Forest Management Planning Research Articles

Assessment of Mycological Possibility Using Machine Learning Models for Effective Inclusion in Sustainable Forest Management

The integral role of wild fungi in ecosystems, including provisioning, regulating, cultural, and supporting services, is well recognized. However, quantifying and predicting wild mushroom yields is challenging due to spatial and temporal variability. In Mediterranean forests, climate-change-induced droughts further impact mushroom production. Fungal fruiting is influenced by factors such as climate, soil, topography, and forest structure. This study aims to quantify and predict the mycological potential of Lactarius deliciosus in sustainably managed Mediterranean pine forests using machine learning models. We utilize a long-term dataset of Lactarius deliciosus yields from 17 Pinus pinaster plots in Soria, Spain, integrating forest-derived structural data, NASA Landsat mission vegetation indices, and climatic data. The resulting multisource database facilitates the creation of a two-stage ‘mycological exploitability’ index, crucial for incorporating anticipated mycological production into sustainable forest management, in line with what is usually done for other uses such as timber or game. Various Machine Learning (ML) techniques, such as classification trees, random forest, linear and radial support vector machine, and neural networks, were employed to construct models for classification and prediction. The sample was always divided into training and validation sets (70-30%), while the differences were found in terms of Overall Accuracy (OA). Neural networks, incorporating critical variables like climatic data (precipitation in January and humidity in November), remote sensing indices (Enhanced Vegetation Index, Green Normalization Difference Vegetation Index), and structural forest variables (mean height, site index and basal area), produced the most accurate and unbiased models (OAtraining = 0.8398; OAvalidation = 0.7190). This research emphasizes the importance of considering a diverse array of ecosystem variables for quantifying wild mushroom yields and underscores the pivotal role of Artificial Intelligence (AI) tools and remotely sensed observations in modeling non-wood forest products. Integrating such models into sustainable forest management plans is crucial for recognizing the ecosystem services provided by them.

Elms (Ulmus L.) in the Branch “Myrhorod Forestry” of the State Specialized Forest Enterprise “Forests of Ukraine”

Introduction Elms are spread in forests, landscaping settlements, and protective forest belts. After several waves of elm mortality from Dutch elm disease (DED) in the 20th century, individual populations recovered or formed resistant hybrids. However, epiphytoties of bacterial and fungal diseases sometimes develop, particularly due to pathogen vectoring by bark beetles. In Ukraine, the spread of elms and their damage by different causes are relatively poorly investigated. This research aimed to discover the features of Ulmus sp. distribution by stand origin, age, and forest site condition in the forest fund of the Branch “Myrhorod Forestry”. Materials and Methods The data as of 2010 and 2017 on the forest fund of enterprises belonging to the Branch “Myrhorod Forestry” of the State Specialized Forest Enterprise “Forests of Ukraine” were selected from the database of Ukrainian State Forest Management Planning Association “Ukrderzhlisproekt” using SQL-query and converted to the *.xls files. Area distribution by types of forest site conditions was assessed following the Ukrainian school of forest typology. Such distributions for the entire area covered with forest vegetation, for subcompartments with elms in the stand composition, and with elms as the dominant species were compared for various elm species and stand origins, using c2-test. The percentages of stand areas of different origins and elm species in 2010 and 2017 were compared using z-test in the two proportions comparisons. The data for subcompartments with various elms as the dominant species and as a part of stands composition have been analyzed by 10-year age classes for different origins (natural and planted). The proportion of the stand area of each 10-year age class was assessed for each set of subcompartments. Then the proportion of the stands’ area, which survives up to a certain age, was evaluated. Results and Conclusions In the Branch “Myrhorod forestry”, an area of natural stands with Ulmus sp. as the dominant species for 2010–2017, and the proportion of U. minor has significantly increased. The most dramatic decrease in the survival rate of all elm species of natural stands occurred in the VI age class and planted stands in the V age class. Respective natural and planted stands were formed in the 1960s and 1970s when the peak of DED occurred in many regions. In the Branch “Myrhorod Forestry”, U. minor prefers moist fertile forest site conditions. U. laevis grows mainly in moist forest site conditions, U. pumila occurs in dry to wet habitats, U. glabra is present only in natural stands and absent in plantations. As part of stand composition, the same elm species grow in a wider range of forest site conditions than elms as the dominant species. 2 Figs., 8 Tables, 31 Refs.

Possible Changes in Red Pine (Pinus brutia Ten.) Distribution Areas in Kastamonu due to Global Climate Change

In this study, it was aimed to determine the current status of red pine (Pinus brutia Ten.) distribution areas in Kastamonu Forestry Regional Directorate, which is the Forestry Regional Directorate with the highest production in Türkiye, and the possible changes in suitable distribution areas due to global climate change. In the study, in addition to the current suitable distribution areas of red pine (Pinus brutia Ten.), suitable distribution areas in 2040, 2070 and 2100 according to SSP 126, SSP 370 and SSP 585 scenarios were determined. The results of the study show that there may be a loss of more than 15% (approximately 114,5 km2) in the suitable distribution areas of red pine populations in Kastamonu until 2100 due to the effects of climate change. It does not seem possible for the species to adapt to these changes without human intervention. Therefore, it is recommended that necessary adjustments should be made in forest management plans taking into account the results of the study.

How can optimized forest management plan regulate noise levels from wind turbines?

Wind energy has emerged as one of the most economically viable renewable energy options in the transition towards a fossil-free society. In Finland, wind farms, consisting of several wind turbines, are commonly located in forested areas, prompting concerns about their potential audio-visual impacts. Despite this, research into how forests might mitigate the adverse effects of wind farms are limited. Forests can effectively serve as noise barrier, with their noise attenuation capacity varying based on the forest’s characteristics. Specifically, the attenuation level depends on the sound’s travel distance through the forest, as well as the size and density of trees. Our study findings indicate that forests can provide up to 10 dB of additional noise attenuation. This was achieved by integrating a forest structure-based model into forest planning calculations, aimed at mitigating noise pollution from wind turbines. Incorporating this noise model as a management objective significantly reduced noise levels in the pilot study area, outperforming traditional business-as-usual management strategies. Furthermore, adapting a combination of uneven-aged and even-aged forest management approaches resulted in more forested landscape, which was more effective in mitigating higher noise levels. Our results contribute important insights that, along with further research, can guide future forest planning and management towards enhanced sustainability.

Post-fire resurveys reveal predictability of long-term conifer recruitment in severely burned California dry forests

High severity fire is far outpacing post-fire reforestation capacity across the American West, highlighting the need to better understand when and where natural regeneration is sufficient to meet short- and long-term reforestation goals. The vast majority of available post-fire data represents regeneration in the first few years following a fire, raising the question of how well recovery trajectories can be predicted from early post-fire snapshots. We utilize a unique dataset from seven wildfires and 78 plots surveyed twice following stand-replacing fire in two California ecoregions, the northern Sierra Nevada and Klamath Mountains, to ask (1) how are post-fire vegetation and conifer densities changing over time and (2) how well do relatively early post-fire sampling efforts and biophysical factors (availability of seed source, shrub competition, and microclimate) explain longer-term (12–23 year) conifer recovery in California dry forests. Change in conifer seedling density between survey periods was highly variable. Overall, densities of all conifer species during the first decade after fire provided reliable estimates of longer-term conifer recovery in northern California dry forests, though in different ways for different species. Early post-fire seedling density readily explained longer-term densities for less shade-tolerant species, yellow pine (Pinus ponderosa and Pinus jeffreyi) and Douglas-fir (Pseudotsuga menziesii). In contrast, early seedling density was a poor predictor for shade-tolerant species, predominantly white fir (Abies concolor), with site conditions better explaining longer-term density. In general, the probability of a site experiencing net seedling mortality between surveys was highest in sites with high shrub cover and the probability of net recruitment increased with decreasing heat load, suggesting a continued role of microclimate and competition in forest recovery long after the initial post-fire period. Our results lend support to the use of relatively early post-fire surveys to infer longer-term forest recovery trajectories for forest management planning and can help refine reforestation prioritization tools.

Construction of Compatible Volume Model for Populus in Beijing, China

The accurate assessment of tree volume is crucial for developing forest management plans, and this can be achieved using tree volume models. In this study, data on individual trees were collected and calculated, including the diameter at breast height (D), ground diameter (DG), tree height (H), and tree volume (V). A total of 400 Populus × tomentosa Carrière, 400 Populus tomentosa Carr, and 400 Populus × canadensis Moench trees were sampled. Two compatible volume model systems were established using corresponding methods. The models consisted of the following five types: V-DH, V-D, V-DG, H-D, and DG-D. In our calculations, before the horizontal line was the dependent variable, and behind the horizontal line was the independent variable. Variations in preferences for the H-D models were observed among the tree species, with the logistic function proving the most suitable for Populus × tomentosa Carrière, Chapman–Richard function for Populus tomentosa Carr, and power function for Populus × canadensis Moench. Among the three volume models, the V-DH model exhibited a superior performance, with its R2 values ranging from 0.965 to 0.984 and mean estimated error (MPE) values ranging from 1.26% to 1.78%. Following this was the V-D model, with R2 values between 0.9359 and 0.9704 and MPE values between 1.71% and 2.46%. The V-DG model ranked third, with R2 values ranging from 0.8746 to 0.9501 and MPE values ranging from 2.33% to 3.16%. In the H-D model, the R2 and MPE values ranged from 0.4998 to 0.7851 and from 1.31% to 1.45%, respectively. For the DG-D model, the R2 values ranged from 0.9563 to 0.9868 and the MPE values ranged from 0.47% to 0.68%. Comparing both compatible methods, the nonlinear seemingly uncorrelated regression (NSUR) was more effective. The three volume models demonstrated high levels of accuracy and compatibility, providing a reliable scientific foundation for forest management and the formulation of harvesting plans in Beijing, with significant practical implications.

Forest management plan pathways for non-industrial private forest owners in Norway: from acquisition to implementation

AbstractForest management plans (FMP) are key decision-support tools for forest managers and a central policy instrument in many countries. Despite its pivotal importance in forest management, there is a lack of studies that analyze the acquisition, familiarity and implementation of the FMP. These steps collectively contribute to making FMP a successful decision-support tool and forest policy instrument. We surveyed a sample representative of the population of Norwegian non-industrial private forest (NIPF) owners to quantify the importance of a broad set of factors, including ownership objectives, socio-demographic, property and information sources variables on the FMP pathway, i.e. acquisition of FMP, awareness of its content and implementation of the plans’ proposals. Applying principal component analysis on twelve ownership objectives, we found that most forest owners are multiobjective. We combined conditional classification trees and logistic regression analyses on the FMP pathway to unveil familiarity and use of FMP in forest owner subgroups as well as individual drivers. The results indicate that 37% of Norwegian NIPF owners have a FMP. Among the owners having FMP, 66% have good knowledge of its content and 40% implement its proposals. The strongest variables to predict FMP familiarity and use were previous harvests, productive area, advice about forestry from the public management, knowledge of public support schemes for forestry and perceived relevance of the FMP. We conclude that FMP suppliers, consultants, forestry bureaucracy and policy-makers should emphasize information dissemination and relevance of the FMP to increase its success as a decision-support tool and forest policy instrument.

Applying taper function models for black locust plantations in Greek post-mining areas

A key process in forest management planning is the estimation of tree volume and, more specifically, merchantable volume. The ability to predict the cumulative stem volume relative to any upper stem diameter on standing trees or stands is essential for forest inventories and the management of forest resources. In the 1980s, the Hellenic Public Power Corporation (HPPC) started the rehabilitation of lignite post-mining areas in Greece by planting mainly black locust (Robinia pseudoacacia, L.). Today, these plantations occupy an area of approximately 2570 ha, but the stem volume has not yet been estimated. Therefore, we aimed to estimate the over- and under-bark stem volume using taper function models for 30 destructively sampled trees. Of the nineteen calibrated fixed-effects models, Kozak’s (2004) equation performed best for both the over-bark and under-bark datasets, followed by Lee’s (2003) and Muhairwe’s (1999) equations. Two fixed effect models were compared with fitted coefficients from Poland and the United States confirming that the local model fits were better suited, as the foreign model coefficients caused an increase in root mean square error (RMSE) for stem diameter predictions of 13% and 218%, respectively. The addition of random effects on a single-stem basis for two coefficients of Kozak’s (2004) equation improved the model fit significantly at 86% of the over-bark fixed effect RMSE and 69% for the under-bark model. Integrated taper functions were found to slightly outperform three volume equations for predictions of single stem volume over and under bark. Ultimately it was shown that these models can be used to precisely predict stem diameters and total stem volume for the population average as well as for specific trees of the black locust plantations in the study area.

Territorial planning support tool for sustainable forest management in the Amazon

In forest management, it is crucial to ensure process efficiency to maintain economic viability, especially in native forests where timber volume varies due to the uneven distribution of commercially valuable species. This variability poses a challenge for harvest planning, as the inherent heterogeneity of these forests often results in inconsistent productivity. This study employs the SOTER-PA system, an algorithm-based tool utilizing a minimalist tabu search, to achieve a more homogeneous territorial planning structure for better utilization of natural resources and increased economic returns from forestry activities. The study area is the Tapajós National Forest in the state of Pará, with a forest inventory database covering 2,218.7 hectares and 28,369 trees across 93 species. After applying exclusion criteria and data processing, 8,644 trees were deemed suitable for harvest. The tests suggested a new organization of Work Units, enabling a more balanced income distribution among them, and delineating the areas to be exploited in each cutting cycle. The results show that SOTER-PA not only improves the equitable distribution of natural resources but also enhances economic productivity. This improvement is attributed to the more homogeneous and optimized distribution of high-value species like red cedar and purpleheart. Furthermore, when comparing the SOTER-PA generated parcels with conventional management, the parcels created by SOTER-PA were more homogeneous, with better productivity quality and lower standard deviation, indicating greater uniformity. Thus, the use of SOTER-PA proves to be an efficient alternative for optimizing sustainable forest management planning, offering a more effective method for sustainable territorial planning in native forests, providing both economic and environmental benefits.

Integration of field measurements with unmanned aerial vehicle to predict forest inventory metrics at tree and stand scales in natural pure Crimean pine forests

ABSTRACT Inventorying forest ecosystems is an essential part of forest management planning. However, it is quite costly and time-consuming, particularly for larger areas. Recently, significant developments have been made in unmanned aerial vehicle (UAV) technology to improve the cost and time efficiency in forest inventory. Therefore, UAV images have become one of the inventory tools that produces data with high spatial resolution in determining forest resources. This study aims to investigate the contribution of UAV data to forest inventory in a case study area with a total of 30 sample plots located in pure and natural Crimean pine (Pinus nigra J.F. Arnold ssp. pallasiana (Lamb.) Holmboe) stands in the Black Sea backward region of Türkiye. Total tree height (h) and stem volume (v) were recorded at individual tree level (n = 367), and the number of trees (N), mean height (hmean), top height (htop), stand basal area (BA) and stand volume (V) were calculated at sample plot level (n = 30) from both the field and UAV-based data. Pearson’s correlation coefficients (r) for h and v were 0.96 and 0.72, respectively, the highest correlation at the sample plot level was observed for the hmean - htop (r = 0.96), while the lowest correlation was found for BA (r = 0.54). The suitability of the observation and prediction values was assessed using a t-test at both individual tree and sample plot levels. According to the t-test results, the observation and prediction values for h, v, hmean, htop, BA and V metrics were found to be compatible (p > 0.05), but not for N (p < 0.05). Overall results indicated that UAV technology has a potential to be used in forest inventory and can contribute to the determination of individual tree and stand metrics. Thereby, it saves cost and time in forest inventory studies and helps monitoring the dynamic structure of the forest ecosystem with an effective approach in forest inventory.

Modelling Diameter at Breast Height Distribution for Eight Commercial Species in Natural-Origin Mixed Forests of Ontario, Canada

Diameter at breast height (DBH) is a unique attribute used to characterize forest growth and development for forest management planning and to understand forest ecology. Forest managers require an array of DBHs of forest stands, which can be reconstructed using selected probability distribution functions (PDFs). However, there is a lack of practices that fit PDFs of sub-dominating species grown in natural mixed forests. This study aimed to fit PDFs and develop predictive models for PDF parameters, so that the predicted distribution would represent dynamic forest structures and compositions in mixed forest stands. We fitted three of the simplest forms of PDFs, log-normal, gamma, and Weibull, for the DBH of eight tree species, namely balsam fir (Abies balsamea [L.] Mill.), eastern white pine (Pinus strobus L.), paper birch (Betula papyrifera Marshall), red maple (Acer rubrum L.), red pine (Pinus resinosa Aiton), trembling aspen (Populus tremuloides Michx), and white spruce (Picea glauca [Moench] Voss), all grown in natural-origin mixed forests in Ontario province, Canada. We estimated the parameters of the PDFs as a function of DBH mean and standard deviation for these species. Our results showed that log-normal fit the best among the three PDFs. We demonstrated that the predictive model could estimate the recovered parameters unbiasedly for all species, which can be used to reconstruct the DBH distributions of these tree species. In addition to prediction, the cross-validated R2 for the DBH mean ranged between 0.76 for red maple and 0.92 for red pine. However, the R2 for the regression of the standard deviation ranged between 0.00 for red pine and 0.69 for sugar maple, although it produced unbiased predictions and a small mean absolute bias. As these mean and standard deviations are regressed with dynamic covariates (such as stem density and stand basal area), in addition to climate and static geographic variables, the predicted DBH distribution can reflect change over time in response to management or any type of disturbance in the regime of the given geography. The predictive model-based DBH distributions can be applied to the design of appropriate silviculture systems for forest management planning.

Factors enhancing and restricting the success of SRA compliance on the FLEGT/VPA initiative in Ghana. Insights from Juaso forest district in Ghana

This study examines the Voluntary Partnership Agreement (VPA) between Ghana and the European Union (EU) within the Forest Law Enforcement Governance and Trade Initiative (FLEGT). The VPA aims to enhance forest governance, reduce deforestation, combat illegal practices, and improve livelihoods of forest fringe communities. The research focuses on the implementation of social responsibility agreements (SRAs) under the VPA framework and identifies factors contributing to their success or presenting challenges. Data collection involved mixed methods, including literature review and a survey of individuals involved in SRAs. Descriptive and inferential statistical analyses, including exploratory factor analysis, were conducted. Principal component analysis revealed that accountability, monitoring of implementation and progress of SRAs, and documentation of SRA agreements were key factors contributing to the success of negotiated SRAs, explaining about 68.36 % of success variance. Challenges and constraints were categorized into two main factors: weak community capacity to negotiate SRAs and weak community capacity to enforce compliance, explaining about 71.4 % of challenge variance. The study found that the exclusion of the local SRA committee (LSRAC) from certain decision-making processes affected trust and transparency in calculating SRA benefits. Elite capture of benefits was identified as an issue, as the LSRAC did not conduct sufficient consultations with community members before negotiations. The findings emphasize the importance of including local communities in all forest management activities and call for increased awareness of SRAs, particularly for the LSRAC. The study highlights the need for proper representation of community interests during negotiations and their inclusion in forest management plans.

A Five-Step Framework for Creating Forests for the Future

Changing environmental conditions, disturbances, and escalating demands for forest ecosystem services require foresters to restore present forestlands with new forest generations that will exhibit ecological integrity, diversity, high adaptive capacity, and the ability to provide essential ecosystem services. Establishing such forests requires careful consideration of the forest landscape and site dynamics. In pursuit of these requirements, we developed a novel framework that enables the restoration of forest sites and promotes the desired features of the forest complex at the same time. This framework was designed with the methods of system engineering and was organized in the same way as the forest planning process. It was tested in the habitat type of Illyrian Fagus sylvatica forests belonging to the Natura 2000 network. The environmental, vegetation, and site conditions were investigated via field inspections, available forest management plans, and simple GIS analyses. Additionally, we established a seminatural stand composed of European beech, sessile oak, sycamore maple, silver fir, and some wild fruit tree species. The survival of planted species was assessed using census and simple random sampling, the performance of provenances by the Student’s test, while microhabitat factors were explored by a one-way ANOVA. The survival rate of key species was estimated to be 55.6%, while that of fruit species was estimated to be 94.5%. Our framework demonstrated satisfactory performance and contained sufficient benchmarks to facilitate consistent decision-making. In the discussion, we elucidate the framework’s primary features and attributes of the mixed stand, where we also expose some open issues to be addressed in the future.

Estimating Forest Aboveground Biomass Using a Combination of Geographical Random Forest and Empirical Bayesian Kriging Models

Accurately estimating forest aboveground biomass (AGB) is imperative for comprehending carbon cycling, calculating carbon budgets, and formulating sustainable forest management plans. Currently, random forest (RF) and other machine learning models are widely used to estimate forest AGB, as they can effectively handle nonlinear relationships. However, by constructing a global model using all the samples collected from a study area, these models fail to account for the spatial heterogeneity in the AGB and cannot correct the prediction biases, thereby constraining the estimation accuracy. To overcome these limitations, we proposed a novel approach termed geographical random forest and empirical Bayesian kriging (GRFEBK). This hybrid model combines the localized modeling capability of geographical random forest (GRF) with the bias correction strength of empirical Bayesian kriging (EBK). GRF adapts RF to account for the spatial heterogeneity of the AGB, while EBK utilizes the spatial autocorrelation of residuals to correct the prediction deviations. This study was conducted in Hainan Island, utilizing spectral bands, vegetation indices, tasseled cap components derived from Landsat-8 imagery, backscattering coefficients from ALOS-2 synthetic aperture radar, topographic features, and the forest canopy height as the explanatory variables. A total of 195 forest aboveground biomass (AGB) samples were collected for modeling and assessing the predictive accuracy. The results demonstrate that, among the tested models, including GRFEBK, RF, support vector machine (SVM), k-nearest neighbor (KNN), geographically weighted regression (GWR), GRF, and EBK, GRFEBK attains the highest R2 (0.78) and the lowest RMSE (36.04 Mg/ha) and RRMSE (22.87%), significantly outperforming the conventional models and using GRF or EBK alone. These results demonstrate that by accounting for local non-stationarity in AGB and correcting prediction biases, GRFEBK achieves significantly higher accuracy than conventional RF and other models. While the results are promising, the computational cost of GRFEBK and its performance under varying geographical conditions warrant further investigation at larger scales to assess its broader applicability. Nevertheless, GRFEBK provides an innovative and more reliable approach for accurate forest AGB estimation with great potential to support global forest resource monitoring.

Changes in carbon stocks according to stand development stages in oriental beech forests in the Marmara Region of Türkiye.

This study was conducted to determine the changes in carbon stocks of oriental beech (Fagus orientalis) according to stand development stage in the Marmara Region of Türkiye. For this purpose, sample plots were taken from a total of 32 areas encompassing four stand development stages (young, middle age, mature and overmature stand). The diameter at breast height and height of all trees in the sample plots were measured, and only three dominant trees's ages per plot were determined. Aboveground carbon stock was calculated using equations developed for beech forests, while the coefficients in the Agriculture, Forestry and Other Land Use guide were used to determine belowground carbon stocks. A soil pit was dug in each plot and soil samples were taken at different depths (0-10, 10-30, 30-60, 60-100cm). In addition, litters were sampled from four different 25 × 25cm sections in each plot, and then the physical and chemical properties of the soil and litters were analysed. The variations in carbon stocks in above- and below-ground tree mass, litter and soil, and in ecosystem carbon stocks according to development stage were examined by analysis of variance and Duncan test, and the relationships between the carbon stocks were investigated by correlation analysis. Aboveground (AG) and belowground (BG) tree, soil and ecosystem carbon stocks showed significant differences between the four stand development stages (P < 0.05), but not the litter carbon stocks (P > 0.05). AG and BG tree and ecosystem carbon stocks increased with progressive stand development stages, while the soil carbon stock was the highest at the young stage. These findings will contribute to the preparation of forest management plans and the national greenhouse gas inventory.

Forest Management Type Identification Based on Stacking Ensemble Learning

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

A thorough assessment of various forest management planning initiatives and development of improvement strategies towards an ecosystem-based planning

Management planning for forests in Turkey has undergone gradual evolution over the last century. Developing and implementing a comprehensive management planning framework present significant challenges. This paper evaluates the effectiveness of various forest management planning approaches implemented across the country over different time periods, with overarching planning principles. It formulates a robust planning framework and proposes improvements grounded in scientific advancements and international standards. The assessment indicated that the management plans were developed using reputable scientific methods and principles aimed at ensuring the sustainable management of forest resources with certain strengths and opportunities presented by SWOT analysis. All management plans shared a common planning concept primarily focused on maximizing wood production through the area-control harvest scheduling method (except for continuous cover forest, which employed the single tree selection method). Each management plan established its unique vision, targets, policies, objectives, and planning guidelines. Forest inventory data were gathered through a combination of ground surveys and remotely sensed data to characterize and stratify the landscape. A robust in-house management authority and governance system with appropriate technical capacity and guidelines were developed and implemented, fostering a sound common working culture and tools. However, some notable drawbacks were identified, including political pressure, biomass/carbon accounting, growth-yield modelling, economic analysis, limited characterization of the full range of ecosystem services, risk and uncertainty analysis, food security and, particularly, long-term sustainability and scenario analysis with the appropriate decision-making tools and methods. Despite a few strengths, these limitations may raise concerns about the far-sighted design and application, potentially jeopardizing the sustainable management of forest ecosystems. Proposed improvement strategies for an efficient forest ecosystem management planning system include characterization of ecosystem services, modelling their productivity, scenario analysis with a decision support system, stakeholder involvement, balancing utilization and conservation targets, conducting risk and uncertainty analysis and economic analysis of management actions.

Enhancing ecosystem productivity and stability with increasing canopy structural complexity in global forests.

Forest canopy structural complexity (CSC) plays a crucial role in shaping forest ecosystem productivity and stability, but the precise nature of their relationships remains controversial. Here, we mapped the global distribution of forest CSC and revealed the factors influencing its distribution using worldwide light detection and ranging data. We find that forest CSC predominantly demonstrates significant positive relationships with forest ecosystem productivity and stability globally, although substantial variations exist among forest ecoregions. The effects of forest CSC on productivity and stability are the balanced results of biodiversity and resource availability, providing valuable insights for comprehending forest ecosystem functions. Managed forests are found to have lower CSC but more potent enhancing effects of forest CSC on ecosystem productivity and stability than intact forests, highlighting the urgent need to integrate forest CSC into the development of forest management plans for effective climate change mitigation.

Forest Protection Tax Policy in the Context of Portuguese Societies

This article aims to study the fiscal incentives for forest protection in Portugal. Understanding the reality of this fiscal policy is fundamental in the context of sustainable forest management. After reviewing the literature focused on the importance of the forest as an essential asset to meet human needs, the Portuguese tax regime in this area is analysed. This is followed by a study of tax incentives for Forest Intervention Areas (FIA) and Forest Management Plans (FMP) in the Portuguese business context in the period 2015-2020. The main results of the research show that companies connected to the sector have become more aware of the importance of adopting more sustainable practices, by adhering to these tax benefits over time, showing the role of environmental tax policy in the sustainable growth of the economic tissue. The results show that the number of beneficiary companies is reduced compared to the total number of companies operating in the forestry sector in Portugal, also verifying that most of these companies are concentrated in a small number of municipalities, some of which have no ZIF. Ther is still important work to be done with a view to greater dissemination of the tax benefits under study.

Economic impacts of climate change on forests: a PICUS–LANDIS–CGE modeling approach

Climate change is expected to alter both forest stand- and landscape-level dynamics through a change in environmental characteristics. While numerous studies have employed models to assess the ecological and/or economic impacts of such changes on forests throughout the world, there is need to further refine such analyses. In this paper, we contribute to this literature by coupling an ecological (PICUS–LANDIS II) modeling framework with an economic (computable general equilibrium) model to better account for the economic impact associated with climate-induced impacts on forest stand- and landscape-level structure and composition dynamics. Applying this framework to a case-study region of New Brunswick, Canada, we estimate that climate change will reduce softwood supply by 16%–73% and impact hardwood supply in the range of −2% to +4% by 2150. The change in wood supply is estimated to reduce the value of the softwood and hardwood forestry and logging sector output by up to 51% and 17%, respectively, by 2150. These sector-level impacts may lead to a 0.08%–0.88% reduction in annual gross domestic product by 2150. The methodological advances established in this study can be used to better inform future forest management and economic plans that aim to lessen both the ecological and economic impact of climate change.