Search Results (169)

Chainsaws are widely used in various occupational settings, including forestry, landscaping, farming, and by homeowners for tasks like tree felling, brush clearing, and firewood cutting. However, the use of chainsaws poses significant risks to operators and bystanders. This research quantified and compared occupational and nonoccupational injuries caused by contact with chainsaws and related objects during the period from 2018 to 2022. The emergency department and OSHA (Occupational Safety and Health Administration) data were used to characterize the cause and nature of the injuries. Results suggest that for this five-year period an estimated 127,944 people were treated in U.S. emergency departments for chainsaw-related injuries. More than 200 non-fatal and 57 fatal occupational chainsaw-involved injuries were found during the same period. Landscaping and forestry were the two industries where most of the occupational victims were employed. Upper and lower extremities were the most likely injured body parts, with open wounds from cuts being the most common injury type. The majority of fatal injuries were caused by falling objects such as trees and tree limbs while using a chainsaw. Our suggestions to reduce injuries include proper training and wearing personal protective equipment, as well as making sure any bystanders are kept in a safety zone away from trees being cut. Full article

The use of Google Earth Engine (GEE), a cloud-based computing platform, in spatio-temporal evaluation studies has increased rapidly in natural sciences such as forestry. In this study, Sentinel-2 satellite imagery and Shuttle Radar Topography Mission (SRTM) elevation data and image classification algorithms based on two machine learning techniques were examined. Random Forest (RF) and Classification and Regression Trees (CART) were used to classify land use and land cover (LULC) in western Oregon (USA). To classify the LULC from the spectral bands of satellite images, a composition consisting of vegetation difference indices NDVI, NDWI, EVI, and BSI, and a digital elevation model (DEM) were used. The study area was selected due to a diversity of land cover types including research forest, botanical gardens, recreation area, and agricultural lands covered with diverse plant species. Five land classes (forest, agriculture, soil, water, and settlement) were delineated for LULC classification testing. Different spatial points (totaling 75, 150, 300, and 2500) were used as training and test data. The most successful model performance was RF, with an accuracy of 98% and a kappa value of 0.97, while the accuracy and kappa values for CART were 95% and 0.94, respectively. The accuracy of the generated LULC maps was evaluated using 500 independent reference points, in addition to the training and testing datasets. Based on this assessment, the RF classifier that included elevation data achieved an overall accuracy of 92% and a kappa coefficient of 0.90. The combination of vegetation difference indices with elevation data was successful in determining the areas where clear-cutting occurred in the forest. Our results present a promising technique for the detection of forests and forest openings, which was helpful in identifying clear-cut sites. In addition, the GEE and RF classifier can help identify and map storm damage, wind damage, insect defoliation, fire, and management activities in forest areas. Full article

The forestry sector is increasingly dealing with a significant lack of labor and faces the difficult task of securing a professional, stable and sustainable manpower. In this study, different strategic directions for strengthening forestry workforce sustainability are presented and evaluated. The considered strategic directions were developed with respect to forestry employees’ views on necessary measures for making the forestry occupation more appealing. Those measures were observed in three categories: (I) stronger recruiting, (II) stronger retention and (III) higher work commitment. The findings of the survey and other performed analyses resulted in the creation of four different strategic directions: (1) the direct financial strategy, implying increased direct monetary compensation as the main instrument and putting focus on labor productivity; (2) the indirect financial strategy, stressing worker wellbeing through indirect material benefits and aiming at performance quality; (3) the educational strategy, focusing on worker training and education and (4) the technical–technological strategy, aiming at the increased utilization of modern machinery and advanced technologies in forest operations. The results of the study include a comparison of the defined strategies by SWOT analysis and the construction of An analytic Hierarchy Process (AHP) model as the multi-criteria tool for strategy evaluation. Considering the possibility and conditions of its implementation in the national forestry sector, the technical–technological strategy has been evaluated as best option to pursue. The objective of the study is to contribute to enhancing the sustainability of forestry workforce by defining critical issues and pointing to specific cornerstones that can assist in formulating effective future policies and strategies in the forestry sector. Full article

Efficient and accurate acquisition of tree distribution and three-dimensional geometric information in forest scenes, along with three-dimensional reconstructions of entire forest environments, hold significant application value in precision forestry and forestry digital twins. However, due to complex vegetation structures, fine geometric details, and severe occlusions in forest environments, existing methods—whether vision-based or LiDAR-based—still face challenges such as high data acquisition costs, feature extraction difficulties, and limited reconstruction accuracy. This study focuses on reconstructing tree distribution and extracting key individual tree parameters, and it proposes a forest 3D reconstruction framework based on high-resolution remote sensing images. Firstly, an optimized Mask R-CNN model was employed to segment individual tree crowns and extract distribution information. Then, a Tree Parameter and Reconstruction Network (TPRN) was constructed to directly estimate key structural parameters (height, DBH etc.) from crown images and generate tree 3D models. Subsequently, the 3D forest scene could be reconstructed by combining the distribution information and tree 3D models. In addition, to address the data scarcity, a hybrid training strategy integrating virtual and real data was proposed for crown segmentation and individual tree parameter estimation. Experimental results demonstrated that the proposed method could reconstruct an entire forest scene within seconds while accurately preserving tree distribution and individual tree attributes. In two real-world plots, the tree counting accuracy exceeded 90%, with an average tree localization error under 0.2 m. The TPRN achieved parameter extraction accuracies of 92.7% and 96% for tree height, and 95.4% and 94.1% for DBH. Furthermore, the generated individual tree models achieved average Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM) scores of 11.24 and 0.53, respectively, validating the quality of the reconstruction. This approach enables fast and effective large-scale forest scene reconstruction using only a single remote sensing image as input, demonstrating significant potential for applications in both dynamic forest resource monitoring and forestry-oriented digital twin systems. Full article

The Brazilian forestry sector comprises 9.94 million hectares of plantations, with eucalyptus dominating 75% of this area for pulp production. Technological advances have enhanced machinery performance, with the cut-to-length system being the primary method for pulpwood production. This study aimed to optimize the operational cycle of the log loader by evaluating productivity, operational cycles, and loading costs. Data were collected in Bahia, northeastern Brazil, from a forestry company operating under varying productivity scenarios and forest rotations. Time and motion studies were conducted to assess the log loader’s cycles, while productivity and cost analyses were performed. The results indicated that predictive models effectively explained productivity variations. The log loader’s productivity increased with the average volume per tree (AVT) and decreased with the number of movements, which consumed 68% of the cycle time due to wood adjustment and stack organization. Stages such as personal breaks, truck movements, crane adjustments, and cleaning of fallen material showed no significant statistical differences. Loading costs rose by up to 154% with increased movements and decreased with a higher AVT. Additionally, loading tri-train trucks significantly influenced transportation efficiency, emphasizing the importance of optimizing the log loader’s cycle to balance costs and enhance transportation operations. Full article

Simulator training offers a safe and cost-effective approach to providing new operators opportunities to become familiar with operating modern machinery. However, in Korea, the current programs are insufficient in training skilled operators capable of handling advanced forestry machinery. Consequently, these programs fall short of developing the required technical expertise, leading to difficulties in workforce employment. We compared the performance of simulator-trained participants with that of machine-trained participants by testing operators on real equipment and assessing their stress levels. Participants were categorized as those with and without excavator certificates. Within each category, participants were further divided into those receiving training via simulators or those who were trained using actual equipment. Although we detected no significant differences in the overall performance of simulator- and machine-trained participants, compared with real machine training, simulator training promoted better performance, lower levels of frustration, and a reduced mental workload due to the safer and more controlled virtual environment. These findings can be used to develop more effective training programs by incorporating simulator-based modules that enhance skill acquisition whilst reducing risks. They can also inform policy decisions to improve workforce training in industries dependent on the operation of advanced machinery, thereby ensuring that operators achieve higher levels of competence and safety. Full article

As the frequency of strong storms and cyclones increases, understanding wind risk in both existing and newly established plantation forests is becoming increasingly important. Recent advances in the quality and availability of remotely sensed data have significantly improved our capability to make large-scale wind risk predictions. This study models the loss of radiata pine (Pinus radiata D.Don) plantations following a severe cyclone within the Gisborne Region of New Zealand through leveraging repeat regional LiDAR acquisitions, optical imagery, and various surfaces describing key climatic, topographic, and storm-specific conditions. A random forest model was trained on 9713 plots classified as windthrow or no-windthrow. Model validation using 50 iterations of 80/20 train/test splits achieved robust accuracy (accuracy = 0.835; F1 score = 0.841; AUC = 0.913). In comparison to most European empirical models (AUC = 0.51–0.90), our framework demonstrated superior discrimination, underscoring its value for regions prone to cyclones. Among the 14 predictor variables, the most influential were mean windspeed during February, the wind exposition index, site drainage, and stand age. Model predictions closely aligned with the estimated 3705 hectares of cyclone-induced forest damage and indicated that 20.9% of unplanted areas in the region would be at risk of windthrow at age 30 if established in radiata pine. The resulting wind risk surface serves as a valuable decision-support tool for forest managers, helping to mitigate wind risk in existing forests and guide adaptive afforestation strategies. Although developed for radiata pine plantations in New Zealand, the approach and findings have broader relevance for forest management in cyclone-prone regions worldwide, particularly where plantation forestry is widely practised. 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

Deep learning-based super-resolution (SR) is an effective state-of-the-art technique for enhancing low-resolution images. This study explains a hierarchical dataset structure within the scope of enhancing grayscale historical aerial photographs with a basic SR model and relates it to non-reference image quality metric. The dataset was structured based on the hierarchy of photo interpretation elements. Images of bare land and forestry areas were evaluated as the primary category containing tone and color elements, images of residential areas as the secondary category containing shape and size elements, and images of farmland areas as the tertiary category containing pattern elements. Instead of training all images in all categories at once, which is the issue that any SR model with low number of parameters has difficulty handling, each category was trained separately. Test images containing the features of each category were enhanced separately, which means three enhanced images for one test image. The obtained images were divided into equal parts of 5 × 5 pixel size, and the final image was created by concatenating those that were determined to be of higher quality based on the Blind/Referenceless Image Spatial Quality Evaluator (BRISQUE) metric values. Subsequently, comparative analyses based on visual interpretation and reference-based image quality metrics proved that the approach to the dataset structure positively impacted the results. Full article

Although the circular economy (CE) has emerged as an innovative approach to address the challenges of protecting natural resources, the use of its strategies remains in its infancy, particularly in West Africa. This study examines the factors influencing the use of CE strategies in the wood and forestry sector in Benin. This study relied on a methodological approach based on surveys, using interview guides to collect information in both the southern and northern zones of the country. This information was collected at the level of the different actors directly involved in this sector, to identify the factors that influence the use of CE strategies using Probit models. The results show that access to information, the number of years of professional experience, the age of the actors and the type of training received are the determining factors in the use of these strategies (the models statistically significant at the 1% level). Other factors, such as knowledge of the costs and benefits of different strategies, are also identified as fundamental. Furthermore, a high financial capacity and an excess or overload of information are identified as the limiting factors for the use of these strategies. Full article

(1) Background: Salt stress poses a significant challenge to plant productivity, particularly in forestry and agriculture. This research explored the physiological adaptations of Salix matsudana × alba to varying salt stress levels and assessed the utility of hyperspectral imaging (HSI) integrated with machine learning for stress detection; (2) Methods: Physiological metrics, such as photosynthesis, chlorophyll concentration, antioxidant enzyme activity, proline levels, membrane stability, and malondialdehyde (MDA) accumulation, were analyzed under controlled experimental conditions. Spectral data in the visible (Vis) and near-infrared (NIR) ranges were acquired, with preprocessing techniques enhancing data precision. The study established quantitative detection models for physiological indicators and developed a salt stress monitoring model; (3) Results: Photosynthetic efficiency and chlorophyll synthesis while elevating oxidative damage indicators, including enzyme activity, proline content, and membrane permeability. Strong correlations between spectral signatures and physiological changes highlighted HSI’s effectiveness for early stress detection. Among the machine learning models, the Convolutional Neural Network (CNN) trained on Vis+NIR data with standard normal variate (SNV) preprocessing achieved 100% classification accuracy; (4) Conclusions: The results demonstrated that HSI, coupled with modeling techniques, is a powerful non-invasive tool for real-time monitoring of salt stress, providing valuable insights for early intervention and contributing to sustainable agricultural and forestry practices. Full article

This study provides a comparative evaluation of several ensemble model constructions for the prediction of specific methane yield (SMY) from anaerobic digestion. From the authors’ knowledge based on existing research, present knowledge of their prediction accuracy and utilization in anaerobic digestion modeling relative to individual machine learning methods is incomplete. Three input datasets from compiled anaerobic digestion samples using agricultural and forestry lignocellulosic residues from previous studies were used in this study. A total of six individual machine learning methods and five ensemble constructions were evaluated per dataset, whose prediction accuracy was assessed using a robust 10-fold cross-validation in 100 repetitions. Ensemble models outperformed individual methods in one out of three datasets in terms of prediction accuracy. They also produced notably lower coefficients of variation in root-mean-square error (RMSE) than most accurate individual methods (0.031 to 0.393 for dataset A, 0.026 to 0.272 for dataset B, and 0.021 to 0.217 for dataset AB), being much less prone to randomness in the training and test data split. The optimal ensemble constructions generally benefited from the higher number of individual methods included, as well as from their diversity in terms of prediction principles. Since the reporting of prediction accuracy based on final model fitting and the single split-sample approach is highly prone to randomness, the adoption of a cross-validation in multiple repetitions is proposed as a standard in future studies. 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

In recent years, several policies and strategies have been developed by the European Union to promote innovation and digitalization in the agricultural and forestry sector, including the Common Agricultural Policy (CAP), which allocates just under EUR 150 billion for the period of 2023–2027. In Italy, digitalization in the agricultural and forestry sector has grown significantly over the past decade, with 3.8% increasing to 15.8% of farms now being computerized. This growth has been fostered by the Italian strategy for digitalization in agriculture, part of the CAP Strategic Plan, implemented at the regional level through the Rural Development Complements (RDCs), adopted in 2023. This study analyzes the RDCs of Italian regions, comparing the strategies adopted in terms of digitalization and innovation from both technical and economic perspectives. This analysis focuses on the interventions of three regional support groups (SRGs)—SRG07, SRG08, SRG09—assessing whether they have been activated in all regions and delves into the political and technical reasons behind any lack of implementation. The study compares the funding allocated for each intervention, highlighting regional differences and underlying causes. The main strengths, weaknesses, opportunities, and threats of digitalization in the agricultural and forestry sector were prioritized through an A’WOT analysis. The major strengths include the provision of job security and sustainability, while the major weaknesses comprise the challenges of the digital divide and a lack of technical training. The opportunities identified include the potential for the development of precision agriculture and eco-sustainable practices, but these are hampered by critical issues such as spatial fragmentation and limited economic resources. This analytical framework offers a comprehensive view of regional dynamics in Italy, providing useful insights for the development of more effective policies that can promote equitable and innovative digitalization in the agricultural and forestry sector. Full article

This study tackles the challenge of precise wood log counting, where applications of the proposed methodology can span from automated approaches for materials management, surveillance, and safety science to wood traffic monitoring, wood volume estimation, and others. We introduce an approach leveraging Conditional Generative Adversarial Networks (cGANs) for eucalyptus log segmentation in images, incorporating specialized image processing techniques to handle noise and intersections, coupled with the Connected Components Algorithm for efficient counting. To support this research, we created and made publicly available a comprehensive database of 466 images containing approximately 13,048 eucalyptus logs, which served for both training and validation purposes. Our method demonstrated robust performance, achieving an average ${\mathrm{Accuracy}}_{pixel}$ of 96.4% and ${\mathrm{Accuracy}}_{logs}$ of 92.3%, with additional measures such as F1 scores ranging from 0.879 to 0.933 and IoU values between 0.784 and 0.875, further validating its effectiveness. The implementation proves to be efficient with an average processing time of 0.713 s per image on an NVIDIA T4 GPU, making it suitable for real-time applications. The practical implications of this method are significant for operational forestry, enabling more accurate inventory management, reducing human errors in manual counting, and optimizing resource allocation. Furthermore, the segmentation capabilities of the model provide a foundation for advanced applications such as eucalyptus stack volume estimation, contributing to a more comprehensive and refined analysis of forestry operations. The methodology’s success in handling complex scenarios, including intersecting logs and varying environmental conditions, positions it as a valuable tool for practical applications across related industrial sectors. Full article