Hasil untuk "Forestry"

Taige Luo, Junru Xie, Chenyang Fan et al.

Intelligent forest tree breeding has advanced plant phenotyping, yet existing research largely focuses on large-leaf agricultural crops, with limited attention to fine-grained leaf analysis of sapling trees in open-field environments. Natural scenes introduce challenges including scale variation, illumination changes, and irregular leaf morphology. To address these issues, we collected UAV RGB imagery of field-grown saplings and constructed the Poplar-leaf dataset, containing 1,202 branches and 19,876 pixel-level annotated leaf instances. To our knowledge, this is the first instance segmentation dataset specifically designed for forestry leaves in open-field conditions. We propose LeafInst, a novel segmentation framework tailored for irregular and multi-scale leaf structures. The model integrates an Asymptotic Feature Pyramid Network (AFPN) for multi-scale perception, a Dynamic Asymmetric Spatial Perception (DASP) module for irregular shape modeling, and a dual-residual Dynamic Anomalous Regression Head (DARH) with Top-down Concatenation decoder Feature Fusion (TCFU) to improve detection and segmentation performance. On Poplar-leaf, LeafInst achieves 68.4 mAP, outperforming YOLOv11 by 7.1 percent and MaskDINO by 6.5 percent. On the public PhenoBench benchmark, it reaches 52.7 box mAP, exceeding MaskDINO by 3.4 percent. Additional experiments demonstrate strong generalization and practical utility for large-scale leaf phenotyping.

Yida Lin, Bing Xue, Mengjie Zhang et al.

Accurate per-branch 3D reconstruction is a prerequisite for autonomous UAV-based tree pruning; however, dense disparity maps from modern stereo matchers often remain too noisy for individual branch analysis in complex forest canopies. This paper introduces a progressive pipeline integrating DEFOM-Stereo foundation-model disparity estimation, SAM3 instance segmentation, and multi-stage depth optimization to deliver robust per-branch point clouds. Starting from a naive baseline, we systematically identify and resolve three error families through successive refinements. Mask boundary contamination is first addressed through morphological erosion and subsequently refined via a skeleton-preserving variant to safeguard thin-branch topology. Segmentation inaccuracy is then mitigated using LAB-space Mahalanobis color validation coupled with cross-branch overlap arbitration. Finally, depth noise - the most persistent error source - is initially reduced by outlier removal and median filtering, before being superseded by a robust five-stage scheme comprising MAD global detection, spatial density consensus, local MAD filtering, RGB-guided filtering, and adaptive bilateral filtering. Evaluated on 1920x1080 stereo imagery of Radiata pine (Pinus radiata) acquired with a ZED Mini camera (63 mm baseline) from a UAV in Canterbury, New Zealand, the proposed pipeline reduces the average per-branch depth standard deviation by 82% while retaining edge fidelity. The result is geometrically coherent 3D point clouds suitable for autonomous pruning tool positioning. All code and processed data are publicly released to facilitate further UAV forestry research.

Yida Lin, Bing Xue, Mengjie Zhang et al.

Autonomous drone-based tree pruning needs accurate, real-time depth estimation from stereo cameras. Depth is computed from disparity maps using $Z = f B/d$, so even small disparity errors cause noticeable depth mistakes at working distances. Building on our earlier work that identified DEFOM-Stereo as the best reference disparity generator for vegetation scenes, we present the first study to train and test ten deep stereo matching networks on real tree branch images. We use the Canterbury Tree Branches dataset -- 5,313 stereo pairs from a ZED Mini camera at 1080P and 720P -- with DEFOM-generated disparity maps as training targets. The ten methods cover step-by-step refinement, 3D convolution, edge-aware attention, and lightweight designs. Using perceptual metrics (SSIM, LPIPS, ViTScore) and structural metrics (SIFT/ORB feature matching), we find that BANet-3D produces the best overall quality (SSIM = 0.883, LPIPS = 0.157), while RAFT-Stereo scores highest on scene-level understanding (ViTScore = 0.799). Testing on an NVIDIA Jetson Orin Super (16 GB, independently powered) mounted on our drone shows that AnyNet reaches 6.99 FPS at 1080P -- the only near-real-time option -- while BANet-2D gives the best quality-speed balance at 1.21 FPS. We also compare 720P and 1080P processing times to guide resolution choices for forestry drone systems.

Yida Lin, Bing Xue, Mengjie Zhang et al.

Autonomous UAV forestry operations require robust depth estimation with strong cross-domain generalization, yet existing evaluations focus on urban and indoor scenarios, leaving a critical gap for vegetation-dense environments. We present the first systematic zero-shot evaluation of eight stereo methods spanning iterative refinement, foundation model, diffusion-based, and 3D CNN paradigms. All methods use officially released pretrained weights (trained on Scene Flow) and are evaluated on four standard benchmarks (ETH3D, KITTI 2012/2015, Middlebury) plus a novel 5,313-pair Canterbury Tree Branches dataset ($1920 \times 1080$). Results reveal scene-dependent patterns: foundation models excel on structured scenes (BridgeDepth: 0.23 px on ETH3D; DEFOM: 4.65 px on Middlebury), while iterative methods show variable cross-benchmark performance (IGEV++: 0.36 px on ETH3D but 6.77 px on Middlebury; IGEV: 0.33 px on ETH3D but 4.99 px on Middlebury). Qualitative evaluation on the Tree Branches dataset establishes DEFOM as the gold-standard baseline for vegetation depth estimation, with superior cross-domain consistency (consistently ranking 1st-2nd across benchmarks, average rank 1.75). DEFOM predictions will serve as pseudo-ground-truth for future benchmarking.

Tobias Heimann, Lara-Sophie Wähling, Tomke Honkomp et al.

Bioenergy with carbon capture and storage (BECCS) is a crucial element in most modelling studies on emission pathways of the Intergovernmental Panel on Climate Change to limit global warming. BECCS can substitute fossil fuels in energy production and reduce CO _2 emissions, while using biomass for energy production can have feedback effects on land use, agricultural and forest products markets, as well as biodiversity and water resources. To assess the former pros and cons of BECCS deployment, interdisciplinary model approaches require detailed estimates of technological information related to BECCS production technologies. Current estimates of the cost structure and capture potential of BECCS vary widely due to the absence of large-scale production. To obtain more precise estimates, a global online expert survey ( N = 32) was conducted including questions on the regional development potential and biomass use of BECCS, as well as the future operating costs, capture potential, and scalability in different application sectors. In general, the experts consider the implementation of BECCS in Europe and North America to be very promising and regard BECCS application in the liquid biofuel industry and thermal power generation as very likely. The results show significant differences depending on whether the experts work in the Global North or the Global South. Thus, the findings underline the importance of including experts from the Global South in discussions on carbon dioxide removal methods. Regarding technical estimates, the operating costs of BECCS in thermal power generation were estimated in the range of 100–200 USD/tCO _2 , while the CO _2 capture potential was estimated to be 50–200 MtCO _2 yr ^−1 by 2030, with cost-efficiency gains of 20% by 2050 due to technological progress. Whereas the individuals’ experts provided more precise estimates, the overall distribution of estimates reflected the wide range of estimates found in the literature. For the cost shares within BECCS, it was difficult to obtain consistent estimates. However, due to very few current alternative estimates, the results are an important step for modelling the production sector of BECCS in interdisciplinary models that analyse cross-dimensional trade-offs and long-term sustainability.

Anna Wajs-Bonikowska, Ewa Maciejczyk, Łukasz Szoka et al.

This study investigates the essential oil (EO) isolated from the seeds and cones of Canadian hemlock (<i>Tsuga canadensis</i>), highlighting notable differences in their chemical composition and biological activities. The seed EO was uniquely dominated by oxygenated derivatives of monoterpene hydrocarbons, particularly bornyl acetate (40%), whereas the cone EO exhibited higher levels of monoterpene hydrocarbons such as α-pinene (23%), β-pinene (20%), and myrcene (23%). A significant finding was the strong cytotoxic activity of cone EO against melanoma cell lines, with IC₅₀ values as low as 0.104 ± 0.015 μL/mL, compared to the minimal effects of seed EO. Additionally, cone EO demonstrated stronger antimicrobial activity, with lower minimum inhibitory concentrations (MICs) against Gram-positive and Gram-negative bacteria, further highlighting its therapeutic potential. Lipophilic extracts from seeds were characterized by unsaturated fatty acids (linoleic, oleic, and sciadonic acids—specific to conifers) and bioactive molecules with high antioxidant and nutritional potential, such as β-tocopherol, β-sitosterol, and campestrol. These findings underscore the unique chemical composition of <i>T. canadensis</i> seed EO and its lipophilic extract, along with the potent cytotoxic and antimicrobial properties of cone EO, offering insights into their potential applications in natural products for pharmaceutical and therapeutic uses.

Jones Akuaku, Rita Sam

The African baobab (Adansonia digitata L.) is a priority Pan-African tree species. Insect pests that are associated with and damage domesticated baobab are largely unknown in the production areas of baobab. To identify and document insect pests associated with domesticated African baobab for the first time, mature and young domesticated baobab plants were, respectively, surveyed on the research fields and nursery of the Ho Technical University in Ho, Ghana. The survey targeted all insects found on baobab with the goal of documenting pests that infest baobab plants. Collected insect samples were photographed and searched using Google Lens and the iNaturalist insect identification application for their identification and taxonomic classification. The entomological specimens collected were classified into 7 orders, 11 families, and 16 insect species. The most frequent orders were Hemiptera (37.5%) and Coleoptera (31.25%). The incidence of the remaining orders (Orthoptera, Lepidoptera, Hymenoptera, Araneae, and Dictyoptera) was very low with 6.25% abundance each. Regarding absolute counts, the Coleopteran order had a significantly (p≤0.05) higher number of insects (51.48 ± 7.42955) than the other orders; Araneae (4.70 ± 7.42955), Hemiptera (1.10 ± 7.42955), Dictyoptera (0.45 ± 7.42955), Orthoptera (0.40 ± 7.42955), Hymenoptera (0.30 ± 7.42955), and Lepidoptera (0.05 ± 7.42955). No significant difference was observed among these remaining orders. The cocoa weevil (Araecerus fasciculatus) was the most dominant insect pest. Some beneficial insects were also found on the baobab plants. Monitoring and management interventions, particularly integrated pest management (IPM), that target the identified insect pests can be implemented to ensure the sustainable cultivation of baobab. Further research is required to identify and classify insect pests that may not have been captured and identified in this study.

Longfei Li, Zhuxuan Tan, Liuting Li et al.

Abstract Key message Evergreen and deciduous species in a subtropical urban forest of Eastern China exhibit pronounced differences in leaf traits, with evergreens species showing lower photosynthetic rate on a leaf mass basis and leaf nutrient contents, but higher leaf mass per area ratio, leaf thickness, leaf carbon content, and leaf carbon-to-nitrogen ratio, whereas deciduous species show the opposite pattern, reflecting distinct resource-use characteristics. In addition, leaf economic and hydraulic traits are coordinated, with higher vein density associated with higher scores along the leaf economics spectrum PCA axis, reflecting resource-acquisitive characteristics and highlighting vein density as a key trait linking water transport capacity to carbon economy. Context Understanding how leaf economics and hydraulic traits vary and interact among different plant growth forms and leaf habits is essential for elucidating plant adaptability. However, the coupling of these two trait dimensions remains unclear within urban forest ecosystems where environmental conditions differ significantly from natural forests. Aims This study aimed to investigate variation and coordination between leaf economics and hydraulic traits among woody species in a subtropical urban forest of Eastern China, focusing on differences between leaf habits and growth forms. Methods We measured 10 leaf economic traits and 4 hydraulic traits across 53 woody species from a subtropical urban forest. Results Evergreen species exhibited lower photosynthetic rate on a leaf mass basis, leaf nutrient contents, and higher leaf mass per area ratio, leaf thickness, leaf carbon content, and leaf carbon-to-nitrogen ratio, consistent with resource-conserving characteristics. Deciduous species showed higher values of these parameters, indicative of rapid resource acquisition. Shrubs displayed significantly higher phosphorous content in leaves than trees. Vein density was positively correlated with the leaf economic spectrum. Conclusion These findings reveal a coordination between leaf hydraulic and economic traits. This coupling highlights the balance between water transport and resource acquisition characteristics.

Li-Tao Che, Li-Tao Che, Ahmed H. El-Sappah et al.

IntroductionThis study investigated whether glycyrrhetinic acid (GA) can alleviate the reproductive toxicity of Zearalenone (ZEN) in replacement gilts.MethodsEighty Landrace × Yorkshire gilts were randomly assigned to four dietary groups: control (basal diet), ZEN (1 mg/kg), GA (400 mg/kg), and ZEN + GA (1 mg/kg ZEN + 400 mg/kg GA).ResultsThe onset of estrus advanced significantly in all treatment groups, with the GA and ZEN + GA groups showing the most pronounced changes. Puberty onset occurred earlier in the ZEN group and was further advanced by GA supplementation. ZEN exposure impaired uterine and ovarian development, while GA improved organ development and mitigated the abnormalities in the ZEN + GA group. Hormonal analysis revealed that ZEN reduced estradiol (E2) and luteinizing hormone (LH), whereas GA elevated all measured hormones. The ZEN + GA group showed a partial recovery in hormone levels, excluding E2. Histological examination of liver tissue in the ZEN group revealed focal hepatocellular necrosis and lymphocyte infiltration, which GA notably attenuated. ZEN upregulated 3α/3β/17β-hydroxysteroid dehydrogenase (HSD) gene expression in the liver and duodenum, while GA co-administration downregulated most HSD genes except hepatic 3α-HSD.Discussion and conclusionThese findings suggest that GA can alleviate ZEN-induced reproductive toxicity via modulation of endocrine and hepatic metabolic pathways.

Fikret Mert Gultekin, Oscar Lilja, Ranim Khojah et al.

In safety-critical software systems, cybersecurity activities become essential, with risk assessment being one of the most critical. In many software teams, cybersecurity experts are either entirely absent or represented by only a small number of specialists. As a result, the workload for these experts becomes high, and software engineers would need to conduct cybersecurity activities themselves. This creates a need for a tool to support cybersecurity experts and engineers in evaluating vulnerabilities and threats during the risk assessment process. This paper explores the potential of leveraging locally hosted large language models (LLMs) with retrieval-augmented generation to support cybersecurity risk assessment in the forestry domain while complying with data protection and privacy requirements that limit external data sharing. We performed a design science study involving 12 experts in interviews, interactive sessions, and a survey within a large-scale project. The results demonstrate that LLMs can assist cybersecurity experts by generating initial risk assessments, identifying threats, and providing redundancy checks. The results also highlight the necessity for human oversight to ensure accuracy and compliance. Despite trust concerns, experts were willing to utilize LLMs in specific evaluation and assistance roles, rather than solely relying on their generative capabilities. This study provides insights that encourage the use of LLM-based agents to support the risk assessment process of cyber-physical systems in safety-critical domains.

Daniel Steininger, Julia Simon, Andreas Trondl et al.

Timber represents an increasingly valuable and versatile resource. However, forestry operations such as harvesting, handling and measuring logs still require substantial human labor in remote environments posing significant safety risks. Progressively automating these tasks has the potential of increasing their efficiency as well as safety, but requires an accurate detection of individual logs as well as live trees and their context. Although initial approaches have been proposed for this challenging application domain, specialized data and algorithms are still too scarce to develop robust solutions. To mitigate this gap, we introduce the TimberVision dataset, consisting of more than 2k annotated RGB images containing a total of 51k trunk components including cut and lateral surfaces, thereby surpassing any existing dataset in this domain in terms of both quantity and detail by a large margin. Based on this data, we conduct a series of ablation experiments for oriented object detection and instance segmentation and evaluate the influence of multiple scene parameters on model performance. We introduce a generic framework to fuse the components detected by our models for both tasks into unified trunk representations. Furthermore, we automatically derive geometric properties and apply multi-object tracking to further enhance robustness. Our detection and tracking approach provides highly descriptive and accurate trunk representations solely from RGB image data, even under challenging environmental conditions. Our solution is suitable for a wide range of application scenarios and can be readily combined with other sensor modalities.

Ilya Kurinov, Miroslav Ivanov, Grzegorz Orzechowski et al.

Forestry forwarders play a central role in mechanized timber harvesting by picking up and moving logs from the felling site to a processing area or a secondary transport vehicle. Forwarder operation is challenging and physically and mentally exhausting for the operator who must control the machine in remote areas for prolonged periods of time. Therefore, even partial automation of the process may reduce stress on the operator. This study focuses on continuing previous research efforts in application of reinforcement learning agents in automating log handling process, extending the task from grasping which was studied in previous research to full log loading operation. The resulting agent will be capable to automate a full loading procedure from locating and grappling to transporting and delivering the log to a forestry forwarder bed. To train the agent, a trailer type forestry forwarder simulation model in NVIDIA's Isaac Gym and a virtual environment for a typical log loading scenario were developed. With reinforcement learning agents and a curriculum learning approach, the trained agent may be a stepping stone towards application of reinforcement learning agents in automation of the forestry forwarder. The agent learnt grasping a log in a random position from grapple's random position and transport it to the bed with 94% success rate of the best performing agent.

Maciej Wielgosz, Simon Berg, Heikki Korpunen et al.

This paper presents a deep learning-based framework for classifying forestry operations from dashcam video footage. Focusing on four key work elements - crane-out, cutting-and-to-processing, driving, and processing - the approach employs a 3D ResNet-50 architecture implemented with PyTorchVideo. Trained on a manually annotated dataset of field recordings, the model achieves strong performance, with a validation F1 score of 0.88 and precision of 0.90. These results underscore the effectiveness of spatiotemporal convolutional networks for capturing both motion patterns and appearance in real-world forestry environments. The system integrates standard preprocessing and augmentation techniques to improve generalization, but overfitting is evident, highlighting the need for more training data and better class balance. Despite these challenges, the method demonstrates clear potential for reducing the manual workload associated with traditional time studies, offering a scalable solution for operational monitoring and efficiency analysis in forestry. This work contributes to the growing application of AI in natural resource management and sets the foundation for future systems capable of real-time activity recognition in forest machinery. Planned improvements include dataset expansion, enhanced regularization, and deployment trials on embedded systems for in-field use.

Luis Palacios Vela, Christian Wolird

We give a closed-form expression for $\varphi(1+\varphi(2+\varphi(3+...+\varphi(n)$, where $\varphi$ is Euler's totient function. More generally, for an integer sequence $A=\{a_j\}$ we study the value of $A^\varphi(n)=\varphi(a_1+\varphi(a_2+\varphi(a_3+...+\varphi(a_n)$ when $A$ is the perfect squares or the perfect cubes. We show $A^\varphi(n)$ is bounded for all sequences considered. We also present the Arboreal Algorithm which can sometimes determine a closed form of $A^\varphi(n)$ using tree-like structures.

David-Alexandre Duclos, William Guimont-Martin, Gabriel Jeanson et al.

Interest in robotics for forest management is growing, but perception in complex, natural environments remains a significant hurdle. Conditions such as heavy occlusion, variable lighting, and dense vegetation pose challenges to automated systems, which are essential for precision forestry, biodiversity monitoring, and the automation of forestry equipment. These tasks rely on advanced perceptual capabilities, such as detection and fine-grained species classification of individual trees. Yet, existing datasets are inadequate to develop such perception systems, as they often focus on urban settings or a limited number of species. To address this, we present SilvaScenes, a new dataset for instance segmentation of tree species from under-canopy images. Collected across five bioclimatic domains in Quebec, Canada, SilvaScenes features 1476 trees from 24 species with annotations from forestry experts. We demonstrate the relevance and challenging nature of our dataset by benchmarking modern deep learning approaches for instance segmentation. Our results show that, while tree segmentation is easy, with a top mean average precision (mAP) of 67.65%, species classification remains a significant challenge with an mAP of only 35.69%. Our dataset and source code will be available at https://github.com/norlab-ulaval/SilvaScenes.

Seyyed Khalil HosseiniHashemi, Ahad Rahimi, Nadir Ayrilmis

Polypropylene (PP) with black locust wood flour and maleic grafted polypropylene were used to prepare wood plastic composites (WPC) by injection molding. The effect of the addition of nano titanium oxide (nano TiO2) on the properties of the composites was investigated. The specimens were weathered in an accelerated weathering apparatus using a xenon arc lamp for 2000 h. The physical properties of the composites were evaluated by colorimetry, water absorption, and thickness swelling before and after weathering. Mechanical properties of WPC were also determined before and after weathering. The WPC containing 0.75 phr nano TiO2 showed an improvement in the flexural and tensile strength and flexural and tensile modulus while the WPCs containing 0.2 phr nano TiO2 showed an improvement in the impact strength. The UV resistance of the WPCs also improved with the incorporation of nano TiO2 powder into the composites. Both water absorption and thickness swelling were found to be reduced by the incorporation of nano TiO2 into WPC.

Tesfaye Tadesse, Yericho Berhanu, Ginjo Gitima et al.

Land use and cover changes alter the functions and structures of ecosystem, resulting in variations in Ecosystem Service Values (ESVs). Thus, we examined the impacts of land use/land cover (LULC) changes on ESVs from 1992 to 2052 using geospatial technologies. The Landsat images were classified using the supervised maximum likelihood classification technique, and future changes in LULC were predicted using the CA-Markov model. Ecosystem Service Values coefficients were adopted from empirical studies and ESVs changes were evaluated based on the benefit transfer method using LULC data for the study periods, with their corresponding modified ESVs coefficients. The results revealed that, the proportions of grassland, forestland and shrubland declined by 58.5 %, 48.15 % and 33.48 %, respectively, from 1992 to 2022. Simultaneously, the highest rate of expansions of waterbodies (34 times), farmland and settlement threefold as well as bareland (60.2 %) from 1992 to 2022 was noticed. As a result, decreasing trends were experienced in the total ESVs of the district from US$33.6 million in 1992 to US$27.79 million in 2022, and are anticipated to further decline to US$25.94 million in 2052. The ESVs of forestland, shrubland and grassland shrank from 53.1 %, 40 % and 2.78 % in 1992 to 33.28 %, 33.16 % and 1.4 % in 2022 these changes are anticipated to continue for the next three decades, except trend for the increase in grassland ecosystem service value. Therefore, the government should redesign effective land management strategies to alleviate the negative consequences of LULC changes, facilitate payment for ecosystem services, and design ecotourism to boost the income of residents for major land use management-based production systems to increase the ESV in the district.

MUNEEBA, Abdul KHALIQ, Faran MUHAMMAD et al.

Salinity poses a significant constraint to cereal productivity particularly in arid and semiarid regions. The application of allelochemical has shown promising results in mitigating the intensity of abiotic stresses. A pot experiment was conducted to assess the efficacy of different concentrations of aqueous allelopathic extract derived from moringa leaves in mitigating the adverse impacts of salinity on the germination and growth of maize cultivars via seed priming. The study involved three variables: two cultivars of maize, ‘Pioneer 30Y87’ (salt tolerant) and ‘Pioneer 30T60’ (salt sensitive) e seed priming with moringa leaf extract (MLE) at varying concentrations of 0.5%, 1.0%, 1.5%, 2.0%, 2.5%, 3.0%, and hydro-priming as control; and different salinity levels of 0, 6, and 12 dS m-1. Salinity had a negative impact on the germination process, leading to delayed and suboptimal growth of seedlings. Additionally, salinity reduced the synthesis of photosynthetic pigments (20-50%), photosynthesis, transpiration, internal carbon, and stomatal conductance. Further, MLE also improved the antioxidant activities (catalase: CAT and peroxidase: POD) by 22-56% which reduced the hydrogen peroxide production. Moreover, ‘P-30Y87’ exhibited favorable performance in terms of better germination, growth, photosynthesis and antioxidant activities.  The application of moringa leaf extract (3%) resulted in a more notable hermetic effect in elevating salinity stress thereby enhancing germination, growth, photosynthesis and antioxidant activities. In the conclusion, application of MLE (3%) is a promising approach to mitigate the adverse impacts of salinity by improving germination, growth, photosynthesis and antioxidant activities.

Wei-Yin Chang, Xin Wang, De-Sheng Guo et al.

This study aims to explore the effects of forest travel activities on university students’ stress affection. Forty volunteer university students participated in this study. All participants were asked to complete physiological (Heart Rate Variability) and psychological (Brief Profile of Mood State and State–Trait Anxiety Inventory) tests before and after the travel activities. The results reported that students’ heart rates were significantly lower after the forest travel activities than before. All domains of negative mood and anxiety decreased from the pre-test to the post-test. This study found that university students could feel less stressed if they went on forest travel activities.

Petri P. Karenlampi

Probability theory is applied for the effect of severe disturbances on the return rate on capital within multiannual stands growing crops. Two management regimes are discussed, rotations of even-aged plants on the one hand, and uneven-aged semi-stationary state on the other. The effect of any disturbance appears two-fold, contributing to both earnings and capitalization. Results are illustrated using data from a recently published study, regarding spruce (Picea abies) forests in Austria. The economic results differ from those of the paper where the data is presented, here indicating continuous-cover forestry is financially inferior to rotation forestry. Any severe disturbance may induce a regime shift from continuous-cover to even-aged forestry. If such a regime shift is not accepted, the disturbance losses reduce profits but do not affect capitalization, making continuous-cover forestry financially more sensitive to disturbances. Revenue from carbon rent favors the management regime with higher carbon stock. The methods introduced in this paper can be applied to any dataset, regardless of location and tree species.