Variable retention harvesting (VRH) was designed for timber purposes and biodiversity conservation in natural forests. This system was globally tested, but few studies are related to soil microbial components. The objective was to evaluate different retention types (aggregated and dispersed retention) considering different years-after-harvesting (6, 9, 16 YAH) on soil microbial community attributes compared with unmanaged primary forests (PF) in Nothofagus pumilio forests of Tierra del Fuego (Argentina). We also evaluated the influence of climate, soil and understory vegetation. Results showed that aggregated retention increased microbial biomass carbon (MBC), microbial biomass nitrogen (MBN) and soil basal respiration (SBR) compared to dispersed retention, but with similar values than PF. However, harvested areas decreased MBC:MBN values compared with PF. We found an overall decreasing of microbial biomass and activity in 9 YAH stands, with a positive recovery at 16 YAH. Soil pH, mean annual temperature and understory vegetation cover showed a positive relationship with MBC, MBN, and SBR. The recovery after 16 YAH reaching to a different microbial community. Therefore, maintenance of retention components in managed stands for longer periods is needed. Our results highlight some advantages of VRH as a tool for conservation of forest-dwelling soil microorganisms (microbial biomass and activity).

The evaluation of logging in a forest management area using only the volume variable is not sufficient to assess the effectiveness of the harvest. The objective was to evaluate harvested volume using different logging variables to point out possible divergences between what was planned and what was actually carried out, in order to indicate adjustments to next annual operating plans (AOPs). The database comes from a community forest management area in the Tapajós National Forest, considering three sources: forest inventory, AOP planning and the post-exploration report. The yield considering the number of trees felled and the effective volume were compared with the AOP planning. The volume estimated by the generic equation was compared with the cubed volume and the commercial height variable was evaluated. Of the 6,267 trees selected, 5,090 trees were harvested (3.2 trees ha^-1), indicating a yield of 85% of the estimated volume. Of the 25 commercial species logged, the most representative were: Manilkara huberi, Vochysia maxima, Lecythis lurida, Couratari guianensis and Hymenaea courbaril. It can be concluded that the number of trees, cubed volume and commercial height are post-exploration yield response variables and not just volume, as is currently used in Sustainable Forest Management Plans (SFMPs).

The advancement of mechanization in forestry has increased productivity in the forestry sector, bringing positive and negative impacts that require a deeper understanding for sustainable forest management. This study aimed to apply a simplified instrument for assessing damage and environmental impacts in forest harvesting of commercial eucalyptus plantations, using a combination of methodologies. The methodology used combined interaction networks and impact assessment matrices, carrying out field surveys, transposing them to interaction networks and weighting them through assessment matrices, resulting in environmental indices (ES) for prioritizing actions. The study was conducted on a commercial eucalyptus plantation in the municipality of São Pedro, São Paulo, Brazil. The mechanized harvesting of the area consists of the structure of a module with a mobile unit consisting of a harvester and forwarder. The results indicated that wood transport presented the highest ES, both positive and negative. The most significant negative impacts (ES) were the depletion of water resources and erosion, while the positive impacts included regional development and job creation. The most notable changes, positive and negative, were observed in the physical and anthropic environment, with a lesser impact on the biotic environment.

Polarimetric Synthetic Aperture Radar (PolSAR) images with dual polarization modes have great potential to map FSV by excellent penetration capabilities and distinct microwave scattering processes. However, the response of these SAR data to FSV is still uncertain at non-growing season. To further interpret the response of forest FSV to these dual-polarization SAR images, three types of dual polarization SAR images (GF-3, Sentinel-1, and ALOS-2) were initially acquired in coniferous planted forest at non-growing season. Then, sensitivity between FSV and all alternative features extracted from each type of SAR images were analyzed to express the difference of bands and polarization modes in deciduous (Larch) and evergreen (Chinese pine) forests. Finally, mapped FSVs using single and combined dual polarization images were derived by optimal feature sets and four machine learning models, respectively. The combined effects were also analyzed to clarify the response of FSV to dual polarization SAR images with bands and polarization modes at non-growing season. The results demonstrated that the difference of backscattering energy from different sensors is significant in Chinese pine forests, and the difference is gradually reduced in Larch forests. it is also implied that polarization mode is more important than penetration capability in mapping forest FSV in deciduous forest at non-growing season. By comparing the accuracy of mapped FSV using single and combined images, combined images have more capability to improve the accuracy and reliability of mapped FSV. Meanwhile, it is also confirmed that compensation effect with bands and polarization modes not only have great potential to delay the saturation phenomenon, but also have capability to reduce errors caused by over-estimation.

Visual data on the geographic distribution of carbon storage helps policy maker to formulate countermeasures on global warming. However, Taiwan, as an island showing diversity in climate and topography, had lacked valid visual data in distribution of forest carbon storage between the last to forest surveys (1993-2015). This study established a model to achieve an estimation that capable to demonstrate the distribution of forest carbon storage. This model uses land use, stand morphology, carbon conversion coefficient databases accordingly for 51 types of major forest in Taiwan. An estimation on 2006 was conducted and shows an overall carbon storage of 165.65 Mt C, with forest carbon storage per unit area of 71.56 t C ha–1, where natural forests and plantations respectively contributed of 114.15 Mt C (68.9%) and 51.50 Mt C (31.1%). By assuming no change in land use type, the carbon sequestration from 2006 to 2007 by the 51 forest types was estimated to be 5.21 Mt C yr–1 using historical tree growth and mortality rates. The result reflects the reality of the land use status and event of coverage shifting with time by combining to the two forest surveys in Taiwan.

Growth models of uneven-aged forests on the diameter class level can support silvicultural decision making. Machine learning brings added value to the modeling of dynamics at the stand or individual tree level based on data from permanent plots. The objective of this study is to explore the potential of machine learning for modeling growth dynamics in uneven-aged forests at the diameter class level based on inventory data from practice. Two main modeling approaches are conducted and compared: i) fine-tuned linear models differentiated per diameter class, ii) an artificial neural network (multilayer perceptron) trained on all diameter classes. The models are trained on the inventory data of the Canton of Neuchâtel (Switzerland), which are area-wide data without individual tree-level growth monitoring. Both approaches produce convincing results for predicting future diameter distributions. The linear models perform better at the individual diameter class level with test R2 typically between 50 and 70% for predicting increments in the numbers of stems at the diameter class level. From a methodological perspective, the multilayer perceptron implementation is much simpler than the fine-tuning of linear models. The linear models developed in this study achieve sufficient performance for practical decision support.

This paper compares the Medieval (ca. 400–1500 CE) dynamics of forests from low-mountain (Montcortès; ca. 1000 m a.s.l.) and high-mountain (Sant Maurici; 1900 m a.s.l.) areas of the Iberian Pyrenees, both of which experienced similar climatic forcing but different anthropogenic pressures. The main aim is to identify forest changes over time and associate them with the corresponding climatic and anthropogenic drivers (or synergies among them) to test how different forests at different elevations respond to external forcings. This could be useful to evaluate the hypothesis of general Pyrenean deforestation during the Middle Ages leading to present-day landscapes and to improve the background for forest conservation. The study uses palynological analysis of lake sediments, historical documents and paleoecological reconstructions based on pollen-independent proxies. The two sites studied showed different forest trajectories. The Montcortès area was subjected to intense human pressure during regional deforestation up to a maximum of ca. 1000 CE. Further forest recovery took place until the end of the Middle Ages due to a change in forest management, including the abandonment of slash-and-burn practices. Climatic shifts indirectly influenced forest trends by regulating human migrations and the resulting shifts in the type and intensity of forest exploitation. The highland Sant Maurici forests exhibited a remarkably long-standing constancy and an exceptional resilience to climatic shifts, which were unable to affect forest extension and composition, and to local human pressure, from which they rapidly recovered. The Montcortès and Sant Maurici records did not follow the rule of an irreversible forest clearing during the Middle Ages leading to present-day landscapes. The present Montcortès landscape was shaped after a Medieval forest recovery, a new Modern-Age deforestation and a further forest recovery during the last centuries. The Sant Maurici forests remained apparently untouched since the Bronze Age and were never cleared during the Middle Ages. The relevance of these findings for forest conservation is briefly addressed, and the need for the development of more high-resolution studies on Pyrenean forest dynamics is highlighted.

Forest education is pivotal for achieving the Sustainable Development Goals and promoting sustainable forest management in the face of global challenges. However, existing programs struggle to keep up with rapidly changing crises and uncertainties that contribute to deforestation and forest degradation. To address these challenges, integrating innovative approaches into forest education is crucial. This paper demonstrates the transformative use of a role-playing game as an innovative teaching method for integrated forest management. The game provides students with practical experience and a comprehensive understanding of landscape approaches, using the Congo Basin as a case study. Our research focuses on sustainable forest governance, stakeholder roles, and valuing natural assets and ecosystem services, supporting the transformation of economic, political, and social/cultural relationships at various scales. We highlight the potential of innovative forest education to foster sustainability, trigger critical thinking, resolve conflicts, and prevent costly forest losses.

As time progresses and technology improves, biological data sets are continuously increasing in size. New methods and new implementations of existing methods are needed to keep pace with this increase. In this paper, we present a high performance computing(HPC)-capable implementation of Iterative Random Forest (iRF). This new implementation enables the explainable-AI eQTL analysis of SNP sets with over a million SNPs. Using this implementation we also present a new method, iRF Leave One Out Prediction (iRF-LOOP), for the creation of Predictive Expression Networks on the order of 40,000 genes or more. We compare the new implementation of iRF with the previous R version and analyze its time to completion on two of the world's fastest supercomputers Summit and Titan. We also show iRF-LOOP's ability to capture biologically significant results when creating Predictive Expression Networks. This new implementation of iRF will enable the analysis of biological data sets at scales that were previously not possible.

Prescribed burning and other active forest management treatments have been proven to be essential for maintaining suitable habitat conditions for many wildlife species, including the federally endangered red-cockaded woodpecker (RCW). This study examines the perception of forest management treatments of recreation users participating in various activities (hunting, hiking/backpacking, camping, off-highway vehicle riding, and canoeing/kayaking) in terms of scenic beauty and recreation satisfaction. We used photographic images to capture various forest management treatments of different intensity levels and times after treatments, and assessed users’ perception of scenic beauty and recreation satisfaction. Results indicated variation among users participating in different recreation activities, but that good quality RCW habitat offered both higher scenic beauty and higher recreation satisfaction than poor quality habitat for most user groups. Finally, recreation satisfaction was statistically equal to perceived scenic beauty from both good and poor-quality RCW habitats for most of the user groups, thus suggesting the importance of scenic beauty on forest sites in determining recreation users’ attainment of visit satisfaction. Findings conclude that forest sites developed as good quality RCW habitat in the present state also offer quality experience to recreation users, thus supporting multi-objective forestry practices in public forests.

Forest community connections are crucial to ensure forest stewardship and sustainability. We explored the potential of mushrooming to enable such connections in contexts where these connections have been historically broken, alienating local people from forests. Taking the case of the recent devolution of a community forest (baldios) in central Portugal to the local population, we present a five-year pilot project to rework mycology from a mushroom-centered approach to a mushroom-in-baldios approach. Mushrooms were used as an entry-point to connect the forest ecology with the challenges of governance and community building. The devised activities provided an opportunity for people inside and outside the local community to adventure into the woods and find out more about their socio-ecological history, develop communal and convivial relationships and engage in the responsible gathering of wild mushrooms. However, the hosting of mushroomers to know, value and engage with the community forest recovery has constantly working against the enclosure of mushrooms to provide marketable forms of leisure. The outcome of these activities depends on the relationships established between mushrooms, mycologists, local administrators, commoners and poachers, all operating within a framework that favors the eradication of resources instead of long-term relationships that sustain places.

Logistic Regression and Random Forest are used to identify risk factors that influence traffic accident fatalities in the United Kingdom. The mean decrease accuracy was used to measure variable importance. The speed limit, police attendance and quarter had an increasing influence on accident fatalities. They had a mean decrease of 102.1669, 221.5322, and 120.894 respectively. The speed limit, had a parameter estimate of 0.0046902 and a standard deviation of 0.0004875. Light Conditions: Night had a parameter estimate of 1.2657635 and a standard deviation of 0.0118409. Road Type Round About had a parameter estimate of -0.4055796 and a standard deviation of 0.0210848. Police Attendance classified as Yes had a parameter of 0.8546232 and a standard deviation of 0.0151043. The best predictors were speed limit, police attendance and quarter since they had p values that were less than 0.05. The findings of the study indicated that logistic Regression had a higher accuracy rate 79.85% as compared to 64.00% for Random Forest. A split test was used and a standard deviation of 0.0010486 was obtained for the Logistic Regression model.

This research investigates the exposure of plant species to extreme drought events in the Brazilian Atlantic Forest, employing an extensive dataset collected from 205 automatic weather stations across the region. Meteorological indicators derived from hourly data, encompassing precipitation, maximum and minimum air temperature, were utilized to quantify past, current, and future drought conditions. The dataset, comprising 10,299,236 data points, spans a substantial temporal window and exhibits a modest percentage of missing data. Missing data were excluded from analysis, aligning with the decision to refrain from imputation methods due to potential bias. Drought quantification involved the computation of the Aridity Index, the analysis of consecutive hours without precipitation, and the classification of wet and dry days per month. Mann-Kendall trend analysis was applied to assess trends in evapotranspiration and maximum air temperature, considering their significance. The hazard assessment, incorporating environmental factors influencing tree growth dynamics, facilitated the ranking of meteorological indicators to identify regions most exposed to drought events. The results revealed consistent occurrences of extreme rainfall events, indicated by positive outliers in monthly precipitation values. However, significant trends were observed, including an increase in daily maximum temperature and consecutive hours without precipitation, coupled with a decrease in daily precipitation across the Brazilian Atlantic Forest. No significant correlation between vulnerability ranks and weather station latitudes and elevation were found, suggesting geographical location and elevation does not strongly influence observed dryness trends.

Depression is considered a widespread mental health problem worldwide. Moreover, anxiety symptoms are very closely related to depression patients, and it is known that the coexistence rate of depression and anxiety diagnosed simultaneously is high. Treatment and preventive management of depression and anxiety are essential for public health. Forest healing is attracting attention as a form of low-cost preventive medicine that is safe and has no side effects. However, although the physiological and psychological effects have been scientifically proven, it is insufficient to reveal a direct relationship between forest healing and depression. This study investigated the benefits of an urban forest healing program on depression and anxiety symptoms of depressive disorders. We employed a randomized controlled trial design. Forty-seven depression patients were randomly divided into an urban forest healing program group and a control group. Measures included the Montgomery-Asberg depression rating scale (MADRS), the Hamilton Anxiety Rating Scale (HARS), and the State-Trait Anxiety Inventory (STAI) questionnaires. Our results revealed that the combination of general treatment and forest healing programs for patients with depression is more effective in improving depression and anxiety than routine treatment alone. We expect our work to serve as a starting point for more sophisticated research in discussing the availability of non-pharmacological treatments in forest healing.

This study investigated the presence and characteristics of Juglans neotropica in three ecosystems in Southern Ecuador: Montane Evergreen Forest, Evergreen Seasonal Lower Montane Forest, and Semideciduous Foot Montane Forest. The main focus was on the species' multipurpose nature as both a fruit and timber source. Six study sites, totaling at least 0.5 hectares each, were established, with four in Loja province and two in Zamora Chinchipe province.The results showed significant differences in dendrometric variables across the sites, with the most favorable growth recorded in The Tundo, where trees exhibited an average diameter at breast height (DBH) of 45.16 cm, basal area (G) of 1.41 m2, total height (TH) of 19.22 m, canopy height (CH) of 13 m, cubic volume (CV) of 3.55 m3, and total volume (TV) of 5.22 m3. The species displayed a clumped distribution pattern, as indicated by a Morisita index greater than 1. Regarding abundance, the highest density of 297 trees per hectare was found in Argelia, while Victoria had the lowest density of 46 trees per hectare. This research highlights the importance of Juglans neotropica as a multipurpose species, providing both valuable fruits and wood resources in the studied ecosystems of Southern Ecuador.

The study was carried out in zone 7 of Ecuador, in ecosystems of the Southern Montane Evergreen Forest of the Eastern Cordillera of the Andes that goes from 2200 to 3000 masl. In the Evergreen Seasonal Lower Montane Forest of Catamayo-Alamor, which ranges from 1600-2000 meters above sea level and finally in the Semideciduous Foot Montane Forest of Catamayo-Alamor, which ranges from 400-1600 meters above sea level. The objective was to know the occurrence, structure and abundance of J. neotropica. The investigation consisted of collecting secondary information on the occurrence of the species, to later verify it in situ. Six sites with areas ≥ 0.5 hectares were identified, four in the province of Loja and two in the province of Zamora Chinchipe. Regarding the structure, it was determined that there are significant statistical differences as well as in the dasometric variables between one site and another, presenting better growths El Tundo with average values in DAP(cm)= 45.16; G(m2) = 1.41; HT(m)= 19.22; HC(m)= 13; VC(m3) = 3.55; VT(m3) = 5.22. Regarding the Morisita index, the species presents adistribution pattern >1. Regarding abundance, it was determined that Algeria presented a maximum of 297 (ind/ha), and La Victoria 46 (ind/ha) with a minimum.

In tropical Africa, forests and savannas are the two most widespread biomes and potentially represent alternative stable states with divergent species composition. A classic, but untested, hypothesis posited by White (1983) suggests that the transition zones between forests and savannas contain a floristically impoverished assemblage with few representatives from each biome. Further, the evolutionary dimension of diversity has received limited attention, despite its importance for understanding the biogeographic history of biomes. Here, we quantify species richness and several measures of evolutionary diversity in 1° grid cells, using c. 300K occurrence records of trees and shrubs combined with biome affiliation data for 3,125 species. We find that assemblages in transition zones hold fewer woody species than assemblages in forest and savanna zones, as posited by White. However, transition zones hold more phylogenetic diversity than expected given their species richness, whether one considers forest and savanna assemblages separately or together. We also show that the Congo basin forest has low levels of phylogenetic diversity given the number of species and highlight south-eastern African savannas as a centre of savanna woody species richness and phylogenetic diversity. Regions with high phylogenetic diversity given the number of both forest and savanna species were centred around the Dahomey Gap and Cameroon, mainly in transition zones. Overall, our study shows that even if floristically impoverished, transition zones lead to unexpectedly high evolutionary diversity, suggesting they are important centres of evolutionary innovation and diversification.

Ecological corridors function as a viable way to mitigate the environmental impact arising from forest fragmentation by interconnecting forest fragments through various techniques. In this context, the objective of this study is to propose a route for the implementation of an ecological corridor in the Itapemirim river watershed. The specific objectives were: (i) to delimit the permanent preservation areas (APP) of the Itapemirim river watershed and compare land use and land cover in the delimited areas; (ii) to calculate landscape ecology indices and select the forest fragments with the highest potential for ecological corridor implementation using Fuzzy logic; (iii) to assess costs and trace the best route for ecological corridor implementation, considering distance and physical impediments; (iv) to assess land costs and expropriation costs to delimit the ecological corridor in the study area. To map land use and land cover, the MapBiomas platform was used, based on Landsat 8 satellite images. The permanent preservation areas were delimited according to criteria established by Law No. 12.651 of May 25, 2012, which establishes parameters, definitions, and limits for APP. The characterization and structural quantification of some landscape ecology indices were performed using the QGIS 3.26 computational application, through the LecoS 3.0.1 plugin and Fragstats 4.2. The connected forest fragments were the Caparaó National Park, the Serra das Torres State Natural Monument, and the fragments selected through the application of Fuzzy logic to the landscape ecology indices. The corridor was delimited according to the lowest cost route, considering land use and land cover, APP, fragment potential, slope, and subnormal clusters. For each cost raster image, its respective statistical weights were calculated using the Analytic Hierarchy Process (AHP) hierarchical method, as well as for the analysis of priority areas for forest restoration, considering land use and land cover, APP, pedology, lithology, and biological importance. The interconnection between protected areas and fragments with the ecological corridor followed the orientation described by CONAMA nº 09/96. Based on the development of the work, the following results were identified: the highest land use and land cover class is pasture. Of the area designated for permanent preservation, 68.58% is in conflicting use with the legislation. The bare land value per hectare of the pasture class is the second highest among the bare land value per LULC values, representing 64.28% of the total. The priority area map showed that 31.86% of the area was classified as of very high or high importance and 42.97% as low or very low priority for forest restoration. Thus, it is concluded that the least cost path algorithm associated with the result generated by the multi-criteria decision method (AHP) constitutes an important tool for planning and implementing an ecological network by taking into account the primary factors for decision-making regarding the location of the best route.

Trees provide key ecosystem services, but the health and sustainability of these plants is under increasing biotic and abiotic threat, including from the growing incidences of non-native invasive plant pests (including pathogens). The island of Ireland (Ireland and Northern Ireland) is generally accepted to have a high plant health status, in part due to its island status and because of the national and international regulations aimed at protecting plant health. To establish a baseline of the current pest threats to tree health for the island of Ireland, the literature and unpublished sources were reviewed to produce a dataset of pests of trees on the island of Ireland. The dataset contains 396 records of pests of trees on the island of Ireland, the majority of pests being arthropods and fungi, and indicating potentially more than 44 non-native pest introductions. The reliability of many (378) of the records was judged to be high, therefore the dataset provides a robust assessment of the state of pests of trees recorded on the island of Ireland. We analyse this dataset and review the history of plant pest invasions, including (i) discussion on notable native and non-native pests of trees, (ii) pest interceptions at borders and (iii) pests and climate change. The dataset establishes an important baseline for the knowledge of plant pests on the island of Ireland, and will be a valuable resource for future plant health research and policy making.

The purpose of this study was to systematically review forest therapy programs designed to decrease the level of depression among adults and subsequently identify the gaps in the literature. This systematic review was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The authors independently screened full-text articles from various databases using the following criteria: 1) intervention studies assessing the effects of forest therapy on depression in adults aged 18 years and over; 2) studies including at least one control group or condition; 3) been peer-reviewed; and 4) been published either in English or Korean before July 2016. The Scottish Intercollegiate Guideline Network (SIGN) measurement tool was used to assess the risk of bias in each trial. In the final sample, a total of 28 articles (English: 13, Korean: 15) were included in the present systematic review. This review concluded that forest therapy is one of the emerging and effective interventions for decreasing the level of depression in adults. However, the studies included in this review lacked methodological rigor. Future studies assessing the long-term effect of forest therapy on depression using rigorous study designs are needed.

Following the adoption of the Kunming-Montreal Global Biodiversity Framework (KM-GBF), with the Convention on Biological Diversity serving as its guiding treaty, the European Union (EU) has just reached an agreement on an EU Nature Restoration Regulation. This study carries out a systematic cross-walk between the restoration regulation and the KM-GBF, focusing on their implications for forest ecosystems. This paper identifies areas of alignment, divergence, and potential gaps related to habitat restoration, species protection, climate resilience, and the sustainable use of natural resources. The methodology adopts a grounded approach, starting with the 23 targets outlined in the KM-GBF and proceeding to the 28 articles set out in the restoration regulation. The results highlight the need for better alignment between the KM-GBF, the restoration regulation, and other forest-relevant EU policy instruments. The study stresses the need for a coherent and integrated EU policy approach that can address the diverse challenges and policy objectives facing forests. It concludes that amendments to the restoration regulation have significantly diluted its potential impact, limiting the EU Members States accountability and ability to meet KM-GBF goals and targets. It further stresses the need for strategies that can reconcile divergent EU policy pathways, support forest management and restoration efforts, while aligning with global biodiversity objectives.

We propose a silviculture with a purely biological logic that takes soil into account. The article is divided into three parts: 1) presentation of the issue in the context of biodiversity and forest pro-tection; 2) impact of the soil functioning on forestry; 3) possible implementation of soil functioning in forestry practice, to support and protect a non-or minimally anthropized evolution of the forest. The first chapter illustrates the importance of the forest for the future of the planet and our human species, referring to recently published works on the topic of climate warming and biodiversity. The second illustrates the aspects that link soil dynamics to the silvogenetic cycle, focusing on the humipedon (organic and organo-mineral surface horizons, approximately the first 30 cm of forest soil), and more significant carbon storage in old forests since up to 2/3 is found in the soil. In the third chapter we consider the possibility of lengthening the forestry cycle, to allow the return of nutrients to the soil. Theoretical graphs and distribution models of the number of trees as a func-tion of diameter and age are reported, with examples of logging in uneven Alpine forests, with reference to Swiss fir-beech forests published on the Pro-Silva website.

Preserving natural biodiversity of forest ecosystems is one of the greatest challenges of modern, multifunctional forest management. Among the many different groups of forest organisms trees are of major importance as they often act as the so-called foundation species, i.e. species that create conditions on which the existence of virtually all other organisms depends, either directly or indirectly. The essential condition for trees to permanently play this role is their demographic sustainability, which is reflected in the balanced proportions of individuals representing different developmental stages (from seedlings and saplings to mature trees). The main goal of this study was to assess the possibility of tree species occurring in the Białowieża Forest (important biodiversity hotspot) to maintain the role of foundation species in a sustainable manner. Comparison of species-specific, equilibrium vs. actual size distributions revealed that almost half of all trees growing in the Białowieża Forest represent "inappropriate", i.e. occurring in excess compared to the balanced model, species and/or diameter ranges. Similarly high was also the amount of deficits, concerning primarily the smallest trees. It is suggested that, in order to permanently preserve a high biological diversity of the Białowieża Forest, active actions should be taken to bring the current tree diameter distributions as close as possible to the desired sustainable model.

Unmanaged forest ecosystems play a critical role in addressing the ongoing climate and biodiversity crises. As there is no commercial interest in monitoring the health and development of such inaccessible habitats, low-cost assessment approaches are needed. We used a method combining RGB imagery acquired using an Unmanned Aerial Vehicle (UAV), Sentinel-2 data and field surveys to determine the carbon stock of an unmanaged forest in the UNESCO World Heritage Site wilderness area Dürrenstein-Lassingtal in Austria. The entry-level consumer drone (DJI Mavic Mini) and free of charge Sentinel-2 multispectral datasets were used for the evaluation. We merged the Sentinel-2 derived vegetation index NDVI with aerial photogrammetry data and used an orthomosaic and a Digital Surface Model (DSM) to map the extent of woodland in the study area. The Random Forest (RF) Machine Learning (ML) algorithm was used to classify land cover. Based on the acquired field data, the average carbon stock per hectare of forest was determined to be 371.423 ± 51.106 t of CO2 and applied to the ML-generated classification. An overall accuracy of 80.8% with a Cohen’s kappa value of 0.74 was achieved for the land cover classification, while the carbon stock of the living Above-Ground Biomass (AGB) was estimated with an accuracy of -1.0% (± 5.9%). In conclusion, the proposed approach demonstrated that the combination of low-cost remote sensing data and field work can predict above-ground biomass with high accuracy. The results and the estimation error distribution highlight the importance of accurate field data.

Urban forests are critical infrastructure for mitigating environmental and social challenges cities face. Municipalities and non-governmental entities, among others, often set goals (e.g., tree planting or canopy targets) to support urban forests and their benefits. We focus on canopy goals and develop conceptual underpinnings for an analysis of where additional canopy, as one important dimension of the urban forest, can fit within the landscape, while considering factors that influence where trees can be planted and where canopy can grow – ‘practical canopy.’ We apply this in New York City (NYC) to inform the setting of a canopy goal by the NYC Urban Forest Task Force (UFTF) for the NYC Urban Forest Agenda, which may trigger a virtuous cycle that supports the urban forest there. We further develop framing for a ‘priority canopy’ analysis to understand where urban forest expansion should be prioritized given more context (e.g., environmental hazards, local preferences), which can inform how expansion of the urban forest is achieved. We estimate an opportunity for 15,899 ha of new canopy in NYC given existing opportunities and constraints (practical canopy), which, if leveraged, could result in nearly doubling the canopy as of 2017 (17,253 ha). However, like existing canopy, practical canopy is not evenly distributed, in general, or across jurisdictions and land uses. Relying solely on areas identified as practical canopy to expand the urban forest would exacerbate inequities in its distribution. We discuss how the NYC UFTF established an aspirational but achievable goal of 30% canopy cover by 2035, which was informed by this analysis and guided by priorities of equity, health, and resilience. Achievement of this goal will ultimately require a combination of protecting and stewarding the existing resource, and leveraging opportunities for tree planting. Achieving a more equitable urban forest will also require identification of priority canopy, and, in cases, creation of new opportunities for tree planting and canopy expansion. Overall, the collaborative establishment of such goals based on local context can be instrumental in creating a virtuous cycle, moving conservation actors toward exercising influence and agency within the social ecological system.

The forest transition – or forest-area transition – has been put forward as a land-use concept by A.S. Mather in 1992 (The forest transition. Area 24, 367-379), to describe the historical trend generally observed in the forest area of developed countries, embodied in a V-shaped curve of the forest area over time, and that may serve as a paradigm to understand and anticipate deforestation in the developing world. Well in line with a geographical approach to forests, forest transition has thus been defined as one-dimensional, forest area being the reference state variable. From a forestry perspective, the analysis appears to be reductive, as forests are described by many other state variables than area, including forest growing stock, composition in tree species, or stand structure. Whether the drivers of forest transition (population dynamics, economic modes of production and consciousness, as classified by Mather) also impact these other forest state variables in a general way thus comes forth as a logical issue.From a deductive analysis of forest transition drivers, and from forest trends brought to light in Europe, France, and at other places in the world, we here argue that the forest transition concept can be extended to a multi-dimensional space of forest attributes, characterized by typical ideal dynamics. Cumulative impacts onto forests and irreversible losses in forest biodiversity over a forest transition are hence highlighted. Global change, as a parallel consequence of countries’ developing process, further appears as one additional albeit less coupled dimension of forest transition, as it modifies forest productivity and vitality over time. Since forest ecosystem services and forest profitability primarily depend on such attributes, we argue that the extension of the forest transition concept has significance for land-use change and forest protection issues. A prospect on future changes in the forests of developed countries with the European space as a benchmark is finally proposed that leads to extend the temporal significance of forest transition. Though poorly described, returning forests on abandoned agricultural lands are significant, and deserve greater attention.

The prolonged lockdown imposed to contain the COVID-19 pandemic prevented many people from direct contact with nature and greenspaces, raising alarms for a possible worsening of mental health. This study investigates the effectiveness of a simple and affordable remedy for improving psychological well-being, based on audio-visual stimuli brought by a short computer video showing forest environments, with an urban video as a control. Randomly selected participants were assigned the forest or urban video, to look at and listen early in the morning, and filled questionnaires. In particular, the State-Trait Anxiety Inventory (STAI) Form Y, collected in baseline condition and at the end of the study, and the Part II of the Sheehan Patient Rated Anxiety Scale (SPRAS), collected every day immediately before and after watching the video. The virtual exposure to forest environments showed effective to reduce perceived anxiety levels in in people forced by lockdown in limited spaces and environmental deprivation. Although significant, the effects were observed only in the short term, highlighting the limitation of the virtual experiences. The reported effects might also represent a benchmark to disentangle the determinants of health effects due to real forest experiences, for example, the inhalation of biogenic volatile organic compounds (BVOC).

Traditional disease severity monitoring is subjective and inefficient. This study employs a Parrot multispectral sensor mounted on an unmanned aerial vehicle (UAV) to apply machine learning algorithms, such as Random Forest, for categorizing defoliation levels in R7-stage soybean plants. The research assesses the effectiveness of vegetation indices, spectral bands, and relative vegetation cover as input parameters, demonstrating that machine learning approaches combined with multispectral imagery can provide a more accurate and efficient assessment of Asian soybean rust in commercial soybean fields. The Random Forest algorithm exhibited satisfactory classification performance when compared to recent studies, achieving accuracy, precision, recall, F1-score, specificity and AUC values of 0.94, 0.92, 0.92, 0.92, 0.97 and 0.97, respectively. The input variables identified as most important for the classification model were the WDRVI and MPRI indices, the RedEdge and NIR bands, and relative vegetation cover, with the highest Gini importance index.

This study evaluates the effectiveness of a Random Forest model for predicting above-ground biomass in South Carolina (SC), utilizing diverse remote sensing and climatic data sources. SC, with its humid subtropical climate and varied geography, including the Atlantic coastal plain, Piedmont, and Blue Ridge Mountains, poses unique challenges for biomass estimation. We integrated global biomass datasets for 2010, MODIS vegetation indices (NDVI and EVI), Leaf Area Index (LAI) from MOD15A2H, and climate data from TerraClimate. The model was trained using 2010 data and applied to 2022 datasets to assess biomass changes. To validate the model, plot-level biomass estimates from the 2023 FIA data were interpolated using Inverse Distance Weighting (IDW). Performance evaluation showed a strong positive correlation between predicted and observed biomass, with a correlation coefficient of 0.77 and an R² value of 0.62, indicating that the model explains 62% of the variability in biomass. Comparison with IDW-interpolated biomass data resulted in a correlation coefficient of 0.64, confirming the model's validity. Although the Random Forest model demonstrated reliable predictions, the study suggests potential improvements by incorporating additional data sources and advanced modeling techniques. The findings emphasize the value of integrating remote sensing data, machine learning, and interpolation methods to enhance biomass estimation accuracy. This research provides crucial insights into biomass distribution in SC and establishes a basis for future studies on forest monitoring and carbon accounting, highlighting the importance of combining various data sources for comprehensive environmental analysis.

The increasing demand for large-scale, high-frequency environmental monitoring has driven the adoption of satellite-based technologies for effective forest management, especially in the context of climate change. This study explores the potential of SAR for estimating the mass of harvesting residues, a significant component of forest ecosystems that impacts nutrient cycling, fire risk, and bioenergy production. The research hypothesizes that while the spatial distribution of residues remains stable, changes in moisture content—reflected in variations in the dielectric properties of the woody material—can be detected through SAR techniques. Two models, generalized linear model (GLM) and random forest (RF), were used to predict the mass of residues using interfer-ometric variables (phase, amplitude and coherence) as well as the backscatter signal from several acquisition pairs. The models provided encouraging results (R2 of 0.48 for GLM and 0.13 for RF), with acceptable bias and RMSE. We concluded that SAR data are feasible to predict the mass of harvesting residues and the findings could lead to improved monitoring and management of forest residues, contributing to sustainable forestry practices and enhanced bioenergy resource utilization.

Protected areas serve as an effective mechanism usually supported by a statute to conserve and protect wildlife-rich areas worldwide. Such extensive external management needs elaborate planning, regular monitoring, and periodic assessment of results. Thus, management effective evaluation (MEE) has emerged as one of the major tools for quantifying the performance of any protected area, globally since 2006. India has successfully conducted a complete round of management effectiveness evaluation of all its terrestrial protected areas (except conservation reserves and community reserves), with 25 such areas already undergoing a repeat assessment. Simultaneously, India biennially carries out a country-wide status of forest cover in all its states and union territories. This study has correlated the trend in change of forest cover for the states that contain those 25 repeat evaluated protected areas with their change in MEE scores over the same period. Our study found a positive correlation between the change in forest cover and the change in management effectiveness score from 2005-06 to 2018-19. Owing to increased protection regimes in the PAs including the degraded areas if they make part of such areas, the habitat often shows recovery. Effective habitat conservation is thus intertwined with the monitoring and preserving the wildlife populations in the protected areas, thereby, reduction in forest cover equally impacts the management scores. However, in states where the loss in forest cover has been reported, more protected areas need to be assessed to plan complementary strategies for both habitat and species conservation.

Conifer bark beetles (Coleoptera: Curculionidae: Scolytinae) are major agents of natural disturbance in temperate forests of the northern hemisphere, capable of killing billions of trees in native forests. The dynamics of these insect populations are influenced by climatic variability. To understand such a relationship, it is necessary to monitor the population fluctuation of the species present over time. In this regard, the objective of the present study was to determine the species community of the genus Dendroctonus, the population fluctuation throughout the months of the year, and the effects of relative humidity, temperature, and precipitation on the fluctuation, at three sites of mixed forests in the Municipality of Bocoyna, Chihuahua, in northern Mexico. For insect monitoring, we used Lindgren funnel traps (with 8 funnels each) baited with semiochemicals. Every two weeks, and during 2 years, we collected, identified, and counted the insects. The identification of the species was based on the external morphology and characteristics of the male seminal rod. Temperature and relative humidity were measured using climate data loggers (HOBOs). We found six Dendroctonus species. We also determined the population fluctuation patterns of D. mexicanus Hopkins, D. frontalis Zimmermann, and D. brevicomis LeConte, species with the highest number of captures at each site. Additionally, the relationship between species abundance and climatic variables was determined. The variable that significantly influenced (P<0.05) abundance was temperature. Relative humidity and precipitation variables did not show a significant relationship (P>0.05).

We investigated the response of leaf photosynthetic capacity to climate warming and its variation among provenances of Larix gmelinii (Dahurian larch). Seedlings of 11 L. gmelinii provenances were transplanted into two common gardens with different climate conditions (control and warming climate). We measured the leaf photosynthetic capacity and explored its influencing factors. The warming treatment significantly increased the maximum net photosynthetic rate (Pmax-a), photosynthetic nitrogen use efficiency (PNUE), maximum carboxylation rate (Vcmax), maximum electron transport rate (Jmax), triose phosphate utilization rate (TPU), mesophyll conductance (gm), leaf nitrogen content (Narea), and chlorophyll content (Chlm). The Pmax-a was significantly positively associated with Vcmax, Jmax, TPU, gm, and Narea, and the slope of the correlations between Pmax-a and Vcmax, Jmax, and TPU was steeper in the warming treatment. The responses of Pmax-a, PNUE, Vcmax, Jmax, TPU, Narea, and Chlm to warming differed among provenances. The effects of warming on Pmax-a, Vcmax, Jmax, and TPU increased and then decreased as the aridity index of the original site increased. Overall, the warming treatment improved the photosynthetic capacity of L. gmelinii, but the extent of the improvement varied among provenances. These findings provide insights into the mechanisms underlying the responses of L. gmelinii to climate warming.

In regenerated oak forests, weeds are present throughout the year, the ones appearing in early spring representing a major problem. Hence, the aim of this study was to examine herbicides for early spring broadleaved weed control in the regenerated oak forests while the seedlings are in a dormant stage. During 2019 and 2020 two experiments were set up in regenerated pedunculate oak forests with 2-yar and 3-year-old seedlings and two herbicides were applied in two doses: fluroxypyr at dose 360 g a.i. ha-1 and 540 g a.i. ha-1 and clopyralid at dose 100 g a.i. ha-1 and 120 g a.i. ha-1. The results from the two-year investigations showed that fluroxypyr and clopyralid significantly reduced early spring broadleaved weeds in the regenerated pedunculate oak forests but both doses of fluroxypyr provided greater control of presented weeds than the applied doses of clopyralid. The manual weeding reduced broadleaved weeds in experiments, but that method did not have a long-term effect on the reduction of weeds. Applied doses of herbicides fluroxypyr and clopyralid did not cause phytotoxicity symptoms in dormant oak seedlings. All investigated treatments significantly reduced fresh broadleaved weed biomass compared to the control. Generally, it can be concluded that fluroxypyr and clopyralid can be successfully used for control of many early spring broadleaved weeds in the regenerated pedunculate oak forests, but oak seedlings must be in the dormant stage.

Ammonia is an atmospheric pollutant, predominantly emitted from agriculture, leading acidification and eutrophication of soil and water and contributing to secondary PM2.5. The implementation of accurate emission inventories with high spatial and time resolution plays a fundamental role in the development of air modelling simulation and in the impact assessment of actions for air quality improvement. The development and release of new algorithms and the increase of data availability are supporting the implementation of machine learning approaches in environmental and air quality data analysis. In this paper we present a methodology developed by the application of the Random Forest algorithm to bottom-up local emission inventories of ammonia to validate annual time series of ammonia emissions and calculate high resolution temporal profiles. The model has been trained and tested by the hourly measurements of ammonia concentrations and atmospheric turbulence parameters starting from a constant emission scenario. The initial values of emissions are calculated based on a bottom-up emission inventory detailed at the municipal basis and considering a circular area of about 4 km radius centered on measurement sites. By comparing predicted and measured concentrations, the emissions are modified, the model's training and testing are repeated, and the model converges to a very high performance in predicting ammonia concentrations and establishing an hourly time changing emission profile. The site-specific emissions profiles, estimated by the proposed methodology, clearly show a nonlinear relation with measured concentrations and allow to identify the effect of atmospheric turbulence on pollutant accumulation. The estimated time series well confirm the available data of the emission inventories and the monthly emission profiles have been compared with estimated data from satellite.

Tropical forest and savanna biomes are pivotal in the functioning of the Earth system. Tropical forests are one of the largest terrestrial biosphere carbon pools, whereas savannas exchange carbon between the biosphere and atmosphere via frequent and extensive landscape fires. Both are biodiverse and under increasing threat due to land clearing and anthropogenic climate change. Reliable mapping of tropical forest and savanna is essential to provide understanding of how anthropogenic impacts are affecting the extent of these biomes. Using Google Maps satellite imagery, we manually classified 24,239 random points as forest, savanna, or anthropogenic landscapes within the tropics. Because fire and climate are correlated, we developed separate geospatial models to rank the importance of climate, topography, and human influence on vegetation present. This modelling confirmed that those areas with more fires had lower probabilities of tropical forest, that forest was most likely in areas with high mean annual rainfall with little seasonal variation in precipitation, and that anthropogenic factors disrupt this environmental predictability. We found there were environments where tropical forest and savanna were equally probable are geographically restricted. These relationships suggest that future drier climates projected under anthropogenic climate change, combined with clearing and burning that have reduced tropical forest extent to a subset of its theoretical distribution, will lead to irreversible loss of tropical forests. Our modelling provides global mapping that can be used track further changes to distribution of rainforests.

Mount Hamiding Protected Forest is located in North Halmahera Regency, North Maluku Province, the Wallace zone has an important role in providing protection functions, life support systems and regulating water management, preventing floods, preventing seawater intrusion, and controlling soil fertility. The protected forest area in the Wallace Zone is globally renowned for its endemic characteristics of flora and fauna. The condition of the protected forest of Mount Hamiding HMPF is currently experiencing anthropogenic damage as a result of forest encroachment, illegal logging, shifting cultivation, grazing and poaching to meet social needs. Anthropogenic damage has been in the spotlight for decades and has become a global issue. This study aims to determine damage to protected forests, changes in land cover, and mitigation of protected forest areas. This research uses quantitative and descriptive qualitative methods. Determination of research locations by purposive sampling. Parameters measured in this study were damage to protected forests, changes in land cover and area mitigation strategies. To identify forest damage, snowball sampling was carried out, and land cover changes through land cover data processing were obtained from the Ministry of Environment and Forestry of the Republic of Indonesia. Then using Imagery 7 ETM+ and 8 OLI to calculate changes in forested land to non-forested from 2006 -2021, analysis of land cover change using supervised classification, developing a strategy based on identity verification of forest damage and analysis of land cover change. The results showed that damage to protected forests was caused by anthropogenic disturbances in the form of forest encroachment, shifting cultivation, illegal logging, grazing and hunting of wild animals. Changes in land cover experienced the greatest damage in 2006-2021 amounting to 1,796.54 ha (16.17%). Strategic efforts are carried out using forest engineering, namely the agroforestry system and social engineering through community empowerment, namely training and outreach.

The Botryosphaeriales order are best known for the diseases they cause in woody plants, as primary pathogens or latent pathogens residing in the woody tissue of asymptomatic hosts. In the first instance, Botryosphaeriales species have been identified in Venezuela using morphological de-scriptions in the 80's and 90's, and later, the mid-2000s using molecular techniques. The mor-phological descriptions of the asexual morphs were initially used for the identification of Botry-osphaeriales genera and species. Lasiodiplodia spp., (as L. theobromae) was the most isolated fungus in Venezuela within the Botryosphaeriales and has been found in more than 50% of the hosts in native and non-native plants, followed by Diplodia, Dothiorella, Fusicoccum, Lasiodiplodia, Micro-diplodia, Macrophomina, Neofusicoccum, Sphaeropsis, and Botryosphaeria, considered all of them cosmopolitan group. With molecular studies, that included DNA sequence data from multiple genes, such as the internal transcribed spacer of rDNA (ITS), translation elongation factor-1α (tef1), and β-tubulin (btub) used on the fungi isolated from woody plants, mainly trees or forest species, resulted in the presence of two families within the Botryosphaeriales order for Venezuela. Botryosphaeriaceae family with the genera: Botryosphaeria, Cophinforma, Diplodia, Lasiodiplodia and Neofusicoccum, and the Pseudofusicoccumaceae family that includes the genus Pseudofusicoccum. In Botryosphaeriaceae family was again the Lasiodiplodia genus the most predominant in most hosts, and the specie L. theobromae the most isolated in native and non-native plants; Botryosphaeria dothidea, Cophinforma atrovirens, Diplodia scrobiculata (syn. Diplodia guayanensis), Lasiodiplodia brasi-liensis, L. crassispora, L. pseudotheobromae, Neofusicoccum arbuti (syn. N. andinum), N. parvum, and N. ribis are cosmopolitan species, and they were isolated from native and non-native plants; while Pseudofusicoccum stromaticum was found in plantations non-native of Acacia mangium, E. urophylla x E. grandis, Eucalyptus urophylla, and reported exclusively in South America; Lasiodiplodia venezue-lensis has only been reported in Venezuela, from native and non-native plants. The presence, distribution, diversity, and symptoms of these fungi, mainly of the new genus, new species, and reports found in Venezuela and other parts of the world, were also reviewed.

A primary challenge facing conservationists is reconciling the human needs of forest adjacent communities with the needs of conserving forest biodiversity, especially in tropical regions with growing populations of rural poor. Agroforestry has the potential to simultaneously provide for human needs and enhance forest biodiversity, but the complex interactions and feedbacks between the social and natural dimensions are relatively undescribed and thus systematic implementation is rare. The attributes of trees on farms required for human needs and conservation value may conflict. For example, exotic species in monoculture may provide the most economic value for farmers, while relic or planted indigenous tree mixtures may be more valuable for biological conservation. The objective of this study was to explore whether agroforestry practices in a moist tropical forest ecosystem in Kenya can simultaneously provide timber and fuelwood value to small-holder farmers while extending forest tree biodiversity. We described the agroforestry attributes on farms around a tropical forest, assessed the relationship between number and biomass of timber/fuelwood trees and tree biodiversity, and explored the relationships between forest tree diversity attributes and farm tree diversity attributes on a landscape scale using spatial analysis. We found that the diversity and number of trees on farms in this area are extensive yet variable, but that no significant relationship exists between the number of timber/fuelwood trees and tree diversity. This suggests that the two values of agroforestry may not be in conflict, due mainly to the high diversity of trees used for fuelwood. We also found that trees on farms in the larger landscape add to the conservation value of forest tree biodiversity and could be important components in conservation management. If agroforestry is to play an increasingly active role in conserving biodiversity in human-dominated landscapes, particularly in areas of dense subsistence farmer populations, increase recognition needs to be given to farmer’s perception of the value of trees and their selection of what trees to plant or maintain.

Portable Document Format (PDF) is one of the most widely used files types worldwide in data exchange, this has encourage hackers to utilize such files to spread any malicious content through PDF, utilizing different methods and techniques to accomplish that, on the other hand, security researches kept trying to improve detection methods to cope up to the rapidly increasing number of malwares daily, one of the commonly used detection technique nowadays is by utilizing artificial intelligence and Machine learning classificat; thision to help detecting PDF Malwares, in this paper, we utilize machine learning classifier Random Forest on a newly released PDF Malware dataset CIC-Evasive-PDFMal2022 to achieve the main goal of detecting malicious PDF documents, results showing a detection accuracy of around 99.5%

Antimicrobial peptides (AMPs) are considered as promising alternatives to conventional antibiotics in order to overcome the growing problems of antibiotic resistance. Computational prediction approaches receive an increasing interest to identify and design the best candidate AMPs prior to the in-vitro tests. In this study, we focused on the linear cationic peptides with non-hemolytic activity, which are downloaded from the Database of Antimicrobial Activity and Structure of Peptides (DBAASP). Referring to the MIC (Minimum inhibition concentration) values, we have assigned a positive label to a peptide if it shows antimicrobial activity; otherwise the peptide is labeled as negative. Here, we focused on the peptides showing antimicrobial activity against Gram-negative and against Gram-positive bacteria separately, and we created two datasets accordingly. Ten different physico-chemical properties of the peptides are calculated and used as features in our study. Following data exploration and data preprocessing steps, a variety of classification algorithms are used with 100-fold Monte Carlo Cross Validation to build models and to predict the antimicrobial activity of the peptides. Among the generated models, Random Forest has resulted in the best performance metrics for both Gram-negative dataset (Accuracy: 0.98, Recall: 0.99, Specificity: 0.97, Precision: 0.97, AUC: 0.99, F1: 0.98) and Gram-positive dataset (Accuracy: 0.95, Recall: 0.95, Specificity: 0.95, Precision: 0.90, AUC: 0.97, F1: 0.92) after outlier elimination is applied. This prediction approach might be useful to evaluate the antibacterial potential of a candidate peptide sequence before moving to the experimental studies.

Forest fire identification is important for forest resource protection. Effective monitoring of forest fires requires the deployment of multiple monitors with different viewpoints, while most traditional recognition models can only recognize images from a single source. By ignoring the information from images with different viewpoints, these models produce high rates of missed and false alarms. In this paper, we propose a graph neural network model based on the similarity of dynamic features of multi-view images to improve the accuracy of forest fire recognition. The input features of the nodes on the graph are converted into relational features of different gallery pairs by establishing pairs (nodes) representing different viewpoint images and gallery images. The new feature library relationship is used to update the image gallery with dynamic features in order to achieve the estimation of similarity between images and improve the image recognition rate of the model. In addition, to reduce the complexity of image pre-processing process and extract key features in images effectively, this paper also proposes a dynamic feature extraction method for fire regions based on image segment ability. By setting the threshold value of HSV color space, the fire region is segmented from the image, and the dynamic features of successive frames of the fire region are extracted. The experimental results show that, compared with the baseline method Resnet, this paper's method is more effective in identifying forest fires, and its recognition accuracy is improved by 2%. And the scheme of this paper can adapt to different forest fire scenes, with better generalization ability and anti-interference ability.

Tree biomass and diversity relationship in mixed forest impacts on forest ecosystem services provisions. Tree biomass yield is driven by several aspects such as species identity, site condition, stand density, tree age as well as tree diversity expressed as species mingling and structural diversity. By comparing diverse degrees of tree mixture in natural forests we can insight on the ecosystem services provision level and dynamic. Two monitoring sites in the Castilian Northern Plateau (Spain) have been analyzed to disentangle the relationships between biodiversity levels and tree biomass yield. Two permanent one ha squared plots were established at Llano de San Marugan and Valdepoza. In each plot all individual trees were measured (diameter and height), georeferenced and its species identity defined. Tree species in the two sites were Pinus sylvestris, Pinus nigra, Pinus pinea, Quercus pyrenaica, Quercus ilex, Quercus faginea and Juniperus thurifera. From these datasets ten diversity indices that fall in three categories (species richness indices, species compositional/mingling indices and vertical structural indices) were used as predictor variables to fit several candidate models. By merging the trees by site (without considering the species identity) selected models include individual tree basal area as explanatory variable combining by addition or interaction with diversity indices. When species are analyzed independently structural diversity impacts on biomass yield in combination (additive or multiplicative) with tree size is negative Pinus nigra and positive for the other species.

A strategy to reduce qualitative and quantitative losses in crop-yields refers to early and accurate detection of insect-damage caused in plants. Remote sensing systems like hyperspectral proximal sensors are a promising strategy for managing crops. In this aspect, machine learning predictions associated with clustering techniques may be an interesting approach mainly because of its robustness to evaluate high dimensional data. In this paper, we model the spectral response of insect-herbivory-damage in maize plants and propose an approach based on machine learning and a clustering method to predict whether the plant is herbivore-attacked or not using leaf reflectance measurements. We differentiate insect-type damage based on the spectral response and indicate the most contributive wavelengths to perform it. For this, we used a maize experiment in semi-field conditions. The maize plants were submitted to three different treatments: control (health plants); plants submitted to Spodoptera frugiperda herbivory-damage, and; plants submitted to Dichelops melacanthus herbivory-damage. The leaf spectral response of all plants (controlled and submitted to herbivory) was measured with a FieldSpec 3.0 Spectroradiometer from 350 to 2500 nm for eight consecutive days. We evaluated the performance of different learners like random forest (RF), support vector machine (SVM), extreme gradient boost (XGB), neural networks (MLP), and measured the impact of a day-by-day analysis into the prediction. We proposed a novel framework with a ranking strategy, based on the accuracy returned by predictions, and a clusterization method based on a self-organizing map (SOM) to identify important regions in the reflectance measurement. Our results indicated that the RF-based framework algorithm is the overall best learner to deal with this type of data. After the 5th day of analysis, the accuracy of the algorithm improved substantially. It separated the three treatments into different groups with an F-measure equal to 0.967, 0.917, and 0.881, respectively. We also verified that the most contributive spectral regions are situated in the near-infrared domain. We conclude that the proposed approach with machine learning methods is adequate to monitor herbivory-damage of S. frugiperda and stink bugs like Dichelops melacanthus in maize, differentiating the types of insect-attack early on. We also demonstrate that the framework proposed for the analysis of the most contributive wavelengths is suitable to highlight spectral regions of interest.

The many strains of Cannabis spp. are associated with many effects on users and contain many different potentially psychoactive metabolites, but the links between metabolite profiles and user effects are unclear. Here we take a statistical approach to linking cause (i.e. metabolites) to effects in Cannabis spp. through the prism of strains, using quantitative data for metabolite composition and user effects. We find that species (indica vs. sativa) explains <2% of the variability in metabolite profiles, while strain explains 1/3 of variability, indicating species is nonindicative of metabolite composition, while strain is approximately indicative. Using random forests we generate a table of potential metabolite-effect links. We also find that effect-weighted metabolite composition can effectively be described in terms of four values representing the concentrations of pairs or triplets of particular compounds.

Soil proteases are involved in the transformation of organic matter and thus influence the nutrient turnover in the ecosystem. Phytohormones, similarly to proteases, are synthesized and secreted into the soil by fungi and microorganisms and regulating their activity in the rhizosphere. The aim of our work was to find out how the presence of auxins, cytokinins, ethephone and chlorocholine chloride affects the activity of native soil proteases at the spruce tree stand. Auxins stimulated the native proteolytic activity in the spruce tree stand. Synthetic auxins most stimulated the activity of 2-naphthoxyacetic acid and the naturally occurring auxins of indole-3-acetic acid in the organic horizon of the spruce forest. Cytokinins, ethephone and chlorocholine chloride inhibited the activity of native soil proteases in the spruce tree stand. The highest inhibitory effect was found in ethephone and chlorocholine chloride. Overall, the negative effect of phytohormones on the activity of the native proteolytic activity may slow down the decomposition of organic matter and thus make plant nutrition more difficult. The outcomes of our work assist with understanding of the effect of substances produced by the rhizosphere on the activity of soil microorganisms and the soil nitrogen cycle.

Mapping settlement extents at the annual time step has a wide variety of applications in demography, public health, sustainable development, and many other fields. Recently, while more multitemporal urban feature or human settlement datasets have become available, issues still exist in remotely-sensed imagery due to coverage, adverse atmospheric conditions, and expenses involved in producing such feature sets. These challenges make it difficult to increase temporal coverage while maintaining high fidelity in the spatial resolution. Here we demonstrate an interpolative and flexible modeling framework for producing annual built-settlement extents. We use a combined technique of random forest and spatio-temporal dasymetric modeling with open source subnational data to produce annual 100m x 100m resolution binary settlement maps in four test countries of varying environmental and developmental contexts for test periods of five-year gaps. We find that in the majority of years, across all study areas, the model correctly identified between 85-99% of pixels that transition to built-settlement. Additionally, with few exceptions, the model substantially out performed a model that gave every pixel equal chance of transitioning to the category “built” in each year. This modelling framework shows strong promise for filling gaps in cross-sectional urban feature datasets derived from remotely-sensed imagery, provide a base upon which to create future built/settlement extent projections, and further explore the relationships between built area and population dynamics.

The effects of heterogeneity of cooperative membership on cooperative and collective action sustainability has been previously discussed. However, despite the importance of membership heterogeneity in recent theoretical frameworks, empirical examinations have been limited. We determine the effect of changes to cooperative member heterogeneity on cooperative sustainability and discuss changes to heterogeneity overtime that can advance our understanding to cooperative sustainability long-term. This study uses USDA Agricultural Management Resource Survey data, coupled with USDA-Rural Development cooperative financial data at the state level, to quantify effects of cooperative member heterogeneity to sustainability of U.S. farmer cooperatives. We use random forest regression to interpret the significance of heterogeneity with cooperative sustainability at an aggregate level. The findings of this empirical study narrowly reconciles the theoretical understanding of the emergence of intra-cooperative issues while providing consistent empirical evidence and expectations for the sustainability of cooperatives in the near term.

Rapid urbanization has changed the structure and function of natural ecosystems, especially the floodplain ecosystems in SE Asia. This paper describes the ecological structure of vegetation stands and the usefulness of satellite images to characterize a disturbed tropical urban forest located in the lower floodplain of the Chao Phraya River, Thailand. Nine representative plots were established in Bang Kachao peninsula in 4 tropical urban forest types: rehabilitation forest, home-garden agroforestry, mangrove and park. The correlation between NDVI and LAI obtained from satellite images and plant structure from field surveys were analyzed. The NDVI had the highest relationship with stand factors for the number of families, number of species, Shannon-Weiner’s diversity index and total basal area. The LAI had the highest correlation with total basal area, number of canopy layers, stand density and canopy density. Linear regression predicted the correlation between NDVI and LAI with stand factors as show above. The trend in NDVI and LAI reflected the urban forest type, being high in rehabilitation and mangrove forests, moderate in home-gardens and low in parks. Future urban planning of the Bang Kachao peninsula should focus on rehabilitation to increase the biodiversity and complexity of the urban forest.

Wildfires have always been a part of the history of Mediterranean forests. However, forest regeneration after a wildfire is not certain. It depends on many factors, some of which may be influenced by land management activities. Failure of regeneration will cause a regime shift in the ecosystem, reducing the provision of ecosystem services and ultimately leading to desertification. How can we increase Mediterranean forests’ resilience to fire? To answer this question, we did a literature review, investigating chains of processes that allow forests to regenerate (which we label “regeneration mechanisms”), and assessed the impact of selected management practices documented in the WOCAT database on the regeneration mechanisms. We identified three distinct regeneration mechanisms that enable Mediterranean forests to recover, as well as the time frame before and after a fire in which they are at work, and factors that can hinder or support resilience. The three regeneration mechanisms enabling a forest to regenerate after a fire consist of regeneration (1) from a seed bank; (2) from resprouting individuals; and (3) from unburned plants that escaped the fire. Management practices were grouped into four categories: (1) fuel breaks, (2) fuel management, (3) afforestation, and (4) mulching. We assessed how and under what conditions land management modifies the ecosystem’s resilience. The results show that land management influences resilience by interacting with resilience mechanisms before and after the fire, and not just by modifying the fire regime. Our analysis demonstrates a need for adaptive – i.e. context- and time-specific – management strategies.

Increased wildfire activity is the most significant natural disturbance affecting forest ecosystems, with a strong impact on their natural regeneration. This study presents a comprehensive analysis of post-fire forest recovery using Landsat time series data from 2000 to 2023 in the Middle Volga region of the Russian Federation. The analyses utilised the LandTrendr algorithm in Google Earth Engine (GEE) cloud computing platform to examine Normalized Burn Ratio (NBR) spectral metrics and quantify the forest recovery at low, moderate, and high burn severity (BS) levels. To assess the spatio-temporal trends of the recovery, the Mann–Kendall statistical test and Theil–Sen’s slope estimator was applied. The results suggested that the post-fire spectral recovery is significantly influenced by the degree of the BS on affected areas. The higher the class of BS, the faster and more extensive the reforestation of the area occurs. About 91% (40,446 ha) of the first 5-year forest recovery after the wildfire belong to the BS classes of moderate and high severity. A regression model showed that land surface temperature (LST) was more significant to post-fire recovery than the variability in precipitation (Pr), explaining about 65% of the variance in post-fire recovery. This study provides new insights into the post-fire forest recovery dynamics and scientific bases for the cost-effective management strategies under changing climate conditions.

Historical forest management practices in the southwestern US have left forests prone to high intensity, stand-replacement fires. Effective management to reduce the cost and impact of forest-fire management and allow fires to burn freely without negative impact depends on detailed knowledge of stand composition, in particular, above-ground biomass (AGB). Lidar-based modeling techniques provide opportunities to reduce costs and increase ability of managers to monitor AGB and other forest metrics. Using Bayesian Model Averaging (BMA), we develop a regionally applicable lidar-based statistical model for Ponderosa pine and mixed conifer forest systems of the southwestern USA, using previously collected field data. The selected regional model includes a mid and low canopy height metric, a canopy cover, and height distribution term. It explains 72% of the variability in field estimates of AGB, and the RMSE of the two independent validation data sets are 23.25 and 32.82 Mg/ha. The regional model developed is structured in accordance with previously described models fit to local data, and performs equivalently to models designed for smaller scale application. Developing regional models for broad scale application provides a cost-effective, robust approach for managers to monitor and plan adaptively at the landscape scale.

Tropical forests harbor complex communities that are linked together by biotic relationships. Asian forests in particular have lost many apex predators due to habitat loss. We studied a small forest patch in northeastern Bangladesh, Satchari National Park, to determine density and diversity of nocturnal mammals and evaluate their relationships. Transects were walked from February 2015 to July 2016 and density was estimated using distance sampling. Nine species of mammals (5 arboreal and 4 ground-dwelling) were encountered. Densities of the common palm civets, Paradoxurus hermaphrodites, Bengal slow loris, Nycticebus bengalensis, were the highest (19.48 and 15.03 individuals/km2). Density of small Indian civets, large Indian civets and Indian mongoose were lower (2.31-5.55 individuals/km2). Unexpectedly, a wide range of nocturnal mammals co-existed in this forest patch, in spite of fragmentation and severe disturbance. We did not find any significant association between any of the species studied, although this could be an artifact of low sample size. Conservation in Bangladesh remains a challenge due to high human population density. Thus, strict conservation measures are needed to permit the long-term survival of these species.

Developed within the scope of a collaborative R&DT project, a 1 kW pilot unit aimed to perform the recovery of previously liquefied biomass from lignocellulosic forest resi-dues, later used as a carbon source in a co-electrolysis process, intended for the pro-duction of synthesis gas (H2, CO, CO2), also known as syngas. The syngas will be trans-formed into other value-added products, namely synthetic biofuels such as methane, methanol or biodiesel. This paper presents the results of the research currently being carried out at ISEL with the objective of further advancing development on the pilot unit by the execution of optimization tests with and without the addition of two dif-ferent biomass types.

In this report, we present a study demonstrating how machine learning methods can reveal the interaction of different risk factors of post-transplant leukemic relapse and obtain robust predictions even with a modest clinical dataset. Using a cohort of 63 pediatric patients with acute lymphocytic leukemia (ALL) and 46 patients with acute myeloid leukemia (AML) who underwent stem cell transplant at a single institution, we built predictive models of leukemic relapse with both pretransplant and posttransplant patient variables (specifically lineage-specific chimerism) using the random forest classifier. The random forest classifier revealed different important predictive factors between ALL and AML in our relapse models consistent with previous knowledge. Furthermore, it also distinguished donor CD34 chimerism as most impactful in relapse prediction compare to donor chimerism of other cellular lineages in our dataset for ALL but CD3 for AML. Our models greatly improved sensitivity and specificity at predicting relapses in cross validation compared to a reference model based on our own institutional incidence of relapse and inferential statistical principles. Local Interpretable Model-Agnostic Explanations, an interpretable machine learning tool, confirmed our Random Forest Classification result and provided an intuitive explanation of how our machine learning models made the relapse prediction for each individual patient.

Despite increased protection globally, climate change and other anthropogenic factors have caused a significant decline in seagrass cover. Identifying the specific causes of this decline is paramount if they are to be addressed. Therefore, we identified the causes of long-term change in seagrass/submerged aquatic vegetation (SAV) percentage cover and extent in the Bluefields Bay Special Fish Conservation Area (BBSFCA), a marine protected area on Jamaica’s southern coast. We used two random forest regression (RFr) models that included 2013 hydroacoustic survey SAV percentage cover data (dependent variable), and auxiliary data, reflectance data from level 2 processed 2013 Landsat 7 and 8 images and image band texture statistics maps (mean and variance) as predictors to generate 24 SAV percentage cover maps for the period 1984 – 2021 (37 years) using data from Landsat 4-5, 7 and 8 images., from which benthic features maps (SAV present, absent and coral reef) were created. Rainfall and map data were used to determine if SAV extent/area (km2) and average percentage cover and annual rainfall changed significantly over time and to evaluate the influence of rainfall. Rainfall impact on the overall spatial patterns of SAV loss, gain, and percentage cover change was assessed using a pixel-based regression. Finally, the most important spatial pattern predictors (two rainfall proxies (distance and direction from river mouth), benthic topography, depth, and hurricane exposure (a measure of hurricane disturbance)) of SAV loss, gain, and percentage cover change during 23 successive 1-to-4-year periods were identified using spatial Bayesian INLA generalized linear mixed models. SAV area/extent was largely stable with &gt; 70% mean percentage cover for multiple years. However, Hurricane Ivan (in 2004) caused a significant decline in SAV area/extent (by 1.62 km2, or a 13%) during 2002 – 2006 and a second hurricane (Dean) in 2007 delayed recovery until 2015. Additionally, rainfall declined significantly by &gt;1000 mm since 1901, and mean monthly rainfall positively influenced SAV percentage cover change. The pixel-based regression highlighted areas where mean monthly rainfall had a positive overall effect on SAV cover percentage change (across the entire bay) and gain (closer to the mouth of a river). The most frequently selected important spatial pattern predictors were the two rainfall proxies (areas closer to the river mouth were more likely to experience SAV loss and gain) and depth, with shallow areas generally having a higher probability of SAV loss and gain. Three hurricanes had significant but different impacts depending on their distance from the southern coastline. Hurricane Gilbert, which made landfall in 1988, resulted in higher SAV percentage cover loss in 1987 - 1988. Benthic locations with a northwestern/northern facing aspect (the predominant direction of Ivan’s leading edge wind bands) experienced higher SAV losses during 2002 – 2006. Although some locations impacted by Ivan and not by Dean recovered during 2006 – 2008, exposure to Ivan explained percentage cover loss during 2006 – 2008 and average exposure to (the cumulative impact of) Ivan and Dean (both with tracks close to the southern coastline) explained SAV loss during 2013 – 2015. Therefore, despite historic lows in annual rainfall, overall, higher rainfall was beneficial, multiple hurricanes impacted the site, and despite two hurricanes in three years SAV recovered within a decade. Hurricanes and a further reduction in rainfall may pose a serious threat to SAV persistence in the future.

Adaptations to climate change rely on understanding the dynamics of plant biomass stocks on the planet. The high levels of deforestation in Cerrado have transformed this biome into the second-largest Brazilian source of carbon emissions. The objective of this study was to develop a method to accurately estimate aboveground and total biomass values among shrublands, savannas, and forests located in the Cerrado biome, using an allometric equation adjusted from canopy height, obtained through optical and laser sensors. The results show similarity between the estimates employed by our method and the data found in the literature review for different phytophysiognomies in the Cerrado biome. Shrubland formations showed higher biomass estimation uncertainties due to the discontinuity of isolated trees and the lower canopy height when compared to more clustered tree canopies in savannas and taller canopies in forests. Aboveground biomass estimates were related to expansion factors, and specific maps were developed for each compartment by root, litter, and necromass. The sum of these compartments is presented in the aboveground and below forest biomass map. This study presents, for the first time, the mapping of total biomass in 10 m pixels of all regions of the Cerrado biome.

The use of hyperthermia is one of the effective methods of treating cancer. In this case, the problem arises of constructing and predicting the distribution of the thermal field inside tissues depending on their type. This paper discusses the use of linear and polynomial regression methods, as well as regression algorithms based on decision tree, random forest and K nearest neighbors methods to recover missing temperature values. The influence of the size of the training sample when performing regression on the quality of the reconstructed values is investigated. Experimental recommendations on sample size are given for the selected decision tree and random forest methods. The possibility of a significant (10 times or more) reduction in the initial sample size, without leading to a decrease in the R² metric by more than 0.05 for various tissues, has been shown.

Natural vegetation restoration has become an effective and rapid way for eco-logical restoration in fragile areas. However, the response of soil microorgan-isms remains unclear. Using high-throughput sequencing methods, we evalu-ated the dynamics of soil bacterial and fungal communities during forest suc-cession (shrubland, secondary forest, and primary forest) in the karst region of Southwest China. Secondary forest had significantly higher bacterial α-diversity than the shrubland and primary forest. Soil bacterial community in the primary forest was close to the shrubland but diverged from the secondary forest, and that the fungal community could be discriminated along forest suc-cession. Also, the microbial co-occurrence network indicated that soil fungi had less but more intense relationships than bacteria in the karst forests. Further-more, soil properties (pH, SOC, TN, moisture, and AK), soil microbial biomass (MBP, and MBN), and plant factors (Shannon index of woody plants) drove the dynamics of the soil bacterial community, while soil properties (i.e., pH) mostly explained the variation of the soil fungal community along karst forest succession. Different responses of soil bacteria and fungi to forest succession in a karst region give hints for ecological restoration along forest succession in the karst region.

UAVs are becoming a crucial tool in modern warfare, primarily due to their cost-effectiveness, risk reduction, and ability to perform a wider range of activities. The use of autonomous UAVs to conduct strike missions against highly valuable targets is the focus of this research. Due to developments in ledger technology, smart contracts, and machine learning, such activities formerly carried out by professionals or remotely flown UAVs are now feasible. Our study provides the first in-depth analysis of challenges and potential solutions for successful implementation of an autonomous UAV mission.

Landscape changes based on spectral responses allow showing plant cover changes through diversity, composition, and ecological connectivity. The spatial and temporal vegetation dynamics of the Bijagual Massif from 1986 to 2021 were analyzed as a measure of ecological integrity, conservation, and territory. The covers identified were high open forest (Hof), dense grassland of non-wooded mainland (Dgnm), a mosaic of pastures and crops (Mpc), lagoons (Lag), and bare and degraded lands (Bdl). The Bijagual Massif has 8,574.1 ha. In 1986, Dgnm occupied 42.6% of the total area, followed by Mpc (32.8%) and Hof (24.5%); by 2000, Mpc and Hof increased (43.7 and 28.1%, respectively), while Dgnm decreased (28%); by 2021, Dgnm was restricted to the northeastern zone and continued to decrease (25.2%), Mpc occupied 52.9%, Hof 21.7% and Bdl 0.1%. Of the three fractions of the connectivity probability index, only dPCintra and dPCflux contribute to ecological connectivity. Hof and Dgnm show patches with biota habitat quality and availability. Between 1986 and 2021, Dgnm lost 1,489 ha (41%) and Hof 239.5 ha (11%). Mpc replaced various covers (1,722.2 ha; 38%) in 2021. Bijagual has a valuable biodiversity potential limited by Mpc. Territorial planning and sustainable agroecological and ecotourism proposals are required due to the context of the ecosystems.

Keywords: Forest management; sustainability; FSC; PEFC; research; market share

Forests, as a form of natural resource, play a significant role in the social, ecological, and economic aspects of community life. One of the communities that live in the forest and dependent on the forest is Orang Rimba in Jambi. The objectives of this research are to investigate changes in ownership and access systems of natural resources within the Orang Rimba community, the factors driving these changes, and the impacts of changes in access and control over natural resources on the socio-cultural conditions of the Orang Rimba community. This research was conducted using a qualitative approach. The result of this research shows that the Orang Rimba community has experienced changes in access and control of forest resources. Many factors contribute to these changes, mainly from the Government and market forces. Moreover, these changes bring about some fundamental changes in Orang Rimba’s livelihood and even their live in general.

The study sheds light on the impact of urbanization on fragile ecosystems such as the western Himalayas. We use Haldwani in Uttarakhand as an example of human encroachment and loss of ecosystem services. Several environmental parameters such as Nighttime light (NTL), Land Surface Temperatures (LSTs), Aerosol Optical Depth (AOD) and forest cover are used based on satellite imagery to allow a bidecadal comparison (between 2000 and 2020) of the status of these parameters for the city based on these parameters shows a decline in ecosystem services. Significant statistical differences for LSTs and AOD (p < 0.001) can be found in the bidecadal comparison. Furthermore, a strong negative correlation was found between LST-NDVI (r = -0.69) and between NTL-NDVI (r = -0.58) in earlier and last decade intervals. In addition, long-term multi-spectral satellite imagery also shows a decline in tree cover in the reserved forest. Therefore, focusing on ecosystem services related to tree cover in reserved forest areas, particularly in the Indian Himalayan Region (IHR) must be part of a broader action plan to address these issues to further protect fragile Himalayan ecosystems.

Affem Boussou community forest (AFC) abounds in important biological resources. This study, which contributes to its better management, examines the spatiotemporal dynamics of the vegetation and its ecological and structural characteristics to propose a zoning plan for the said forest. The analysis of the spatiotemporal dynamics of land use in the AFC from Google Earth images of 2015, 2018, and 2021 revealed a regressive trend of formations: crops and fallows (-33.98%), dense dry forests (-7.92%), gallery forests (-3.46%), plantations (-100%), grassy savannahs and meadows (-18.84%) except for tree/shrub savannahs (484.23%). The floristic inventory identified 163 species divided into 129 genera and 55 families. Fabaceae (14.02%), and Combretaceae (10.55%) are the most represented families. Anogeissus leiocarpa (5.19%) and Vitellaria paradoxa (4.72%) are the most frequent species. We note the dominance of individuals of small diameters. The regeneration potential of the AFC is 64 feet/ha due to 21 feet/ha of suckers, 29 feet/ha of seedlings, and 14 feet/ha of shoots. As a zoning plan, the AFC was subdivided into four series: the agroforestry zone (18.80%), the sustainable production forest zone (42.22%), the buffer zone (11%), and the biological conservation zone (28%). These results constitute a scientific basis for testing ecological indicators of sustainable management of community forests in Togo.

Soil enzyme activity can be affected by both production and degradation, as enzymes can be degraded by proteases. However, the impact of nutrient addition on enzyme activity is often solely attributed to changes in enzyme production without fully considering degradation. Here, we demonstrated that the activities of β-1,4-glucosidase (BG), β-D-cellobiohydrolase (CBH), β-1,4-Xylosidase (BX), and β-1,4-N-acetyl-glucosaminnidase (NAG) were comparable in nitrogen (N) and phosphorus (P) fertilized soils and the unfertilized control under field conditions, but the reduction in activity was substantially greater in the fertilized soils during short-term laboratory incubation. The results show that the interruption of the natural, continuous supply of organic matter or non-soil microbial-derived enzymes, which typically occurs under field conditions, leads to a more significant reduction in apparent enzyme activities in fertilized soils compared to unfertilized control. This may be attributed to the higher abundance of protease in fertilized soils, resulting in faster enzyme degradation. Interestingly, P fertilization alone did not have a similar effect, indicating that N fertilization is likely the main cause of the larger decreases in enzyme activity during incubation in fertilized soils compared to unfertilized control soils, despite our study site being poor in P and rich in N. These findings highlight the importance of considering enzyme degradation when investigating material dynamics in forest ecosystems, including the impact of nutrient addition on enzyme activity, as enzyme production alone may not fully explain changes in apparent enzyme activity.

The amino acid composition of the active site in an enzyme is influential on its substrate selectivity. For CYP2E1, the role of PHE residues in the formation of effective orientations for its activity toward aromatic substrates remains unclear. In this study, molecular docking and molecular dynamics analysis were performed toward the interactions between PHEs in the active site of human CYP2E1 and various aromatic compounds as confirmed CYP2E1 substrates. The results indicated that the orientation of 1-methylpyrene (1-MP) in the active site was highly modulated by the PHEs, PHE478 contributing to the binding free energy most significantly. Furthermore, by building a random forest model the relationship between each of 19 molecular descriptors of PCB congeners (from molecular docking, quantum mechanics, and physicochemical properties) and established human CYP2E1-dependent mutagenicity of a series of polychlorinated biphenyls (PCBs), which have been proven to be human carcinogens and endocrinal disrupters, was investigated. The presence of PHEs did not appear to significantly modify the electronic or structural feature of each bound ligand (PCB), instead, the flexibility of the molecular conformation of PHEs contributed substantially to the effective binding energy and orientation. It is supposed that PHE residues adjust their own conformation to permit a suitable space for the ligand binding and form an orientation favorable for a biochemical reaction. This study has provided some insights into the role of PHEs in guiding the interactive adaptation of the active site of human CYP2E1 for the binding and metabolism of aromatic substrates.

High PM10 concentrations are still a significant problem in many parts of the world. In many countries, including Poland, 50μg/m3 is the permissible threshold for a daily averaged PM10 concentration. The number of people affected by this threshold’s exceedance is challenging to estimate and requires high-resolution concentration maps. This paper presents an application of random forests for downscaling regional model air quality results. As policymakers and other end users are eager to receive a detailed resolution PM10 concentration maps, we propose a technique which utilizes the results of regional CTM (GEM-AQ, with 2.5km resolution) and local Gaussian plume model. As a result, we receive a detailed, 250-meter resolution PM10 distribution, which resembles the complex emission pattern in a foothill area in southern Poland. The random forest results are highly consistent with the GEM-AQ and observed concentration. We also discuss different strategies of data training random forest - using additional features and selecting target variables.

Successful fertilization treatments targeted to improve stand productivity while reducing operational complexities and cost depend on a clear understanding of soil nutrient availability under varying environmental conditions. Soil nutrient data collected from 154 forest sites throughout the Inland Northwest, USA were analyzed to examine soil nutrient characteristics on different geologic soil parent materials and to rank soil fertility. Results show that soil parent material explains significant differences in soil nutrient availability. Soils developed from volcanic rocks have the highest CEC and are relatively high in P, K, S, Mg, Cu, Ca, and B, but generally poor in N. Forest soils developed from plutonic rocks exhibit the lowest CEC and are low in N, S, K, Mg, Cu, and Ca, but higher in P. Some soils located on mixed glacial till are low only in K, Cu, Mg, and Ca, but many glacial soils are relatively rich in other nutrients, albeit the second lowest CEC. Soils developed from metasedimentary and sedimentary rocks are among those with lowest soil nutrient availability for P and B. Sulfur was found to have the highest concentrations in metasedimentary influenced soils and the least in sedimentary derived soils. Our results should be useful in designing site-specific fertilizer and nutrient management prescriptions for forest stands growing on soils developed from these major geologies within the Inland Northwest region of the United States.

Forest enterprise has been identified as a means of generating income among people; plays a vital role in enhancing the quality of life of forest-dependent people. Despite the opportunities timber marketing offers the people, the disparities in the income generation of the marketers in the Bodija sawn wood Market and the effect of socio-economic factors on income generation of the marketers is not well understood. This study was conducted to assess the socio-economic determinants of contributions of timber marketing to the income of timber merchants in Bodija sawn-wood Market. One hundred structured questionnaires were administered randomly in five zones of the sawn wood Market to obtain information on the socio-economic background of the sawn wood marketers and the contribution of timber trade to their incomes. The result indicated that 99.0% of the respondents were male while females constituted 1.00%. Seventy-five percent of the marketers had post-primary education and 25% had primary education. Two percent of the marketers had below 10 years of marketing experience, twenty-six percent had between 11 and 20 years, 57.00% had between 21 and 30 years, and 15.00% had more than 30years experience. Fifty-eight percent of the respondents earned between ₦10000-₦60000 (1US$ = 360.00) from timber marketing, thirty-one percent earned between ₦60001 and ₦110000, 7% earned between ₦110001 and ₦160000, while 4% earned above ₦160000 per month. Chi-square analysis of the socio-economic characteristics of the respondents and income generation at α level of 0.05 indicated that ethnicity (0.001) and years of experience (0.009) significantly influenced income while the level of education (0.101), age (0.122), and religion (0.745) had no significant influence on the incomes of marketers. Experience is an important factor in sawn wood marketing and a major determinant of the contribution of timber marketing to the income of timber marketers in Bodija sawn wood Market.

Forests are critical in regulating the world’s climate and they maintain overall Earth’s energy balance. The variability in forest canopy structure, topography and underneath vegetation background condition creates uncertainty in the estimation and modelling of Earth’s surface radiation particularly for boreal regions in high latitude. We studied seasonal variation in surface reflectance with respect to land cover classes, canopy structures, and topography in a boreal region of Alaska by fusing together Landsat 8 surface reflectance and LiDAR-derived canopy matrices. Our study shows that canopy structure and topography interplay and influence surface reflectance in a complex way particularly during the snow season. Topographic aspect and elevation control vegetation growth, type and structure. The southern slope is featured with more deciduous and taller trees having greater rugosity than the northern slope. Higher elevation is associated with taller trees for both vegetation types, particularly in the southern slope. In general, surface reflectance shows similar relationships with canopy cover, height and rugosity, mainly due to close relationships between these parameters. Surface reflectance decreases with canopy cover, tree height, and rugosity especially for evergreen forest. Deciduous forest shows larger variability of surface reflectance, particularly in March, mainly due to the mixing effect of snow and vegetation. The relationship between vegetation structure and surface reflectance is greatly impacted by topography. The negative relationship between elevation and surface reflectance may be due to taller and denser vegetation distribution in higher elevation. Surface reflectance in the southern slope is slightly larger than the northern slope for both deciduous and evergreen forest. The shadow effect from topography and tree crowns on surface reflectance play a different role for deciduous and evergreen forests. For deciduous forest, topographic shadow effect on surface reflectance is stronger than from tree shadowing in all seasons. For evergreen forest, shadow effects from topography and tree crowns on surface reflectance are both equally dominant, however tree shadow effect is more significant in March than in May and August. The generalized additive models (GAM) based on non-linear relationships between response (surface reflectance) and predictor (canopy structures and topography) variables confirms such observations. Our study not only provides accurate quantification of surface radiation budget but also helps in parametrization of climate change models.

Within the town, Abiotic is a built environment that includes buildings, roads, pedestrians, and other elements that interact with biotics, which are living things including plants, animals, and humans. From a landscape ecological perspective, the urban structure consists of (1) a matrix, which is a collection of dominant buildings and homogeneous elements, (2) Patches are grouped as housing, urban forests, parks, lakes, and finally (3) Corridors such as roads, rivers, and pedestrians. The dominance of watertight areas over green open spaces in urban development can lead to increased temperatures and runoff. The condition of the soil structure and the steep slope of the soil can cause landslides, therefore urban development must pay attention to the natural conditions of the area being built. This research was conducted in Kota Baru, Bogor, South Tangerang, and Cikarang (Bekasi Regency). The purpose of this study is to determine the natural environment and the built environment as well as changes in the ecosystem and their consequences for the new town and its surroundings. This research uses quantitative and qualitative approaches. Analysis of land-use change uses spatial and temporal methods, while Nieuwolt's equation is used to measure comfort. This study finds comfortable environmental planning, with green open spaces such as urban forests, city parks, and bodies of water, such as lakes, as a space for interaction between fellow new city residents and people outside the new towns.

Produced in many world’s countries at over 1 million tonne/year rate by extraction of certain woods and barks with boiling water, tannin is a class of high molecular weight biophenols increasingly used in a number of industries. This study offers a new bioeconomy insight into an old natural product that, we argument in this study, will play a crucial role in the development of the bioeconomy of forest regions. After providing an updated picture of key economic and production aspects, we show how flourishing research on tannin’s biological activity and technological applications has revealed many new properties which are likely to drive significant growth in demand in the near and mid-term future. The study concludes with selected recommendations for bioeconomy scholars and for policy-makers based in forest areas.

A botnical survey was conducted in Kaptai reserve forests under Rangamati district in Bangladesh to study the flora of Karnaphuli range from May 2015 to October 2018. The survey was accompanied by a collection of voucher specimens enumerates 464 plant species belonging to 334 genera under 117 families from the forest range. The survey has confirmed 31 threatened forest species from this area along with many near threatened plant species.

A botnical survey was conducted in Kaptai reserve forests under Rangamati district in Bangladesh to study the flora of Karnaphuli range from May 2015 to October 2018. The survey was accompanied by a collection of voucher specimens enumerates 464 plant species belonging to 334 genera under 117 families from the forest range. The survey has confirmed 31 threatened forest species from this area along with many near threatened plant species.

The European heatwave of 2018 led to record-breaking temperatures and extremely dry conditions in many parts of the continent resulting in widespread decrease in agricultural yield, early tree-leaf senescence, and increase in forest fires in Northern Europe. Our study aims to capture the impact of the 2018 European heatwave on terrestrial ecosystem through the lens of a high-resolution solar-induced fluorescence (SIF) data acquired from the Orbiting Carbon Observatory (OCO-2) satellite. SIF is proposed to be a direct proxy for gross primary productivity (GPP) and thus can be used to draw inferences about changes in photosynthetic activity in vegetation due to extreme events. We explore spatial and temporal SIF variation and anomaly during spring and summer months across different vegetation types (agriculture, broadleaved forest, coniferous forest, and mixed forest) during the European heatwave of 2018 and compare it to non-drought conditions (most of Southern Europe). About one-third of Europe’s land area experienced a consecutive spring and summer drought in 2018. Comparing 2018 to mean (2015-2017) conditions, we found a change in intra-spring season SIF dynamics for all vegetation types, with lower SIF during the start of spring followed by an increase in fluorescence from mid-April. Summer, however, showed a significant decrease in SIF. Our results show that particularly agricultural areas were severely affected by the hotter drought of 2018. Furthermore, the intense heat wave in Central Europe showed about 31% decrease in SIF values during July and August as compared to the mean over three previous years. Furthermore, our MODIS and OCO-2 comparative results indicate that especially for forests, OCO-2 SIF has a quicker response and possible higher sensitivity to drought in comparison to MODIS’s fPAR and NDVI when considering shorter reference periods, which highlights the added value of remotely sensed solar-induced fluorescence for studying the impact of drought on vegetation.

We propose a new estimator for creating expansion factors for survey plots in the USDA Forest Inventory and Analysis program. This is a regularized version of the raking estimator widely used in sample surveys. The regularized raking method differs from other predictive modeling methods for integrating survey and ancillary data in that it produces a single set of expansion factors that can have general purpose use to produce small area estimates and wall-to-wall maps of any plot characteristic. This method also differs from other more widely used survey techniques, such of GREG estimation, in that it is guaranteed to produce positive expansion factors. We extend the previous method here to include cross-validation, and provide a comparison to expansion factors between the regularized raking and ridge GREG survey calibration.

Background: Diabetes is among the most prevalent diseases worldwide, of all the affected individuals a significant proportion of the population remains undiagnosed because of a lack of specific symptoms early in this disorder and inadequate diagnostics. Diabetes and its associated sequela, i.e., comorbidity are associated with microvascular and macrovascular complications. As diabetes is characterized by an altered metabolism of key metabolites and regulatory pathways. Metabolic phenotyping can provide us with a better understanding of the unique set of regulatory perturbations that predispose to diabetes and its associated comorbidities. Methodology: The present study utilizes the analytical platform NMR spectroscopy coupled with Random Forest statistical analysis to identify the discriminatory metabolites of diabetes (DB) and diabetes-related comorbidity (DC) along with the healthy control (HC) subjects. A combined and pairwise analysis was performed, between the serum samples of HC (n=50), and DB (n=38), and DC (n=35) individuals to identify the discriminatory metabolites responsible for class separation. The perturbed metabolites were further rigorously validated using t-test, AUROC analysis to examine the statistical significance of the identified metabolites. Results: The DB and DC patients were well discriminated from HC. However, 15 metabolites were found to be significantly perturbed in DC patients compared to DB, the identified panel of metabolites are TCA cycle (succinate, citrate), methylamine metabolism (trimethylamine, methylamine, betaine), -intermediates; energy metabolites (glucose, lactate, pyruvate); and amino acids (valine, arginine, glutamate, methionine, proline and threonine). The metabolites were further used to identify the perturbed metabolic pathway and correlation of metabolites in DC patients. Conclusion: The 1H NMR metabolomics may prove a promising technique to differentiate and predict diabetes and its comorbidities on their onset or progression by determining the altered levels of the metabolites in serum.

Forest certification is considered a viable market-based policy instrument to promote forest sustainability. It has an important role of play in meeting the objective of modern forestry development in China, which is to sustain ecological and environmental benefits of forests. To understand differences in attitudes, opinions, and interests in forest certification, this study segmented respondents of a landowner’s survey in Shandong, China based on their level of interest in participating in forest certification under different program requirements. Multivariate cluster analysis revealed three distinct groups: likely-, potential-, and unlikely-landowners. We further examined the heterogeneity of these groups in terms of their demographics, ownership characteristics, management objectives, and perceived benefits and challenges with adopting forest certification. The results suggested the necessity of differentiating landowners in formulating and designing specific motivation-based incentives and tailor outreach efforts and communication strategies to improve their interests in forest certification. Findings are useful and interesting to forest policymakers interested in promoting forest certification among landowners in China and other countries facing similar circumstances.

Forest fire is a major ecological disaster, which has economic, social and environmental impacts on humans and also causes the loss of biodiversity. Forest officials issue the warnings to the public on the basis of fire danger index classes. There is no fire danger index for the country India due to the sparsely distributed meteorological stations. Previous studies suggest that Static Fire Danger Index (SFDI) as well as Dynamic Fire Danger Index (DFDI) have been derived from the satellite datasets. In this study, we have made an attempt to integrate both the Static and Dynamic fire danger indices and also used the Near Real Time (NRT) data sets that can be available for download through NASA FTP server after one hour of the satellite overpass. In this study, DFDI has been calculated from the MODIS Terra NRT Land Surface Temperature (MOD11_L2) and MODIS TERRA NRT surface reflectance MOD09. Finally, The Forest Fire Danger Index (FFDI) has been developed from the static and dynamic fire danger indices by the additive model and the overall accuracy was around 81.27%. Thus, the FFDI has been useful to predict the fire danger accurately and can be useful anywhere, where the meteorological stations are un-available.

Land use land cover change(LULCC) plays a significant role in understanding the global change. Particularly in regions with high population growth and climate change, anthropogenic activities have drastically altered land use and land cover (LULC). The geographic information system (GIS) and remote sensing are prominent tools for monitoring LULC changes. The land use land cover(LULC) analysis and carbon loss assessment were carried out in Ayubia national park. The area was classified into five major LULC classes, namely grassland, built-up areas, mixed forests, conifer forests and bare land. Landsat temporal satellite data of Ayubia national park acquired on 1992,2002,2012 and 2022 were analysed to access land use change and Markov’s Chain Model was used to predict the land use land cover changes for 2040. The accuracy of the images were assessed using Kappa matrix. Our results showed that a total of 371.94 ha forest land has been converted into other land uses from 1992 to 2022 and Indicating the increasing scale of anthropogenic pressure on forest resources of the Park. The carbon pool was assessed in two forest types including temperate coniferous forest and Mixed forest. The estimated carbon value recorded in TCF is 135.19 ±9.74 MgC ha-1, whereas it is in 86.43 ±8.25 MgC ha-1 in MF. Using satellite data, a thorough examination of the forest inventory was conducted to determine the LULCC, the rate of biomass carbon loss, and the factors that influenced these trends.

Climate analogues provide forestry practice empirical evidence of how forests are managed in “twin” regions, i.e. regions where the current climate is comparable to the expected future climate at a site of interest. But the uncertain future climate creates uncertainty in how to adapt the forests. We therefore investigate how the uncertainty in future climate affects tree species suitability and whether there is a common underlying pattern. Like most studies we employ different ensemble variants of RCP 4.5 and 8.5. But instead of focusing on a single point in future time, we resolve each variant in a climate trajectory from 2000 to 2100. We calculate climatic distances between the climate trajectories of our site of interest and the current climate in Europe, generating maps with twin regions from 2000 to 2100. Forest inventories from the twin regions allow us to trace the changes in the prevalence of 23 major tree species. We find that it is not the direction but rather the velocity of the change that differs between the scenarios. We use this pattern to propose a tree species suitability concept that integrates the uncertainty in future climate. Twin regions provide further information on silvicultural practices, pest management, product chains etc.

Forest carbon sequestration is a widely accepted natural climate solution, however, methods to determine net carbon offsets are limited to commercial carbon proxies and CO2 eddy covariance research. Non-CO2 greenhouse gases (GHG) (e.g., CH4, N2O) receive less attention in the context of forests, in part, due to emphasis on CO2 and the operational requirements and cost for three-gas eddy covariance platforms. In this study, Howland forest flux tower (CO2, CH4) and soil flux data (CO2, CH4, N2O), representing net emission reductions, are linked to their respective social costs to estimate commercial revenue if sold as a GHG social cost forest offset product (GHG-SCF). Estimated annual revenue for GHG-SCF products, applicable to realization of a Green New Deal, range from 120,000 covering the site area of 557 acres in 2021, to 12,000,000 for extrapolation to 40,000 acres in 2040, assuming a 3% discount rate. The Howland Forest CO2 flux record for two adjacent towers is compared to California Air Resources Board forest carbon proxy data for compliance sequestration offsets, the only project site where these approaches overlap. Overcrediting, incomplete carbon accounting with annual errors of up to 2,256%, inadequate third-party verification, and limited application to non-CO2 GHG’s are established. In contrast, direct measurement of one or more GHG’s offers new forest products and revenue incentives to restore and conserve forests worldwide.

This study aims to evaluate the potential of machine learning algorithms (Random Forest and Support Vector Machine) in predicting the open porosity of a general-use industrial mortar applied to different substrates based on the characteristics of both the mortar and substrates. For this purpose, an experimental database comprising 1592 samples of industrial mortar applied to five different substrates (hollowed ceramic brick, solid ceramic brick, concrete block, concrete slab, and lightweight concrete block) was generated by an experimental program. The samples were characterized by bulk density, open porosity, capillary water absorption coefficient, drying index, and compressive strength. This database was then used to train and test the machine learning algorithms to predict the open porosity of the mortar. The results indicate that it is possible to predict the open porosity of the mortar with good prediction accuracy and that both Random Forest (RF) and Support Vector Machine (SVM) algorithms (RF =0.880; SVM = 0.896) are suitable for this task. Regarding the main characteristics that influence the open porosity of the mortar, bulk density and the open porosity of the substrate are significant factors. Furthermore, the study employs a straightforward methodology with a machine learning no-code platform, enhancing the replicability of its findings for future research and practical implementations.

Linear programming models have been used in forest management planning since the 1960s. These models have been formulated in three basic ways: Models I, II, and III, which are defined by the sequences of management unit states represented by the variables. In Model I, variables represent sequences of states from the beginning of the planning horizon to the end. In Model II, variables represent sequences of states from one intervention to the next. Finally, in Model III, variables represent a single arc in a management unit’s decision tree, i.e., two states. The objectives of this paper are to clarify the definitions of these model variations and evaluate the advantages and disadvantages of each model. This second objective was achieved by formulating a case study problem using each model type. The case study includes three increasingly complex scenarios, each incorporating additional ecosystem services. Results show that despite having more variables and constraints, Model III requires the least time to formulate due to its less dense parameter matrix. Model II has the shortest solution times, followed closely by Model III, while Model I requires the longest times for both formulation and solution. These results are increasingly apparent in more complex scenarios.

Based on the land use data of Qingmei Harbor Mangrove Nature Reserve in Sanya, China, in 2009, 2015 and 2022, this paper utilizes the PLUS model to predict the land use changes in the region in 2035 under three development scenarios, namely natural development, urban construction and ecological protection. The results of the study show that the area of building land in this region increased rapidly from 2009-2015, and the corresponding cropland, parkland and arbor forest land as a whole showed a different degree of decline, while the expansion of the area of manmade land, such as building land, basically came to a standstill after 2015. However, the mangrove area in this region has maintained a stable growth trend from 2009-2022. The results of multi-scenario modeling of land in this region showed that the area of building land, unutilized land, and artificial land such as parkland and grassland increased the most under the urban development scenario. In the ecological protection scenario, the area of ecological land, such as arbor forest land and river and lagoon, increases significantly. However, there is no significant increase in the area of mangrove forests under any of the three development scenarios. At present, the task of “returning ponds to wetlands” has been basically completed in this area, and the space for large-scale expansion of mangrove forests in Qingmei Harbor is limited in the future, and the focus of subsequent work should be shifted to the enhancement of the quality and function of mangrove ecosystems.

Forest fires can occur suddenly and have significant environmental, economic, and social consequences. Timely and accurate evaluation and prediction of their progression, particularly the spread speed in difficult-to-access areas, are essential for emergency management departments to proactively implement prevention strategies and scientifically extinguish fires. This paper provides a comprehensive analysis of advanced technologies for predicting forest fire spread in China and globally. Incorporating remote sensing (RS) technology and forest fire science as the theoretical foundation, and utilizing the Wang Zhengfei forest fire spread model as the technical framework, this study constructs a forest fire spread model based on remote sensing interpretation. The model optimizes the method for acquiring parameters and enhances algorithm accuracy. By considering regional landforms (ridge lines, valley lines, slopes) and vegetation coverage, this paper establishes visual interpretation markers for identifying hotspots within the spread model. Utilizing statistical data from pixels within the fire line zone transforms the methodology for predicting forest fire spread speed, resulting in greater accuracy compared to conventional fixed-point predictions. Finally, the model was applied to the case of a forest fire in Mianning County, Sichuan Province, China and verified using high-time resolution mid-infrared image data and previous research findings. The results demonstrate that the predicted fire spread area from this RS-enabled model is consistent with both high-time resolution geostationary satellite data and previous research outcomes, indicating high reliability. This model can provide crucial fire spread information to relevant emergency departments, enabling effective preemptive measures and scientific firefighting strategies.

One of the main products of the forest, which has the greatest economic impact, is wood. Due to its characteristics, the wood requires certain types of vehicles for transport it. For this reason, the accessibility of the stands with forest roads plays significant role in the efficiency of the wood logging process, which aims to bring the wood from the forest to the harvesting area located, frequently, next to a permanent transport route. The process of wood transport from the forest initially takes place on forest roads and after that on public roads. The technological process of wood transport represents an important stage in this process, and its good organization can have implications on the final price of the wood material. Based on those mentioned above, the paper aims to highlight the factors that can influence productivity in the case of loading wood in forestry trucks and forestry platforms, in different conditions. Thus, the research was carried out in four counties in Romania and addressed the loading of wood in different forestry trucks and platforms, with or without trailers. Following the evaluation of the work phases specific to the process of round wood loading in vehicles of transport, it was found that sometimes a significant importance is given to the working phases that can be carried out before the loading process, as phases of preparation of the wooden material in order to load it. Also, the facilities of the transport vehicles, the assortments of wood material, as well as the experience of the operators are the main factors that can lead to an increase in productivity.

Wood quality has been an ongoing concern for specialists, which has become increasingly important in the current context, where the demand for wood is increasing and forest fires are more frequent and violent. The article aims to evaluate the quality of wood in trees affected by fires and the negative impact of these phenomena on the speed of wood degradation, as a result of the weakening of the trees due to the action of disturbing factors. Investigations were carried out using modern techniques on the beech trees (Fagus sylvatica L.) remaining in an area affected by a litter fire in 2017. Measurements were taken were made with the Arbotom Rinntech sound tomograph, the IML F-500 Resi resistograph and the Pressler core sampler to observe the quality of the wood inside the trunk. It was found that all the trees are in various stages of decaying the tomograms being able to highlight the severity of the defects only in the case of the total destruction of the wood structure as a result of the action of xylophages fungi, whose harmful influence is more pronounced when the injuries sustained by the trees from the fire were greater. Although the trees tried to close the fire wounds through their own defense mechanisms, the destructive action of the fungi intensified with the time.After the forest fires, for quickly and clear results regarding the internal quality of the wood could be used the resistograph. For valuable trees could be used the tomograph, but the measurements have to be taken only by qualified operators.

Since the 1960s, forest planners have used linear programming models to develop management plans for large, forested areas. Hundreds of academic papers have presented such models, incorporating multiple objectives, a growing diversity of management interventions, and uncertainty, among other things. Three basic ways to formulate these models have been used: Model I, III, and III. We define these models based on the sequence of management unit states represented by the variables. In Model I, variables represent a sequence of states from the beginning of the planning horizon to the end. In Model II, variables represent a sequence of states from one intervention to the next. Finally, in Model III, variables represent a single arc in a management unit’s decision tree, including only a beginning and an ending state. We formulate each type of model for a case study with three increasingly complex scenarios incorporating additional ecosystem services. Our results indicate that, despite requiring more variables and constraints, Model III requires the least time to formulate, largely because it has the least dense parameter matrix. Model II has the shortest solution times, with Model III close behind. Model I requires both the longest formulation and solution times.

By sinking and storing carbon from the atmosphere via photosynthesis, forests play a significant role in combating climate change. Global warming is currently the most pressing global issue. Forests play an important role in the global carbon cycle, covering 30% of the world's terrestrial land area and conserving 81% of the earth's terrestrial carbon biomass. The aim of the study was to quantify and map the Carbon Sink and Stock Values of the Yeraba state forest ecosystem. Primary and secondary data sources were used in this study. Primary data such as plot length and width, DBH (diameter at breast height), soil data, and ground control point (GCP) were obtained in the field using a Garmin 72 GPS, a clinometer, and a diameter tape. Diameter tape, caliper and Soil ogre materials. Beside from this sentimental 2 satellite image, there was also another important primary data. In addition to the primary data source, secondary data sources were used. It includes published books, journals and studies, reports, website sources, and other materials that are publicly available. The systematic sampling technique for identifying each intersection point in the area at regular intervals was the most appropriate sampling design for this study. The study used data from four forest carbon pools, including AGB, BGB, soil, and litter, to calculate the carbon sink and stock values of the Yeraba state forest ecosystem. A total of 17 quadrats, each measuring 20m x 20m and spaced 428 m apart, were used to collect vegetation data. For litter and soil sample collection, five 1 m x 1 m sub-quadrats were established at each quadrat's four corners and center. This data was correlated with five Sentinel 2A level 1c imagery data derived vegetation indices of 2023 to obtain a model and estimate of AGB and AGC at the study site. The total mean carbon stock density of Yeraba state Forest was 404.895 t/ha, with 223.656t/ha, 58.15t/ha, 3.41t/ha, and 119.679 t/ha in the above ground carbon, below ground carbon, litter carbon, and soil carbon pools, respectively. The three environmental factors that influence the distribution of different carbon pools in the forest are altitudinal gradient, slope, and aspect. Standing from the result and inputs the researcher recommended that, Future research may find that using LiDAR and Radar imageries, which can address the saturation and canopy penetration issues, will increase the accuracy of carbon stock estimation.

Climate change has altered the frequency and severity of extreme weather, which can affect vegetation condition and habitat quality for wildlife. Declines in vegetation productivity during droughts and heatwaves can negatively impact animals that depend on vegetation for water and nutrition. The ability to detect such effects on habitat suitability can reveal refugia, which can be prioritised for protection to improve threatened species conservation. We used the normalised difference vegetation index (NDVI) to look at relationships between vegetation productivity and the presence of koalas (Phascolarctos cinereus) in potential habitat throughout much of their range. Using a large, long-term koala presence dataset, we tested the hypothesis that locations where koalas had been observed would exhibit higher NDVI values than a random, representative sample from the same vegetation group (i.e. woodlands, open forests or tall open forests). We also identified the minimum NDVI threshold at which koalas occurred across time for each vegetation group and compared these to the minimum NDVI values observed across potential koala habitat before and during the millennium drought; one of the worst recorded in Australia. Additionally, we investigated whether vegetation above the minimum NDVI thresholds was significantly closer to perennial water than unsuitable vegetation. We found that koalas tend to occur at locations exhibiting higher NDVI than average for all vegetation groups. We also found that 49% of all vegetation groups maintained a minimum NDVI above the koalas’ NDVI threshold before drought, equating to 190,227 km2, which declined to 166,746 km2 during drought (a 12% reduction). We also found that unsuitable vegetation tended to occur farther from perennial water than suitable vegetation for some vegetation groups. Areas that remained above the NDVI thresholds during the drought should be considered potential refugia for populations during an event of similar magnitude and could indicate future core habitat extent.

Nitrogen is one of the most important macronutrients for crops, and in conjunction with artificial intelligence algorithms, it is possible to estimate it with the aid of vegetation indices through remote sensing. Various indices were calculated and those with a correlation ≥ 0.7 were selected for subsequent use in Random Forest, Gradient Boosting, and Artificial Neural Networks to determine their relationship with nitrogen levels measured in the laboratory. Random Forest showed no relationship, yielding an R2 of zero, whereas Artificial Neural Networks yielded the best results with an R2 of 0.93. Thus, it is reliable to estimate nitrogen levels using this algorithm by feeding it with data from TCARI, MCARI, TCARI/OSAVI, and MCARI/OSAVI, assisted by technological tools.

Forests are crucial natural resources in Indonesia, storing significant biodiversity and providing essential benefits for both the environment and people's livelihoods. They contribute significantly to the country's economy, support local communities, and play a vital role in mitigating climate change through carbon sequestration. This study examines the carbon storage valuation in tree forests of Telaga Warna National Park, Indonesia, highlighting the importance of preserving forests for carbon sequestration and climate resilience. The data was collected within a subplot measuring 10x10 meters, and for trees within a plot measuring 20x20 meters. The study focuses on pole and tree species. Based on the data obtained, it is found that the dominant vegetation in Telaga Warna National Park is Riung Anak / Casitanopsis javanica, with a potential carbon value of US$ 2.96 per Ha for pole species and US$ 98.49 for tree species.

Emerald ash borer (EAB), Agrilus planipennis, an invasive phloem-boring beetle, was detected in the Luhansk Region of Ukraine in 2019. In the following years, it expanded to a large part of the Kharkiv region and the parks of Kyiv. Previous research proved that the climate in Luhansk and neighboring regions is favorable for EAB, and the lack of a host plant (Fraxinus sp.) cannot prevent pest expansion in Ukraine. Considering the necessity to detect inhabited trees in time, our current research aimed to identify the most attractive EAB forest subcompartments considering forest site conditions and stand structure. MaxEnt model predicted the potential distribution of EAB with high accuracy (AUC = 0.842). The most significant five variables provided 82.9% to the model, particularly «Age of trees, years», «Area of forest subcompartment, ha», «Mean height of trees, m», «The proportion of Fraxinus excelsior in the stand composition, %», and «Hygrotope index (humidity level), point». A very high probability of EAB expansion in the Luhansk region (&gt;90 %) is predicted at 20 % of the forest area with Fraxinus sp. in the stand composition. The probability of EAB spread above 30% is predicted for 75 % of such area. To improve the forecasting accuracy, it could be necessary to consider the data on road localization along which the pest can spread passively, as well as dominate wind speed.

The current communication presents the application of a consolidated model combination strategy to analyze the medium-term carbon fluxes in two Mediterranean pine wood ecosystems. The modelling strategy is based on the use of a NDVI-driven parametric model, Modified C-Fix, and of a bio-geochemical model, BIOME-BGC, the outputs of which are combined taking into account the actual development phase of each ecosystem. The two pine ecosystems examined correspond to an old-growth forest and to a secondary succession after clearcuts, which differently respond to the same climatic condition during a ten-year period (2013-2022). Increasing dryness, in fact, exerts a fundamental role in controlling the gross primary and net ecosystem production of the mature stand, while the effect of forest regeneration is prevalent for the uprising of the same variables in the other stand. In particular, the simulated net carbon exchange fluctuates around 200 g C m-2 year-1 in the first stand and rises to over 600 g C m-2 year-1 in the second stand; correspondingly, the accumulation of new biomass is nearly undetectable in the former case while becomes notable in the latter. The study therefore supports the potential of the applied strategy for predicting the forest carbon balances consequent on diversified natural and human-induced factors.

Non-timber forest Products (NTFPs) are a broad category of natural resources harvested from forests that do not involve cutting down trees for their wood or timber. NTFPs play a vital role in the livelihoods and economies of many rural communities, particularly in forest-dependent regions. Despite the benefits obtained from NTFPS, little is known about its potential to alleviate poverty in the local communities. The objective of this review is to comprehensively assess the potential of NTFP's contribution to poverty reduction within forest fringe communities. The study employed the systematic review method to delve into the multifaceted relationship between NTFPs and poverty alleviation. We followed the preferred reporting items for systematic reviews and meta-analyses protocol (PRISM-P). In all, 58 research articles were reviewed. The results indicated that NTFPs hold significant promise as a tool for poverty alleviation. Globally, NTFPs have the potential to alleviate poverty and increase household incomes between 19% and 78% within forest fringe rural communities. In addition, the review indicated that countries in Africa and Asia depend highly on NTFPs and provide substantial amounts of income to the locals. However, the potential for NTFPs to reduce poverty is not uniform. It is highly contingent upon local ecological conditions, market accessibility, community involvement, and supporting policies and interventions. In conclusion, this systematic review demonstrates that NTFPs have a substantial role in poverty alleviation. This study underscores the need for continued research and targeted development initiatives to unlock the vast potential of NTFPs in addressing poverty challenges.

Honey has been employed for the treatment of wounds and diseases for ancient time, thanks to its antimicrobial and curative properties. This study aims to present the biochemical characteristics, including the amount of sugar, total phenols, phenolic chemicals, antioxidant properties, and antibacterial activities of a specific type of French honey (Sologne summer forest) in comparison with a manuka honey. According to our findings, the Manuka honey tested has 23% fewer polyphenols total than Sologne summer forest honey. The results showed that the Sologne summer honey had a different polyphenols profile than Manuka honey with specific polyphenol like p- Hydroxybenzoic acid. Additionally, the antioxidant activity of the two honeys is comparable (9 μmole Trolox.g-1 honey). Furthermore, the antibacterial activity of the two honeys is comparable against Pseudomonas aeruginosa and Staphylococcus aureus. Our research indicates for the first time that there are significant nutritional and biological potentials for a French honey from the Sologne summer forest.

The adoption of sustainable forest management practices emerged as a contemporary imperative for forest biodiversity preservation, functional integrity, and mitigation of climate change. Within the spectrum of these practices, thinning is recognized as an environmentally sustainable strategy. The primary objective of thinning is the equitable redistribution of aboveground growth, diverting resources from younger trees to promote the accelerated development of more valuable specimens compared to unthinned forests. Thinning, while fostering aboveground changes, induces substantial alterations in soil chemical and biochemical properties. This review addresses the often-debated consequences of thinning on soil characteristics, focusing on the intricate interplay involving different thinning intensities and their impact on soil chemical and biochemical parameters. Specifically, the analysis delves into the modulation of organic carbon levels, microbial biomass, and enzymes crucial to the organic matter cycle. Two key aspects are scrutinized: firstly, the influence of thinning intensity and the temporal dimension post-thinning on soil properties; secondly, the comparative impact of thinning intensity versus the time elapsed from the thinning event on soil properties and associated biological processes. The findings underscore pivotal insights: a) temporal dynamics significantly affect soil organic matter across broadleaved (excluding beech), coniferous, and mixed forests, manifesting as early as 2-7 years post-thinning; b) thinning intensity, up to a threshold of 50%, enhances soil quality by augmenting organic carbon content, bolstering microbial communities, and amplifying associated enzyme activities. This review consolidates valuable information for forest managers, providing guidance to optimize natural processes in tandem with achieving specific management objectives. By illuminating the nuanced interactions between thinning practices and soil dynamics, this knowledge equips stakeholders to make informed decisions in the pursuit of sustainable forest management and ecological resilience.

Predicting nurse turnover is a growing challenge within the healthcare sector, profoundly impacting healthcare quality and the nursing profession. This study employs the Synthetic Minority Over-sampling Technique (SMOTE) to address class imbalance issues in the 2018 National Sample Survey of Registered Nurses (NSSRN) dataset and predict nurse turnover using machine learning (ML) algorithms. Four ML algorithms, namely logistic regression (LR), random forests (RF), decision tree (DT), and extreme gradient boosting (XGBoost), are applied to the SMOTE-enhanced dataset. The data is randomly split into an 80% training set and a 20% validation set. Eighteen carefully selected variables from the NSSRN database serve as predictive features, and the machine learning model identifies feature importance concerning nurse turnover. The study includes a performance comparison based on metrics such as Accuracy, Precision, Recall (Sensitivity), F1-score, and AUC. In summary, the results demonstrate that SMOTE-enhanced random forests (SMOTE_RT) exhibit the most robust predictive power, both in the classical approach (with all 18 predictive variables) and an optimized approach (utilizing eight key predictive variables). XGBoost, decision tree, and logistic regression follow in performance. Notably, age emerges as the most influential factor in nurse turnover, with working hours, EHR/EMR usability, individual income, and region also playing significant roles. This research offers valuable insights for healthcare researchers and stakeholders, aiding in selecting suitable ML algorithms for nurse turnover prediction.