly faced by people living below the poverty line. This study has been carried in Kailali and Doti districts of Sudur Pashchim Province of Nepal to explore the impact of climate change on livelihood of poor and marginalized communities using mixed-method approach which combined quantitative and qualitative methods of research. Based on household survey, focus group discussion, interaction with the key informants and local authorities, poor and marginalized communities in both Doti and Kailali districts noticed that climate is changing, although slowly, and that people have been experiencing several impacts of climate change. The result indicated that out of total respondents 45.1 percent has been reported declining yields of major crops, 25.3 percent has been shared in changes in cropping pattern, 58.5 percent has been informed the increased in pest and disease infestation, 69.8 percent has been reported decreased in availability of water in nearby streams and rivers in recent years in all study sites compared to the past situation. Similarly, 53.1 percent has been shared the deceasing on movement of wild habitats, 73.5 percent has been informed in loss of dependent wild species and 46.4 percent of respondents has been expressed the decreasing of crown density in forests and grasslands coverage. This study helps policymakers and other stack holders to develop suitable policies and effective adaptation strategies to enhance the livelihoods within this context of local marginalized communities’ context.

Classification of crown density and foliage transparency scale for broadleaf tree using VGG-16

Crown density and foliage transparency are important parameters for tree crown conditions. Previously, observers carried out crown density and foliage transparency assessments manually, which could be a less efficient process.This research aims to use the VGG-16 deep learning architecture to classify the density and transparency of broadleaves tree crowns. In this study, broadleaves tree crown datasets were collected for four types of broadleaves tree: cacao (theobroma cacao), durian (durio zibethinus), rubber (havea brasiliensis), candlenut (aleurites moluccana); then the data is labeled based on the crown density and foliage transparency scale card. The research applies resize and augmentation preprocessing. The model training process uses a scenario of 80% train data, 10% test data, and 10% validation data. After training using the VGG-16 model, the test results showed impressive accuracy, with the highest accuracy reaching 98.40% for candlenut trees, rubber (96.00%), cacao (92.00%), and durian (86.60%). This research shows quite good results in classifying the scale of crown density and foliage transparency with four types of broadleaves tree (cacao, durian, rubber and candlenut) using VGG-16.

Crown Density Relationship to Microclimate and Comfort Index in Telaga Sari City Forest, Balikpapan, East Kalimantan, Indonesia

The development of urban areas that are increasingly developing causes the area of green open space to decrease, and urban buildings that are increasingly denser result in an increase in local temperatures in the city. The presence of vegetation will provide cooler air. Furthermore, the urban microclimate is an interesting thing to correlate with the presence of vegetation somewhere. The Telaga Sari City Forest is one of Balikpapan’s green open space places. This research analyz the relationship between crown density microclimate and comfort index. The method for the percentage of crown density used Habitapp App. To measure the microclimate used Thermohygrometer, and Lux Meter. From the research results, it is known that, at the Telaga Sari City Forest there is a strong relationship between crown density and microclimate, namely light intensity, temperature, humidity, and the comfort index/THI, successively the multiple R values of 0.87, 0.83, 0.78 and 0.81. The linear equations namely y = -23.677x + 1986, y = -0.0317x + 31.096, y = 0.119x + 65.891 and y = -0.0232x + 29.007. In order to create a favorable microclimate in the city and provide green open space, it required to plant trees with high canopy density values.

The contributions of stand characteristics on carbon sequestration potential are triple that of climate variables for Larix spp. plantations in northeast China

Planted forests play a crucial role in addressing global climate change and are also valued globally for their numerous ecosystem services. Therefore, it is essential to understand how biotic and abiotic factors affect the carbon sequestration potential. This study focuses on quantifying the effects of 26 different variables on the carbon sequestration potential of Larix spp. plantations in northeast China, utilizing the random forest algorithm (RF). To eliminate the age-related tendency of stand carbon stock, a novel carbon sequestration index (CSI) was defined, which measures the ratio of actual to predicted stand carbon stocks for a stand at a specific age. The results indicated that the developed RF model explained approximately 64.75 % of the variations of CSI. Among the four categories of variables analyzed, stand variables (35.73 %) contributed significantly more than terrain variables (3.31 %), soil variables (3.68 %), and climate variables (9.06 %). The partial dependence analysis revealed that the Larix spp. plantations had a potential maximum carbon stock of approximately 73.34 t·ha−1. This potential was associated with certain attributes, including a stand mean diameter of 15 cm, a stand density of 1700 trees·ha−1, a stand basal area of 30 m2·ha−1, and a crown density of 0.7, respectively. These findings provide insightful information for plantation management to improve stand carbon stocks in northeast China with attempting to mitigate climate change.

Using high-resolution images to analyze the importance of crown size and competition for the growth of tropical trees

The influence of canopy structure on tropical tree growth has been scantly studied because of the difficulties making field measurements in these dense multi-layered ecosystems. The recent advent of unmanned aerial vehicles (UAVs), has made it easier to collect canopy data, so offering a way to gain a better understanding of forest productivity and thereby improve forest management. In this study, we assessed tree growth prediction using UAV-derived crown measurements as an alternative for field data.Four experimental 9 ha plots were sampled in two forest sites, Yoko in the Democratic Republic of the Congo and Loundoungou in the Republic of Congo. Field inventories were made between 2015 and 2020. For each tree, we computed the diameter increment (DBHI) using censuses and diameter-based competition indices (diameter-based CIs) using the first census. High-resolution orthoimages and digital surface models were acquired with UAVs in 2016 and 2018 in the two sites. They gave estimates of crown characteristics (size, relative elevation, shape) and crown-based competition indices (crown-based CIs). Co-recorded UAV and field measurements were obtained for 1558 trees. The diameter increment of these trees was then modelled using supervised component generalized linear regression, and 20 % of trees were kept for cross-validation.Combined field and UAV data predicted tree DBHI twice better than either taken separately. Diameter at breast height (DBH) and crown area (CA) were found to be complementary predictors. Crown-based CIs significantly improved predictions of models already containing DBH and CA. Adding diameter-based CIs to models containing DBH, CA, and crown-based CIs only marginally improved growth predictions, showing that tree competition can be well-described with UAV data. The model calibrated at one site predicted the growth at the other site well, suggesting that a general model could be devised for multiple sites. Growth variance was better explained in the site (Yoko) where the crown density was higher and the crown smaller. Further data are now needed from multiple sites with ranging stand structures and compositions to build a general model.

Species and Structural Diversity of Coniferous-Broadleaved Mixed Forests of the Pshekha River, North-Western Caucasus

The article presents the species and structural diversity of coniferous-broadleaved forests of the Pshekha river – the largest tributary of the Kuban river. All in all, 7 main types of forest were identified and described: fir-beech fern-forb forest, fir-hornbeam blackberry-forb forest, aspen-hornbeam honeysuckle-small-herb forest, hornbeam-black alder nitrophilic-tall-herb forest, hornbeam-oak rhododendron-blackberry forest, hornbeam-beech blackberry-forb forest and hornbeam honeysuckle-blackberry forest. The composition of the forest flora of the Pshekha river includes 270 species, including 221 species of vascular plants and 49 species of mosses. Floristic diversity of coniferous-broadleaved forests of the Pshekha river is inversely related to the crown density of the stand and the proportion of oriental beech (Fagus orientalis). The highest species saturation was noted in the hornbeam-black alder nitrophilic-high-grass forest type, the lowest – in the fir-beech fern-forb type. Ecological and coenotic structure of coniferous-broadleaved forest types of the Pshekha river depends on the height above sea level, which determines the indicators of the average annual precipitation and average annual temperature – with a decrease in altitude above sea level. m. the proportion of boreal species in the composition of the flora decreases, while the proportion of nemoral and meadow-edge species increases. In the coniferous-broadleaved forests of the river. Pshekha, 11 species of earthworms were identified, belonging to four morpho-ecological groups: epigeic, epi-endogeic and endogeic, as well as anecic. In forests with a high quality of litter (hornbeam-dominated), the greatest contribution to the biomass is made by epigeic (up to 41%) and endogeic (up to 45%) groups of earthworms. In forests with low quality litter (fir- and beech-dominated), the share of endogeic earthworms is high (up to 66%). Anecic worms are mostly confined to forests on moist soils. In all types of forests, Crimean-Caucasian endemics make a large contribution to biomass and abundance: polymorphic species D. schmidti, anecic D. mariupolienis, as well as Mediterranean species: litter D. attemsi (often inhabiting oak and hornbeam deadwood) and proper soil A. jassyensis. Cosmopolitan species (D. r. tenuis, D. octaedra) are numerous among the epigeic worms and often inhabit deadwood of coniferous tree species.