Search Results (112)

In the fields of forestry, ecology, and pedology, different slope aspects exhibit significantly different microenvironments and soil conditions, which ultimately lead to disparities in seedling regeneration. Therefore, studying the effects of soil nutrients on seedling regeneration under different microenvironmental conditions can provide critical data for the artificial promotion of natural regeneration. In July 2021, the seedling regeneration status in 900 m² artificial Pinus sylvestris var. mongolica forests with different slope aspects was investigated. Soil nutrient indices were obtained through the collection and measurement of soil samples. Geostatistics were used to quantify the spatial heterogeneity of soil nutrients at a small scale. Soil nutrient information from the seedling growth locations was acquired by combining geographic information system (GIS) technology and laboratory experiments to analyze the effects of soil nutrients on seedling regeneration. The spatial heterogeneity of soil nutrients and their effects on seedling regeneration change with different slope aspects. Even at a small scale (3 m), spatial heterogeneity remains evident. Shaded slopes are more prone to supporting biennial seedlings and older saplings, while seedlings on sunny slopes exhibit superior growth indicators (height and ground diameter). The correlation calculations and redundancy analysis (RDA) of the relationship between soil nutrients and seedling regeneration show that although the soil nutrient content inhibits seedling quantity, they can enhance seedling growth indicators, among which soil organic matter plays the most critical role. Different slope aspects affect soil nutrients and seedling spatial patterns, and increased soil nutrients can promote the natural regeneration of Pinus sylvestris var. mongolica seedlings. Full article

The accelerated penetration of digital technology into natural ecosystems has led to the digital transformation of forest ecological spaces. Smart forestry, as a key pathway for digital-intelligence-enabled ecological governance, plays an important role in global sustainable development and multi-level governance. However, due to differences in functional positioning, resource capacity, and policy translation mechanisms, semantic shifts and disconnections arise between central policies, local policies, and practical implementation, thereby affecting policy execution and governance effectiveness. Fujian Province has been identified as a key pilot region for smart forestry practices in China, owing to its early adoption of informatization strategies and distinctive ecological conditions. This study employed the Latent Dirichlet Allocation (LDA) topic modeling method to construct a corpus of smart forestry texts, including central policies, local policies, and local media reports from 2010 to 2025. Seven potential themes were identified and categorized into three overarching dimensions: technological empowerment, governance mechanisms, and ecological goals. The results show that central policies emphasize macro strategy and ecological security, local policies focus on platform construction and governance coordination, and local practice features digital innovation and ecological value transformation. Three transmission paths are summarized to support smart forestry policy optimization and inform digital ecological governance globally. Full article

After the fall of communism, Romania embarked on a comprehensive land restitution process through Law No. 18/1991, aiming to re-establish private ownership rights, particularly for agricultural and forestry lands. Divergent historical legacies across regions have resulted in heterogeneous land administration systems, contributing to inconsistencies, overlapping claims, and prolonged legal disputes. To address these challenges, the Romanian government introduced the National Cadastre and Land Registration Program, which promotes systematic property registration across the country. Keeping in mind the fact that there is no integrated study that analyses national challenges from multiple dimensions such as history, law, institutions, technology, and socioeconomics and proposes systematic optimization strategies, this article provides a critical analysis of the legal and institutional framework governing land restitution and cadastral reform, highlighting the influence of historical administrative structures and the adoption of modern geospatial technologies such as Geographic Information Systems (GISs) and Unmanned Aerial Vehicles (UAVs). By adopting a qualitative and document-based research approach, focusing on the analysis of legislative frameworks, institutional procedures, and technical instruments used in systematic land registration in Romania, this study emphasizes the benefits of systematic registration, including increased legal certainty, investment stimulation, improved access to credit, and better planning and taxation. Despite progress, implementation remains uneven, hindered by documentation gaps, institutional capacity limitations, and administrative obstacles. Recent legislative adjustments and the integration of advanced geospatial tools aim to improve data quality and accelerate the registration process. Ultimately, the integration of legal, institutional, and geospatial components is essential for achieving transparent and accountable land governance, efficient resource management, and sustainable rural development in Romania. Full article

Forests face increasing threats due to climate change and anthropogenic pressures, exacerbating plant stress and disease susceptibility. Norway spruce (Picea abies (L.) H. Karst.), a key conifer species in European forestry, is particularly vulnerable. Developing innovative seed treatments to enhance tree resilience is crucial for sustainable forest management. Despite the growing interest in cold plasma (CP) technology for seed treatment, research on its long-term effects on trees, particularly Norway spruce, remains scarce. This study aimed to investigate the effects of pre-sowing CP treatment on Norway spruce seeds from 10 half-sib families over two vegetation seasons. Results indicate that CP treatment influenced key physiological and biochemical parameters in a genotype-specific and treatment duration-dependent manner (1 or 2 min). In some cases, CP-treated seedlings exhibited increased chlorophyll levels (e.g., increased chlorophyll a by up to 49% in some genotypes treated with CP for 1 min, and by up to 35% in those treated with CP for 2 min), reduced malondialdehyde (MDA) content in second-year samples (by up to 52% in some genotypes), and enhanced production of phenolics (by up to 21% in some genotypes in both treatment groups), suggesting improved stress tolerance. The 541 half-sib family is particularly noteworthy, as first-year seedlings exhibited increased levels of chlorophylls, flavonoids, and total phenols following a 2 min treatment. In contrast, second-year seedlings of the same family showed an increase in flavonoids and a reduction in MDA levels compared to the control, indicating a sustained and possibly age-modulated physiological response to CP exposure (2 min). However, responses varied across genetic backgrounds, highlighting the importance of genotype in determining treatment efficacy. These findings underscore the potential of CP technology as a tool for improving Norway spruce resilience and inform future strategies for seed enhancement in forestry. Full article

The accurate and efficient 3D reconstruction of trees is beneficial for urban forest resource assessment and management. Close-range photogrammetry (CRP) is widely used in the 3D model reconstruction of forest scenes. However, in practical forestry applications, challenges such as low reconstruction efficiency and poor reconstruction quality persist. Recently, novel view synthesis (NVS) technology, such as neural radiance fields (NeRF) and 3D Gaussian splatting (3DGS), has shown great potential in the 3D reconstruction of plants using some limited number of images. However, existing research typically focuses on small plants in orchards or individual trees. It remains uncertain whether this technology can be effectively applied in larger, more complex stands or forest scenes. In this study, we collected sequential images of urban forest plots with varying levels of complexity using imaging devices with different resolutions (cameras on smartphones and UAV). These plots included one with sparse, leafless trees and another with dense foliage and more occlusions. We then performed dense reconstruction of forest stands using NeRF and 3DGS methods. The resulting point cloud models were compared with those obtained through photogrammetric reconstruction and laser scanning methods. The results show that compared to photogrammetric method, NVS methods have a significant advantage in reconstruction efficiency. The photogrammetric method is suitable for relatively simple forest stands, as it is less adaptable to complex ones. This results in tree point cloud models with issues such as excessive canopy noise and wrongfully reconstructed trees with duplicated trunks and canopies. In contrast, NeRF is better adapted to more complex forest stands, yielding tree point clouds of the highest quality that offer more detailed trunk and canopy information. However, it can lead to reconstruction errors in the ground area when the input views are limited. The 3DGS method has a relatively poor capability to generate dense point clouds, resulting in models with low point density, particularly with sparse points in the trunk areas, which affects the accuracy of the diameter at breast height (DBH) estimation. Tree height and crown diameter information can be extracted from the point clouds reconstructed by all three methods, with NeRF achieving the highest accuracy in tree height. However, the accuracy of DBH extracted from photogrammetric point clouds is still higher than that from NeRF point clouds. Meanwhile, compared to ground-level smartphone images, tree parameters extracted from reconstruction results of higher-resolution and varied perspectives of drone images are more accurate. These findings confirm that NVS methods have significant application potential for 3D reconstruction of urban forests. Full article

Forests are one of the most predominant types of land usage in Portugal and are highly relevant in terms of environmental, economic, social, and political factors. Increasing the value and the resilience of the Portuguese forest, defining adequate policies, and aligning forest research with society needs requires a truthful comprehension of the most relevant challenges in this sector. This study identifies and analyzes the most relevant needs and challenges impacting the Portuguese forestry sector, both currently and over a five-year period, from the stakeholder’s perspective. A participatory approach was employed, engaging national and regional forest stakeholders, to ensure a realistic vision of the forest sector in Portugal. A total of 116 topics were identified, with a predominance of immediate challenges over future information needs, underscoring the urgent pressures on the sector. Environmental/ecological and policy issues dominated the identified needs and challenges, reflecting the urgency for strategic interventions in these areas. A significant emphasis was placed on the mitigation of climate change impacts, mainly associated with biotic and abiotic risks, promoting technological advanced forest management, and the sector valorization. Policy and legal issues, such as fragmented ownership and adequate economic and fiscal incentives, were also identified as major concerns. The findings highlight the interconnected nature of forestry challenges and the need for integrated, multidisciplinary, and transdisciplinary approaches, prioritizing research on climate impacts, developing adaptive management strategies, promoting stakeholder engagement, and enhancing capacity-building initiatives. The results of this study make it a relevant case study for other forest stakeholders in similar regions in Europe with comparative forest management models and can inspire new solutions for common challenges opening new research avenues for other forest related academics. Full article

Agroforestry residues are a promising source of organic matter and energy. These organic wastes are often poorly managed by incineration or open-air composting, resulting in the emission of greenhouse gases. Solid-state anaerobic digestion has recently attracted considerable attention to converting organic waste with a high total solids content, such as agroforestry residues, into renewable energy. However, the complex structure of these residues is still a defiance to this technology. Their degradation requires a long period, resulting in low heat and mass transfer. In addition, the process is often inhibited by the accumulation of toxic compounds. An efficient management process has remained under development. Comprehending the challenges faced when treating agroforestry waste is necessary to create practical applications. This review provides essential information for more effective management of complex agricultural and forestry residues using the SS-AD process. It covers the different parameters and experiments that have successfully managed these residues for renewable energy production. Various solutions have been identified to overcome the drawbacks encountered. These include co-digestion, which brings together different residues for better sustainability, and the strategies used to improve energy production from these residues at different levels, involving efficient pretreatments and appropriate operational reactor designs. Full article

The European biofuel and bioenergy industry faces increasing challenges in achieving sustainable energy production while meeting carbon neutrality targets. This study provides a detailed analysis of biogenic emissions from biofuel and bioenergy production, with a focus on key sectors such as biogas, biomethane, bioethanol, syngas, biomass combustion, and biomass pyrolysis. Over 18,000 facilities were examined, including their feedstocks, production processes, and associated greenhouse gas emissions. The results highlight forestry residues as the predominant feedstock and expose significant disparities in infrastructure and technology adoption across EU Member States. While countries like Sweden and Germany lead in emissions management and carbon capture through bioenergy production with carbon capture and storage systems (BECCS), other regions face deficiencies in bioenergy infrastructure. The findings underscore the potential of BECCS and similar carbon management technologies to achieve negative emissions and support the European Green Deal’s climate neutrality goals. This work serves as a resource for policymakers, industry leaders, and researchers, fostering informed strategies for the sustainable advancement of the biofuels sector. Full article

Forests are critical ecosystems, supporting biodiversity, economic resources, and climate regulation. The traditional techniques applied in forestry segmentation based on RGB photos struggle in challenging circumstances, such as fluctuating lighting, occlusions, and densely overlapping structures, which results in imprecise tree detection and categorization. Despite their effectiveness, semantic segmentation models have trouble recognizing trees apart from background objects in cluttered surroundings. In order to overcome these restrictions, this study advances forestry management by integrating depth information into the YOLOv8 segmentation model using the FinnForest dataset. Results show significant improvements in detection accuracy, particularly for spruce trees, where mAP50 increased from 0.778 to 0.848 and mAP50-95 from 0.472 to 0.523. These findings demonstrate the potential of depth-enhanced models to overcome the limitations of traditional RGB-based segmentation, particularly in complex forest environments with overlapping structures. Depth-enhanced semantic segmentation enables precise mapping of tree species, health, and spatial arrangements, critical for habitat analysis, wildfire risk assessment, and sustainable resource management. By addressing the challenges of size, distance, and lighting variations, this approach supports accurate forest monitoring, improved resource conservation, and automated decision-making in forestry. This research highlights the transformative potential of depth integration in segmentation models, laying a foundation for broader applications in forestry and environmental conservation. Future studies could expand dataset diversity, explore alternative depth technologies like LiDAR, and benchmark against other architectures to enhance performance and adaptability further. Full article

Assessing the functions of forest ecosystems is important for a proper understanding of their role in the natural environment and society. Ecotourism emphasizes minimizing negative impacts on the environment and supports environmental education. Modern information and communication technologies, including forest apps, are helping in this regard. Precision forestry uses GIS technologies and remote sensing to obtain spatial data, identify the components of the natural environment, and evaluate the changes that they are subject to. A tool enabling the evaluation of synergy between ecosystem functions and tourism, in addition to traditional field research and surveys, is remote sensing. This paper aims to show the feasibility of evaluating the synergy of ecosystem and tourism services in forests using remote sensing as an alternative to traditional terrestrial measurements. This study’s temporal scope is from 2019 (i.e., the introduction of the pilot program on making forests available for bushcraft and survival activities in Poland) until the beginning of 2024. Thus, it covers the time when the State Forests program called “Stay Overnight in the Forest” related to dispersed camping in forests was in force. Additionally, online surveys were conducted using the Microsoft Forms platform among representatives of all forest districts participating in implementing the “Stay Overnight in the Forest” program from 1 May 2021. This program is a crucial element of the contemporary tourist and recreational offer of the State Forests in Poland and influences the course of the ecosystem and tourist services in the forests. From the recorded digital images, it is possible to obtain information about threats in forest ecosystems caused by natural disasters, such as windstorms and fires. The precise provision of information about degraded forest areas can contribute to the more efficient management of forest reclamation works and the restoration of damaged stands. On the other hand, the rehabilitated forest can be a destination point for educational trails in forests. Full article

LiDAR is an active remote sensing technology widely used in forestry applications, such as forest resource surveys, tree information collection, and ecosystem monitoring. However, due to the resolution limitations of 3D-laser scanners and the canopy occlusion in forest environments, the tree point clouds obtained often have missing data. This can reduce the accuracy of individual tree segmentation, which subsequently affects the tree species classification. To address the issue, this study used point cloud data with RGB information collected by the UAV platform to improve tree species classification by completing the missing point clouds. Furthermore, the study also explored the effects of point cloud completion, feature selection, and classification methods on the results. Specifically, both a traditional geometric method and a deep learning-based method were used for point cloud completion, and their performance was compared. For the classification of tree species, five machine learning algorithms—Random Forest (RF), Support Vector Machine (SVM), Back Propagation Neural Network (BPNN), Quadratic Discriminant Analysis (QDA), and K-Nearest Neighbors (KNN)—were utilized. This study also ranked the importance of features to assess the impact of different algorithms and features on classification accuracy. The results showed that the deep learning-based completion method provided the best performance (avgCD = 6.14; avgF1 = 0.85), generating more complete point clouds than the traditional method. On the other hand, compared with SVM and BPNN, RF showed better performance in dealing with multi-classification tasks with limited training samples (OA-87.41%, Kappa-0.85). Among the six dominant tree species, Pinus koraiensis had the highest classification accuracy (93.75%), while that of Juglans mandshurica was the lowest (82.05%). In addition, the vegetation index and the tree structure parameter accounted for 50% and 30%, respectively, in the top 10 features in terms of feature importance. The point cloud intensity also had a high contribution to the classification results, indicating that the lidar point cloud data can also be used as an important basis for tree species classification. Full article

The application of LiDAR technology in extracting individual trees and stand parameters plays a crucial role in forest surveys. Accurate identification of individual tree trunks is a critical foundation for subsequent parameter extraction. For LiDAR-acquired forest point cloud data, existing two-dimensional (2D) plane-based algorithms for tree trunk detection often suffer from spatial information loss, resulting in reduced accuracy, particularly for tilted trees. While cylinder fitting algorithms provide a three-dimensional (3D) solution for trunk detection, their performance in complex forest environments remains limited due to sensitivity to parameters like distance thresholds. To address these challenges, this study proposes an improved individual tree trunk detection algorithm, Random Sample Consensus Cylinder Fitting (RANSAC-CyF), specifically optimized for detecting cylindrical tree trunks. Validated in three forest plots with varying complexities in Tianhe District, Guangzhou, the algorithm demonstrated significant advantages in the inlier rate, detection success rate, and robustness for tilted trees. The study showed the following results: (1) The average difference between the inlier rates of tree trunks and non-tree points for the three sample plots using RANSAC-CyF were 0.59, 0.63, and 0.52, respectively, which were significantly higher than those using the Least Squares Circle Fitting (LSCF) algorithm and the Random Sample Consensus Circle Fitting (RANSAC-CF) algorithm (p < 0.05). (2) RANSAC-CyF required only 2 and 8 clusters to achieve a 100% detection success rate in Plot 1 and Plot 2, while the other algorithms needed 26 and 40 clusters. (3) The effective distance threshold range of RANSAC-CyF was more than twice that of the comparison algorithms, maintaining stable inlier rates above 0.9 across all tilt angles. (4) The RANSAC-CyF algorithm still achieved good detection performance in the challenging Plot 3. Together, the other two algorithms failed to detect. The findings highlight the RANSAC-CyF algorithm’s superior accuracy, robustness, and adaptability in complex forest environments, significantly improving the efficiency and precision of individual tree trunk detection for forestry surveys and ecological research. Full article

This article focuses on the need for digitalization in the forestry and timber sector using information from CT scans of logs. The National Forest Centre (Slovak Republic) operates a unique 3D CT scanner for wooden logs at the Stráž Biotechnology Park. This real-time scanner generates a 3D model of a log, displaying the wood’s internal features/defects. To optimize log-cutting plans effectively, it is necessary to automatically detect and classify these features and defects in real time, leveraging computer vision principles. Artificial intelligence, specifically neural networks, addresses this need by enabling solutions for tasks of this nature. Building a highly efficient neural network for detecting wood features and defects requires creating a database of log scans and training the network on these data. This is a time-intensive process, as it involves manually marking internal features and defects on hundreds of CT scans of various wood types. A functional neural network for detecting internal wood defects represents a significant advancement in sector digitalization, paving the way for further automation and robotization in wood processing. For the forestry sector to remain competitive, efficiently process raw materials, and improve product quality, the effective application of CT scanning technology is essential. This technological innovation aligns the sector more closely with leaders in other fields, such as the automotive, engineering, and metalworking industries. Full article

Forest plot surveys are vital for monitoring forest resource growth, contributing to their sustainable development. The accuracy and efficiency of these surveys are paramount, making technological advancements such as Simultaneous Localization and Mapping (SLAM) crucial. This study investigates the application of SLAM technology, utilizing LiDAR (Light Detection and Ranging) and monocular cameras, to enhance forestry plot surveys. Conducted in three 32 × 32 m plots within the Tibet Autonomous Region of China, the research compares the efficacy of LiDAR-based and visual SLAM algorithms in estimating tree parameters such as diameter at breast height (DBH), tree height, and position, alongside their adaptability to forest environments. The findings revealed that both types of algorithms achieved high precision in DBH estimation, with LiDAR SLAM presenting a root mean square error (RMSE) range of 1.4 to 1.96 cm and visual SLAM showing a slightly higher precision, with an RMSE of 0.72 to 0.85 cm. In terms of tree position accuracy, the three methods can achieve tree location measurements. LiDAR SLAM accurately represents the relative positions of trees, while the traditional and visual SLAM systems exhibit slight positional offsets for individual trees. However, discrepancies arose in tree height estimation accuracy, where visual SLAM exhibited a bias range from −0.55 to 0.19 m and an RMSE of 1.36 to 2.34 m, while LiDAR SLAM had a broader bias range and higher RMSE, especially for trees over 25 m, attributed to scanning angle limitations and branch occlusion. Moreover, the study highlights the comprehensive point cloud data generated by LiDAR SLAM, useful for calculating extensive tree parameters such as volume and carbon storage and Tree Information Modeling (TIM) through digital twin technology. In contrast, the sparser data from visual SLAM limits its use to basic parameter estimation. These insights underscore the effectiveness and precision of SLAM-based approaches in forestry plot surveys while also indicating distinct advantages and suitability of each method to different forest environments. The findings advocate for tailored survey strategies, aligning with specific forest conditions and requirements, enhancing the application of SLAM technology in forestry management and conservation efforts. Full article

Digitalization has transformed various sectors, including forestry, by introducing specialized digital tools and ICTs. This study explores the impact of digitalization on the Italian forestry sector, focusing on socio-economic indicators. Data on these indicators were gathered from the “National Forest Information System” (SINFor), while the digitalization level in Italian regions came from previous studies. The methods used included correlation analysis between digitalization and socio-economic indicators, along with linear regression models. The study also presents three digital progress scenarios, predicting significant socio-economic improvements with increased digitalization. The results show a strong correlation between digitalization and forestry indicators such as employment, value creation, and certification. These findings highlight the transformative potential of digitalization for sustainable forest management, emphasizing the need for further investment in digital infrastructure to boost productivity, inclusivity, and environmental conservation. The study also discusses challenges in fully understanding the effects of digitalization and suggests future research directions to examine specific technological features and broader industry impacts. Full article