Forest Planning Research Articles

Dynamic Changes of Air Particle Pollutants and Scale Regulation of Forest Landscape in a Typical High-Latitude City

Particulate pollutants, particularly PM2.5 and PM10, pose serious threats to human health and environmental quality. Therefore, effectively mitigating and reducing the concentrations of these pollutants is crucial for human survival and development. In this study, we analyzed the distribution characteristics of air particulate pollutants in a typical high-latitude city, extracted urban forest areas from high-resolution remote sensing images, and examined the changing characteristics of PM concentration and the relationship between landscape pattern indexes and PM at different scales. The results showed that the concentrations of PM2.5 and PM10 were highest in winter and lowest in summer. At the small scales of 0.5 km × 0.5 km to 1.5 km × 1.5 km, PM concentration decreased with the decrease in PARA (Perimeter–Area Ratio). At the mesoscales of 2 km × 2 km to 2.5 km × 2.5 km, both PARA and CIRCLE (Related Circumscribing Circle) were highly significant (p < 0.001) correlated with PM concentration. At the large scales of 3 km × 3 km to 4 km × 4 km, PARA and PAFRAC (Perimeter–Area Fractal Dimension) were positively correlated with PM concentration. Our study indicates that reducing the complexity of forest patches in small-scale planning can help mitigate particulate air pollution. In the medium scale of urban forest planning, the more regular the forest patch shape and the more similar the patch shape to the strip, the better PM can be alleviated, while in large-scale planning, increasing the forest area and making the patches more normalized and simplified can reduce PM concentration. Moreover, reducing the complexity of forest patches can significantly mitigate PM pollution at all scales. The results of this research provide theoretical support and guidance for improving air quality in urban forest planning at different scales.

Improving airborne laser scanning-based species-specific forest volume estimation using sentinel-2 time series

ABSTRACT Species-specific timber volume estimates are required to support forest planning and conservation. We evaluated whether additional predictors from a Sentinel-2 time series can improve airborne laser scanning (ALS)-based estimation of species-specific timber volumes. Furthermore, we determined the satellite image dates that provided the greatest improvement in accuracy. The Sentinel-2 time series was constructed to cover 1 March–30 November time-period, with a focus on late spring and early summer. The estimation was done using the k Most Similar Neighbor method and predictors extracted from the ALS data and Sentinel-2 images. Our best model included both ALS and Sentinel-2 time-series predictors, and the relative root mean square error (RMSE) values for pine, spruce and deciduous timber volumes were 40.8%, 57.0% and 51.3%, respectively (mean 49.7%). All deciduous trees were treated as one species. When bands from an individual image were used instead of the time series, the best result was obtained with an image from September where the respective relative RMSE values were 42.2% (deciduous), 58.4% (pine) and 60.6% (spruce) with a mean value of 53.7%. A fusion of a Sentinel-2 time-series and ALS data can improve species-specific estimation results compared to the use of individual Sentinel-2 images or ALS only.

Impact of Indonesia's Forest Management Units on the reduction of forest loss and forest fires in Sulawesi

The establishment of Forest Management Units (Kesatuan Pengelolaan Hutan/ KPH) represents one of Indonesia's most significant forest governance reforms, covering the 120 million hectares of designated Forest Areas. Sharing features with the Landscape Approach, KPHs are expected to reduce deforestation and forest degradation through improved forest planning, oversight, open access avoidance, and fire prevention and response. This paper assesses the impact of KPHs on the reduction of forest loss, including forest loss due to fire, and the factors that influence this impact (e.g. KPH's primary objective, deforestation risk). We use remotely sensed data to estimate difference-in-differences models accounting for differences in baseline characteristics and multiple time periods. Our case study, Sulawesi Island, is primarily shaped by smallholder farmer land use, where KPHs can exert significant influence. We do not find evidence of an overall effect of KPHs on deforestation. However, we observe intriguing heterogeneous effects depending on the year, including significantly less deforestation in already-established KPHs during the El Niño years of 2015–16, and a notable impact on the reduction of forest loss due to fire in some early-established KPHs. These findings warrant further investigation.

Estimation Stand Volume, Basal Area and Quadratic Mean Diameter Using Landsat 8 OLI and Sentinel‐2 Satellite Image With Different Machine Learning Techniques

ABSTRACTThe data required for sustainable forest planning is provided by traditional forest inventories, which are labor, time, and cost‐intensive. Providing this data quickly, reliably, and accurately is crucial for planners and researchers. The objective of this study was to predict stand basal area (BA), stand volume (V), and quadratic mean diameter (dq) by leveraging vegetation indices (VIs) and reflectance (R) derived from Landsat 8 OLI and Sentinel 2 satellite images, along with topographic (T) data obtained from ALOS‐PALSAR satellite imagery. Forest inventory data for a total of 250 sample plots were used for modeling in the study. Stand parameters were estimated using support vector machines (SVM), multiple linear regression (MLR), decision tree (DT), and random forest (RF) algorithms. In modeling V, BA, and dq, both individual and combinations of R, VIs, and T values obtained from satellite imagery were used as independent variables. Using the generated datasets, each of the stand parameters was modeled separately with MLR, SVM, RF, and DT algorithms, and the success of the models was compared to determine the modeling technique and dataset with the highest success for the relevant parameter. The results showed that for each stand parameter, the highest model success was achieved in the combined dataset, which was created by combining all datasets. However, in terms of modeling techniques, the highest success for each stand parameter was achieved with different modeling techniques. The highest success for V is obtained in the model using the SVM method (R2 = 0.78; RMSE = 0.28 m3/ha), the RF method yielded the highest model performance for BA (R2 = 0.70; RMSE = 2.53 m2/ha), and finally, the highest success for dq was obtained in the DT method (R2 = 0.74; RMSE = 0.02 cm). In general, the datasets obtained from Sentinel 2 images showed higher model success than the datasets obtained from Landsat 8 OLI images.

The use of digital tools in forest tourism and recreation - Experiences from Warsaw agglomeration in Poland

Tourism and recreation in forest areas around large urban agglomerations are becoming increasingly important and pose major management challenges in terms of sustainability, environmental impact and balancing the needs of visitors with conservation efforts. Digital tools are critical to the planning and management of suburban forests as they improve data collection, enable efficient resource allocation and enhance the visitor experience through better navigation and information dissemination. The aim of our work was to find out whether people visiting urban and suburban forests in Warsaw consciously plan their visits to forest areas, what ICT tools they use during and after a forest visit, and whether they share their experiences related to forest recreation on social media after the visit. The research focused on urban and suburban forests in the Warsaw metropolitan area, Poland. Data on visitors’ characteristics, recreation habits and digital skills were collected in an online panel survey in November 2023 (n = 1413). The results showed that respondents most frequently visited forest areas around Warsaw 2–3 times a month and several times a year. The vast majority of respondents planned their visits directly on the day of the trip (34.96 %) and used their own knowledge and experience when planning their trip to the forest. More than half of respondents (69 %) stated that they use various types of digital tools when visiting forest areas, most commonly smartphones with GPS functionality. Most respondents stated that they had advanced (43.38 %) or at least advanced (32.06 %) knowledge of using digital tools. 39.49 % of respondents stated that they do not use the internet to share information about their forest visits. The research results presented in the sentence indicate the complexity of using digital tools in the management of outdoor recreation and tourism. On the one hand, our research clearly shows that digital tools are an integral part of modern outdoor recreation and tourism management and provide valuable insights, on the other hand, most respondents planned their visits directly on the day of departure, relying mainly on their own knowledge and experience, rather than on digital tools. This knowledge can be used to develop forest management strategies, environmental education, monitoring tourist flows and promoting responsible recreation worldwide. Further research is needed to analyse the use of digital tools in digital forest recreation.

Monitoring of Land Cover Changes Between 1990 and 2014 over Cyprus using Multi-Temporal Landsat Imagery

Determining changes in forest resources and, consequently, land cover/land use is crucial for sustainable forest planning. This study aims to determine changes in land use classes, including Forest areas, Agricultural areas, Settlement areas, Other non-forest areas, and Water bodies in the study area located in Cyprus between 1990 and 2014. The study utilized digital management plans, a high-resolution base map, and Landsat satellite data for the relevant years. Necessary preprocessing steps were applied to prepare the satellite data for classification. Initially, unsupervised classification was conducted on the images to determine the number of distinguishable sub-information classes. Subsequently, supervised classification was performed using the maximum likelihood algorithm with the provision of training areas. The sub-information classes obtained from the supervised classification were consolidated into five predetermined main information classes. Following the classification process, the accuracy of the classification for each image was determined. Accordingly, the classification accuracy of the 1990 Landsat 5 TM satellite image was found to be 92% (Overall Classification Accuracy: 92%, Overall Kappa Statistics: 0.9000), while the classification accuracy of the 2014 Landsat 8 OLI satellite image was 89.20% (Overall Classification Accuracy: 89.20%, Overall Kappa Statistics: 0.8650). Subsequently, change analysis was conducted using the post-classification comparison method, and changes in land use within the study area over a twenty-four-year period were examined.

Environmental Fragility in Commercial Forest Plantations Aiming at Sustainable Management

Objective: The objective of this study is to investigate the environmental fragility of two landscapes in which there is a predominance of commercial forest plantations, with the aim of analyzing this fragility through multicriterial evaluation, focusing on the planning of forest roads. Theoretical Framework: The theory of environmental fragility, multi-criteria evaluation methods, and principles of landscape management stand out, providing a solid basis for understanding the context of the investigation. These frameworks are crucial for analyzing the impact of forest roads on environmental variables such as water dynamics, sediment production, and soil erodibility. The integration of these theories allows for a comprehensive assessment of environmental fragility, essential for effective forest planning and road maintenance in forested areas Method: The methodology adopted involves a multi-criteria evaluation approach to assess environmental fragility in forest landscapes. Key criteria, including slope, distance from water bodies, soil erodibility, and topographic humidity, were integrated to create fragility maps. Data were collected through geographical and environmental analysis, processed with spatial analysis tools, and supplemented by principal component analysis to identify influential criteria. This approach enabled a detailed assessment of forest roads' environmental impact, supporting informed decisions in road planning and landscape management. Results and Discussion: The results revealed notable variations in environmental fragility. The analysis of forest roads highlighted their varying impact on environmental fragility. These results are framed within the context of environmental fragility and multi-criteria evaluation, emphasizing the role of criteria such as slope, proximity to water bodies, soil erodibility, and topographic humidity in assessing landscape degradation. The study underscores the need for careful forest road planning to mitigate environmental impacts and promotes the use of comprehensive evaluation methods in sustainable landscape management. Research Implications: The practical and theoretical implications of this research are discussed, providing insights into how the results can be applied or influence practices in the field of forest management and environmental conservation. These implications could encompass forest road planning, sustainable landscape management, and the development of environmental policies aimed at mitigating the impact of infrastructure on natural ecosystems.The study's findings emphasize the need for incorporating multi-criteria evaluation methods into forest planning processes to identify and mitigate areas of high environmental fragility. This approach can guide decision-makers in prioritizing areas for road maintenance, rehabilitation, or even rerouting to minimize ecological damage. Furthermore, the research underscores the importance of integrating environmental fragility assessments into broader forest management strategies, enhancing the sustainability of commercial forestry operations while preserving essential ecosystem services. Originality/Value: This study contributes to the literature by introducing an innovative application of multi-criteria evaluation in assessing environmental fragility within forest landscapes, specifically in the context of forest road planning. The research combines various environmental criteria into a comprehensive fragility map, offering a nuanced understanding of landscape vulnerability that is less commonly addressed in existing studies. Notably, this methodology focused on forest roads has not been previously explored in the literature, highlighting the unique contribution of this research to the field.The relevance and value of this research are evidenced by its potential to influence forest management practices, particularly in the sustainable planning and maintenance of forest roads.

Review of biodiversity assessment and resource management orientation for sustainable rural development in Lang Son province, Vietnam

This study examines the conservation and biodiversity management efforts in Lang Son province, Vietnam, in the context of global environmental concerns. Under Decision No 2447/QD-UBND, the province’s Special-use Forest Planning focuses on conserving areas such as the Huu Lien Nature Reserve and the Mau Son special-use forests. This initiative covers a substantial planned land area to sustain biodiversity and foster rural development up to 2030. Recent surveys indicate a notable increase in plant species within these areas, from 842 species in 2017 to 961 in 2019, and significant efforts towards conserving endangered species like the Asiatic black bear and the Annam flying frog. The province encompasses diverse ecosystems spread across 832,075.82 ha, characterized by varied terrains and climates contributing to its rich biodiversity. These regions are crucial for various species, including those listed in conservation documents, such as the 77 plant species recorded. RAP-Plant analysis and Monte Carlo simulation indicate that Lang Son’s biodiversity, with a Shannon index of 2.85 and Simpson index of 0.75, is threatened by a projected forest area decline to 514,578.61 ha by 2040, risking extinction for rare species like Hoang dan and Lan kim tuyen. Furthermore, Lang Son’s strategic initiatives towards sustainable agricultural practices, forest management, and restoration efforts highlight the integration of biodiversity conservation into broader socio-economic development plans. The province’s approach aligns with global strategies emphasizing the protection of endangered and endemic species as essential to maintaining ecological balances and supporting sustainable development. This study underscores the importance of comprehensive regional planning and community engagement in biodiversity conservation efforts to mitigate economic development, illegal activities, and climate change challenges. Threats such as illegal logging and habitat fragmentation persist, necessitating more vital community-based conservation efforts and robust legal frameworks.

A priory allocation of ecosystem services to forest stands in a forest management context considering scientific suitability, stakeholder engagement and sustainability concept with multi-criteria decision analysis (MCDA) technique: A case study in Turkey

Various research endeavours are designed to identify ecosystem services, assess their spatial distribution, and prioritize them in a given forest landscape. The Turkish State Forest Organization has introduced an ecosystem-based multiple-use forest management philosophy since 2008, which emphasizes the need for identifying and allocating ecosystem services to each forest planning unit. This paper aims to investigate the use of Multiple Criteria Decision Analysis (MCDA) techniques and explores their effectiveness and suitability in identifying and allocating ecosystem services to forest units, considering scientific suitability, stakeholder engagement and the sustainability concept in the context of ecosystem-based forest management decision-making processes in a case study area of Turkey. We propose a framework that entails an iterative process comprising various stages, starting from identifying ecosystem services (ES) to allocating them to forest stands with a participatory approach. We employed the Analytical Hierarchy Process (AHP) and the Delphi method to determine stakeholder preferences and allocate ecosystem services to forest stands. This was achieved through an equation newly developed using scientific suitability, stakeholder preferences, and the sustainability concept. The landscape percentage allocated primarily to ES was as follows: water regulation (55.44%), soil protection (16.47%), biodiversity conservation (14.03%), wood production (13.08%), and aesthetic-recreation (0.84%). Notably, no allocations were made for national defence and climate regulation services. In conclusion, the stratification of Posof forests into zones was efficiently achieved a priori, considering both scientific-technical and socio-cultural criteria through MCDA techniques based on stakeholder preferences. This study streamlines the decision-making process involved in spatially allocating ecosystem services and provides crucial information instrumental in determining management objectives and optimal forest activities.

Using a location-based game to collect preference information for urban and rural forest planning

Location-based games provide an opportunity to collect crowdsourced, spatially explicit data that can be used in decision-making processes in forest planning. While these games have been more widely used in collecting mapping and modelling data, there is limited research on their suitability for collecting location-based data on human preferences, opinions, and emotions in these contexts. Collecting human-centred data can be resource-intensive and costly, hence exploring the potential of location-based games is important. This study investigates the use of geocaching in gathering meaningful data in urban and rural settings. A methodology was developed wherein surveys were embedded in geocaches on trails located in urban and rural forests in Finland. These surveys collected quantitative, qualitative, and photographic data relating to human-forest relationships and landscape preferences. The findings indicate that trails closer to urban areas with higher population density showed increased cache log volume and survey response rates. Moreover, trails tended to collect most information in the first summer after establishment. It is recommended that future studies employing this methodology create shorter, easily accessible, circular trails and that survey questions should be clear and incorporate a method to verify that submitted responses reflect experiences at the chosen location. Furthermore, forest planning professionals and researchers seeking to utilise location-based games in data collection are advised to engage with the communities active in playing them to ensure success..

Assessment of Woody Species Biodiversity and Carbon Stock Potentials in the Arboretum of National School of Water and Forests of Mbalmayo, Center Region of Cameroon

Assessing forest biodiversity is crucial for supporting sustainable forest planning. We investigated the potential for biodiversity and carbon sequestration in both artificial and natural forests of the arboretum of the National School of Water and Forests (ENEF) of Mbalmayo. A floristic inventory was carried out in 72 plots measuring 25m x 25m in the artificial forest covering an area of 4.5ha, and in a quadrat measuring 250m x 180m in the natural forest of the arboretum. Carbon stocks were determined by the non-destructive method using allometric equations. The results of the woody biodiversity inventories identified 77 species belonging to 69 genera and 33 families. This species richness varied from 69 species in the natural forest to 58 species in the artificial forest. Tree density was slightly higher in the artificial forest (616 ±339 ind/ha) than in the natural forest (312 ±176 ind/ha), without being significantly different. Basal area was constant in both types of forest studied, with an average of 55.97 ±36.28 m2/ha in the artificial forest and 58.33 ±19.42 m2/ha in the natural forest. The greatest quantity of sequestered carbon was found in the natural forest with 432±146.49 tC/ha compared with 400.69±328.37 tC/ha in the artificial forest. Nevertheless, these rates were not significantly different according to the ANOVA test. Given the importance of this massif in carbon sequestration and the needs of local populations, ENEF should support these populations in agroforestry practices and in the domestication of priority tree species.

An examination of opportunities and risks to private forest planning efforts among Connecticut private forest owners

Private landowners own 56 % of forest lands in the United States, yet only 11 % of forest landowners have a management plan despite the ability of a plan to help establish goals and actions to promote forest stewardship. We conducted a web-based survey targeting private forest owners in Connecticut to understand the motivations and barriers to participating in a management planning process and paired the results with economic and environmental targets spatially. Landowners without plans differed from plan holders by gender identity and average parcel size, in addition to being less familiar with educational programs, and having personal priorities that did not include harvesting timber. Our findings indicate that barriers still inhibit engaged owners from seeking out and obtaining plans, supporting the notion that financial incentives associated with planning are less of a driver for active stewardship than alignment with landowner priorities. Survey results informed the development of forest opportunity and risk zones that incorporated social, economic, and environmental variables.

Urban foresters’ perceptions about the role of soil and fungi in urban forest management and climate mitigation

The benefits of soil and mycorrhizal fungi in mitigating climate impacts through their vital role in carbon sequestration are well recognised in the academic literature but are not well incorporated in current urban forest planning and policies. We conducted a survey to examine urban foresters’ perceptions about the incorporation of current scientific understanding of the role of soil and fungi, and stakeholders’ engagement in urban forest management. Overall, urban foresters perceive that soil and fungi are important, however, little consideration is given to their incorporation in urban forest management practices and policies. Many stakeholders’ engagement is low in urban forest management. Urban foresters perceive that decision makers have little knowledge about the role of soil and fungi which may pose barriers to its integration in planning and policies. The key challenges revealed by the survey include the lack of funding, conflicting priorities, and the lack of mechanisms to transfer scientific knowledge to urban foresters and inter-departmentally. Municipalities should develop policies that enhance knowledge transfer and integration to enhance the efficacy of urban forest’s strategies that work to support municipal climate goals. Furthermore, collaboration with diverse stakeholder groups may enhance communication and subsequently increase support for urban forest initiatives.

Use of artificial neural networks with the physiological principles to predict growth model

Process-based models have become essential tools for forest planning and management due to their ability to assess the impacts of climate and operational changes on productivity. Physiological Principles Predicting Growth (3-PG) is a process-based/hybrid model widely used, associated with allometric models to estimate dendrometric variables. However, the accuracy of the model outputs still needs to be improved, especially those of interest to forest management. In this study, we tested the efficiency of artificial neural networks (ANNs) to estimate the average diameter at 1.30 m height (dbh), total height, stand volume, and root, stem, and foliage biomass. We also evaluated the prediction of two parameters that estimate the proportion of biomass allocated aboveground (ap and np). A database from published 3-PG parameterizations was generated and used to train the ANNs. Two networks were validated against observed data from two municipalities in the MG state of Brazil. Potential productivity maps at six and seven years were generated for Brazil, to evaluate the performance of the 3-PG + ANN model. All ANNs presented satisfactory statistical results in predicting the ap and np parameters, foliage biomass, dbh, total height and stand volume. The use of artificial neural networks integrated with the 3-PG model or another process-based model is a promising alternative to improve the accuracy of estimates, especially those of interest for forest management. Artificial neural networks offer a simple, flexible way to insert important variables into a process-based model. The parameters and 3-PG outputs can be estimated efficiently using artificial neural networks.

Virtual forests for decision support and stakeholder communication

Challenges in forest management are increasing due to climate change and its associated risks. Considering the needs and demands of various stakeholders leads to more complex decision-making. The increasing amount and quality of available geographic, forest and individual tree data, the combination of this data, and the use of forest growth simulators make it possible to support forest managers in this decision-making process. Our aim was to develop a strong visualization instrument that can be used in both forest planning and stakeholder communication. We present a solution based on a game engine, where data from multiple sources (terrain data, satellite imagery, tree data) is combined into a virtual environment. The user can move freely inside this virtual forest, look at the forest from arbitrary perspectives, and observe its development over the years under different management scenarios. We demonstrate the usefulness of this approach with a study region in Switzerland.

Carbon storage-driven multi-objective spatial planning: simulation and practice at the management unit level in Northeast China

In recent years, global attention to forest ecological environment, carbon, wood yield, and biodiversity has been increasing, which requires forest managers to make reasonable forest planning in time and space to meet the needs of multi-objectives and multi-resources. In this study, the resources of business units in Northeast China were analyzed, and the FPS-ATLAS model was established. A multi-objective decision supporting spatial forest planning scheme for 16629.53 ha of forest in Jingouling forest farm of Wangqing Forestry Bureau in Jilin Province was proposed. In this paper, the volume growth model of six forest types was established, and the carbon storage model was introduced to show the total carbon, biomass carbon, dissolved organic carbon, and total soil carbon of the ecosystem. The dynamic change in carbon during the whole planning period has achieved good results. After 20 time periods, the total soil carbon increased to 7,627,208 Mg (+6.63%), the total ecosystem carbon increased to 14,889,876 Mg (+27.51%), the biomass carbon increased to 5,362,779 Mg (+59.70%), and the dissolved organic carbon increased to 9,531,906 Mg (+14.59%). The purpose of this article is to achieve dynamic management of forest resources, meet multiple constraint settings, achieve multiple management objectives, harvest wood products, and ensure the ecological and social functions of forests so that forests ultimately achieve the ideal state of sustainability.

The inclusion of improved forest management in strategic forest planning and its impact on timber harvests, carbon and biodiversity conservation

In forestry, although the so-called nature-based climate solutions have usually been focused on the calculation of carbon captured in new afforestation projects, it should be noted that the increase in carbon associated with improvements in their management (Improved Forest Management) can also be computed. This type of carbon is not usually integrated into strategic forest planning models, nor has its possible degree of conflict with other regulation ecosystemic services, like biodiversity conservation, been verified. In this research, those two issues have been approached by calculating a baseline in an emblematic forest with an extensive forestry history. For this purpose, we have designed two scenarios, i.e., one linked to its current management (Business As Usual, BAU) and another justified by the inclusion of Improved Forest Management (IFM). The results reveal a notable conflict between the carbon captured and the values of the indicators used to measure biodiversity. In order to reach a compromise between both scenarios, a multi-criteria model has been proposed that could be more attractive than the above ones. In addition, the carbon profit credits in the first ten years have been computed under the IFM scenario; the latter could be, a priori, an object of transaction in a voluntary carbon market. In conclusion, our model generates feasible solutions that allow the integration of IFM into strategic planning. Besides, those solutions show interesting tradeoffs between carbon and biodiversity. This discord must be distinguished by the current state of the forest and its expected growth, as well as their influence on the values associated with provision ecosystem services, such as the present net value associated with timber harvests.

From single trees to country-wide maps: Modeling mortality rates in Germany based on the Crown Condition Survey

Most years in the period from 2018 to 2022 have been exceptionally dry in Central Europe. In Germany’s forests, this long-lasting drought has caused unprecedented tree mortality. Systematic ground-based surveys, such as the annual Crown Condition Survey, provide information on the vitality status of the different tree species and their mortality rates. However, models are needed to be able to map the spatial patterns of mortality for each tree species based on cause-effect relationships derived from field observations. In this study, logistic regression models were used to identify the most important drivers of mortality for the most important tree species in Germany. For this purpose, the dead and surviving trees from the Crown Condition Survey were combined with a large set of potential predictor variables from the domains of climate, topography, soil, land cover and deposition. After feature selection, the models were evaluated using the area under the curve (AUC) statistic. Norway spruce (Picea abies; AUC = 0.9) showed by far the greatest increase in mortality, with the country-wide average observed and predicted rates approaching almost 10% per year from 2020 to 2022, and much higher predicted rates at the regional level. Much of the spruce mortality was explained by the climatic water balance of the driest summer in previous years. The other main tree species also showed clear mortality responses to the drought conditions. However, in the case of European beech (Fagus sylvatica; AUC = 0.94) and Pedunculate and Sessile oak (Quercus robur and petraea; AUC = 0.88), the peaks in the time series of the country-wide mortality rates stayed below 1%. For these broadleaved species, mortality was more dependent on a range of site conditions, i.e., soil and topography. For Scots pine (Pinus sylvestris; AUC = 0.76), for which the observed mortality rate peaked at 1.2% in 2020, the given drivers could explain mortality only to a lesser degree than for the other species. The regression models were used for spatial prediction to produce country-wide maps of species-specific mortality rates at annual temporal and 100-m spatial resolution, covering all years from 1998 to 2022. The maps visualize the spatial patterns of mortality over time. The regions in western and central Germany, which were most seriously affected by spruce dieback can clearly be identified. The models and maps presented can be used for risk assessment, forest planning, and tree species selection, providing decision support for forest practitioners.

Spatio-Temporal Analysis of Erosion Risk Assessment Using GIS-Based AHP Method: A Case Study of Doğancı Dam Watershed in Bursa (Türkiye)

Soil erosion, one of the most serious phenomena in watershed management, can be estimated based on various criteria. Land use change is one of the most important factors affecting the susceptibility of soil erosion. In this study, the effect of land use change on soil erosion risk in two plan periods (2005 and 2017) was investigated using Analytical Hierarchy Process (AHP) and Geographic Information Systems (GIS) for the forest planning units in the Doğancı Dam Watershed, located in Bursa, Türkiye. Eight criteria were evaluated including erosion-related slope, bedrock type, land use/land cover, precipitation, relative relief, aspect, drainage frequency, and density. According to the results, the most effective factor in soil erosion was slope (0.29), while bedrock type and land use/land cover ranked second with 0.19. It was found that full closure forests were characterized by high erosion resistance (0.3), while bare land was characterized as the most sensitive area to erosion (0.39). In terms of spatio-temporal changes in a 12-year period, the areas in the medium and high erosion risk decreased, while low and very low-risk areas increased. The ROC method showed a satisfactory accuracy of 72.8% and 80.2% for the 2005 and 2017 erosion risk maps, respectively.

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.