Hasil untuk "Forestry"

J.R. Jensen

D. Mohan, and Charles U. Pittman, P. Steele

M. Stokes, T. L. Smiley

Sydney Houston, Jun-Jun Liu, Mike Cruickshank et al.

Western redcedar (WRC; Thuja plicata) root- and butt-rot diseases are caused by a set of wood-decay fungal pathogens, which have posed a significant threat to forest health and resulted in substantial economic losses of WRC production. Traditional approaches for disease detection are labor-intensive and more suitable on mature trees at late infection stages. This study developed and validated internal transcribed spacer region next-generation sequencing (ITS-NGS) and quantitative polymerase chain reaction (qPCR) assays for detecting decay-disease infections in WRC seedlings with high sensitivity and specificity. The efficiencies of ITS-PCR amplification were in silico predicted and validated through ITS-NGS using a pure fungal DNA mixture. For diagnosis of decay pathogens in WRC seedlings, fine root and root collar samples were collected from greenhouse inoculation trials. ITS-NGS identified positive infection rates of 100% for Armillaria ostoyae, Heterobasidion occidentale, and Poriella subacida in diseased seedlings, but the diagnostic efficiency for Coniferiporia weirii was affected by the types of sampled tissues. Species-specific qPCR assays were developed for C. weirii and revealed positive infection rates up to 100% in inoculated seedlings. Relative fungal abundances measured by ITS-NGS and qPCR were highly comparable, with significant correlation, demonstrating the reliability of both molecular diagnostic approaches. Moreover, qPCR provided higher quantification accuracy for a trace amount of the pathogen in total DNA extracted from host tissues. These results provided evidence for the application of ITS-NGS and qPCR assays as robust and reliable molecular tools for detecting early and latent infections of fungal pathogens in complex tissue samples for enhancing WRC disease management. [Figure: see text] Copyright © 2026 His Majesty the King in Right of Canada, as represented by the Minister of Natural Resources Canada. This is an open access article distributed under the CC BY 4.0 International license.

Anton Grafström, Wilmer Prentius

We propose Distributionally Balanced Designs (DBD), a new class of probability sampling designs that target representativeness at the level of the full auxiliary distribution rather than selected moments. In disciplines such as ecology, forestry, and environmental sciences, where field data collection is expensive, maximizing the information extracted from a limited sample is critical. More precisely, DBD can be viewed as minimum discrepancy designs that minimize the expected discrepancy between the sample and population auxiliary distributions. The key idea is to construct samples whose empirical auxiliary distribution closely matches that of the population. We present a first implementation of DBD based on an optimized circular ordering of the population, combined with random selection of a contiguous block of units. The ordering is chosen to minimize the design-expected energy distance, a discrepancy measure that captures differences between distributions beyond low-order moments. This criterion promotes strong spatial spread, and yields low variance for Horvitz-Thompson estimators of totals of functions that vary smoothly with respect to auxiliaries. Simulation results show that approximate DBD achieves better distributional fit than state-of-the-art methods such as the local pivotal and local cube designs. Hence, DBD can improve the reliability of estimates from costly field data, making distributional balancing effective for constructing representative surveys in resource-constrained applications.

Fang Nan, Meher Malladi, Qingqing Li et al.

Forestry plays a vital role in our society, creating significant ecological, economic, and recreational value. Efficient forest management involves labor-intensive and complex operations. One essential task for maintaining forest health and productivity is selective thinning, which requires skilled operators to remove specific trees to create optimal growing conditions for the remaining ones. In this work, we present a solution based on a small-scale robotic harvester (SAHA) designed for executing this task with supervised autonomy. We build on a 4.5-ton harvester platform and implement key hardware modifications for perception and automatic control. We implement learning- and model-based approaches for precise control of hydraulic actuators, accurate navigation through cluttered environments, robust state estimation, and reliable semantic estimation of terrain traversability. Integrating state-of-the-art techniques in perception, planning, and control, our robotic harvester can autonomously navigate forest environments and reach targeted trees for selective thinning. We present experimental results from extensive field trials over kilometer-long autonomous missions in northern European forests, demonstrating the harvester's ability to operate in real forests. We analyze the performance and provide the lessons learned for advancing robotic forest management.

Sánchez-Ledesma Judith A., Águila Bernardo, Garibay-Orijel Roberto et al.

Using ITS1-based metabarcoding, we investigated the structure of the soil fungal communities in the central and peripheral zones of a 25-hectare pecan nut (Carya illinoinensis) orchard located in the arid region of Coahuila, Mexico. While environmental conditions such as soil moisture and temperature varied between zones, physicochemical soil properties (pH, organic carbon, total carbon, organic matter, electrical conductivity, and zinc) remained homogeneous. A total of 4,443 fungal OTUs were detected at 97% similarity. Alpha diversity indices did not differ significantly between zones. The fungal community was dominated by the phyla Ascomycota and Basidiomycota, with Pezizomycetes, Dothideomycetes, and Agaricomycetes as dominant classes. No statistically significant differences in beta diversity or community composition were found between zones (PERMANOVA p = 0.662). Redundancy analysis also revealed no clear clustering by zone, though localized differences were observed. Our findings suggest that agronomic management in this system promotes environmental homogeneity, leading to relatively uniform fungal communities. This exploratory study highlights the need for future research incorporating comparisons with adjacent natural ecosystems to better assess spatial patterns and potential edge effects in agroecosystems.

Marzia Gabriele, Raffaella Brumana, Nicola Genzano

In environmental management, monitoring transitions toward regenerative agriculture (RA) supports carbon offset initiatives aligned with Regulation (EU) 2018/841. Current Land Use, Land Use Change, and Forestry (LULUCF) platforms primarily analyze macro-scale Earth Observation (EO) vegetation trends, yet are increasingly enhancing ground-based data collection. This study integrates these approaches through a methodological workflow comprising: (1) a survey segment with a 30 × 30 m pixel sampling grid for landscape-scale trend assessment and sub-hectare Survey Validation Areas delineating specific RA management practices; and (2) an EO monitoring segment using Landsat 5, 7, and 8 time series, processed in R and Google Earth Engine (GEE) to model 30 m phenological dynamics, alongside 10 m Sentinel-2 NDVI 15-day Maximum Value Composites published via a GEE application (RegenAPP). Applied to an experimental RA site, La Junquera – Camp Altiplano (Murcia, Spain), the workflow enabled fine-scale analyses, identifying greening trends in no-till RA plots in contrast to browning in adjacent tilled organic fields. Sub-hectare analyses further detailed phenological patterns linked to specific RA practices. This integrated EO–Survey approach complements LULUCF assessments by coupling EO-derived vegetation analytics with targeted field validation, capturing spatial and temporal RA transition dynamics.

Hassan-Roland Nasser, Marianne Cockburn, Marie Schneider

Studying animals’ rhythmicity provides insights into their physiological and psychological states. The degree of functional coupling (DFC) is one of the algorithms available to assess rhythmicity in activity-related time series data, such as accelerometer or GPS data. However, DFC computation is complex, as it includes frequency spectrum analysis and statistical significance testing. This paper introduces digiRhythm, an R package that makes the DFC-based rhythmicity analysis easily accessible. Beyond the DFC, the package includes an additional set of tools, which are crucial for rhythmicity investigations, such as actogram generation, daily activity visualization, and diurnality index computation.

Erika Loučanová, Miriam Olšiaková, Zuzana Štofková

Digitization and innovation supported by various innovation systems have become key factors in the sustainable development of companies, countries (including UE countries), and the economy as a whole. The primary objective of this study is to explore the interconnections between the perspectives of the Quintuple Helix model and digitalization as a comprehensive innovation system supporting digitalization in EU countries. The study is grounded in the innovation systems theory, specifically employing the Quintuple Helix Model as a comprehensive framework, and addresses the challenge of digital divide across the EU. The research was conducted using K-means cluster analysis to identify homogeneous groups of countries within the EU. Subsequently, correlation analysis was applied to identify statistically significant relationships between the individual variables examined within the Quintuple Helix model and digitization within EU countries. Based on the results, we identified four distinct clusters of EU countries characterized by different degrees of digitization, governance, and intellectual Capital. It was found that countries with the highest level of digitization are also characterized by the highest levels of governance and intellectual Capital. Correlation analysis confirmed a strong interconnection between the examined perspectives of the Quintuple Helix model and their relationship with digitization.

Dominik Sturm, Ivo F. Sbalzarini

We propose a method for variable selection in the intensity function of spatial point processes that combines sparsity-promoting estimation with noise-robust model selection. As high-resolution spatial data becomes increasingly available through remote sensing and automated image analysis, identifying spatial covariates that influence the localization of events is crucial to understand the underlying mechanism. However, results from automated acquisition techniques are often noisy, for example due to measurement uncertainties or detection errors, which leads to spurious displacements and missed events. We study the impact of such noise on sparse point-process estimation across different models, including Poisson and Thomas processes. To improve noise robustness, we propose to use stability selection based on point-process subsampling and to incorporate a non-convex best-subset penalty to enhance model-selection performance. In extensive simulations, we demonstrate that such an approach reliably recovers true covariates under diverse noise scenarios and improves both selection accuracy and stability. We then apply the proposed method to a forestry data set, analyzing the distribution of trees in relation to elevation and soil nutrients in a tropical rain forest. This shows the practical utility of the method, which provides a systematic framework for robust variable selection in spatial point-process models under noise, without requiring additional knowledge of the process.

Mohammad Wasil, Ahmad Drak, Brennan Penfold et al.

Unmanned Aerial Vehicles (UAVs) are increasingly used for reforestation and forest monitoring, including seed dispersal in hard-to-reach terrains. However, a detailed understanding of the forest floor remains a challenge due to high natural variability, quickly changing environmental parameters, and ambiguous annotations due to unclear definitions. To address this issue, we adapt the Segment Anything Model (SAM), a vision foundation model with strong generalization capabilities, to segment forest floor objects such as tree stumps, vegetation, and woody debris. To this end, we employ parameter-efficient fine-tuning (PEFT) to fine-tune a small subset of additional model parameters while keeping the original weights fixed. We adjust SAM's mask decoder to generate masks corresponding to our dataset categories, allowing for automatic segmentation without manual prompting. Our results show that the adapter-based PEFT method achieves the highest mean intersection over union (mIoU), while Low-rank Adaptation (LoRA), with fewer parameters, offers a lightweight alternative for resource-constrained UAV platforms.

Anisha Dutta

Accurate assessment of urban canopy coverage is crucial for informed urban planning, effective environmental monitoring, and mitigating the impacts of climate change. Traditional practices often face limitations due to inadequate technical requirements, difficulties in scaling and data processing, and the lack of specialized expertise. This study presents an efficient approach for estimating green canopy coverage using artificial intelligence, specifically computer vision techniques, applied to aerial imageries. Our proposed methodology utilizes object-based image analysis, based on deep learning algorithms to accurately identify and segment green canopies from high-resolution drone images. This approach allows the user for detailed analysis of urban vegetation, capturing variations in canopy density and understanding spatial distribution. To overcome the computational challenges associated with processing large datasets, it was implemented over a cloud platform utilizing high-performance processors. This infrastructure efficiently manages space complexity and ensures affordable latency, enabling the rapid analysis of vast amounts of drone imageries. Our results demonstrate the effectiveness of this approach in accurately estimating canopy coverage at the city scale, providing valuable insights for urban forestry management of an industrial township. The resultant data generated by this method can be used to optimize tree plantation and assess the carbon sequestration potential of urban forests. By integrating these insights into sustainable urban planning, we can foster more resilient urban environments, contributing to a greener and healthier future.

Tzu-I Liao, Mahmoud Fakhry, Jibin Yesudas Varghese

Pith detection in tree cross-sections is essential for forestry and wood quality analysis but remains a manual, error-prone task. This study evaluates deep learning models -- YOLOv9, U-Net, Swin Transformer, DeepLabV3, and Mask R-CNN -- to automate the process efficiently. A dataset of 582 labeled images was dynamically augmented to improve generalization. Swin Transformer achieved the highest accuracy (0.94), excelling in fine segmentation. YOLOv9 performed well for bounding box detection but struggled with boundary precision. U-Net was effective for structured patterns, while DeepLabV3 captured multi-scale features with slight boundary imprecision. Mask R-CNN initially underperformed due to overlapping detections, but applying Non-Maximum Suppression (NMS) improved its IoU from 0.45 to 0.80. Generalizability was next tested using an oak dataset of 11 images from Oregon State University's Tree Ring Lab. Additionally, for exploratory analysis purposes, an additional dataset of 64 labeled tree cross-sections was used to train the worst-performing model to see if this would improve its performance generalizing to the unseen oak dataset. Key challenges included tensor mismatches and boundary inconsistencies, addressed through hyperparameter tuning and augmentation. Our results highlight deep learning's potential for tree cross-section pith detection, with model choice depending on dataset characteristics and application needs.

Ethan Fettes, Pablo G. Madoery, Halim Yanikomeroglu et al.

Internet of Things (IoT) devices have become increasingly ubiquitous with applications not only in urban areas but remote areas as well. These devices support industries such as agriculture, forestry, and resource extraction. Due to the device location being in remote areas, satellites are frequently used to collect and deliver IoT device data to customers. As these devices become increasingly advanced and numerous, the amount of data produced has rapidly increased potentially straining the ability for radio frequency (RF) downlink capacity. Free space optical communications with their wide available bandwidths and high data rates are a potential solution, but these communication systems are highly vulnerable to weather-related disruptions. This results in certain communication opportunities being inefficient in terms of the amount of data received versus the power expended. In this paper, we propose a deep reinforcement learning (DRL) method using Deep Q-Networks that takes advantage of weather condition forecasts to improve energy efficiency while delivering the same number of packets as schemes that don't factor weather into routing decisions. We compare this method with simple approaches that utilize simple cloud cover thresholds to improve energy efficiency. In testing the DRL approach provides improved median energy efficiency without a significant reduction in median delivery ratio. Simple cloud cover thresholds were also found to be effective but the thresholds with the highest energy efficiency had reduced median delivery ratio values.

Matías Mattamala, Nived Chebrolu, Jonas Frey et al.

Legged robots are increasingly being adopted in industries such as oil, gas, mining, nuclear, and agriculture. However, new challenges exist when moving into natural, less-structured environments, such as forestry applications. This paper presents a prototype system for autonomous, under-canopy forest inventory with legged platforms. Motivated by the robustness and mobility of modern legged robots, we introduce a system architecture which enabled a quadruped platform to autonomously navigate and map forest plots. Our solution involves a complete navigation stack for state estimation, mission planning, and tree detection and trait estimation. We report the performance of the system from trials executed over one and a half years in forests in three European countries. Our results with the ANYmal robot demonstrate that we can survey plots up to 1 ha plot under 30 min, while also identifying trees with typical DBH accuracy of 2cm. The findings of this project are presented as five lessons and challenges. Particularly, we discuss the maturity of hardware development, state estimation limitations, open problems in forest navigation, future avenues for robotic forest inventory, and more general challenges to assess autonomous systems. By sharing these lessons and challenges, we offer insight and new directions for future research on legged robots, navigation systems, and applications in natural environments. Additional videos can be found in https://dynamic.robots.ox.ac.uk/projects/legged-robots

Anis Ur Rahman, Einari Heinaro, Mete Ahishali et al.

Mapping standing dead trees is critical for assessing forest health, monitoring biodiversity, and mitigating wildfire risks, for which aerial imagery has proven useful. However, dense canopy structures, spectral overlaps between living and dead vegetation, and over-segmentation errors limit the reliability of existing methods. This study introduces a hybrid postprocessing framework that refines deep learning-based tree segmentation by integrating watershed algorithms with adaptive filtering, enhancing boundary delineation, and reducing false positives in complex forest environments. Tested on high-resolution aerial imagery from boreal forests, the framework improved instance-level segmentation accuracy by 41.5% and reduced positional errors by 57%, demonstrating robust performance in densely vegetated regions. By balancing detection accuracy and over-segmentation artifacts, the method enabled the precise identification of individual dead trees, which is critical for ecological monitoring. The framework's computational efficiency supports scalable applications, such as wall-to-wall tree mortality mapping over large geographic regions using aerial or satellite imagery. These capabilities directly benefit wildfire risk assessment (identifying fuel accumulations), carbon stock estimation (tracking emissions from decaying biomass), and precision forestry (targeting salvage loggings). By bridging advanced remote sensing techniques with practical forest management needs, this work advances tools for large-scale ecological conservation and climate resilience planning.

Ebasa Temesgen, Mario Jerez, Greta Brown et al.

Wildlife-induced crop damage, particularly from deer, threatens agricultural productivity. Traditional deterrence methods often fall short in scalability, responsiveness, and adaptability to diverse farmland environments. This paper presents an integrated unmanned aerial vehicle (UAV) system designed for autonomous wildlife deterrence, developed as part of the Farm Robotics Challenge. Our system combines a YOLO-based real-time computer vision module for deer detection, an energy-efficient coverage path planning algorithm for efficient field monitoring, and an autonomous charging station for continuous operation of the UAV. In collaboration with a local Minnesota farmer, the system is tailored to address practical constraints such as terrain, infrastructure limitations, and animal behavior. The solution is evaluated through a combination of simulation and field testing, demonstrating robust detection accuracy, efficient coverage, and extended operational time. The results highlight the feasibility and effectiveness of drone-based wildlife deterrence in precision agriculture, offering a scalable framework for future deployment and extension.

Richard T. Benders, Joshua A. Dijksman, Thomas M. M. Bastiaansen et al.

Pellet manufacturing of biomass (food, feed, bioenergy) presses powders or particles into dense pellets with improved nutritional, calorific, and handling properties. This process upgrades industrial co-products from agriculture, forestry, and bioenergy into higher-value products. However, processing particulate streams raises the scientific question: Under which conditions do loose particles bind to form rigid, durable pellets? This work answers this question for biomass extrusion. Systematic experiments reveal how steam conditioning temperature, production rate, and die geometry interact to determine pellet quality. We propose an overarching framework introducing the stickiness temperature ($T^*$), marking the onset of enthalpic reactions required for particle agglomeration. $T^*$ serves as the boundary for inter-particle bond formation and is reached through a combination of steam conditioning and friction, both controllable via process parameters. Results highlight the combined role of pellet temperature and die residence time in optimizing pellet durability while lowering specific energy use (J/kg). Validation with experiments and literature confirms that this framework offers practical guidance to enhance efficiency and sustainability of pelleting. By providing operational parameters to control bonding and energy input, this work supports a more circular economy through efficient conversion of diverse biomass streams into valuable products while reducing energy consumption and greenhouse gas emissions.

Xingzhi Li, Yanan Wang, Juanjuan Zhang et al.

Abstract Background Photodegradation of plant litter plays a pivotal role in the global carbon (C) cycle. In temperate forest ecosystems, the exposure of plant litter to solar radiation can be significantly altered by changes in autumn phenology and snow cover due to climatic change. How this will affect litter decomposition and nutrient dynamic interacting with forest canopy structure (understorey vs. gaps) is uncertain. In the present study, we conducted a field experiment using leaf litter of early-fall deciduous Betula platyphylla (Asian white birch) and late-fall deciduous Quercus mongolica (Mongolian oak) to explore the effect of change in autumn solar radiation on dynamics of litter decomposition in a gap and understorey of a temperate mixed forest. Results Exposure to the full-spectrum of not only significantly increased the loss of mass, C, and lignin, but also modified N loss through both immobilization and mineralization during the initial decomposition during autumn canopy opening, irrespective of canopy structure and litter species. These effects were mainly driven by the blue-green spectral region of sunlight. Short-term photodegradation by autumn solar radiation had a positive legacy effect on the later decomposition particularly in the forest gap, increasing mass loss by 16% and 19% for Asian white birch and Mongolia oak, respectively. Conclusions Our results suggest that earlier autumn leaf-fall phenology and/or later snow cover due to land-use or climate change would increase the exposure of plant organic matter to solar radiation, and accelerate ecosystem processes, C and nutrient cycling in temperate forest ecosystems. The study provides a reference for predictive research on carbon cycling under the background of global climate change.