Search Results (224)

Redwood (Sequoia sempervirens (Lamb. ex D. Don) Endl.) is a fast-growing, long-lived conifer native to a narrow coastal zone along the western seaboard of the United States. Redwood can accumulate very high amounts of carbon in plantation settings and continuous cover forestry (CCF) represents a highly profitable option, particularly for small-scale forest growers in the North Island of New Zealand. We evaluated the profitability of conceptual CCF regimes using two case study forests: Blue Mountain (109 ha, Taranaki Region, New Zealand) and Spring Creek (467 ha, Manawatu-Whanganui Region, New Zealand). We ran a strategic harvest scheduling model for both properties and used its results to guide a tactical-spatially explicit model harvesting small 0.7 ha units over a period that spanned 35 to 95 years after planting. The internal rates of return (IRRs) were 9.16 and 10.40% for Blue Mountain and Spring Creek, respectively, exceeding those considered robust for other forest species in New Zealand. The study showed that small owners could benefit from carbon revenue during the first 35 years after planting and then switch to a steady annual income from timber, maintaining a relatively constant carbon stock under a continuous cover forestry regime. Implementing adjacency constraints with a minimum green-up period of five years proved feasible. Although small coupes posed operational problems, which were linked to roading and harvesting, these issues were not insurmountable and could be managed with appropriate operational planning. Full article

The ecosystem services (ESs) of forests are the benefits that people derive from forest ecosystems. Their precise recognition is important for differentiating and determining the optimal principles of multifunctional forest management. The aim of this study is to identify some important ESs based on a site classification system at the lowest level—i.e., forest stands, at the forest owner level—as a tool for differentiated management. ESs were assessed within the Czech Republic and are expressed in units in accordance with the very sophisticated Forest Site Classification System. (1) Biomass production: The vertical differentiation of ecological conditions given by vegetation tiers, which reflect the influence of altitude, exposure, and climate, provides a basic overview of biomass production; the highest value is in the fourth vegetation tier, i.e., the Fageta abietis community. Forest stands are able to reach a stock of up to 900–1200 m³·ha⁻¹. The lowest production is found in the eighth vegetation tier, i.e., the Piceeta community, with a wood volume of 150–280 m³·ha⁻¹. (2) Soil conservation function: Geological bedrock, soil characteristics, and the geomorphological shape of the terrain determine which habitats serve a soil conservation function according to forest type sets. (3) The hydricity of the site, depending on the soil type, determines the hydric-water protection function of forest stands. Currently, protective forests occupy 53,629 ha in the Czech Republic; however, two subcategories of protective forests—exceptionally unfavorable locations and natural alpine spruce communities below the forest line—potentially account for 87,578 ha and 15,277 ha, respectively. Forests with an increased soil protection function—a subcategory of special-purpose forests—occupy 133,699 ha. The potential area of soil protection forests could be up to 188,997 ha. Water resource protection zones of the first degree—another subcategory of special-purpose forests—occupy 8092 ha, and there is potentially 289,973 ha of forests serving a water protection function (specifically, a water management function) in the Czech Republic. A separate subcategory of water protection with a bank protection function accounts for 80,529 ha. A completely new approach is presented for practical use by forest owners: based on the characteristics of the habitat, they can obtain information about the fulfillment of the habitat’s ecosystem services and, thus, have basic information for the determination of forest categories and the principles of differentiated management. Full article

The accurate measurement of the tree diameter is vital for forest inventories, urban tree quality assessments, the management of roadside and railway vegetation, and various other applications. It also plays a crucial role in evaluating tree growth dynamics, which are closely linked to tree health, structural stability, and vulnerability. Although a range of devices and methodologies are currently under investigation, the widespread adoption of laser scanners remains constrained by their high cost. This study therefore aimed to compare high-end laser scanners (Trimble TX8 and GeoSLAM ZEB Horizon) with cost-effective alternatives, represented by the Apple iPhone 14 Pro and the LA03 scanner developed by mapry Co., Ltd. (Tamba, Japan). It further sought to evaluate the feasibility of employing these more affordable devices, even for small-scale forest owners or managers. Given the growing availability of 3D-based forest inventory algorithms, a selection of such processing pipelines was used to assess the practical potential of the scanning devices. The tested low-cost device produced moderate results, achieving a tree detection rate of up to 78% and a relative root mean square error (rRMSE) of 19.7% in diameter at breast height (DBH) estimation. However, performance varied depending on the algorithms applied. In contrast, the high-end mobile laser scanning (MLS) and terrestrial laser scanning (TLS) systems outperformed the low-cost alternative across all metrics, with tree detection rates reaching up to 99% and DBH estimation rRMSEs as low as 5%. Nevertheless, the low-cost device may still be suitable for scanning small sample plots at a reduced cost and could potentially be deployed in larger quantities to support broader forest inventory initiatives. Full article

The forestry industry, both primary (sawn wood production) and secondary (output of reels, pallets, boxes, cooperage, and tool handles), is growing in importance in Honduras. In 2023, exports from this industry were mainly destined for Central America (58.19%), followed by North America (22.92%) and the Caribbean region (17.20%), with hardly any Honduran wood reaching Asia or Europe. Objective: The goal of this paper is to analyze the current situation of the Honduran timber industry, which is defined by its environmental deterioration caused by the overexploitation of timber resources. Methodology: Using secondary data from official national and international sources regarding the forestry industry in Honduras, we conducted a Welch’s ANOVA analysis added to two post hoc tests (Tukey and Bonferroni), complemented by a linear regression analysis using JASP software, version 0.19.3.0. to carry on our analysis. Findings: The results of our analysis underscore the urgent need to implement a series of public policies in both the medium and short term to strengthen the forestry industry in Honduras. One of Honduras’ greatest strengths is its civil society, particularly its indigenous communities, which are actively working to protect their land from deforestation and soil degradation. If public policies are not implemented in collaboration with private firms to foster the Honduran forest industry cluster, it could lead to significant socioeconomic and environmental consequences. These may include increased pressure on natural forests, rising unemployment, and the loss of an essential income source for forest owners, ultimately exacerbating poverty. Full article

Land management decisions and conservation value are heavily influenced by land ownership, land cover, and land use. Our research aimed to examine ownership and land cover distribution, classify landowners based on land cover composition, and evaluate the ability of land cover clustering to be predictive of landowner motivations and behaviors in Maryland, USA. We tabulated a high-resolution land cover map against ownership boundaries, applied hierarchical clustering, and identified five landowner types characterized by a dominant land cover: (1) forest, (2) turf grass, (3) developed, (4) hay/pasture, and (5) crops. We analyzed a landowner survey of 3344 respondents to reveal how clusters predicted recreation, conservation, income, and other motivations. We found a skewed ownership distribution: 95.3% of smaller ownerships (<5 acres) cover 27.3% of the land, while 4.7% of larger owners hold 72.7%. Ownership patterns vary by cover, with forests and wetlands showing bimodal distributions, unimodal for cropland and hay/pasture, and turf grass concentrated in smaller properties. Survey analysis showed that crop, hay/pasture, and forest clusters had income percentages increasing with property size, with crop and hay/pasture accelerating more; conservation interest rose with size for forest and crop, but not hay/pasture; hunting motivation was highest in forest but increased with size similarly across clusters; non-hunting recreation motivation was highest in smaller hay/pasture properties, but decreased with size for all. Although each landowner has unique motivations and goals, our results reveal trends mediated by size of property and land cover that can be used to target outreach and improve conservation outcomes across Maryland’s diverse landscape. Full article

Smart buildings’ role is crucial for advancing smart cities’ performance in achieving environmental sustainability, resiliency, and efficiency. The integration barriers continue due to technology, infrastructure, and operations misalignments and are escalated due to inadequate assessment frameworks and classification systems. The existing literature on assessment methodologies reveals diverging evaluation frameworks for smart buildings and smart cities, non-uniform metrics and taxonomies that hinder scalability, and the low use of machine learning in predictive integration modelling. To fill these gaps, this paper introduces a novel machine learning model to predict smart building integration into smart city levels and assess their impact on smart city performance by leveraging data from 147 smart buildings in 13 regions. Six optimised machine learning algorithms (K-Nearest Neighbours (KNNs), Support Vector Regression (SVR), Random Forest, Adaptive Boosting (AdaBoost), Decision Tree (DT), and Extra Tree (ET)) were employed to train the model and perform feature engineering and permutation importance analysis. The SVR-trained model substantially outperformed other models, achieving an R-squared of 0.81, Root Mean Square Error (RMSE) of 0.33 and Mean Absolute Error (MAE) of 0.27, enabling precise integration prediction. Case studies revealed that low-integration buildings gain significant benefits from progressive target upgrades, whilst those buildings that have already implemented some integrated systems tend to experience diminishing marginal benefits with further, potentially disruptive upgrades. The conclusion of this study states that by utilising the developed machine learning model, owners and policymakers are capable of significantly improving the integration of smart buildings to build better, more sustainable, and resilient urban environments. Full article

In recent years, Europe has experienced an unexpectedly high frequency of natural disturbances. Private forest owners (PFOs), who manage a significant proportion of European forests and have diverse objectives and approaches to forest management, play a crucial role in salvage logging. The aim of this study was to (i) categorise PFOs based on their forest management objectives, experience with regular forest management, and responses to natural disturbances, and (ii) propose policy implications for particular PFO groups to improve salvage logging operations and meet legal requirements. A survey was conducted among a random sample of PFOs whose forests were affected by natural disturbances (n = 547). The survey data were analysed using K-means cluster analysis, and three groups of PFOs were identified. The outsourcing-oriented managers (32%) responded most strongly to natural disturbances, with 96.0% carrying out salvage logging. This highly co-operative group often relies on forest contractors and demonstrates the highest commitment to performing forest management activities among the three groups. The self-reliant managers (42%) also responded strongly (92.6% carried out salvage logging) and are characterised by a strong preference for performing the work themselves. The group of less active managers (26%) included the highest proportion of PFOs who did not conduct salvage logging (19.0%) and those with no previous forest management experience (12.0%). Despite these differences, common policy instruments based on smart regulation principles are proposed to promote efficient salvage logging. The results may contribute to the holistic transformation of forest policy and management in response to the current challenges posed by large-scale natural disturbances. Full article

Vehicle owners often use certified service centers throughout the warranty period, which usually extends for five years after buying. Nonetheless, after this timeframe concludes, a large number of owners turn to unapproved service providers, mainly motivated by financial factors. This change signifies a significant drop in income for automakers and their certified service networks. To tackle this issue, manufacturers utilize customer relationship management (CRM) strategies to enhance customer loyalty, usually depending on segmentation methods to pinpoint potential clients. However, conventional approaches frequently do not successfully forecast which clients are most likely to need or utilize maintenance services. This research introduces a machine learning-driven framework aimed at forecasting the probability of monthly maintenance attendance for customers by utilizing an extensive historical dataset that includes information about both customers and vehicles. Additionally, this predictive approach supports workforce planning and scheduling within after-sales service centers, aligning with AI-driven labor optimization frameworks such as those explored in the AI4LABOUR project. Four algorithms in machine learning—Decision Tree, Random Forest, LightGBM (LGBM), and Extreme Gradient Boosting (XGBoost)—were assessed for their forecasting capabilities. Of these, XGBoost showed greater accuracy and reliability in recognizing high-probability customers. In this study, we propose a machine learning framework to predict vehicle maintenance visits for after-sales services, leading to significant operational improvements. Furthermore, the integration of AI-driven workforce allocation strategies, as studied within the AI4LABOUR (reshaping labor force participation with artificial intelligence) project, has contributed to more efficient service personnel deployment, reducing idle time and improving customer experience. By implementing this approach, we achieved a 20% reduction in information delivery times during service operations. Additionally, survey completion times were reduced from 5 min to 4 min per survey, resulting in total time savings of approximately 5906 h by May 2024. The enhanced service appointment scheduling, combined with timely vehicle maintenance, also contributed to reducing potential accident risks. Moreover, the transition from a rule-based maintenance prediction system to a machine learning approach improved efficiency and accuracy. As a result of this transition, individual customer service visit rates increased by 30%, while corporate customer visits rose by 37%. This study contributes to ongoing research on AI-driven workforce planning and service optimization, particularly within the scope of the AI4LABOUR project. Full article

Due to the increase in recreational demands, the significance of protected areas and forests with recreational potential in forests increased with the demands of nature tourism, which in turn provided new income sources to the forestry industry. In the current study, the economic values of the Uzungöl Nature Park’s lake view, an international tourist destination, were estimated using the hedonic pricing method. In the study, 188 questionnaires were conducted with 89 businesses, and the hedonic price function (HPF) was determined based on the study data collected from the businesses in Uzungöl Nature Park. It was estimated that the mean lake view in-room accommodation price for the hotels in Uzungöl Nature Park was USD 207.38 and the lake causes an increase of $2.8 per square meter and $144.67 in total on the room price of the hotels. The study findings demonstrated that the lake view was a desirable quality for hotel rooms, which is reflected in the prices in Uzungöl. The significant contribution of the lake view to room prices would support the planning and management of protected areas that are usually rich in natural resources. Determining the economic value of the lake view will enable business owners operating in the region or those planning to establish new businesses to make more informed pricing strategies. It will also strengthen hotel owners’ marketing campaigns and enable them to think more rationally about new investments (such as adding rooms or services). Business owners will be able to optimize their rooms based on lake views in order to offer more lake-view rooms to customers. Determining the economic value of the lake view will raise awareness about the protection of natural areas. By investing in eco-friendly and sustainable practices, hotel owners will contribute to the conservation of natural resources. The value estimates determined in the present study would also contribute to the employment of total forest value calculations and resource accounting systems. Full article

Lithuania has initiated the development of voluntary ecosystem protection measures within private forests, establishing protection agreements between the state and private forest owners. This article examines the priorities and motivations of private forest owners in the voluntary protection of ecosystems, based on the analysis of expert opinions. The Delphi sociological method was employed to assess expert opinions on the priorities and motivations of private forest owners regarding the voluntary protection of ecosystems. Twenty-nine experts responded to the survey, providing insights into the attitudes of Lithuanian private forest owners towards voluntary forest protection models and contract types, potential environmental protection instruments, the necessity of compensation for losses incurred due to forest management restrictions in protected areas, the proportion of protected forests, factors influencing the intention to engage in forest protection, motivations for voluntary forest protection, the “crowd-out” effect, sources of compensation for losses, the effectiveness of ecosystem protection mechanisms in Lithuanian forests, and the factors that diminish their effectiveness. Summarizing the experts’ findings, it can be concluded that the forest protection priorities of Lithuanian private forest owners, concerning the expansion of protected areas in private forests, protection models, and incentives for protection, are likely to align with the priorities and motivations identified in other European countries. A heterogeneity of priorities and motives was identified. Almost three-quarters of experts thought the current amount of protected forest in Lithuania is sufficient or is already more than necessary, and only about one in ten thought that is necessary for owners to protect more forest. Lithuanian private forest owners are mostly motivated by full financial compensation for losses. According to experts, the majority of private forest owners do not support forest protection models that lack financial compensation. It would be appropriate to implement both permanent and fixed-term protection agreements (contracts) with compensation, alongside the option of selling forests to the state. The level of compensation is identified as the most-significant factor influencing private forest owners’ willingness to engage in ecosystem protection. Experts highlight that the primary reasons for the ineffectiveness of private forest protection measures in Lithuania include inadequate and unjustified compensation, compensation amounts that are too low relative to forest owners’ income, insufficient information, and complex bureaucratic procedures. Full article

Findings from monitoring Greek fir forests in central Greece regarding radial growth, insect infestations, plant communities, and climate response have highlighted the urgent need for expanded field research to address critical conservation challenges within the fir forest ecosystem on Giona and Parnassus mountains. This underscores the urgency of adopting measures to mitigate the impacts of both abiotic and biotic factors. Our findings so far, encompassing biometric data, tree ring analysis, observations on plant communities, climatic response, and insect infestations—primarily Choristoneura murinana (European Bud Moth)—across various stands on the mountain, have revealed significant local infestations of varying degrees. In many instances, these infestations were detected in adult trees, particularly in sunny areas or near country roads. Furthermore, our research has revealed the varied ability of the fir trees to adapt to both minor and significant climatic variations. The proposed research aims to monitor, preserve, and protect the fir trees, utilizing new knowledge for informed decision-making in their management. The project’s scope includes studying the growth characteristics of the fir forest, mitigating threats from biological factors (primarily the moth Choristoneura murinana-Lepidoptera, Tortricidae) by involving pheromones and other biological methods, promoting natural regeneration, preserving biodiversity, and evaluating the water status of the fir trees in response to climate change. Investigating their interactions and understanding the ecosystem’s status concerning the previously mentioned aspects is a significant priority for biological and genetic diversity, landscape aesthetics, recreation, and sustainable regional development (with economic impacts on local beekeepers and guesthouse owners). The evaluation of the implementation of the National Biodiversity Strategy in Greece has shown partial or minimal progress in addressing new threats and challenges, as well as in promoting new approaches. The proposed project, with specific actions, aligns with the spirit of the National Biodiversity Strategy and contributes to the strategy goals within the framework of conservation, restoration, and the strengthening of nationwide enhancement actions. Full article

Forest fires, i.e., wildfires, often cause an inevitable strain on society and human living conditions. Incident Commanders (IC) at the Fire and Rescue Services (FRS) are challenged to handle forest fires and at the same time address the forest owners’ needs; this stipulates a need for collaboration, information, and communication. Hence, the aim of this study was to explore and describe the ICs’ experiences and actions in their interactions with forest owners during forest fires on private property. Interviews were conducted and analyzed using Flanagan’s Critical Incident Technique (CIT) to describe the experiences and actions of 22 ICs. The results showed that a firefighting operation needs clarity in information exchange with the forest owner as a stakeholder, not a victim. The trust between forest owner and IC accelerated the operational phase. The ICs demonstrate more care than the law stipulates, and they worry about the forest owners. Therefore, the FRS needs to form a strategic partnership with forest owners and their network on a local level. Also, future forest fire drills should not only include emergency stakeholders (i.e., police, ambulance, etc.) but also forest owners and local volunteer organizations. For a resilient community, FRS and forest owner collaboration is vital. Full article

Accounts are an integral part of most modern information systems and provide their owners with the ability to authenticate within the system. This paper presents an analysis of existing methods for detecting simple account passwords in automated systems. Their advantages and disadvantages are listed. A method was developed to detect simple exploitable passwords that administrators can use to supplement other existing methods to increase the overall security of automated systems against threats from accounts potentially compromised by attackers. The method was based on the analysis of commands executed in automated or manual modes with the indication of credentials in plain text. Minimum password strength requirements are provided based on the security level. A special case was considered in which all passwords analyzed in this way were found explicitly in the system logs. We developed a unified definition of the classification of passwords into simple and strong, and also developed machine learning technology for their classification. The method offers a flexible adaptation to a specific system, taking into account the level of significance of the information being processed and the password policy adopted, expressed in the possibility of retraining the machine learning model. The experimental method using machine learning algorithms, namely the ensemble of decision trees, for classifying passwords into strong and potentially compromised by attackers based on flexible password strength criteria, showed high results. The performance of the method is also compared against other machine learning algorithms, specifically XGBoost, Random Forest, and Naive Bayes. The presented approach also solves the problem of detecting events related to the use and storage of credentials in plain text. We used the dataset of approximately 770,000 passwords, allowing the machine learning model to accurately classify 98% of the passwords by their significance levels. Full article

The values of forests have been extensively researched by focusing on general public perspectives with different frameworks used to categorise them. Studies have also explored forest manager values; however, there is limited evidence on the values they associate with specific tree species. Understanding more about managers’ values regarding a particular species is important when considering how they make decisions and might respond to tree pests and disease threats. In this study, we explored forest managers’ values regarding oak trees and the effects of a particular pest and disease risk known as Acute Oak Decline on these. This paper outlines the results from interviews with forest managers in England and a survey of private forest managers in Britain to capture the ways in which they value the oak trees they own, manage, or influence. Forest manager types included private owners of single or multiple properties, forestry professionals, businesses, and tenants. The results show that oaks were highly valued by forest managers as an iconic cultural species in the landscape and for their timber. Veteran and ancient oak trees were considered very important, and managers were more likely to spend time and resources attempting to conserve these oaks due to their perceived cultural value. Those who had trees that were suffering from Acute Oak Decline were also more likely to spend resources on them to save the trees or try to reduce the impact of the disease. Gaining a better understanding of forest managers’ attitudes towards protecting species they value is important as it has implications for their decision-making and management behaviours. It can also help to provide relevant bodies with information on how best to develop and communicate guidance and advice on monitoring and reporting disease symptoms, as well as managing oak tree health. Full article

The development of a monitoring system for the socio-economic indicators of private forest owners includes the comprehensive collection of various socio-economic data. These data encompass general information about private forests owners, the need for monitoring, periodicity, and other relevant factors. This holistic approach allows for a detailed assessment of the social and economic conditions of private forest owners, as well as the economic efficiency of their operations, ultimately aiming to achieve sustainable forest management. This research builds upon previous studies by the authors that investigated the need for monitoring of the socio-economic indicators of private forests and involved interviews with forest experts. Based on the data obtained from these earlier efforts, this study aims to present a refined methodology for monitoring of the socio-economic indicators of Lithuanian private forest owners. Based on the findings of this research, it is highly recommended that monitoring be implemented at the state level across the entire territory of the Republic of Lithuania. The results of this research show that the monitoring of the economic and social indicators of private forest owners benefits not only the owners but also the state and society as a whole. Residence size, property size, and association membership emerged as the most significant factors influencing the need for forest monitoring, while knowledge in forestry, distance to the forest, and gender showed weaker but still notable effects. The research results suggest that monitoring can help to ensure the sustainable management of forests and the maintenance of ecosystem services, in addition to contributing to the region’s economic development. It is recommended that this monitoring be conducted every five years. Full article