Implementing inventory in remote and hard-to-reach forests is rather challenging. The study aims to develop methods for identifying qualitative and quantitative characteristics of mixed forests using Sentinel-1 imagery. Research area covered the taiga zone of Russian plane. Various forest ecosystems (in term of species composition, age, growing class, forest type etc.) were examined.

Three options of radar satellite images were analyzed: images processed with incoherent accumulation (Multilooking) and Frost filter in SNAP software, and original images without processing. To determine the relationship between forest parameters (standing volume, forest density, age, number of trees) and radar survey indicators statistical methods of multiple regression were applied. Data processing was implemented using the Scikit-learn machine learning library in Python programming environment.

Determining forest characteristics applying various pre-processing methods showed similar efficiency (R=0.7-0.8). However, the highest correlations (up to 0.86) are obtained with Multilooking procedure. Imagery processing with no filter and using BFGS neural networks exhibited the possibility of determining dominant species with a correlation coefficient of 0.6 and higher. The most accurate determination of the standing volume and forest density was acquired using multiple factor regression models.

We revealed relationships between standing volume, forest density, age, and number of trees and following radar indicators: SRCS, GammaVV, GammaVH, GLCMMean, GLCMVariance, GLCMCCorrelation, GammaVH - GammaVV (Diff), and GammaVH + GammaVV (Sum). The results were compared with forest inventory materials for a part of the study area. For most stands there is a similarity in standing volume and forest density definition. The study results demonstrate that it is possible to identify quantitative and qualitative forests characteristics using radar survey data.

The term circular bioeconomy refers to the situation in which non-renewable fossil resources are being replaced by renewables and other naturally generated resources, which is now encouraging economic growth and development in order to become more sustainable. The importance of bio-based materials grows by the day due to their positive life cycle assessment (LCA) and carbon footprint. Unlike fossil-based raw materials, lignocelluloses derived from wood and forests are renewable raw materials. Lignocellulosic materials are valuable feedstock for the transition from a petroleum-based economy, also known as black gold, to a green gold economy. There has been a lot of interest in using lignin as a bio-based alternative to fossil-based products. Lignin is the second most abundant organic polymer in nature and is playing an increasingly leading role in the forest-based bioeconomy. Each year, the pulp and paper industry produces around 50 million tons of lignin, only 2% of which is used commercially for added-value applications, while the remaining 98% is directly burned to generate energy in the pulping and biorefinery industries. It is significant to unlock the potential of lignin and utilize it in sustainable products. LCA analyses indicated that lignin-based products generally outperform fossil-based products in terms of environmental performance, especially when it comes to climate change. This review focused on the potential, valorization, and role of lignin in the bioeconomy as well as LCA analyses.

Wildfire is a critical environmental disturbance affecting forest dynamics, succession, and the carbon cycle in Siberian forests. In recent decades forests of southern and central Siberia experienced an increase in fire-disturbed area. The main goal of this study was to assess the degree of fire disturbance in the southern regions of central Siberia, as well as the dynamics of post-fire changes for fires of different intensity. Remote sensing data from MODIS and VIIRS sensors were used to estimate burned area, fire radiative power (FRP) and post-fire dynamics using Normalized Burn Ratio (NBR) and Normalized Difference Index Vegetation (NDVI). Mean annual burned area between 2001 and 2021 in the region was about 950 thousand ha per year with the largest burned areas observed in mixed and larch-dominant forests. Fires detected in the dark-needle coniferous (DNC) and larch-dominant forests were found to have higher (by about 25%) fire radiative power comparing to fires in pine-dominant and mixed forests. The analysis of FRP together with NBR showed a significant correlation (R = 0.68; p < 0.05) between these variables, indicating that fires with higher intensity generally result in higher degree of fire disturbance. Evaluation of the post-fire dynamics showed that NBR is more sensitive to fire-related disturbances comparing to NDVI and requires up to 20 years to return to pre-fire values. At the same time, the recovery of the NDVI to background values took about 7–10 years after the fire.

The study was supported by the Russian Science Foundation and the Government of Republic of Khakassia (grant #22-17-20012, https://rscf.ru/en/project/22-17-20012/).

In the practice of forest resource field sample surveys, tree species is an essential survey factor. With the continuous development of LiDAR technology, the use of ground-based LiDAR systems for sample plot scanning can quickly and accurately obtain the 3D structural parameters of forest trees. The accurate identification of tree species information from individual tree laser point clouds is a key focus and difficulty of current research. Traditional machine learning methods require manual extraction of a large amount of 3D structural information for modeling, and the accuracy of recognition is not high. In the field of computer vision, a breakthrough has been made in the classification of 3D object shapes using point cloud deep learning techniques, which provides a new practical direction for tree species classification. To explore the effectiveness of point cloud deep learning in classifying individual tree point cloud species, we use three point-by-point point cloud-based deep learning methods (PointNet, PointNet++, PointMLP) to identify individual tree point clouds of seven types of tree species. We downsampled the number of points in each individual tree point cloud to 1024 and 2048 using the farthest point sampling. We have achieved very exciting experimental results. The experiments using 2048 points involved can obtain higher classification accuracy. PointMLP, the current optimal pointwise MLP-based method, PointNet++ achieved the highest classification accuracy (0.90) in tree species classification. The tree species classification accuracies for the experiments using PointMLP and PointNet methods were 0.80 and 0.40, respectively. Our study illustrates that tree species information can be well identified from individual tree point clouds using pointwise MLP-based deep learning methods. As the current pointwise MLP-based approach of SOTA, PointMLP does not obtain the highest classification accuracy. We are analyzing the reasons for this phenomenon.

The monitoring of burned areas by forest fires is essential to know their dynamics and the use of orbital data and remote sensing techniques are fundamental in this process. The Global Ecosystem Dynamics Investigation (GEDI) program produces high-resolution laser observations of the earth, measuring vertical structure and canopy height, in addition to surface elevation, which are key data for understanding ecosystem services such as the carbon cycle. In the present study, we analyzed the vegetation structure of four areas affected by forest fires in the summer of 2020 in northern Portugal and Galicia (Spain). We used the Google Earth Engine platform to analyze satellite imagery, digital elevation model and GEDI data to measure vegetation height before and after fires. Our results indicated that before the fires the height varied from 5.21 to 20.16 meters, and the training and validation data obtained, respectively, r² values of 0.82 and 0.67. After the fires, heights of 5.55 to 9.12 meters were recorded, with values of r² 0.47 and 1 in the training and validation data. These r² values after fires indicate the absence or limitation of sample data. The training data recorded an RMSE value of 3.47 before and 3.36 after the fires. The validation data recorded an RMSE value of 5.23 before and 3.34 after the fires. The most important variables for the measurement were identified through the Random Forest algorithm and training and validation data, they are: VV_iqr, elevation and B8 of the GEDI, after the fires the most important variables were the bands B2, B3 and B11. We conclude that the GEDI data has great potential to assist in the mapping of areas affected by forest fires, with the potential to measure the height of vegetation and contribute to the monitoring of areas affected by fires.

Leaf area is related to tree growth, water balance and mechanical resistance to physical and biotic agents. Currently there are no studies of its estimation focused on studying its importance in forest species in Peru. Hence, the purpose of the present study was to compare two non-destructive methods of leaf area estimation: ImageJ free software vs. a manual method using graph paper, in seedlings of the Peruvian endemic species “Quina”. Three young leaves and three mature leaves were evaluated on 18 seedlings of Cinchona, for a total of 108 data points. The leaves were identified, coded and placed on an A4 sheet of paper with a millimetric scale, photographed at 30 cm perpendicularly and evaluated using ImageJ software. In the same way, the measurement was carried out on sheets of graph paper, delimiting the leaf lamina and calculating the dimensions considered. Descriptive statistics were obtained for both methods. They were compared using the non-parametric Kruskall Wallis test, and a simple linear and quadratic regression equation were estimated based on the parameters of width and length. We obtained a greater adjustment in the quadratic one.

The beginnings of the petrol chainsaw in forestry date back to the early 20th century. For more than a century, engineers have been refining the chainsaw to make it as efficient and comfortable as possible for woodcutters. In recent years, environmental protection and reduction of CO₂ emissions policies have been particularly prominent. As a consequence, the use of battery-powered electric tools, including chainsaws, has become increasingly widespread, especially in gardening. However, electric chainsaws have limited battery capacity and, therefore, are not used daily in forestry. This study aimed to determine the efficiency of a battery-powered chainsaw during commercial thinning.

The research compared the work efficiency of the petrol chainsaw Dolmar PS 5000 and the battery-powered Echo ECCS-58V during commercial-thinning in a 14 years old pine stand. In seven repeats the following variables were measured each time: working time, working area and noise load to which the logger was exposed. Obtained results were used to calculate average productivity, a weighted equivalent continuous sound pressure level (L_Aeq) and a weighted noise exposure level normalized to a nominal 8h working day (L_EX,8h).

The average operating length of the battery-powered chainsaw was 00:41:26 and was comparable to the working length of a petrol chainsaw for which the average working time was 00:41:41. The average work output of the petrol chainsaw was 100 m²/h higher. The recorded noise exposure L_Aeq and L_EX,8h were lower for the battery-powered chainsaw.

Using a battery-powered chainsaw was less workload, because of smaller noise levels and zero emissions. This study found that 6 fully charged batteries allowed the user to effectively complete a work shift. It can be concluded that battery-powered chainsaws can be used effectively during commercial thinning. Further tests should be run in winter to determine the effect of low temperatures on battery consumption.

High water consumption, together with the use of synthetic dyes and metallic mordant agents, contribute to the high environmental impact of the textile industry. Numerous investigations have focused on the search for more sustainable raw materials and processes for this sector. One of the most promising solutions is to look towards forest by-products as a sustainable source of fibrous raw materials to substitute plastic fibres and replace partially cotton. In addition, forest by-products could also be a good source of natural dyes and textile additives, replacing synthetic ones. The main by-product generated in the forestry industry is bark, derived from the debarking process. Pinus and eucalyptus are nowadays two of the most important tree species exploited by the forestry industry in southwestern Europe. This work investigates the application of Maritime pine (Pinus pinaster, Ait.) and the Eucalyptus (Eucalyptus Globulus) barks as a source of high polyphenolic content extracts, to be used as natural dyes in the textile industry. Extraction was performed with water in alkali conditions. The influence of the extraction conditions to obtain the extracts used as natural colorant and the dyeing conditions (pH, time, temperature, use of mordant) on the properties of the dyed textile sample was evaluated. It was shown that the pH and the extraction conditions used were the variables with the greatest influence on the final properties of the dyed textile. In addition, this work also demonstrated that it is possible to use the extracts obtained from both forest by-products as textile dyes without need for using any metallic mordant.

Bursaphelenchus xylophilus, commonly known as pinewood nematode (PWN), is considered one of the greatest threats to pine forest ecosystems. The most recent invasion of this phytoparasite occurred in Europe, in Portugal. Pest management strategies based on chemical nematicides are highly effective but can lead to negative ecological impacts and human health concerns. Research on sustainable alternatives is now a priority. The use of essential oils (EOs) as nematicides has gained renewed interest due to the advantages of being easily obtained, biodegradable and showing low toxicity to mammals. The present work aimed at screening the activity of four EOs against the PWN and analyzing possible synergistic interactions, in combinations of two EOs, towards anti-PWN activity. The EOs of Cymbopogon citratus, Eucalyptus globulus, Mentha piperita and Satureja montana were acquired from commercial sources and analyzed with Gas Chromatography coupled to Mass Spectrometry (GC-MS). Anti-PWN activity was screened by determining in vitro nematode mortality at several concentrations of EOs, or EO mixtures, per mL of PWN suspension. The combination of C. citratus and M. piperita EOs resulted in higher activities than those obtained for each one tested solely, suggesting the occurrence of synergistic interactions between the compounds of these EOs. Research on the combination of synergistic EOs may lead the development of plant based biopesticides with optimized activities against the PWN.

The evaluation of benefits provided by the natural environment in the form of a quantification of natural capital is an effort that has intensified in recent years and links natural ecosystems with human evolution and society’s well-being. Ecosystem scientists and especially the foresters can study the management practices adopted so far and evaluate the policies implemented at various levels of administration (local, regional, national), involving issues of sustainability. False-alpine grasslands also known as summer grasslands or rangelands are mainly associated with transhumance. In the past, transhumance and graze were organized on a mainly family basis and there existed an informal management system for grazing, which was respected by all livestock farmers who used the summer pastures. Nomadic animal husbandry has disappeared, and with it a sense of respect for nature, the rangelands and, more generally, the environment. The aim of this paper is to assess the grazing capacity of rangelands in the Pisoderion Forest which is located at the region of Florina in Greece, under various specifications introduced by Forest Management Plans and relatively recent legislation. The grazing capacity that is theoretically expected following the specifications of previous Forest Management Plans is compared to grazing capacity according to the specifications introduced by relatively recent legislation. The conclusion that can be drawn is that the rangelands are underused and with an appropriate holistic management approach, such as the traditional system of dividing the forest grasslands into yards, the livestock capital can be doubled in these rangelands.