Hasil untuk "Forestry"

J. Penman, Michael Gytarsky, Taka Hiraishi et al.

Katja Fedrowitz, J. Koricheva, S. Baker et al.

Industrial forestry typically leads to a simplified forest structure and altered species composition. Retention of trees at harvest was introduced about 25 years ago to mitigate negative impacts on biodiversity, mainly from clearcutting, and is now widely practiced in boreal and temperate regions. Despite numerous studies on response of flora and fauna to retention, no comprehensive review has summarized its effects on biodiversity in comparison to clearcuts as well as un-harvested forests. Using a systematic review protocol, we completed a meta-analysis of 78 studies including 944 comparisons of biodiversity between retention cuts and either clearcuts or un-harvested forests, with the main objective of assessing whether retention forestry helps, at least in the short term, to moderate the negative effects of clearcutting on flora and fauna. Retention cuts supported higher richness and a greater abundance of forest species than clearcuts as well as higher richness and abundance of open-habitat species than un-harvested forests. For all species taken together (i.e. forest species, open-habitat species, generalist species and unclassified species), richness was higher in retention cuts than in clearcuts. Retention cuts had negative impacts on some species compared to un-harvested forest, indicating that certain forest-interior species may not survive in retention cuts. Similarly, retention cuts were less suitable for some open-habitat species compared with clearcuts. Positive effects of retention cuts on richness of forest species increased with proportion of retained trees and time since harvest, but there were not enough data to analyse possible threshold effects, that is, levels at which effects on biodiversity diminish. Spatial arrangement of the trees (aggregated vs. dispersed) had no effect on either forest species or open-habitat species, although limited data may have hindered our capacity to identify responses. Results for different comparisons were largely consistent among taxonomic groups for forest and open-habitat species, respectively. Synthesis and applications. Our meta-analysis provides support for wider use of retention forestry since it moderates negative harvesting impacts on biodiversity. Hence, it is a promising approach for integrating biodiversity conservation and production forestry, although identifying optimal solutions between these two goals may need further attention. Nevertheless, retention forestry will not substitute for conservation actions targeting certain highly specialized species associated with forest-interior or open-habitat conditions. Our meta-analysis provides support for wider use of retention forestry since it moderates negative harvesting impacts on biodiversity. Hence, it is a promising approach for integrating biodiversity conservation and production forestry, although identifying optimal solutions between these two goals may need further attention. Nevertheless, retention forestry will not substitute for conservation actions targeting certain highly specialized species associated with forest-interior or open-habitat conditions.

Zhuo Lv, Keying Ding, Wenbao Ma et al.

Primula forbesii Franch. is a biennial ornamental species increasingly used in flower landscape design, potted displays, and cut flower production. Nevertheless, studies on its flowering regulation remain limited. This study investigated the effects of gibberellin (GA3) and spermidine (Spd) on the flowering performance of P. forbesii, using flowering time, scape morphology, and physiological parameters as key evaluation indices. At the onset of floral bud differentiation, 15 foliar spray treatments were applied. The most effective treatment contained 200 mg·L-1 GA3 and 0.01 mmol·L-1 Spd, which extended flowering duration by 5 days and promoted earlier bud emergence. Notably, this treatment significantly enhanced scape traits compared to the distilled water treatment, increasing internode length between floral whorls, the main scape height, and diameter. The scape number was increased by 361.54%. At the full flowering stage, the combination of 200 mg·L-1 GA3 and 0.01 mmol·L-1 Spd reduced malondialdehyde content and peroxidase activity in petals, while enhancing superoxide dismutase and catalase activities, along with soluble protein and soluble sugar accumulation. Endogenous hormone profiling showed that the treatment significantly raised levels of GA3, indole-3-acetic acid, and zeatin riboside, while reducing abscisic acid content. These results demonstrate that the combined application of 200 mg·L-1 GA3 and 0.01 mmol·L-1 Spd effectively enhances ornamental quality and delays senescence in P. forbesii. The findings provide valuable insights into the hormonal regulation of flowering senescence in ornamental plants and may help guide future strategies for cut‑flower preservation and quality maintenance.

Yida Lin, Bing Xue, Mengjie Zhang et al.

Manual pruning of radiata pine trees poses significant safety risks due to extreme working heights and challenging terrain. This paper presents a computer vision framework that integrates YOLO object detection with Semi-Global Block Matching (SGBM) stereo vision for autonomous drone-based pruning operations. Our system achieves precise branch detection and depth estimation using only stereo camera input, eliminating the need for expensive LiDAR sensors. Experimental evaluation demonstrates YOLO's superior performance over Mask R-CNN, achieving 82.0% mAPmask50-95 for branch segmentation. The integrated system accurately localizes branches within a 2 m operational range, with processing times under one second per frame. These results establish the feasibility of cost-effective autonomous pruning systems that enhance worker safety and operational efficiency in commercial forestry.

Chaehong Lee, Varatheepan Paramanayakam, Andreas Karatzas et al.

We present GeoLLM-Squad, a geospatial Copilot that introduces the novel multi-agent paradigm to remote sensing (RS) workflows. Unlike existing single-agent approaches that rely on monolithic large language models (LLM), GeoLLM-Squad separates agentic orchestration from geospatial task-solving, by delegating RS tasks to specialized sub-agents. Built on the open-source AutoGen and GeoLLM-Engine frameworks, our work enables the modular integration of diverse applications, spanning urban monitoring, forestry protection, climate analysis, and agriculture studies. Our results demonstrate that while single-agent systems struggle to scale with increasing RS task complexity, GeoLLM-Squad maintains robust performance, achieving a 17% improvement in agentic correctness over state-of-the-art baselines. Our findings highlight the potential of multi-agent AI in advancing RS workflows.

Chiara Fend, Claudia Redenbach

Spatial point processes are used as models in many different fields ranging from ecology and forestry to cosmology and materials science. In recent years, model validation, and in particular goodness-of-fit testing of a proposed point process model have seen many advances. Most of the proposed tests are based on a functional summary statistic of the observed pattern. In this paper, the empirical powers of many possible goodness-of-fit tests that can be constructed from such a summary statistic are compared in an extensive simulation study. Recently introduced functional summary statistics derived from topological data analysis and new constructions for the test statistic such as the continuous ranked probability score are included in the comparison. We discuss the performance of specific combinations of functional summary statistic and test statistic and their robustness with respect to other tuning parameters. Finally, tests using more than one individual functional summary statistic are also investigated. The results allow us to provide guidelines on how to choose powerful tests in a particular test stetting.

Giuseppe Altieri, Sabina Laveglia, Mahdi Rashvand et al.

This study aims to evaluate and classify the ripening stages of yellow-fleshed kiwifruit by integrating spectral and physicochemical data collected from the pre-harvest phase through 60 days of storage. A portable near-infrared (NIR) spectrometer (900–1700 nm) was used to develop predictive models for soluble solids content (SSC) and firmness (FF), testing multiple preprocessing methods within a Partial Least Squares Regression (PLSR) framework. SNV preprocessing achieved the best predictions for FF (R²P = 0.74, RMSEP = 12.342 ± 0.274 N), while the Raw-PLS model showed optimal performance for SSC (R²P = 0.93, RMSEP = 1.142 ± 0.022°Brix). SSC was more robustly predicted than FF, as reflected by RPD values of 2.6 and 1.7, respectively. For ripening stage classification, an Artificial Neural Network (ANN) outperformed other models, correctly classifying 97.8% of samples (R² = 0.95, RMSE = 0.08, MAE = 0.03). These results demonstrate the potential of combining NIR spectroscopy with AI techniques for non-destructive quality assessment and accurate ripeness discrimination. The integration of regression and classification models further supports the development of intelligent decision-support systems to optimize harvest timing and postharvest handling.

Levent Gülüm, Süheyla Esin Köksal, Emrah Güler et al.

The objective of this study was to evaluate the physicochemical, bioactive, and technological properties of pasta made from durum wheat semolina that was partially replaced with Acorn flour at levels of 10%, 20%, and 30%. The incorporation of Acorn flour had a substantial impact on the nutritional composition of the pasta, resulting in increases in total phenolic content (TPC), total flavonoid content (TFC), and antioxidant capacity in comparison with the control sample. The highest values for TPC and TFC were found in the samples containing 20% and 30% Acorn flour (p<0.05), demonstrating the functional potential of this formulation. However, an increase in the quantity of Acorn flour used in the pasta production process resulted in a noticeable darkening of the pasta's colour. This observation is consistent with the findings of previous research conducted on the use of non-traditional flours. While the increased amounts of Acorn flour resulted in enhanced nutritional and antioxidant profiles, the darker appearance and alterations in texture may have implications for sensory and visual acceptability. The present findings are corroborated by extant literature, which demonstrates that functional flours such as buckwheat, chickpea, lentil, chia, and sorghum have exhibited analogous trends in enhancing bioactive compounds and altering technological properties. Incorporation of Acorn flour at levels ranging from 10% to 20% optimises the health benefits of pasta while maintaining its desirable sensory and structural characteristics. Presented research contributes to the valorization of non-wood forest product (NWFP) resources and the development of innovative functional pasta products using sustainable ingredients.

Meixiang Gao, Xiujuan Yan, Xin Li et al.

ABSTRACT The field of soil science has seen significant advancements in recent years, largely due to the integration of computational tools and statistical methods. Among these resources, the programming language R has emerged as a powerful and versatile platform for soil scientists, aiding in a spectrum of tasks from data analysis and modeling to visualization. Nonetheless, the broader trends and specific patterns of R usage in soil research have not been thoroughly documented. Our study investigated the prevalence of R and its package usage in 25,888 research articles published in 10 leading soil science journals over a decade, from 2014 to 2023. A considerable number of these articles, 7899 (or 30.5%), named R as their primary data analysis tool. The use of R has followed a steady linear growth pattern, rising from 13.9% in 2014 to 46.5% in 2023. The most commonly used R packages were “vegan,” “ggplot2,” “lme4,” “nlme,” and “randomForest,” with each journal showcasing unique research focuses, resulting in varying frequencies of R package applications across different publications. Furthermore, there was a notable increase in the average number of R packages used per article throughout the study period. This research highlights the pivotal role of R, armed with its robust statistical and visualization capabilities, in enabling soil scientists to conduct comprehensive analyses and gain in‐depth insights into the complex dimensions of soil science.

M. Salvatore, A. Ferrara, S. Rossi et al.

S. Walker, T. Pearson, Sandra A. Brown

This sourcebook is designed to be a guide for developing and implementing land use, land-use change and forestry (LULUCF) projects for the BioCarbon Fund of the World Bank that meet the requirements for the Clean Development Mechanism (CDM) of the Kyoto Protocol. Only project types and carbon pools that are eligible for credit under the CDM during the first commitment period (2008-2012) are covered. With its user-friendly format, the sourcebook introduces readers to the CDM processes and requirements, and provides methods and procedures to produce accurate and precise estimates of changes in carbon stocks. The sourcebook is not designed as a primer on field measurement tech-niques, although guidance is given. The sourcebook is intended as an addition to the Intergovernmental Panel on Climate Change (IPCC) good practice guidance on land use, land-use change and forestry (2003), providing additional explanation, clarification and enhanced methodologies. It is designed to be used alongside the good practice guidance.

Yawen Lu, Yunhan Huang, Su Sun et al.

Forest monitoring and education are key to forest protection, education and management, which is an effective way to measure the progress of a country's forest and climate commitments. Due to the lack of a large-scale wild forest monitoring benchmark, the common practice is to train the model on a common outdoor benchmark (e.g., KITTI) and evaluate it on real forest datasets (e.g., CanaTree100). However, there is a large domain gap in this setting, which makes the evaluation and deployment difficult. In this paper, we propose a new photorealistic virtual forest dataset and a multimodal transformer-based algorithm for tree detection and instance segmentation. To the best of our knowledge, it is the first time that a multimodal detection and segmentation algorithm is applied to large-scale forest scenes. We believe that the proposed dataset and method will inspire the simulation, computer vision, education, and forestry communities towards a more comprehensive multi-modal understanding.

Stefano Puliti, Carolin Fischer, Rasmus Astrup

This paper presents an automated pipeline for detecting tree whorls in proximally laser scanning data using a pose-estimation deep learning model. Accurate whorl detection provides valuable insights into tree growth patterns, wood quality, and offers potential for use as a biometric marker to track trees throughout the forestry value chain. The workflow processes point cloud data to create sectional images, which are subsequently used to identify keypoints representing tree whorls and branches along the stem. The method was tested on a dataset of destructively sampled individual trees, where the whorls were located along the stems of felled trees. The results demonstrated strong potential, with accurate identification of tree whorls and precise calculation of key structural metrics, unlocking new insights and deeper levels of information from individual tree point clouds.

Muhammad Waseem Akram, Marco Vannucci, Giorgio Buttazzo et al.

The leaf area index determines crop health and growth. Traditional methods for calculating it are time-consuming, destructive, costly, and limited to a scale. In this study, we automate the index estimation method using drone image data of grapevine plants and a machine learning model. Traditional feature extraction and deep learning methods are used to obtain helpful information from the data and enhance the performance of the different machine learning models employed for the leaf area index prediction. The results showed that deep learning based feature extraction is more effective than traditional methods. The new approach is a significant improvement over old methods, offering a faster, non-destructive, and cost-effective leaf area index calculation, which enhances precision agriculture practices.

Maciej Wielgosz, Stefano Puliti, Phil Wilkes et al.

This article introduces Point2Tree, a novel framework that incorporates a three-stage process involving semantic segmentation, instance segmentation, optimization analysis of hyperparemeters importance. It introduces a comprehensive and modular approach to processing laser points clouds in Forestry. We tested it on two independent datasets. The first area was located in an actively managed boreal coniferous dominated forest in Våler, Norway, 16 circular plots of 400 square meters were selected to cover a range of forest conditions in terms of species composition and stand density. We trained a model based on Pointnet++ architecture which achieves 0.92 F1-score in semantic segmentation. As a second step in our pipeline we used graph-based approach for instance segmentation which reached F1-score approx. 0.6. The optimization allowed to further boost the performance of the pipeline by approx. 4 \% points.

Fredrik Warg, Anders Thorsén, Victoria Vu et al.

Various types of vehicle automation is increasingly used in a variety of environments including road vehicles such as cars or automated shuttles, confined areas such as mines or harbours, or in agriculture and forestry. In many use cases, the benefits are greater if several automated vehicles (AVs) cooperate to aid each other reach their goals more efficiently, or collaborate to complete a common task. Taxonomies and definitions create a common framework that helps researchers and practitioners advance the field. However, most existing work focus on road vehicles. In this paper, we review and extend taxonomies and definitions to encompass individually acting as well as cooperative and collaborative AVs for both on-road and off-road use cases. In particular, we introduce classes of collaborative vehicles not defined in existing literature, and define levels of automation suitable for vehicles where automation applies to additional functions in addition to the driving task.

S. M. Riazul Islam

Unmanned Aerial Vehicles (UAVs) have become increasingly popular in recent years due to their versatility and affordability. This article provides an overview of the history and development of UAVs, as well as their current and potential applications in various fields. In particular, the article highlights the use of UAVs in aerial photography and videography, surveying and mapping, agriculture and forestry, infrastructure inspection and maintenance, search and rescue operations, disaster management and humanitarian aid, and military applications such as reconnaissance, surveillance, and combat. The article also explores potential advancements in UAV technology and new applications that could emerge in the future, as well as concerns about the impact of UAVs on society, such as privacy, safety, security, job displacement, and environmental impact. Overall, the article aims to provide a comprehensive overview of the current state and future potential of UAV technology, and the benefits and challenges associated with its use in various industries and fields.

Daniel Althoff, Georgia Destouni

The partitioning of precipitation (P) water input on land between green (evapotranspiration, ET) and blue (runoff, R) water fluxes distributes the annually renewable freshwater resource among sectors and ecosystems. We decipher the worldwide pattern and key determinants of this water flux partitioning (WFP) and investigate its predictability based on a machine learning (ML) model trained and tested on data for 3,614 hydrological catchments around the world. The results show considerably higher WFP to the green (ET/P) than the blue (R/P) flux in most of the world. Land-use changes toward expanded agriculture and forestry will increase this WFP asymmetry, jeopardizing blue-water availability and making it more vulnerable to future P changes for other sectors and ecosystems. The predictive ML-model of WFP developed in this study can be used with climate model projections of P to assess future blue and green water security for various regions, sectors, and ecosystems around the world.

Angela K. Burrow, Kira D. McEntire, John C. Maerz

Among mobile terrestrial animals, movement among microsites can allow individuals to behaviorally moderate their body temperatures and rates of water loss, which can have important consequences for activity times, growth, fecundity, and survival. Ground-layer vegetation can change the availability and variability of microclimates; however, gaps in our understanding of how individuals interact with the microclimates created by vegetation limit our ability to inform management actions for wildlife. Amphibians can simultaneously balance operant body temperatures and water loss and the availability of heterogeneous microclimates should moderate how effectively they are able to do so. However, relatively few studies have attempted to mechanistically demonstrate how ground vegetation-driven effects on microclimatic variation may affect amphibian performance and survival. Agent-based modeling (ABM) can incorporate behavior and other mechanisms to understand how animals interact with their environments to result in larger scale patterns. They are effective for exploring alternative scenarios and representing the uncertainty in systems. Here, we use ABMs to integrate field and laboratory measurements of movement behavior, physiology, and plant effects on near-ground microclimate to explore how ground vegetation and the availability of terrestrial refugia may affect the survival and terrestrial distributions of juvenile gopher frogs (Rana capito) under two weather regimes. We also examine how assumptions regarding micro-scale movement (&lt; 1 m2) affect the influence of ground vegetation on survival and settlement within refugia. While all variables affected settlement and survival, our models predict that inter-annual variation in weather and the density and spatial distribution of permanent refugia likely have the greatest influence on juvenile survival. The benefit of increased ground vegetation was dependent on the reasonable assumption that gopher frogs exhibit microclimate habitat selection throughout the day and night to limit water loss. Our models suggest that vegetation would be most beneficial to amphibians under warmer weather regimes provided there is sufficient rainfall.

J. Franklin, R. Mitchell, B. Palik