Hasil untuk "Forestry"

iaojiang Lia, Chuanrong Zhanga, W. Lia et al.

L. Tang, G. Shao

Michel Bartoli, Jean-Paul Mandin

La forêt domaniale de Valbonne (Gard) est située à l’étage supraméditerranéen. La présence de petits lambeaux de hêtraie a étonné les forestiers dès 1920. Essence relictuelle ou plantation des Chartreux, anciens propriétaires ? Dans les années 1950, des débats sur cette question ont rendu la forêt célèbre. De nombreuses preuves (flore associée, feuilles fossiles, …) ont, alors et depuis, démontré l’indigénat de l’espèce. Elle est arrivée jusqu’à nous protégée par la création d’un quart en réserve en 1670. De récents travaux montrent que ces hêtres sont directement issus d’un refuge interglaciaire proche et forment depuis un isolat génétique original à protéger fortement. Messages clés : - Les hêtres de Valbonne « indigènes ou plantés ? », la question ne se pose plus : ils sont indigènes. - Leur protection a été assurée par la mise en place d’un quart en réserve en 1670. - Ils sont directement issus d’un refuge interglaciaire proche. Sans contact avec les hêtres des Alpes ou du Massif central, ils sont un isolat génétique remarquable.

Longfei Xu, Xuefeng Xu

This paper investigates the coupled relationship between solid-phase temperature fields and droplet evaporation, focusing on the effects of substrate thermal conduction properties on droplet evaporation behavior. A mathematical model is developed to analyze the impacts of substrate thermal conductivity, thickness, and lower-surface temperature on evaporation rate, surface temperature, and evaporation flux. A dimensionless relative evaporation rate (HCs) is introduced to characterize the influence of substrate thermal conduction. Results show that increasing substrate thermal conductivity enhances droplet surface temperature and evaporation flux, thereby monotonically increasing evaporation rate until it approaches the rate of the evaporative cooling model. Conversely, increasing substrate thickness lengthens the heat transfer path, reducing heat conducted to the solid–liquid interface and decreasing evaporation rate. Changes in substrate lower-surface temperature significantly affect evaporation rate, but HCs remains nearly unaffected. The concept of equivalent substrates is proposed and verified through dimensionless analysis and simulations. It is found that different combinations of substrate thickness and thermal conductivity exhibit consistent effects on droplet evaporation, with minimal relative errors in evaporation rate and total heat transfer at the solid–liquid interface. This confirms the existence of the equivalent substrate phenomenon. Additionally, the effects of droplet properties, such as contact angle and evaporative cooling coefficient (<i>Ec</i>), on the equivalent substrate phenomenon are explored, revealing negligible impacts. These findings provide theoretical guidance for optimizing droplet evaporation processes in practical applications, such as micro/nanoscale thermal management systems.

Andrew Crawley, Adam Daigneault, Jonathan Gendron

Several key states in various regions of the U.S. have experienced recent sawtimber as well as pulp and paper mill closures, which raises an important policy question: how have and will key macroeconomic and industry specific indicators within the U.S. forest sector likely to change over time? This study provides empirical evidence to support forest-sector policy design by using a vector error correction (VEC) model to forecast economic trends in three major industries - forestry and logging, wood manufacturing, and paper manufacturing - across six of the most forest-dependent states found by the location quotient (LQ) measure: Alabama, Arkansas, Maine, Mississippi, Oregon, and Wisconsin. Overall, the results suggest a general decline in employment and the number of firms in the forestry and logging industry as well as the paper manufacturing industry, while wood manufacturing is projected to see modest employment gains. These results also offer key insights for regional policymakers, industry leaders, and local economic development officials: communities dependent on timber-based manufacturing may be more resilient than other forestry-based industries in the face of economic disruptions. Our findings can help prioritize targeted policy interventions and inform regional economic resilience strategies. We show distinct differences across forest-dependent industries and/or state sectors and geographies, highlighting that policies may have to be specific to each sector and/or geographical area. Finally, our VEC modeling framework is adaptable to other resource-dependent industries that serve as regional economic pillars such as mining, agriculture, and energy production offering a transferable tool for policy analysis in regions with similar economic structures.

Yunmei Huang, Songlin Hou, Zachary Nelson Horve et al.

The precise identification of tree species is fundamental to forestry, conservation, and environmental monitoring. Though many studies have demonstrated that high accuracy can be achieved using bark-based species classification, these models often function as "black boxes", limiting interpretability, trust, and adoption in critical forestry applications. Attribution-based Explainable AI (XAI) methods have been used to address this issue in related works. However, XAI applications are often dependent on local features (such as a head shape or paw in animal applications) and cannot describe global visual features (such as ruggedness or smoothness) that are present in texture-dominant images such as tree bark. Concept-based XAI methods, on the other hand, offer explanations based on global visual features with concepts, but they tend to require large overhead in building external concept image datasets and the concepts can be vague and subjective without good means of precise quantification. To address these challenges, we propose a lightweight post-hoc method to interpret visual models for tree species classification using operators and quantifiable concepts. Our approach eliminates computational overhead, enables the quantification of complex concepts, and evaluates both concept importance and the model's reasoning process. To the best of our knowledge, our work is the first study to explain bark vision models in terms of global visual features with concepts. Using a human-annotated dataset as ground truth, our experiments demonstrate that our method significantly outperforms TCAV and Llama3.2 in concept importance ranking based on Kendall's Tau, highlighting its superior alignment with human perceptions.

Chenyang Fan, Xujie Zhu, Taige Luo et al.

The pattern analysis of tree structure holds significant scientific value for genetic breeding and forestry management. The current trunk and branch extraction technologies are mainly LiDAR-based or UAV-based. The former approaches obtain high-precision 3D data, but its equipment cost is high and the three-dimensional (3D) data processing is complex. The latter approaches efficiently capture canopy information, but they miss the 3-D structure of trees. In order to deal with the branch information extraction from the complex background interference and occlusion, this work proposes a novel WaveInst instance segmentation framework, involving a discrete wavelet transform, to enhance multi-scale edge information for accurately improving tree structure extraction. Experimental results of the proposed model show superior performance on SynthTree43k, CaneTree100, Urban Street and our PoplarDataset. Moreover, we present a new Phenotypic dataset PoplarDataset, which is dedicated to extract tree structure and pattern analysis from artificial forest. The proposed method achieves a mean average precision of 49.6 and 24.3 for the structure extraction of mature and juvenile trees, respectively, surpassing the existing state-of-the-art method by 9.9. Furthermore, by in tegrating the segmentation model within the regression model, we accurately achieve significant tree grown parameters, such as the location of trees, the diameter-at-breast-height of individual trees, and the plant height, from 2D images directly. This study provides a scientific and plenty of data for tree structure analysis in related to the phenotype research, offering a platform for the significant applications in precision forestry, ecological monitoring, and intelligent breeding.

Andreas Kamilaris, Chirag Padubidri, Asfa Jamil et al.

This paper describes the experiences and lessons learned after the deployment of a country-scale environmental digital twin on the island of Cyprus for three years. This digital twin, called GAEA, contains 27 environmental geospatial services and is suitable for urban planners, policymakers, farmers, property owners, real-estate and forestry professionals, as well as insurance companies and banks that have properties in their portfolio. This paper demonstrates the power, potential, current and future challenges of geospatial analytics and environmental digital twins on a large scale.

Aldino Rizaldy, Fabian Ewald Fassnacht, Ahmed Jamal Afifi et al.

Detailed structural and species information on individual tree level is increasingly important to support precision forestry, biodiversity conservation, and provide reference data for biomass and carbon mapping. Point clouds from airborne and ground-based laser scanning are currently the most suitable data source to rapidly derive such information at scale. Recent advancements in deep learning improved segmenting and classifying individual trees and identifying semantic tree components. However, deep learning models typically require large amounts of annotated training data which limits further improvement. Producing dense, high-quality annotations for 3D point clouds, especially in complex forests, is labor-intensive and challenging to scale. We explore strategies to reduce dependence on large annotated datasets using self-supervised and transfer learning architectures. Our objective is to improve performance across three tasks: instance segmentation, semantic segmentation, and tree classification using realistic and operational training sets. Our findings indicate that combining self-supervised learning with domain adaptation significantly enhances instance segmentation compared to training from scratch (AP50 +16.98%), self-supervised learning suffices for semantic segmentation (mIoU +1.79%), and hierarchical transfer learning enables accurate classification of unseen species (Jaccard +6.07%). To simplify use and encourage uptake, we integrated the tasks into a unified framework, streamlining the process from raw point clouds to tree delineation, structural analysis, and species classification. Pretrained models reduce energy consumption and carbon emissions by ~21%. This open-source contribution aims to accelerate operational extraction of individual tree information from laser scanning point clouds to support forestry, biodiversity, and carbon mapping.

Adas Marčiulynas, Jūratė Lynikienė, Artūras Gedminas et al.

We investigated the diversity and occurrence of wood wasps in Lithuania and determined communities of associated fungi. Trapping of wood wasps resulted in three different species, including <i>Sirex juvencus</i>, <i>Urocerus gigas</i>, and <i>Tremex fuscicornis</i>. Fungal culturing from adult females of <i>T. fuscicornis</i> mainly resulted in fungi from the genera <i>Penicillium</i> and <i>Trichoderma</i>. High-throughput sequencing of ITS2 rDNA resulted in 59,797 high-quality fungal sequences, representing 127 fungal OTUs. There were 93 fungal OTUs detected in <i>U. gigas</i>, 66 in <i>S. juvencus</i>, and 10 in <i>T. fuscicornis</i>. The most common fungi were <i>Fusarium sporotrichioides</i> (63.1% of all fungal sequences), <i>Amylostereum chailletii</i> (14.9%), <i>Penicillium crustosum</i> (7.8%), <i>Microascus</i> sp. 2261_4 (5.0%), and <i>Pithoascus ater</i> (2.1%). Among these, only <i>A. chailletii</i> was found in all three insect species with the highest relative abundance in <i>U. gigas</i> (15.2%), followed by <i>S. juvencus</i> (7.7%), and the lowest in <i>T. fuscicornis</i> (0.3%) (<i>p</i> < 0.0003). Correspondence analysis of fungal communities showed a distant placement of different species of wood wasps, indicating that fungal communities in each of these were largely different. In conclusion, the study showed that the economically important tree pathogen <i>A. chailletii</i> was among the most common fungal OTUs associated with siricid wood wasps.

Riko Wahyudi, Wahyu Marjaka, Christian Silangen et al.

The jurisdictional REDD+ (JREDD+) mechanism, aimed at reducing emissions from deforestation and forest degradation, has been crucial in global climate change mitigation efforts. However, designing effective, efficient, and equitable benefit-distribution policy at the site level remains a challenge. This research assesses three benefit distribution mechanisms in Indonesia for JREDD+ initiatives, facilitated by the Indonesian Environment Fund (IEF). They include: (1) distribution through the provincial revenue and expenditure budget (APBD), (2) distribution through intermediary institutions (LEMTARA), and (3) direct distribution or transfer to beneficiaries. Each mechanism is evaluated on effectiveness, efficiency, and equity, considering bureaucratic processes and stakeholder capacities. The study utilizes public deliberation by involving relevant stakeholders at the national and Jambi province levels and expert judgment by purposively selecting based on certain criteria to help determine the optimal mechanism as the reference for achieving Indonesia's climate mitigation goals and the administrative intricacies involved. The findings suggest that direct distribution to beneficiaries is the most efficient and equitable, although using LEMTARA is deemed slightly more effective for targeted fund allocation. The study provides recommendations for policy makers on enhancing institutional capacities and integrating flexible inclusive mechanisms to optimize JREDD+ benefit distribution at the sub-national level.

Benoit Casseau, Nived Chebrolu, Matias Mattamala et al.

For biodiversity and forestry applications, end-users desire maps of forests that are fully detailed, from the forest floor to the canopy. Terrestrial laser scanning and aerial laser scanning are accurate and increasingly mature methods for scanning the forest. However, individually they are not able to estimate attributes such as tree height, trunk diameter and canopy density due to the inherent differences in their field-of-view and mapping processes. In this work, we present a pipeline that can automatically generate a single joint terrestrial and aerial forest reconstruction. The novelty of the approach is a marker-free registration pipeline, which estimates a set of relative transformation constraints between the aerial cloud and terrestrial sub-clouds without requiring any co-registration reflective markers to be physically placed in the scene. Our method then uses these constraints in a pose graph formulation, which enables us to finely align the respective clouds while respecting spatial constraints introduced by the terrestrial SLAM scanning process. We demonstrate that our approach can produce a fine-grained and complete reconstruction of large-scale natural environments, enabling multi-platform data capture for forestry applications without requiring external infrastructure.

Jan Kotlarz

This study investigates the application of Nash equilibrium strategies in optimizing forest harvesting decisions, focusing on multiple management objectives in forestry. Through simulation-based analysis, the research explores the evolution of various indicators during the game: 1) the mass of CO2 sequestration, 2) forest stands biodiversity, 3) the harvested wood volume, 4) native species fraction, and 5) protective functions. The results underscore the importance of considering diverse objectives and balancing competing interests in forestry decision processes. The forest stands designated for harvesting in the Toruń Forest District were defined as the initial strategy, and indicators for all objectives were calculated accordingly. A Nash equilibrium was identified through a game involving five players representing individual objectives with partially conflicting aims. The final strategy was obtained by modifying specific forest stands designated for harvesting, thereby maintaining the planned wood volume extraction while simultaneously reducing biodiversity loss by nearly 40%, preserving protective functions across over 600 hectares of forested areas, enhancing decadal carbon sequestration in the forest district by 100,000 tons, and additionally improving species suitability by nearly 10%. The findings suggest the potential for further research and refinement of Nash equilibrium-based optimization approaches to enhance the effectiveness and sustainability of forest management practices.

Juan Castorena

This work leverages neural radiance fields and remote sensing for forestry applications. Here, we show neural radiance fields offer a wide range of possibilities to improve upon existing remote sensing methods in forest monitoring. We present experiments that demonstrate their potential to: (1) express fine features of forest 3D structure, (2) fuse available remote sensing modalities and (3), improve upon 3D structure derived forest metrics. Altogether, these properties make neural fields an attractive computational tool with great potential to further advance the scalability and accuracy of forest monitoring programs.

Rohit Parasnis, Saurabh Amin

We address the challenge of promoting sustainable practices in production forests managed by strategic entities (agents) that harvest agricultural commodities under concession agreements. These entities engage in activities that either follow sustainable production practices or expand into protected forests for agricultural growth, which leads to unsustainable production. Our study uses a network game model to design optimal pricing policies that incentivize sustainability and discourage environmentally harmful practices. Specifically, we model interactions between agents, capturing both intra-activity (within a single production activity) and cross-activity (between sustainable and unsustainable practices) influences on agent behavior. We solve the problem of maximizing welfare while adhering to budgetary and environmental constraints - particularly, limiting the aggregate level of unsustainable effort across all agents. Although this problem is NP-hard in general, we derive closed-form solutions for various realistic scenarios, including cases with regionally uniform pricing and the use of sustainability premiums or penalties. Remarkably, we find that it is possible to achieve both welfare improvement and reduction in unsustainable practices without reducing any agent's utility, even when there is no external budget for increasing premiums. We introduce a novel node centrality measure to identify agents whose decisions most influence aggregate unsustainable effort. Empirical validation confirms our theoretical findings, offering actionable insights for policymakers aiming to promote sustainable resource management in agricultural commodity markets. Our work has broader implications for addressing sustainability challenges in the presence of network effects, offering a framework for designing incentive structures that align economic objectives with environmental stewardship.

Mojtaba Houballah, Jean-Yves Courtonne, Henri Cuny et al.

Context: Over the last decade, the forestry sector has undergone substantial changes, evolving from a post-2008 financial crisis landscape to incorporating policies favoring sustainable and green alternatives, especially after the 2015 Paris agreement. This evolution was drastically disrupted with the advent of the COVID-19 pandemic in 2020, causing unprecedented interruptions in supply chains, product markets, and data collection. Grasping the aftermath of the COVID-19, regional instances of the forest supply chain sector need synthetic pictures of their present state and future opportunities for emerging wood products and better regional-scale carbon balance. But given the impact of COVID-19 lock-down on data collection, the production of such synthetic pictures has become more complex, yet essential. This was the case for the regional supply chain of the Grand-Est region in France that we studied. Aims: For this study, our aim was to demonstrate that an integrated methodology could provide such synthetic picture even though we sued heterogenous sources of data and different analytical objectives: i.e. (1) retrospectively evaluate the aftermath of COVID-19 pandemic on the supply chain outcomes within the forestry sector; and then (2) retrospectively explore possible options of structural change of regional supply chain that would be required to simultaneously recover from COVID-19 and transit to new objectives in line with the extraction of new bio-molecules from wood biomass, and with the reduction of the regional scale carbon footprint (in line with the IPCC Paris Agreement) Methods: To achieve this, our methodological approach was decomposed into three steps. We first used a Material Flow Analysis (MFA) recently conducted on the forestry sector in the Grand Est region to establish a Sankey diagram (i.e. a schematic representation of industrial sectors and biomass flows along the supply chain) for the pre-Covid-19 period (2014-2018). Then we compared pre-Covid-19 Sankey diagram to the only source of data we could access from the post-Covid-19 period (2020-2021) in order to estimate the impact of Covid-19. Finally, we used as input the reconciled supply chain model into a consequential Wood Product Model (WPMs), called CAT (carbon accounting tool) in order to compare three prospective scenarios: (1) a scenario that projected 2020-2021 Covid-19 conditions and assumed pre-Covid-19 business as usual practices, (2) a scenario illustrating the consequence or rerouting some of the biomass to satisfy the expected increase in pulp and paper production to satisfy the needs of the industry after Covid-19, and (3) a scenario that explored new opportunities in term of extraction of novel bio-molecules by the emerging biochemical wood industry. For every scenario we also evaluated the regional carbon gains and losses that these changes implied. Results: Our study conducted a detailed analysis of the impacts of the COVID-19 pandemic on the forestry sector's supply chain in the Grand Est region, using a dynamic and integrated Wood Product Model. We found significant disruptions during the pandemic period, with notable declines in industrial wood chips and timber hardwood production by 41.8% and 40%, respectively. Conversely, there were substantial increases in fuelwood, timber sawdust, and timber softwood, rising by 14.15%, 44.23%, and 15.29% respectively. These fluctuations underscore the resilience and vulnerabilities within the regional wood supply chain. Our findings also emphasize the potential for strategic rerouting of biomass flows to meet changing industry demands, which could play a crucial role in supporting the sector's recovery and adaptation to post-pandemic conditions. Discussion and conclusion: In addition, our study recognizes the limitations of the current approach combining MFA and WPM and suggests potential areas of enhancement. Ultimately, our findings shed light on the need to develop more integrated analytical methods to provide useful synthetic pictures of regional scale supply chains, when there is a need to adapt it to evolving situations and complex data landscapes.

Dezhou Wang, Dezhou Wang, Jinghong Zuo et al.

BRI1 EMS SUPPRESSOR1 (BES1) family members are crucial downstream regulators that positively mediate brassinosteroid signaling, playing vital roles in the regulation of plant stress responses and anther development in Arabidopsis. Importantly, the expression profiles of wheat (Triticum aestivum L.) BES1 genes have not been analyzed comprehensively and systematically in response to abiotic stress or during anther development. In this study, we identified 23 BES1-like genes in common wheat, which were unevenly distributed on 17 out of 21 wheat chromosomes. Phylogenetic analysis clustered the BES1 genes into four major clades; moreover, TaBES1-3A2, TaBES1-3B2 and TaBES1-3D2 belonged to the same clade as Arabidopsis BES1/BZR1 HOMOLOG3 (BEH3) and BEH4, which participate in anther development. The expression levels of 23 wheat BES1 genes were assessed using real-time quantitative PCR under various abiotic stress conditions (drought, salt, heat, and cold), and we found that most TaBES1-like genes were downregulated under abiotic stress, particularly during drought stress. We therefore used drought-tolerant and drought-sensitive wheat cultivars to explore TaBES1 expression patterns under drought stress. TaBES1-3B2 and TaBES1-3D2 expression was high in drought-tolerant cultivars but substantially repressed in drought-sensitive cultivars, while TaBES1-6D presented an opposite pattern. Among genes preferentially expressed in anthers, TaBES1-3B2 and TaBES1-3D2 expression was substantially downregulated in thermosensitive genic male-sterile wheat lines compared to common wheat cultivar under sterile conditions, while we detected no obvious differences under fertile conditions. This result suggests that TaBES1-3B2 and TaBES1-3D2 might not only play roles in regulating drought tolerance, but also participate in low temperature-induced male sterility.

Lana Jarža, Anka Ozana Čavlović, Stjepan Pervan et al.

This paper deals with an overview of additive manufacturing and its segment - 3D printing, which is today rapidly and widely used (Agashe et al., 2020) for personal and high-capacity production. The paper discusses the possible positive factors such as small and personalized production series, cheaper design and production process, complex geometry, bionic structures (whose surfaces are complicated to make, and are copy of biological organisms) and negative factors such as lack of educated specialists and trainings. Those facts are affecting the implementation of these technologies in different segments of the design, product development and furniture production. The impacts of new technologies on the design and production of rapid prototypes and finished products in furniture industry are analyzed. The positive results of using additive manufacturing indicate that, in spite of minor obstacles and problems with connecting different production processes, additive production will have a significant place in the future of furniture design and production. The most important advantages of 3D printing is fast prototyping, one piece production, free form designing and the use of bio-based materials and their possibility of recycling.

Petri P. Karenlampi

Nitrogen fertilization of boreal forests is investigated in terms of microeconomics, as a tool for carbon sequestration. The effects of nitrogen fertilization's timing on the return rate on capital and the expected value of the timber stock are investigated within a set of semi-fertile, spruce-dominated boreal stands, using an inventory-based growth model. Early fertilization tends to shorten rotations, reducing timber stock and carbon storage. The same applies to fertilization after the second thinning. Fertilization applied ten years before stand maturity is profitable and increases the timber stock, but the latter effect is small. Fertilization of mature stands, extending any rotation by ten years, effectively increases the carbon stock. Profitability varies but is increased by fertilization, instead of merely extending the rotation.

Juan Lagos, Urho Lempiö, Esa Rahtu

While the availability of large and diverse datasets has contributed to significant breakthroughs in autonomous driving and indoor applications, forestry applications are still lagging behind and new forest datasets would most certainly contribute to achieving significant progress in the development of data-driven methods for forest-like scenarios. This paper introduces a forest dataset called \textit{FinnWoodlands}, which consists of RGB stereo images, point clouds, and sparse depth maps, as well as ground truth manual annotations for semantic, instance, and panoptic segmentation. \textit{FinnWoodlands} comprises a total of 4226 objects manually annotated, out of which 2562 objects (60.6\%) correspond to tree trunks classified into three different instance categories, namely "Spruce Tree", "Birch Tree", and "Pine Tree". Besides tree trunks, we also annotated "Obstacles" objects as instances as well as the semantic stuff classes "Lake", "Ground", and "Track". Our dataset can be used in forestry applications where a holistic representation of the environment is relevant. We provide an initial benchmark using three models for instance segmentation, panoptic segmentation, and depth completion, and illustrate the challenges that such unstructured scenarios introduce.