Search Results (83)

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

The development of a forestry carbon sink project is an important way to achieve carbon neutrality and carbon reduction, and the collective forest carbon sink project is an important part of China’s forestry carbon sink project. As the main management entity of collective forests, whether farmers are willing to produce forestry carbon sinks is directly related to the implementation effect of the project. In this paper, a partial equilibrium model of farmers’ forestry production behavior was established based on production function and utility function, and the path to enhance farmers’ willingness to produce forestry carbon sink through forestry carbon sink projects was analyzed in combination with forest ecological management theory. In terms of empirical analysis, the PSM-DID econometric model was established based on the survey data of LY in Zhejiang Province, China, and the following conclusions were drawn: (1) With the receipt of revenues from forestry carbon sequestration projects and partial cost-sharing by the government, farmers’ participation in forestry carbon sink projects can save investment in forest land management. (2) The saved forestry production costs and forestry carbon sink project subsidies can make up for the loss of farmers’ timber income, so that the net income of forestry will not be significantly reduced. (3) The forestry production factors saved by farmers can be transferred to non-agricultural sectors and increase non-agricultural net income, so that the net income of rural households participating in forestry carbon sink projects will increase. The forestry carbon sink project can improve the utility level of farmers and increase the willingness of farmers to produce forestry carbon sinks by delivering income to farmers and saving forestry production factors. This study demonstrates that a well-designed forestry carbon sink compensation mechanism, combined with an optimized allocation of production factors, can effectively enhance farmers’ willingness to participate. This insight is also applicable to countries or regions that rely on small-scale forestry operations. Full article

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

With the intensification of global climate change and human activities, coniferous species as the main components of natural forests in the Altay Mountains are facing the challenges of aging and regeneration. This study systematically analyzed structural heterogeneity and regeneration of three coniferous stand groups, Larix sibirica Ledeb. stand group, Abies sibirica Ledeb.-Picea obovata Ledeb.-Larix sibirica mixed stand group, and Picea obovata stand group, respectively, across western, central, and eastern forest areas of the Altay Mountains in Northwest China based on field surveys in 2023. Methodologically, we integrated Kruskal–Wallis/Dunn’s post hoc tests, nonlinear power-law modeling (diameter at breast height (DBH)–age relationships, validated via R², root mean square error (RMSE), and F-tests), static life tables (age class mortality and survival curves), and dynamic indices. Key findings revealed structural divergence: the L. sibirica stand group exhibited dominance of large-diameter trees (>30 cm DBH) with sparse seedlings/saplings and limited regeneration; the mixed stand group was dominated by small DBH individuals (<10 cm), showing young age structures and vigorous regeneration; while the P. obovata stand group displayed uniform DBH/height distributions and slow regeneration capacity. Radial growth rates differed significantly—highest in the mixed stand group (average of 0.315 cm/a), intermediate in the P. obovata stand group (0.216 cm/a), and lowest in the L. sibirica stand group (0.180 cm/a). Age–density trends varied among stand groups: unimodal in the L. sibirica and P. obovata stand groups while declining in the mixed stand group. All stand groups followed a Deevey-II survival curve (constant mortality across ages). The mixed stand group showed the highest growth potential but maximum disturbance risk, the L. sibirica stand group exhibited complex variation with lowest risk probability, while the P. obovata stand group had weaker adaptive capacity. These results underscore the need for differentiated management: promoting L. sibirica regeneration via gap-based interventions, enhancing disturbance resistance in the mixed stand group through structural diversification, and prioritizing P. obovata conservation to maintain ecosystem stability. This multi-method framework bridges stand-scale heterogeneity with demographic mechanisms, offering actionable insights for climate-resilient forestry. Full article

Pre-trained foundation models, trained on large-scale datasets, have demonstrated significant success in a variety of downstream vision tasks. Parameter-efficient fine-tuning (PEFT) methods aim to adapt these foundation models to new domains by updating only a small subset of parameters, thereby reducing computational overhead. However, the effectiveness of these PEFT methods, especially in the context of forestry remote sensing—specifically for individual tree detection—remains largely unexplored. In this work, we present a simple and efficient PEFT approach designed to transfer pre-trained transformer models to the specific tasks of tree crown detection and species classification in unmanned aerial vehicle (UAV) imagery. To address the challenge of mitigating the influence of irrelevant ground targets in UAV imagery, we propose an Adaptive Salient Channel Selection (ASCS) method, which can be simply integrated into each transformer block during fine-tuning. In the proposed ASCS, task-specific channels are adaptively selected based on class-wise importance scores, where the channels most relevant to the target class are highlighted. In addition, a simple bias term is introduced to facilitate the learning of task-specific knowledge, enhancing the adaptation of the pre-trained model to the target tasks. The experimental results demonstrate that the proposed ASCS fine-tuning method, which utilizes a small number of task-specific learnable parameters, significantly outperforms the latest YOLO detection framework and surpasses the state-of-the-art PEFT method in tree detection and classification tasks. These findings demonstrate that the proposed ASCS is an effective PEFT method, capable of adapting the pre-trained model’s capabilities for tree crown detection and species classification using UAV imagery. Full article

Hilly mountainous regions are ecologically complex, featuring diverse environmental ecosystem services (ESs) and intricate interactions. However, the variability, drivers, and management of these ESs remain poorly understood, particularly in regions with significant topographical and climatic heterogeneity. This study focuses on the southern hilly mountain belt of China, examining five key ecosystem services: food production (FP), carbon storage (CS), water yield (WY), habitat quality (HQ), and soil conservation (SC). This study examines these ESs across long-term, pixel, and regional scales, exploring the interactive relationships and identifying the driving factors and cluster characteristics. The results indicate the following: (1) Over the past 23 years, although food production and carbon storage have increased, habitat quality has declined. (2) From a spatial perspective, the differences in trade-offs and synergies across the years are relatively small. However, significant differences are observed when considering continuous temporal change, and trade-off relationships are generally prevalent. Additionally, the distribution of trade-offs and synergies is also influenced by a combination of factors. (3) Climatic, vegetation, topographical, and socioeconomic factors are key factors influencing the distribution and changes in ESs. For instance, climate–vegetation interactions enhance carbon storage and soil conservation. Socioeconomic factors, though less impactful, optimize ESs through land management and policy. (4) We found that the ecological priority region covers the largest area, followed by the hilly agricultural development zone, the mountainous agricultural and forestry development zone, and the integrated ecological security zone. These findings deepen our understanding of ESs in hilly mountainous regions, providing actionable insights for enhancing conservation and sustainable management in complex landscapes. Full article

The overexploitation of forest resources in the Lubumbashi Charcoal Production Basin in the southeastern Democratic Republic of the Congo (DR Congo) leads to deforestation and miombo woodlands degradation, threatening local livelihoods. Current forestry policies are ineffective, partly due to neglecting traditional ecological knowledge (TEK). This study identifies and describes TEK and practices related to biodiversity conservation and sustainable miombo woodlands management. Focus groups and interviews were conducted in four villages (Maksem, Mwawa, Nsela, and Texas), selected based on forest resource availability and population size. Data on sacred sites, conservation practices, knowledge transmission, ceremonies, and socio-demographic factors were analyzed using descriptive statistics, Fisher’s exact test, and Jaccard’s similarity index. The findings revealed that 75% of respondents identified sacred sites where logging activities are strictly prohibited. Thirty sacred tree species were identified, with stronger compliance in villages with a high availability of forest resources. This TEK is predominantly transmitted orally through family councils, as well as traditional ceremonies or rituals. Conservation practices include small-scale farming, intercropping, avoiding tree cutting in sacred sites, and using deadwood. However, only farming and intercropping are still commonly practiced, particularly in resource-scarce villages (64%). Women and elders are primary custodians of TEK, though its application is constrained by population growth and dwindling forest resources. The findings emphasize the crucial role of TEK in strengthening forest restoration initiatives by selecting key woody species and sustainable practices, while fostering community involvement. As such, decision makers should prioritize integrating TEK into DR Congo’s forest policies to support biodiversity conservation and miombo woodlands restoration efforts. Full article

Forestry carbon sink projects are an important pathway for achieving China’s carbon neutrality goal, with state-owned forest farms playing a leading role in the development of projects. This study collected data from 14 forestry carbon sink projects in Fujian Province, which are primarily led by state-owned forest farms. Using Data Envelopment Analysis (DEA), the operational efficiency of each forestry carbon sink project was evaluated and calculated, followed by a study on resource optimization allocation based on the efficiency evaluation results. The conclusions are as follows: management capability and climate conditions are key factors affecting the efficiency of state-owned forest farms. The investment required for developing forestry carbon sink projects hinders small-scale forest farms from engaging in such projects. Full article

Poplar (Populus L.) anthracnose is an infectious disease that seriously affects the growth and yields of poplar trees, and large-scale poplar infections have led to huge economic losses in the Chinese poplar industry. To efficiently and accurately detect poplar anthracnose for improved prevention and control, this study collected hyperspectral data from the leaves of four types of poplar trees, namely healthy trees and those with black spot disease, early-stage anthracnose, and late-stage anthracnose, and constructed a poplar anthracnose detection model based on machine learning and deep learning. We then comprehensively analyzed poplar anthracnose using advanced hyperspectral-based plant disease detection methodologies. Our research focused on establishing a detection model for poplar anthracnose based on small samples, employing the Design of Experiments (DoE)-based entropy weight method to obtain the best preprocessing combination to improve the detection model’s overall performance. We also analyzed the spectral characteristics of poplar anthracnose by comparing typical feature extraction methods (principal component analysis (PCA), variable combination population analysis (VCPA), and the successive projection algorithm (SPA)) with the vegetation index (VI) method (spectral disease indices (SDIs)) for data dimensionality reduction. The results showed notable improvements in the SDI-based model, which achieved 89.86% accuracy. However, this was inferior to the model based on typical feature extraction methods. Nevertheless, it achieved 100% accuracy for early-stage anthracnose and black spot disease in a controlled environment respectively. We conclude that the SDI-based model is suitable for low-cost detection tasks and is the best poplar anthracnose detection model. These findings contribute to the timely detection of poplar growth and will greatly facilitate the forestry sector’s development. Full article

Pest infestation poses significant threats to grain storage due to pests’ behaviors of feeding, respiration, excretion, and reproduction. Efficient pest detection and control are essential to mitigate these risks. However, accurate detection of small grain pests remains challenging due to their small size, high variability, low contrast, and cluttered background. Salient pest detection focuses on the visual features that stand out, improving the accuracy of pest identification in complex environments. Drawing inspiration from the rapid pest recognition abilities of humans and birds, we propose a novel Cascaded Aggregation Convolution Network (CACNet) for pest detection and control in stored grain. Our approach aims to improve detection accuracy by employing a reverse cascade feature aggregation network that imitates the visual attention mechanism in humans when observing and focusing on objects of interest. The CACNet uses VGG16 as the backbone network and incorporates two key operations, namely feature enhancement and feature aggregation. These operations merge the high-level semantic information and low-level positional information of salient objects, enabling accurate segmentation of small-scale grain pests. We have curated the GrainPest dataset, comprising 500 images showcasing zero to five or more pests in grains. Leveraging this dataset and the MSRA-B dataset, we validated our method’s efficacy, achieving a structure S-measure of 91.9%, and 90.9%, and a weighted F-measure of 76.4%, and 91.0%, respectively. Our approach significantly surpasses the traditional saliency detection methods and other state-of-the-art salient object detection models based on deep learning. This technology shows great potential for pest detection and assessing the severity of pest infestation based on pest density in grain storage facilities. It also holds promise for the prevention and control of pests in agriculture and forestry. Full article

High-quality pellets are typically produced from coniferous sawdust. However, achieving comparable quality from alternative feedstocks, such as broadleaf wood, often necessitates pre-treatments or additives. Yet, within the framework of small-scale pellet production, local forest enterprises may lack the resources for such treatments and usually produce pellets from the whole trees, including branches, leaves and tops. This can have an impact on the quality of the pellets obtained in this manner. To be classified as high-quality pellets (A1 class), the specific features of the pellet must be higher or fall below the thresholds specified in the EN ISO 17225 standard. In this study, we developed an alternative statistical approach to evaluate pellet quality in comparison to the constant thresholds reported in the technical standard. We applied such an approach to evaluate the quality of pellets produced from the broadleaved species common in the Mediterranean forestry, including European beech (Fagus sylvatica L.), Turkey oak (Quercus cerris L.), Eucalyptus (clone Eucalyptus camaldulensis x C. bicostata), and Poplar clone AF6. In particular, we focused on three variables that are generally the most troublesome for the production of high-quality pellets from the broadleaved species, namely bulk density, ash content, and lower heating value. We found that the beech pellets showed satisfactory bulk density (average effect size of −1.2, with no statistical difference in comparison to the standard’s threshold) and ash content (average effect size of about −5 and significantly lower than the standard’s threshold), but the heating value was significantly lower than the threshold required by the standard (average effect size of about −3). Conversely, other investigated species exhibited notable deficiencies, with turkey oak pellets displaying acceptable heating values. We found a significant improvement in ash content and heating value with increasing stem age within the same species thus suggesting that material derived from thinning interventions might be preferable over coppice-derived biomass for high-quality pellet production. We suggest that future research on the topic should focus on investigating pellets produced from blends of beech and turkey oak biomass. We further recommend a wider application of the proposed statistical approach, considering that it is clear and easy to interpret, and allows for a statistical comparison of the obtained values against the requirements of the technical standard. Full article

Pine wilt disease (PWD) is a forest disease characterized by rapid spread and extremely high lethality, posing a serious threat to the ecological security of China’s forests and causing significant economic losses in forestry. Given the extensive forestry area, limited personnel for inspection and monitoring, and high costs, utilizing UAV-based remote sensing monitoring for diseased trees represents an effective approach for controlling the spread of PWD. However, due to the small target size and uneven scale of pine wilt disease, as well as the limitations of real-time detection by drones, traditional disease tree detection algorithms based on RGB remote sensing images do not achieve an optimal balance among accuracy, detection speed, and model complexity due to real-time detection limitations. Consequently, this paper proposes Light-ViTeYOLO, a lightweight pine wilt disease detection method based on Vision Transformer-enhanced YOLO (You Only Look Once). A novel lightweight multi-scale attention module is introduced to construct an EfficientViT feature extraction network for global receptive field and multi-scale learning. A novel neck network, CACSNet(Content-Aware Cross-Scale bidirectional fusion neck network), is designed to enhance the detection of diseased trees at single granularity, and the loss function is optimized to improve localization accuracy. The algorithm effectively reduces the number of parameters and giga floating-point operations per second (GFLOPs) of the detection model while enhancing overall detection performance. Experimental results demonstrate that compared with other baseline algorithms, Light-ViTeYOLO proposed in this paper has the least parameter and computational complexity among related algorithms, with 3.89 MFLOPs and 7.4 GFLOPs, respectively. The FPS rate is 57.9 (frames/s), which is better than the original YOLOv5. Meanwhile, its mAP@0.5:0.95 is the best among the baseline algorithms, and the recall and mAP@0.5 slightly decrease. Our Light-ViTeYOLO is the first lightweight method specifically designed for detecting pine wilt disease. It not only meets the requirements for real-time detection of pine wilt disease outbreaks but also provides strong technical support for automated forestry work. Full article

In Germany, 24% of the forest area is owned by small-scale private forest owners, whose property is affected by a societal demand for forest-related ecosystem services. In the inhomogeneous group of small-scale private forest owners, different living conditions and lifestyles exist, which are affecting the management of the forest ownership. To support the management activities of small-scale private forest owners, institutional instruments exist, but these are only used by a small proportion of forest owners. In order to gain insights into the accessibility and activation of small-scale private forest owners for management, and to draw conclusions for appropriate support instruments, a large number of studies have analyzed the factors influencing the management activities of forest owners. Some studies identify socio-demographic and socio-economic variables as important influencing factors, but do not take into account their complex interdependencies, which have developed as a result of the pluralization of lifestyles and individualization of forest owners. For this reason, in addition to the influencing factors identified in the literature as relevant for forest management activities of small-scale private forest owners, the lifestyle segmentation approach of Sinus milieus established in German market research was applied in this study. This approach allocates forest owners based on their complex lifestyles into ten different milieu groups. A logistic regression analysis was used to determine the parameters influencing the willingness to engage in eight different forest management activities. The results show that the willingness to engage in forest management activities in the future is higher compared to actions in the past. In addition to timber harvesting-related activities, the small-scale private forest owners show a high action potential for non-commodity-related activities. Non-commodity-related activities can primarily be explained by the Sinus milieu affiliation of the small-scale private forest owners. This result underlines the usefulness of a lifestyle segmentation approach such as the Sinus milieus to address small-scale private forest owners in a target group specific way in line with their willingness to engage in specific forest management activities. Institutional instruments such as the membership in a forest owners association or subsidies do not play a significant role for non-commodity-related activities and seem to be inappropriate for targeting small-scale private forest owners for these activities. Timber harvesting-related activities are primarily influenced by the presence of professional foresters, socio-demographic factors or institutional instruments. Based on these empirical findings, implications can be drawn for the target group-specific addressing of forest owners based on different management activities. In this way, forest owners can be motivated to engage in forest management and the provision of forest-related ecosystem services. Full article

Research on the Ecosystem Conservation Charge Return Project (ECCRP), a restoration initiative guided by the polluter-pays principle, is limited. Moreover, certain projects lack location analyses and specific restoration goals. Here, we examined the ecological restoration trends in South Korea based on the ECCRP. We collected data from 277 projects completed between 2003 and 2022 and explored project characteristics, site types, locations, and restoration target species trends. Wetlands and urban areas were frequently targeted, with forestry as the primary land category. Such projects prioritize restoration in areas with a potential for increased biodiversity. Small-scale projects were the most common, likely because of budget constraints and site selection challenges. The establishment of guidelines in 2010 resulted in considerable changes in restoration projects. Forested areas and parks close to species supply sources became important, and specifying target species for restoration was emphasized. Target species primarily included birds, amphibians, and insects. From a sustainability perspective, it is crucial to consider post-monitoring. Species of high restoration value and animals with a small habitat must be selected as performance indicators of restoration outcomes. Our results can help the ECCRP to strengthen the ecological restoration approaches to better achieve sustainability goals—improving biodiversity and conserving and restoring ecosystems. Full article

In response to socio-ecological challenges, cities around the world are implementing greenification and urban forestry. While these strategies contribute to reducing the ecological footprint, they often overlook various social implications. This explains the increasing global attention to Biophilia, which emphasizes human–nature interaction to enhance the quality of urban life. Despite its historical roots spanning centuries, Biophilia is still considered an emerging research field, as shown by debate on evidence-based research and measurement of its multidimensional impacts. Although the beneficial effects of Biophilic Design (BD) are well documented thanks to the small-scale and immediate outcomes, the long-term potential of Biophilic Urbanism (BU) offers less evidence, limiting its utilization and investment. This paper provides a comprehensive theoretical-practical framework on Biophilia, BD, and BU through a 60-year systematic literature review based on a three-metric approach (quality, quantity, and application). Investigating concepts and practices, we delve into biophilic effects on humans and urban livability, analyze tools to measure them, and explore methods to translate them into the built environment. In spite of the growing body of studies and advancements in the last decade, our review findings highlight the need for further insights, especially regarding BU. The study aims to promote Biophilia Upscaling as a strategy to maximize its direct and indirect benefits across urban scales, thereby promoting BU and expediting a paradigm shift in city planning. In metropolises conceived as bioregional systems, where nature plays a key role in ensuring ecological services and citizens’ well-being, BU can assist designers, planners, and city makers in addressing the urban agenda toward higher environmental and social standards. Full article