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The forest products industries play a vital role in the economic, social, and environmental well-being of the Lake States in the United States. While various economic contribution analyses of forest products industries have been conducted to highlight the importance of such industries to regional economies, little effort has yet been made to parse out the contribution of activities in the value chain. The value chain is a series of steps involved in producing goods or services. This study used a matrix decomposition approach to estimate the economic contribution along the value chain through multiple pathways of four forest resource-based industries using wood as inputs: biomass power generation, sawmills, paper mills, and the construction of new single-family residential structures in the Lake States. The direct and indirect economic output values in 2017 resulting from the construction of new single-family residential structures were $19.1 billion, sawmills were $2.5 billion, paper mills were $17.6 billion, and the biomass power generation industry was $759 million. Of the direct and indirect economic output contributed by each industry, the highest percentage of output attributable to the logging industry was observed from the sawmills industry (12%), followed by biomass power generation (9%), paper mills (1.4%), and the construction of new single-family residential structures (<1%), respectively. The percentage of total economic output attributable to the stumpage industry in the region followed a similar trend as commercial logging for all value-chain industries. The relative economic contribution of the value-chain industries to the total economic contribution of the final industry varied based on whether the industry was a primary or secondary forest products industry and the pathways used for sourcing wood inputs. [ABSTRACT FROM AUTHOR]

This study aims to systematically examine the developmental attributes and trends within China's forestry sector through the lens of a global value chain (GVC) framework. To this end, this research analyzes the Organization for Economic Co-operation and Development Inter-Country Input–Output (OECD-ICIO) database, using a cohesive set of GVC methods, including the forward decomposition of industry value-added, an assessment of industry upstream and downstream positions, the decomposition of export values, and analysis of trade competitiveness indicators. The trajectory of China's forestry industry hinges upon the interplay between foreign demand and domestic demand. The results reveal a transition in China's forestry sector development model from export-focused to a domestically driven approach. The proportion of value-added that is devoted to meeting domestic demands within the wood processing and papermaking industries has surged to 76% and 82%, respectively. Among the major economies, China has the highest output upstream index and input downstream index in the forestry industry, playing a vital role in propelling and pulling other industries into the global value chain system. The proportion of domestic added-value in China's forestry industry exports ranks among the top, reflecting strong self-sufficiency in export production. Although China's forestry industry possesses a high world market share, its overall international competitiveness is weak, especially with clear signs of weakening comparative advantages in the wood processing industry. [ABSTRACT FROM AUTHOR]

The pulp and paper industry leads to the formation of significant amounts of bark and wood waste (BWW), which is mostly dumped, causing negative climate and environmental impacts. This article presents an overview of methods for recycling BWW, as well as the results of assessing the resource potential of old bark waste based on physicochemical and thermal analysis. It was found that using BWW as a plant-growing substrate is challenging because it was observed that bark waste is phytotoxic. The C:N waste ratio is far from optimum; moreover, it has a low biodegradation rate (less than 0.15% per year). The calorific value content of BWW ranged from 7.7 to 18.9 MJ/kg on d.m., the ash content was from 4% to 22%, and the initial moisture content was from 60.8% to 74.9%, which allowed us to draw conclusions about the feasibility of using hydrothermal methods for their processing to obtain biofuel and for the unreasonableness of using traditional thermal methods (combustion, pyrolysis, gasification). [ABSTRACT FROM AUTHOR]

Implementing watershed carbon compensation under the constraint of fiscal imbalance is a crucial approach for China to achieve its "dual carbon" goals. Focusing on 257 counties within the Pearl River Basin (PRB), this paper first measured and modified the land use carbon emissions, carbon absorption, and the land use carbon budget (LUCB) from 2005 to 2020. Subsequently, their spatiotemporal patterns and the changes before and after the modifications were analyzed. Finally, this paper determined the subject–object, value, and priority order of carbon compensation using the modified LUCB as the baseline value, and a carbon compensation mechanism that combines vertical and horizontal directions was constructed. The following findings were obtained: (1) At the time scale, the proportion of construction land and forestland in the land use structure increased, and carbon emissions, carbon absorption, and the LUCB showed an overall upward trend from 2005 to 2020. (2) At the spatial scale, the areas with high carbon emissions and carbon deficits were mainly located in the lower reaches of the basin, whereas the areas with high carbon absorption and carbon surpluses were widely distributed in the upper and middle reaches. The carbon deficit in urban municipal districts and resource-based counties was relatively serious. (3) In 2020, the total amount of carbon compensation in the PRB was CNY −8088.61 million. The number of counties that needed to be paid and compensated was 75 and 182, respectively. The carbon compensation mechanism constructed in this paper can provide a reference for other countries and regions with financial imbalances to achieve regional carbon neutrality. [ABSTRACT FROM AUTHOR]

This paper analyzes the static resilience of global wood forest products trade networks across upstream, midstream, downstream, and recycling sectors using a complex directed weighted network approach. By examining topological features and resilience from 2002 to 2021, this study reveals significant structural evolution and scale expansion in these networks. It finds improvements in network efficiency and resilience, alongside an increase in weighted hierarchy highlighting the prominent roles of core countries like China, the US, and Germany. While these countries bolster network resilience, they also introduce certain vulnerabilities. This study finds notable disassortative mixing without trade volume weights and diversified trends with weights, offering new insights into network dynamics. Core nodes must address disruption risks, enhance diversity, and establish emergency response mechanisms. In the recycling sector, this paper highlights weak trade connections and low resilience, with the US maintaining dominance, China's influence waning, and India's rapid ascent. This paper concludes by emphasizing the need for refined indicator systems and deeper explorations into resilience enhancement strategies for operational and targeted suggestions. [ABSTRACT FROM AUTHOR]

Forest fires are major forestry disasters that cause loss of forest resources, forest ecosystem safety, and personal injury. It is often difficult for current forest fire detection models to achieve high detection accuracy on both large and small targets at the same time. In addition, most of the existing forest fire detection models are single detection models, and using only a single model for fire detection in a complex forest environment has a high misclassification rate, and the accuracy rate needs to be improved. Aiming at the above problems, this paper designs two forest fire detection models (named WSB and WSS) and proposes an integrated learning-based forest fire detection model (named WSB_WSS), which also obtains high accuracy in the detection of forest fires with large and small targets. In order to help the model predict the location and size of forest fire targets more accurately, a new edge loss function, Wise-Faster Intersection over Union (WFIoU), is designed in this paper, which effectively improves the performance of the forest fire detection algorithm. The WSB model introduces the Simple-Attention-Module (SimAM) attention mechanism to make the image feature extraction more accurate and introduces the bi-directional connectivity and cross-layer feature fusion to enhance the information mobility and feature expression ability of the feature pyramid network. The WSS model introduces the Squeeze-and-Excitation Networks (SE) attention mechanism so that the model can pay more attention to the most informative forest fire features and suppress unimportant features, and proposes Spatial Pyramid Pooling-Fast Cross Stage Partial Networks (SPPFCSPC) to enable the network to extract features better and speed up the operation of the model. The experimental findings demonstrate that the WSB model outperforms other approaches in the context of identifying forest fires characterized by small-scale targets, achieving a commendable accuracy rate of 82.4%, while the WSS model obtains a higher accuracy of 92.8% in the identification of large target forest fires. Therefore, in this paper, a more efficient forest fire detection model, WSB_WSS, is proposed by integrating the two models through the method of Weighted Boxes Fusion (WBF), and the accuracy of detecting forest fires characterized by small-scale targets attains 83.3%, while for forest fires with larger dimensions, the accuracy reaches an impressive 93.5%. This outcome effectively leverages the strengths inherent in both models, consequently achieving the dual objective of high-precision detection for both small and large target forest fires concurrently. [ABSTRACT FROM AUTHOR]

Digital twins aim to optimize practices implemented in various sectors by bridging the gap between the physical and digital worlds. Focusing on open-field agriculture, livestock farming, and forestry and reviewing the current applications in these domains, this paper reveals the multifaceted roles of digital twins. Diverse key aspects are examined, including digital twin integration and maturity level, means of data acquisition, technological capabilities, and commonly used input and output features. Through the prism of four primary research questions, the state of the art of digital twins, the extent of their achieved integration, and an overview of the critical issues and potential advancements are provided in the landscape of the sectors under consideration. The paper concludes that in spite of the remarkable progress, there is a long way towards achieving full digital twin. Challenges still persist, while the key factor seems to be the integration of expert knowledge from different stakeholders. In light of the constraints identified in the review analysis, a new sector-specific definition for digital twins is also suggested to align with the distinctive characteristics of intricate biotic and abiotic systems. This research is anticipated to serve as a useful reference for stakeholders, enhancing awareness of the considerable benefits associated with digital twins and promoting a more systematic and comprehensive exploration of this transformative topic. [ABSTRACT FROM AUTHOR]

This document discusses the importance of forest ecosystems in regulating climate change and sustainable development. It highlights the threats that forests face due to climate change and urbanization, which can lead to degradation of their structure and function. The document presents 12 research papers that explore various aspects of forest management and sustainable development, including micro-scale studies on tree growth and soil composition, as well as macro-scale studies on land use and ecosystem services. The ultimate goal of this special issue is to provide strategies for achieving the sustainable management and high-quality development of forest ecosystems. [Extracted from the article]

The National Forestland "One Map" applies the boundaries and attributes of sub-elements to mountain plots by means of spatial data to achieve digital management of forest resources. The change detection and analysis of forest space and property is the key to determining the change characteristics, evolution trend and management effectiveness of forest land. The existing spatial overlay method, rasterization method, object matching method, etc., cannot meet the requirements of high efficiency and high precision at the same time. In this paper, we investigate a fast algorithm for the detection of changes in "One Map", taking Sichuan Province as an example. The key spatial characteristic extraction method is used to uniquely determine the sub-compartments. We construct an unbalanced quadtree based on the number of maximum leaf node elements (NLNE Quad-Tree) to narrow down the query range of the target sub-compartments and quickly locate the sub-compartments. Based on NLNE Quad-Tree, we establish a change detection model for "One Map" (NQT-FCDM). The results show that the spatial feature combination of barycentric coordinates and area can ensure the spatial uniqueness of 44.45 million sub-compartments in Sichuan Province with 1 m~0.000001 m precision. The NQT-FCDM constructed with 1000–6000 as the maximum number of leaf nodes has the best retrieval efficiency in the range of 100,000–500,000 sub-compartments. The NQT-FCDM shortens the time by about 75% compared with the traditional spatial union analysis method, shortens the time by about 50% compared with the normal quadtree and effectively solves the problem of generating a large amount of intermediate data in the spatial union analysis method. The NQT-FCDM proposed in this paper improves the efficiency of change detection in "One Map" and can be generalized to other industries applying geographic information systems to carry out change detection, providing a basis for the detection of changes in vector spatial data. [ABSTRACT FROM AUTHOR]

The easement reform of collective forest land (ERCFL) is an important part of national park system construction, which plays an important role in promoting the employment transfer of forest farmers. On the basis of survey data from forest farmers in Qianjiangyuan–Baishanzu National Park in Zhejiang Province, this paper uses the propensity score matching-difference in differences model to empirically analyze the impact of ERCFL on the transfer employment behavior and employment quality of forest farmers to provide experience for promoting the employment transfer of forest farmers and improving the ERCFL in national parks. This research shows the following: (1) The ERCFL in national parks has significantly promoted the employment transfer and improved the employment quality of forest farmers. This effect remains significant when controlling for possible endogeneity issues. (2) Three mechanisms of the ERCFL affect forest farmers' transfer of employment, namely improving non-agricultural employment skills, expanding the scope of non-agricultural employment, and increasing non-agricultural employment opportunities. (3) Significant gender, age, and education differences exist in terms of the impact of the ERCFL on the employment transfer of forest farmers. The impact on men, middle-aged and elderly groups, and low-education groups is more significant. Finally, this paper proposed forward countermeasures and suggestions to promote the employment transfer of forest farmers. [ABSTRACT FROM AUTHOR]

In the spring and summer of 1906, while visiting the rural village of Gósol in the Spanish Pyrenees, Picasso executed his first woodcut, made two sculptures out of boxwood, and began to focus on the topoi of wood and the forest as avatars of primal matter and of that which lies beyond civilization. In a subsequent series of paintings, he used wooden supports for images that depict male and female heads that look as if they had been chiseled out of wood. Others represent nude figures in forest settings, with explicitly sexual gestures and poses connoting a range of attitudes. These little studied works provide an optic into Picasso's early exploration of the emergence of sexual identity as an inner psychic state, but one whose signs can be read through the body. Later, responding to the proliferation of cheap, industrially produced materials, including trompe l'oeil woodgrain wallpaper, Picasso began to treat woodgrain as a mere surrogate, one that marks its distance from actual wood through a variety of painterly and mechanical effects. No longer associated with "primitive" authenticity and the primordial forces of the forest, woodgrain now appears as a false sign open to conceptual play and metamorphosis. [ABSTRACT FROM AUTHOR]

In 2022, the Russian forest sector was severely affected by the government's ban on the export of unprocessed timber and trade sanctions imposed by several countries. It is generally recognized that the regions of the Russian North-West are the most affected by trade barriers that have emerged. Against this background, the impact of bilateral trade restrictions on timber companies in the Asian part of Russia is not discussed. Nevertheless, the forest industry is an important sector of the Siberian economy that has an economic, social and environmental impact on the life of local communities. This paper analyzes the differences among Siberian timber companies in their response to the crisis depending on three factors: industrial specialization, scale of revenue and regional location. The results show that in 2022 the highest median revenues and net profits were generated by small firms that were focused on the domestic market and benefited from reduced competition due to sanctions. There is also evidence that spatial heterogeneity in the response to the crisis may be due to the different support measures of regional authorities and the proximity of the region to border points. We argue that the current conditions may become a new driver for the timber industry development, aimed at the growth of added value and expansion of domestic demand for wood products. [ABSTRACT FROM AUTHOR]

The rapid-developed scale of China's trade in wood forest products has undergone a considerable uptick. Concomitant with the evolution of high-quality development paradigms, product quality within trade frameworks has gained escalating scrutiny. Based on the statistical analysis of the export characteristics of China's wood forest products, this study leverages BACI data spanning from 1998 to 2017. Utilizing regression-based inverse methods, the study quantifies the quality attributes of these export products, dissects fluctuations in quality, and places particular emphasis on the markets within "Belt and Road Initiative" economies to elucidate dynamic trends and spatial distribution characteristics of export quality in this geoeconomic domain. Based on this, the fixed effect model, random effect model, and system GMM are used to empirically examine the influencing factors of China's wood forest product export quality. This study found that wood-based panel products have the highest quality, followed by paper products and wood furniture among the three major categories of wood forest products. Besides, the overall quality levels of the three products exported to countries participating in the Belt and Road initiative haven't significantly changed, while notable changes are evident across divergent export destination markets. In addition, an empirical study on the influencing factors of the export product quality of wood forest products is conducted, which indicates that total factor productivity, R&D investment, capital intensity, labor costs, and foreign direct investment are influencing factors. Finally, based on the research conclusions, suggestions are provided on how to improve the export quality of wood forest products. [ABSTRACT FROM AUTHOR]

Sustainable Forest Management (SFM) can fully use forest resources and improve the economic, environmental, and social sustainability of forest areas. Forestry enterprises play a crucial role in the implementation of SFM. However, the previous literature on SFM pays little attention to the subject of forestry enterprises. This paper aims to extend research on SFM from a macro perspective to the micro level of forestry enterprises. Taking the Triple Bottom Line (TBL) as a theoretical framework and the Montreal Process Criteria and Indicators (MP C&Is) as a basis, this paper constructs an indicator system to evaluate the performance of SFM of forestry enterprises from economic, social, and environmental aspects. Then, we apply the hybrid Multi-Criteria Decision-Making (MCDM) methods, i.e., the Best–Worst Method (BWM) and the VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) method, to construct the methodological system for SFM performance evaluation of forestry enterprises. Through a questionnaire survey of 68 academics and researchers, we determine the weights of the SFM indicators and select a representative forestry enterprise as a case study. The effectiveness of this SFM performance evaluation model is then demonstrated through its application to the case study of forestry enterprises in China. Through the application of the model, this paper evaluates the enterprise's SFM performance over the five-year period 2017–2021 and proposes appropriate policy recommendations and improvements. It is found that environmental factors are the primary factors of SFM in forestry enterprises. Forestry enterprises should not only pay attention to economic benefits but also to the use of forest resources and the protection of forest ecosystems to better achieve SFM. [ABSTRACT FROM AUTHOR]

Fertility variation, defined as a difference in the ability to create progeny (i.e., reproductive success) among individuals, was reviewed using the related available theoretical and practical literature in an attempt to contribute to and improve future studies on the subject. Fertility variation is a useful guide for various purposes such as gene conservation, seed production programs, forest genetic resource (i.e., seed sources) management, other forestry practices (e.g., regeneration), and evolutional and physiological studies. Many papers and proceedings have been published, including both theoretical and practical approaches, on how fertility variation has improved in the last two decades. Large variations in fertility were widely reported among populations within species and among species. We reviewed the literature and combined our diverse knowledge to examine fertility variations and their linkage parameters. Fertility variations and their related parameters (e.g., gene diversity, status number, effective parent number, parental–balance curves) estimated based on reproductive characteristics have been studied for many years using easy and cheap surveys that are used for different purposes in forest sciences. Their importance is increasing and their use is becoming more widespread because of these advantages, leading to improvements in research papers. While many research papers have recently been published on fertility variations and linkage parameters, a review paper has not been published to date. Therefore, a review paper is needed based on a literature survey and unpublished experience, as a guide for future studies. [ABSTRACT FROM AUTHOR]

The hydraulic system is a key component of intelligent forestry harvesters. In the testing of a forestry harvester, researchers need to analyze the operating efficiency and energy consumption of the forestry harvester based on the pressure and flow rate data in the hydraulic system of the forestry harvester and formulate energy-efficient control strategies. In order to enable researchers to monitor and extract the parameters of the hydraulic system of an intelligent forestry harvester in real time, this paper designs a hydraulic online monitoring system for forestry harvesters based on the LabVIEW 2019 software platform. This system realizes the following functions by reading the CAN (controller area network, a serial communication protocol for multi-host localized networks) bus of the harvester: (1) it collects and stores hydraulic system pressure, flow, and other data and displays the value curve in the system interface in real time, and (2) it monitors the control signals received by the hydraulic system and displays the control signal status and value received by the system interface in real time. This paper used this system to carry out on-site testing of an actual machine, and compared and analyzed the online monitoring data of the hydraulic system and the theoretical pressure data of the main hydraulic valve manifold. The average value of the relative error between the two was 1.65%, and the maximum value of the relative error was 2.75%. The results show that the designed system has good accuracy and stability, and can effectively realize online monitoring of the working status of the hydraulic system of forestry harvesters during their operation. [ABSTRACT FROM AUTHOR]

The digital economy is an important engine for promoting green economic development, and the integration of the digital and real economies can accelerate the transformation of the real economy. In order to explore the multifaceted influence of digital economy on forestry green total factor productivity and its specific presentation form, based on the panel data of 277 cities in China from 2013 to 2019, this paper first used the super SBM model to measure the level of forestry green total factor productivity and adopted the entropy method to measure the level of the digital economy in each region. Secondly, the influence and mechanism of the digital economy on green total factor productivity in forestry were explored by using fixed-effect and intermediate-effect models, and the heterogeneity of the digital economy on forestry green total factor productivity was analyzed based on different regional classification methods. Finally, the spatial spillover effect of the digital economy was explored in depth by the spatial Durbin model. The results are as follows: firstly, there is a significant inverted U-shaped relationship between the digital economy and forestry green total factor productivity, which first promotes and then inhibits. Secondly, the relationship between the digital economy and the level of urban green innovation shows a positive U-shaped relationship, first inhibiting and then promoting, and can have an indirect impact on forestry green total factor productivity by promoting the level of green innovation. Third, China is still on the left side of the inverted U-shaped relationship between the digital economy and forestry green total factor productivity, i.e., it is at a stage where the digital economy can significantly contribute to forestry green total factor productivity. Fourth, the effect of the digital economy on green total factor productivity in forestry is heterogeneous in the east, central, and west and is more pronounced in regions with faster economic development or rich natural resources. Fifth, the impact of the digital economy on forestry green total factor productivity has a significant positive spatial spillover effect. [ABSTRACT FROM AUTHOR]

Urban development boundary (UDB) has always served as a crucial aspect of urban sprawl research. The objective of this paper is to investigate boundary delineation and carbon emission effects. Firstly, we examined the patterns of land use changes. Additionally, this paper utilized the FLUS model and land use carbon emission calculation model to delineate UDB and calculate carbon emission effects within UDB under typical scenarios. The research results are as follows: (1) Xuzhou city witnessed a significant increase in both forest land and construction land from 2010 to 2020. (2) Under the CPS, the area of cultivated land increased by 217.05 km2 compared to the NDS. The UDB area under the NDS and CPS was 971.50 km2 and 968.99 km2, respectively. (3) Compared to the NDS, the CPS led to a net carbon emission increase of 4759.93 t within the UDB. Therefore, we should enhance the carbon sequestration and emission reduction capacity of the agricultural system. This study is beneficial for expanding the depth of research on the UDB and guiding the low-carbon urban development. We sincerely encourage readers to download this paper to improve this paper. [ABSTRACT FROM AUTHOR]

Enhancing the total factor productivity in forestry is an important part of deepening the reform of the collective forest rights system. Based on the survey data of 295 forest plots in 12 towns of Liuyang City, Hunan Province, China, the study utilized a three-stage DEA model to assess the total factor productivity of forestry at the plot level. The empirical study employs Tobit and fractional regression models to investigate the effects and differences of forestry subsidies and forestry regulatory policies on the heterogeneous total factor productivity of different types of forests. The study found that: (1) the mean value of plot-scale forestry total factor productivity is 0.127, and there are obvious differences in total factor productivity among timber forests, economic forests, and mixed forests; and (2) afforestation subsidies and nurturing subsidies significantly positively influence high-level TFP. Ecological benefit compensation positively affects high-level TFP, but is not significant at any level of TFP. Forestry regulatory policies negatively impact high-level TFP, but are not significant at any level of TFP. This paper puts forward countermeasure suggestions to improve forestry subsidy policies, optimize forestry regulatory policies, and improve forestry total factor productivity from the perspective of heterogeneous forest types. [ABSTRACT FROM AUTHOR]

Dynamic detection of forest change is the fundamental method of monitoring forest resources and an essential means of preserving the accuracy and timeliness of forest land resource data. This study focuses on a deep learning-based method for dynamic forest change detection using Sentinel-2 satellite data, especially within mountainous areas. First, the performance of various deep learning models (U-Net++, U-Net, LinkNet, DeepLabV3+, and STANet) and various loss functions (CrossEntropyLoss(CELoss), DiceLoss, FocalLoss, and their combinations) are compared on a self-made dataset. Next, the best model and loss function is used to predict the annual forest change in Hunan Province from 2017 to 2021, and the detection results are evaluated in 12 sample areas. Finally, forest changes are detected in Sentinel-2 images for each quarter of 2017–2021. In addition, a dynamic detection map of forest change in Hunan Province from 2017 to 2021 is drawn. The results reveal that the U-Net++ model and the CELoss performed the best on the self-made dataset, with a Precision of 0.795, a Recall of 0.748, and an F1-score of 0.771. The results of annual and quarterly forest change detection were consistent with the changes in the Sentinel-2 images with accurate boundaries. This result demonstrates the high practicality and generalizability of the method used in this paper. This paper achieves a rapid and accurate extraction of multi-temporal Sentinel-2 image forest change areas based on the U-Net++ model, which can be used as a benchmark for future large territorial areas monitoring and management of forest resources. [ABSTRACT FROM AUTHOR]

The Real-Time Kinematic (RTK) method is currently the most widely used method for positioning using Global Navigation Satellite Systems (GNSSs) due to its accuracy, efficiency and ease of use. In forestry, position is a critical factor for numerous applications, with GNSS currently being the preferred solution for obtaining such data. However, the decreased performance of GNSS observations in challenging environments, such as under the forest canopy, must be considered. This paper analyzes the performance of a survey-grade GNSS receiver under coniferous/deciduous tree cover. Unlike most previous research concerning this topic, the focus here is on employing a methodology that is as close as possible to real working conditions in the field of forestry. To achieve this, short observation times of 30 s were used, with corrections received directly in the field from a Continuously Operating Reference Station (CORS) of the national RTK network in Romania. In total, 84 test points were determined, randomly distributed under the canopy, with reference data collected by topographical surveys using total station equipment. In terms of the overall horizontal accuracy, an RMSE of 2.03 m and MAE of 1.63 m are found. Meanwhile, the overall vertical accuracy is lower, as expected, with an RMSE of 4.85 m and MAE of 4.01 m. The variation in GNSS performance under the different forest compositions was found to be statistically significant, while GNSS-specific factors such as DOP values only influenced the precision and not the accuracy of observations. We established that this methodology offers sufficient accuracy, which is application-dependent, even if the majority of GNSS solutions were code-based, rather than carrier-phase-based, due to strong interference from the vegetation. [ABSTRACT FROM AUTHOR]

Developing an informed lapse management strategy (LMS) is critical for life insurers to improve profitability and gain insight into the risk of their global portfolio. Prior research in actuarial science has shown that targeting policyholders by maximising their individual customer lifetime value is more advantageous than targeting all those likely to lapse. However, most existing lapse analyses do not leverage the variability of features and targets over time. We propose a longitudinal LMS framework, utilising tree-based models for longitudinal data, such as left-truncated and right-censored (LTRC) trees and forests, as well as mixed-effect tree-based models. Our methodology provides time-informed insights, leading to increased precision in targeting. Our findings indicate that the use of longitudinally structured data significantly enhances the precision of models in predicting lapse behaviour, estimating customer lifetime value, and evaluating individual retention gains. The implementation of mixed-effect random forests enables the production of time-varying predictions that are highly relevant for decision-making. This paper contributes to the field of lapse analysis for life insurers by demonstrating the importance of exploiting the complete past trajectory of policyholders, which is often available in insurers' information systems but has yet to be fully utilised. [ABSTRACT FROM AUTHOR]

Wood, as the main product of the forest, must be brought from the forest to various beneficiaries by various vehicles. The choice of a particular type of vehicle should consider the characteristics of the area from which the wood is loaded, the wood-storage yard, and the availability of a certain model. Besides this, the assortments and quantities of wood to be transported influence, to a large extent, the type of vehicle, which has consequences for transport efficiency and productivity. The aim of the paper was to evaluate the wood-loading process in various types of transport vehicles in terms of time consumption for each working phase and the factors that can affect productivity. The research was carried out in four counties of Romania and addressed the loading of wood in different forestry trucks and platforms, with or without trailers. Transport distances were documented in Brasov County based on 200 transport documents. To evaluate the transport distances in the case of wood intended for contractors, field measurements were carried out in three different are as managed by the private Lignum Forest District (Bacău County). The study of the loading process and productivity estimation at loading was carried out in three locations, namely Bacău County, Sibiu County, and Caraș-Sever in County. To observe whether there are differences in the loading of tree lengths from the harvesting area in various types of vehicles (forestry trucks—ATF—and forestry platforms—APF), research was carried out in Caraș-Sever in County. After processing the data, it was found that in the forests managed by the state and in the forests owned by the local public administrations, the fuelwood reaches mainly locals, and the working wood reaches some wood processing companies. For forestry trucks (ATFs), the loading time was somewhat constant, while for forestry platforms (APF), it varied, with differences observed from the first to the last loading. Following the evaluation of the work-element phases specific to the operation of loading round wood into vehicles, it was found that, sometimes, significant importance is given to the working elements as phases of preparation of the wooden material that can be carried out before the loading process, in order to load it. To eliminate delays in the loading process, it is recommended that logs are prepared before loading, which will increase productivity and it will decrease the risk of accidents for workers who perform these tasks during the loading process. [ABSTRACT FROM AUTHOR]

Fine three-dimensional (3D) reconstruction of real forest scenes can provide a reference for forestry digitization and forestry resource management applications. Airborne LiDAR technology can provide valuable data for large-area forest scene reconstruction. This paper proposes a 3D reconstruction method for complex forest scenes with trees, shrubs, and grass, based on airborne LiDAR point clouds. First, forest vertical distribution characteristics are used to segment tree, shrub, and ground–grass points from an airborne LiDAR point cloud. For ground–grass points, a ground–grass grid model is constructed. For tree points, a method based on hierarchical canopy point fitting is proposed to construct a trunk model, and a crown model is constructed with the 3D α-shape algorithm. For shrub points, a shrub model is directly constructed based on the 3D α-shape algorithm. Finally, tree, shrub, and ground–grass models are spatially combined to achieve the reconstruction of real forest scenes. Taking six forest plots located in Hebei, Yunnan, and Guangxi provinces in China and Baden-Württemberg in Germany as study areas, experimental results show that the accuracy of individual tree segmentation reaches 87.32%, the accuracy of shrub segmentation reaches 60.00%, the height accuracy of the grass model is evaluated with an RMSE < 0.15 m, the volume accuracy of shrub and tree models is assessed with R2 > 0.848 and R2 > 0.904, respectively. Furthermore, we compared the model constructed in this study with simplified point cloud and voxel models. The results demonstrate that the proposed modeling approach can meet the demand for the high-accuracy and lightweight modeling of large-area forest scenes. [ABSTRACT FROM AUTHOR]

The rapid increase in population and economic activities has greatly influenced land use and spatial development. In urban agglomerations where socioeconomic activities are densely concentrated, the clash between ecological protection and economic growth is becoming more evident. Therefore, a thorough quantitative assessment of spatial changes driven by land use dynamics, alongside an examination of temporal and spatial driving factors, is crucial in offering scientific backing for the long-term and sustainable growth of urban agglomerations. This paper focuses on the major urban agglomerations in China's Yangtze River Delta region, examining the spatiotemporal evolution of land use and landscape patterns from 2000 to 2020. By employing the standard deviation ellipse technique, coupled with multiple linear regression and the geographical detector model, we conduct a quantitative assessment of the directional trends in urban construction land expansion as well as the diverse impacts of temporal and spatial factors on this expansion across various periods and regions. The findings indicate that over the past 20 years, construction land in the Yangtze River Delta Urban Agglomeration expanded in concentrated patches, showing significant scale effects with relatively intact farmland and forest land being increasingly encroached upon. Landscape-type transitions predominantly occurred in cities around Taihu Lake and Hangzhou Bay, with the most significant transition being farmland converted to construction land, resulting in a greater number of patches and more pronounced land fragmentation. Throughout the 20 years, the standard deviation ellipse of construction land in the Yangtze River Delta Urban Agglomeration expanded and shifted, with the predominant expansion trending from the northwest toward the southeast, and the EN orientation being the most intense expansion area, covering 1641.24 km2. The influence of temporal and spatial driving factors on the expansion of urban construction land differed across various periods and regions. This study thoroughly examines the driving factors that affect the evolution of urban construction land in the region, offering valuable scientific evidence and references for future planning and development of the Yangtze River Delta Urban Agglomeration, aiding in the formulation of more precise and efficient urban management and land use strategies. [ABSTRACT FROM AUTHOR]

Cultivated land is crucial for food production and security. In complex environments like mountainous regions, the fragmented nature of the cultivated land complicates rapid and accurate information acquisition. Deep learning has become essential for extracting cultivated land but faces challenges such as edge detail loss and limited adaptability. This study introduces a novel approach that combines geographical zonal stratification with the temporal characteristics of medium-resolution remote sensing images for identifying cultivated land. The methodology involves geographically zoning and stratifying the study area, and then integrating semantic segmentation and edge detection to analyze remote sensing images and generate initial extraction results. These results are refined through post-processing with medium-resolution imagery classification to produce a detailed map of the cultivated land distribution. The method achieved an overall extraction accuracy of 95.07% in Tongnan District, with specific accuracies of 92.49% for flat cultivated land, 96.18% for terraced cultivated land, 93.80% for sloping cultivated land, and 78.83% for forest intercrop land. The results indicate that, compared to traditional methods, this approach is faster and more accurate, reducing both false positives and omissions. This paper presents a new methodological framework for large-scale cropland mapping in complex scenarios, offering valuable insights for subsequent cropland extraction in challenging environments. [ABSTRACT FROM AUTHOR]

Sustainability has become a key issue in the forest industry; this research aims to analyze the quantitative techniques and metrics applied to the forest-to-lumber supply chain to achieve sustainable decision-making during the last six years. The methodology used was the PRISMA systematic literature review guide, which provides a complete and updated view of the situation. A total of 724 publications were collected from the Web of Science database. Consequently, 85 papers were selected for analysis and synthesis after applying inclusion criteria. The results show a growing interest in sustainability in the forest-to-lumber supply chain, with a peak of publications in 2019. Mathematical Programming and Simulation models are on top of the quantitative techniques applied. These techniques are applied to the supply chain components, classified according to the raw material's degree of processing or transformation level in forest entities, sawmills, transportation, and other entities. The concluding remarks highlighted that 19 published works research the social dimension, 43 explore the environmental dimension, and 55 examine the economic dimension. Moreover, in the environmental dimension, there is a concentration on the use of metrics associated with greenhouse gases, and to a lesser extent, they have been concerned with soil and water. Additionally, in the social dimension, they have concentrated mainly on the workers, leaving the local communities around the supply chain in the second place. Our systematic review reports the techniques or quantitative methodologies applied in the forest-to-lumber supply chain and the metrics used to handle the dimensions of sustainability. [ABSTRACT FROM AUTHOR]

In the process of societal development, forest land categories often conflict with other land use types, leading to impacts on the ecological environment. Therefore, research on changes in forest land categories has increasingly become a globally focused topic. To anticipate potential forest ecological security issues under urbanization trends, studies on regional land use simulation become more important. This paper, based on land use data from the Ganjiang River basin, analyzes the distribution characteristics and changing trends of land use types from 2000 to 2020. Using the CA-Markov model, it predicts the land use pattern of the basin in 2040 and analyzes the transfer characteristics of forest land categories. The conclusions indicate that, between 2000 and 2020, the most significant trend in land use evolution was the transfer between various subcategories of forest land, especially frequent in the high-altitude mountainous areas in the southern and western parts of the basin. The land use pattern prediction model constructed in this paper has a kappa index of 0.92, indicating high accuracy and reliability of the predictions. In 2040, the most significant land evolution phenomenon would be from forest land to arable land to construction land, particularly pronounced around large cities. Over the next 20 years, the focus of land use evolution may shift from the southern part of the basin to the central and northern parts, with urban expansion possibly becoming the main driving force of land use changes during this period. Forest land restoration work is an effective method to compensate for the loss of forest land area in the Ganjiang River basin, with key areas for such work including Longnan, Yudu, Xingguo, Ningdu, Lianhua, and Yongxin counties. [ABSTRACT FROM AUTHOR]

Forestry is the underpinning of economic and environmental civilization for sustainable economic development. Forestry benefits ecosystems and local dwellings; thus, transforming and advancing forest products in a civilized society is critical to building a progressive community. This research aims to analyze the merits of forest products based on static, dynamic, and scale efficiency. It employed the super-efficient data envelopment analysis—Charnes, Cooper and Rhodes, and Malmquist index methods with an enhanced robustness check by applying the Stochastic Frontier Analysis. The results show that, first, from the perspective of static efficiency, the forestry industry's operating performance is maintained at a relatively high level, with a minimal fluctuation range; however, the scale efficiency is observed at a deficient level. Limited by the scale efficiency results, the aggregate comprehensive technical efficiency was at a low standard. The average total factor productivity of dynamic efficiency for each company reached 1.029, indicating that the resource allocation of listed companies is relatively reasonable, operating efficiency is on the rise, and technical level changes are a substantial source of total factor productivity growth. Furthermore, the super efficiency DEA findings indicated that 11 of the most high-performance companies attained an efficiency value above criterion value one after re-ranking. The comprehensive efficiency value of Sun Paper reached 1.438 in 2019, Sophia 1.393, Hexing Packaging 1.383, Fujian Jinsen 1.326, Bunny 1.271, Meike Home Furnishing 1.235, Jingxing Paper 1.222, Zhongshun Jiezou 1.116, Meiying Sen 1.099, Chenming Paper 1.064, Da Ya Dekor 1.015. Second, the comprehensive efficiency value of Fujian Jinsen's company has a more significant impact on the overall average efficiency of the forestry listed companies. After excluding Fujian Jinsen, the average comprehensive efficiency of 2018–2019 indicated an upward trend for the listed companies. This study suggests that policymakers should strengthen the forestry industry development plan and policy implementation regulations to focus on the forward-looking and guiding principles of industrial and technological innovation policies. [ABSTRACT FROM AUTHOR]

The establishment of the ecological protection redline (EPR) in China plays a crucial role in safeguarding key ecological functional areas and sustaining biodiversity. Besides their significance, the impact of these redlines on regional carbon sequestration remains unclear due to current inconsistencies in their implementation and adjustment. This paper conducts a thorough analysis of the evolution of two EPR versions on Hainan Island and employs the InVEST model to assess the effects of two EPR areas on regional carbon storage between 2000 and 2020. Additionally, a spatial autoregressive model is integrated to explore the socioeconomic and environmental driving mechanisms behind changes in regional carbon storage, thereby revealing complex interactions between ecological protection policy adjustments and ecosystem services of carbon storage. Key findings include: (1) The 2022 EPR area has improved the management of ecological protection boundaries, permanent cultivated land, and urban development boundaries, outperforming the 2016 version, particularly in control of construction land and water resource management. (2) The EPR2022 exhibits higher carbon storage per area, indicating a more effective zoning system. The increase in carbon storage is primarily observed in transitions from cultivated land to forest, grassland to forest, and water bodies to grassland. (3) Elevation exhibits a relatively strong positive correlation with carbon storage within the EPRs, with high-altitude areas typically encompassing the core zones of tropical rainforest national parks that possess diverse vegetation and stable ecosystems, conducive to maintaining or increasing carbon storage. Evaluating the efficacy of the EPR in enhancing regional carbon storage is critical for refining management strategies and achieving carbon neutrality goals. Future strategies should incorporate a comprehensive analysis of ecosystem services of carbon storage into the EPR framework to enhance multiple ecosystem functions, thereby establishing a resilient and functionally stable ecological landscape. This research introduces an insightful estimation method crucial for developing more effective ecological protection policies and provides valuable insights for managing ecological protection areas globally. [ABSTRACT FROM AUTHOR]

Amid unprecedented challenges to protect arable land, China's permanent capital farmland (PCF) has played a crucial role in grain production. However, a clear legal and physical boundary between PCF and arable land seems to be unable to stop the spread of non-grain production. To address it, an analysis framework for PCF non-grain production was developed to examine the state and drivers of village-scale PCF non-grain production based on the logical relationship between PCF and arable land in the rapid urbanization of Liyang. The results suggested that PCF comprised approximately 70% arable land and 30% adjustable land. Meanwhile, forest land and aquaculture ponds occupied over 25% of PCF, while nearly 20% of PCF is unsuitable for the resumption of crop cultivation. The transition state (scenario SR) offered a realistic representation of PCF non-grain production, with an average non-grain production of 48.88%. This is 14.00% lower than the current state (scenario SD) and 9.65% higher than the future state (scenario ST). Furthermore, PCF area and agricultural income per capita significantly encouraged PCF non-grain production, with explanatory powers of 51.60% and 42.40%, respectively. In contrast, urbanization rate (with an explanatory power of 35.30%) significantly discouraged it. Therefore, this paper proposed PCF redefinition, flexible PCF, and diversified economic incentives to mitigate PCF non-grain production at the village scale. [ABSTRACT FROM AUTHOR]

Diameter at breast height (DBH) is a unique attribute used to characterize forest growth and development for forest management planning and to understand forest ecology. Forest managers require an array of DBHs of forest stands, which can be reconstructed using selected probability distribution functions (PDFs). However, there is a lack of practices that fit PDFs of sub-dominating species grown in natural mixed forests. This study aimed to fit PDFs and develop predictive models for PDF parameters, so that the predicted distribution would represent dynamic forest structures and compositions in mixed forest stands. We fitted three of the simplest forms of PDFs, log-normal, gamma, and Weibull, for the DBH of eight tree species, namely balsam fir (Abies balsamea [L.] Mill.), eastern white pine (Pinus strobus L.), paper birch (Betula papyrifera Marshall), red maple (Acer rubrum L.), red pine (Pinus resinosa Aiton), sugar maple (Acer saccharum Marshall), trembling aspen (Populus tremuloides Michx), and white spruce (Picea glauca [Moench] Voss), all grown in natural-origin mixed forests in Ontario province, Canada. We estimated the parameters of the PDFs as a function of DBH mean and standard deviation for these species. Our results showed that log-normal fit the best among the three PDFs. We demonstrated that the predictive model could estimate the recovered parameters unbiasedly for all species, which can be used to reconstruct the DBH distributions of these tree species. In addition to prediction, the cross-validated R2 for the DBH mean ranged between 0.76 for red maple and 0.92 for red pine. However, the R2 for the regression of the standard deviation ranged between 0.00 for red pine and 0.69 for sugar maple, although it produced unbiased predictions and a small mean absolute bias. As these mean and standard deviations are regressed with dynamic covariates (such as stem density and stand basal area), in addition to climate and static geographic variables, the predicted DBH distribution can reflect change over time in response to management or any type of disturbance in the regime of the given geography. The predictive model-based DBH distributions can be applied to the design of appropriate silviculture systems for forest management planning. [ABSTRACT FROM AUTHOR]

An accurate analysis of the relationship between humans and the land in oasis areas is essential for the formulation of policies for differentiated oasis urban development and resource management measures. Therefore, researchers have conducted numerous studies based on the linear relationship between "people and land" in oasis areas. To address this gap, this paper takes the Wuwei oasis as an example and re-examines the impact of human activities on the sustainability of natural capital from a new research perspective (nonlinear perspective). The study selected four variables, namely planted forests, scientific R&D investment, urbanization, and population density, as the main human activities affecting the Wuwei oasis area. The NARDL model, the nonlinear Granger causality test, is employed to investigate the interactions between the variables and the oasis natural capital in both the short and long term. The results indicate the following: (1) During the study period, the consumption of natural capital in the Wuwei oasis has been increasing annually and has long exceeded the local ecological carrying capacity. (2) In the short term, planting forests is the most beneficial human activity. The most beneficial human activity is the planting of trees, while in the long term, scientific R&D investment has the most positive effect on enhancing the oasis' resource reserves. (3) Urbanization, population density, and planted forest land all have a direct effect on the sustainable development of natural resources in the oasis. The findings of the study indicate that the application of scientific and technological innovation to promote the sustainable development of resources and the environment is a more reliable approach for oasis cities with a high degree of industrialization. [ABSTRACT FROM AUTHOR]

Global positioning systems often fall short in dense forest environments, leading to increasing demand for innovative localization methods. Notably, existing methods suffer from the following limitations: (1) traditional localization frameworks necessitate several fixed anchors to estimate the locations of targets, which is difficult to satisfy in complex and uncertain forestry environments; (2) the uncertain environment severely decreases the quality of signal measurements and thus the localization accuracy. To cope with these limitations, this paper proposes a new method of trajectory localization for forestry environments with the assistance of UAVs. Based on the multi-agent DRL technique, the topology of UAVs is optimized in real-time to cater for high-accuracy target localization. Then, with the aid of RSS measurements from UAVs to the target, the least squares algorithm is used to estimate the location, which is more flexible and reliable than existing localization systems. Furthermore, a shared replay memory is incorporated into the proposed multi-agent DRL system, which can effectively enhance learning performance and efficiency. Simulation results show that the proposed method can obtain a flexible and high-accuracy localization system with the aid of UAVs, which exhibits better robustness against high-dimensional heterogeneous data and is suitable for forestry environments. [ABSTRACT FROM AUTHOR]

The paper took Guang'an City in Sichuan Province of China as a research region, and it built an evaluation index system based on 14 evaluation indices according to four criteria, namely, ecological background, ecological structure, ecological benefits, and ecological stress. The Delphi algorithm and entropy weight (a combination of subjective and objective methods) were used to calculate the weight value of each evaluation index, and an ideal point model was used to calculate the ideal point value. The ideal point ecological grade was classified, and the principal component analysis method was used to obtain the main control factors of each year, which revealed the relationship between the ecological distribution of ideal points and the environmental impact factor, with the changing characteristics of the ecology in the research region according to the hotspot model of the spatial differentiation of land ecological quality as the main control factor. Lastly, the paper also analyzed the land ecological quality of Guangan City in 2000, 2005, 2010, and 2015. The study results show that the overall land ecological quality in Guang'an City generally increased from 2005 to 2015. The proportion of forest land area and the temperature were the most important main control factors. The air temperature and the land ecological quality were positively correlated. [ABSTRACT FROM AUTHOR]

Environmental regulatory instruments are key to achieving synergy between high-quality economic development and ecological civilization construction. This paper measures the green overall factor productivity of the forestry in the Yangtze River Economic Belt by using the super-efficient nonexpected output SBM-ML index model. Additionally, it investigates the environmental regulation's impact on forestry's overall green factor productivity by using the conventional panel regression and panel smooth transformation model. The model was based on the relevant data obtained from eleven provinces along the Yangtze River Economic Belt in China from 2006 to 2021. This study concludes that command-and-control regulation of the environment and public engagement with environmental regulation can significantly promote the forestry green overall factor productivity in the Yangtze River Economic Belt, and the environmental regulation's effects on the forestry green overall efficiency in the economic region of the Yangtze River are regionally and temporally heterogeneous. The command-and-control environmental regulation also needs to exceed a certain level of regulatory coercion to promote the forestry green overall factor productivity positively. The effects of market-incentive environmental regulation were more pronounced with the increase in the regulatory intensity. When the regulation intensity surpasses the threshold, the public participation form of environmental regulation has a depressing impact on the forestry green overall factor output. To promote the development of the forestry industry in the Yangtze River Economic Zone, it is therefore necessary to strengthen the coordination of different environmental regulations, implement measures in each region, build a market-oriented green innovation system, and promote the structuralization and upgrading of the forestry industry. [ABSTRACT FROM AUTHOR]

Corporate social responsibility (CSR) disclosure serves as a vital bridge for forestry firms to communicate with their stakeholders and obtain legitimacy support. Existing studies focus on forestry firms' CSR disclosures based on CSR reports but lack consideration of such disclosures on social media. In this study, based on WeChat, the most widely used social media platform in China, we obtained 3311 tweets from 36 WeChat Official Accounts (WOA) of 63 Chinese-listed forestry firms in 2018 and used content analysis to classify the CSR information involved in these tweets based on the stakeholder dimensions. The main analysis results show that the top three CSR dimensions disclosed by Chinese forestry firms in social media are the shareholder (28.21%), customer (26.20%), and employee (23.64%) dimensions, and there are also great differences in the subcontent of disclosure concerns in each stakeholder dimension, e.g., approximately 86% of CSR disclosures for customers are product and service information. Additionally, we conducted a content analysis on the CSR reports of forestry firms using WOA. The results show that firms express different concerns in CSR reports than on social media, and the most mentioned dimensions in their reports are the environment (23.69%), employees (20.91%), and shareholders (20.21%). This indicates that there is a significant difference between the stakeholders that Chinese forestry firms focus on in social media and those that they focus on in CSR reports. This paper is the first study to focus on the CSR disclosure of Chinese forestry firms in social media and provides a reference for scholars to understand the information activities of forestry firms in social media. [ABSTRACT FROM AUTHOR]

Based on panel data from 30 provinces from 2001 to 2018 in China, this paper explores the effects and mechanisms of green urbanization on the forestry green total factor productivity (FGTFP) in the context of carbon neutral strategy using a two-way fixed effects model and instrumental variables. The results show that: Firstly, as a sector with ecological and economic benefits, ignoring carbon sink output tends to make FGTFP overestimated. Secondly, green urbanization has a significant positive contribution effect on FGTFP, and this finding still holds after a series of robustness tests including instrumental variables. Thirdly, green urbanization can indirectly promote FGTFP by stimulating the integration of forestry and tourism and strengthening environmental regulations. Fourthly, there is regional heterogeneity in the impact of green urbanization on FGTFP, i.e., the promotion effect of green urbanization on FGTFP is more significant in non-state forest areas compared with state-owned forest areas. Based on the above conclusions, the following countermeasures are proposed: firstly, attaching importance to green urbanization and strengthening environmental constraints; secondly, relying on green urbanization to drive the integration of forestry and tourism; thirdly, actively promoting the construction of green urbanization and green development of forestry in non-state forest areas, while vigorously developing the carbon sink economy to crack the transformation dilemma of backward state forest areas. [ABSTRACT FROM AUTHOR]

Simple Summary: The trajectory data of crested ibis (Nipponia nippon Temminck, 1835) have been obtained by HQBG3621L backpack-style tracker. By combining the spatial and temporal features of the trajectory data, an improved spatiotemporal clustering-based DBSCAN method was adopted to detect crested ibis's stopping points and identify the crested ibis's habitat. The clustering results are consistent with those from remote sensing images and field surveys. To address the current challenges of the heavy workload, time-consuming nature and labor-intensiveness involved in existing crested ibis's (Nipponia nipponTemminck, 1835) habitat identification approaches, this paper proposes an automatic habitat identification method based on spatiotemporal density detection. With consideration of the characteristics of the crested ibis's trajectory data, such as aggregation, repeatability, and uncertainty, this method achieves detecting the crested ibis's stopping points by using the spatial characteristics of the trajectory data. On this basis, an improved spatiotemporal clustering-based DBSCAN method is proposed in this paper, incorporating temporal characteristics of the trajectory data. By combining the spatial and temporal features, the proposed method is able to accurately identify the roosting and foraging sites among the crested ibis's stopping points. Supported by remote sensing images and field investigations, it was found that the method proposed in this paper has a good clustering effect and can effectively identify the crested ibis's foraging sites and overnight roosting areas. Specifically, the woodland, farmland, and river areas are the common foraging sites for the crested ibis, while the woodland with large trees is their common overnight site. Therefore, the method proposed in this paper can provide technical support for identifying and protecting the crested ibis's habitats. [ABSTRACT FROM AUTHOR]

Forestry work, which is considered one of the most demanding and dangerous professions in the world, is claiming more and more lives. In a country as small as Austria, more than 50 forestry workers are killed in accidents every year, and the number is increasing rapidly. This serves as a catalyst for us to implement more stringent measures for workplace safety in order to achieve the sustainability objective of SDG 3, which focuses on health and well-being. This study contributes to the analysis of occupational accidents and focuses on two large real-world datasets from both the Austrian Federal Forests (ÖBf) and the Austrian Workers' Compensation Board (AUVA). Decision trees, random forests, and fully connected neural networks are used for the analysis. By exploring different interpretation methods, this study sheds light on the decision-making processes ranging from basic association to causal inference and emphasizes the importance of causal inference in providing actionable insights for accident prevention. This paper contributes to the topic of explainable AI, specifically in its application to occupational safety in forestry. As a result, it introduces novel aspects to decision support systems in this application domain. [ABSTRACT FROM AUTHOR]

Central European countries are strategic wood exporters in the European and international wood trade. In the context of wood exporters, Czechia, Germany, Poland, Slovenia, and Austria are the important countries providing the development and stability of the forestry and wood sector. The share of these countries' exports in total EU exports represents more than 52% of the volume of wood exported over 5 years. After the Netherlands, the five mentioned countries recorded the highest increase in roundwood production of all EU and EFTA countries in the years 2000–2022 (from 46% (AT)–126% (CZ)), and the European average is 26%. The last decade has been characterised by a significant destabilisation of the Central European timber market as a result of natural disturbances linked to climate change, which also threatens the long-term competitiveness and preservation of this sector in the economic systems of national economies. The article provides an analytical insight into the historical development of natural calamities and their macroeconomic consequences in Central European countries. The data forecast an unfavourable development of the timber market in Europe caused by the ongoing calamity situation, the pricing policy of the timber trade, the oversaturation of the market with lower-quality calamity timber, insufficient processing capacities for primary processing of wood and its use in secondary processing, and, last but not least, the limited stock of timber in the coming years in selected Central European countries (due to an extreme increase in harvesting plans within the last 5 years, with consequences that will be felt in the coming decade). For the continued economic function of forests and the availability of wood supply for future generations, the current evolution of calamities in forest ecosystems is critical, and long-term strategic measures based on empirical data are necessary. The information presented in this paper can serve as a tool for the objectification of strategic decisions in the development of forestry policies in selected countries. [ABSTRACT FROM AUTHOR]

The paper looks at the potential benefits of mushrooms in terms of health, recreation, and tourism. Mycotourism is an innovative, specialised tourism product that has been successfully introduced in some regions of the world, helping to combat seasonality and promote economic development in rural areas. The aim of the study was to analyse the development of mycotourism in Poland in comparison with other countries. As a method, a questionnaire on the cultural significance of mushrooms was developed and disseminated via social media. It was found that there are strong mycophilic tendencies in Poland, leading to a strong interest in mushrooms, both in terms of cultural and culinary traditions. They are also a factor that attracts tourists to the forest areas. In contrast, the UK and Germany are more mycophobic, resulting in a lower interest in mushrooms, limited culinary use, and a lower awareness of their role in the ecosystem and their potential health benefits. The low average score for knowledge of mushrooms in countries such as the UK indicates a need for increased mycological education that can contribute to a better understanding of the conservation of these important components of the forest floor. The high proportion of people who see mushroom picking in Poland as a form of recreation emphasises its role as an important cultural and potential nature tourism asset. In Poland, where long traditions are associated with mushrooms and mushroom picking, they have a strong influence on the choice of mycological tourism, especially in regions with a rich tradition of mushroom picking. The high percentage of mushrooms used for culinary purposes, especially in Poland, emphasises their value and influences the health component of the diet, which is rich in proteins, vitamins, and minerals. Research has shown that mushroom consumption and its effects on health are perceived positively. This is evidenced by higher ratings in Poland than in the UK and Germany, indicating a general awareness of the health benefits of mushrooms, even though their use in medicine remains limited. The low level of awareness of the role of mushrooms and their potential health benefits suggests that educational and promotional activities should be increased to sensitise the public to mushrooms. [ABSTRACT FROM AUTHOR]

Noise is a major physical hazard in agricultural activities, and numerous research activities have managed to detect its effects, resulting in surveys and measurements which help to define exposure limits, prevention methods, and control strategies. This review aims to collect and analyse the data from research studies and to provide a comprehensive overview on the subject. Thus, a set of 81 papers, gathered from the Scopus and PubMed scientific databases, has been analysed to provide information regarding the evolution of noise exposure levels over time, to highlight findings on noise-induced hearing loss (NIHL), and to list strategies for noise prevention and control in agriculture. Bibliographic research showed that noise measurements between 1991 and 2022, included in scientific research on farming, forestry, and animal husbandry, mainly reported values beyond the threshold of 85 dB(A); furthermore, several research activities on NIHL showed that farmers' family members and children are often exposed to high levels of noise. Lastly, an analysis of the prevention and control strategies over time is provided, focusing on prevention programmes, screening, and the use of hearing protection devices (HPD). The identified literature suggests that additional efforts are required in regards to machinery design relating to the socio-technical aspects of agricultural activities and that side-effects of NIHL, as well as the negative impact of noise on other risks, might deserve further investigation. [ABSTRACT FROM AUTHOR]

Remote sensing images change detection technology has become a popular tool for monitoring the change type, area, and distribution of land cover, including cultivated land, forest land, photovoltaic, roads, and buildings. However, traditional methods which rely on pre-annotation and on-site verification are time-consuming and challenging to meet timeliness requirements. With the emergence of artificial intelligence, this paper proposes an automatic change detection model and a crowdsourcing collaborative framework. The framework uses human-in-the-loop technology and an active learning approach to transform the manual interpretation method into a human-machine collaborative intelligent interpretation method. This low-cost and high-efficiency framework aims to solve the problem of weak model generalization caused by the lack of annotated data in change detection. The proposed framework can effectively incorporate expert domain knowledge and reduce the cost of data annotation while improving model performance. To ensure data quality, a crowdsourcing quality control model is constructed to evaluate the annotation qualification of the annotators and check their annotation results. Furthermore, a prototype of automatic detection and crowdsourcing collaborative annotation management platform is developed, which integrates annotation, crowdsourcing quality control, and change detection applications. The proposed framework and platform can help natural resource departments monitor land cover changes efficiently and effectively. [ABSTRACT FROM AUTHOR]

Wildfires are important natural drivers of forest stands dynamics, strongly affecting their natural regeneration and providing important ecosystem services. This paper presents a comprehensive analysis of spatiotemporal burnt area (BA) patterns in the Middle Volga region of the Russian Federation from 2000 to 2022, using remote sensing time series data and considering the influence of climatic factors on forest fires. To assess the temporal trends, the Mann–Kendall nonparametric statistical test and Theil–Sen's slope estimator were applied using the LandTrendr algorithm on the Google Earth Platform (GEE). The accuracy assessment revealed a high overall accuracy (>84%) and F-score value (>82%) for forest burnt area detection, evaluated against 581 reference test sites. The results indicate that fire occurrences in the region were predominantly irregular, with the highest frequency recorded as 7.3 over the 22-year period. The total forest BA was estimated to be around 280 thousand hectares, accounting for 1.7% of the land surface area or 4.0% of the total forested area in the Middle Volga region. Coniferous forest stands were found to be the most fire-prone ecosystems, contributing to 59.0% of the total BA, while deciduous stands accounted for 25.1%. Insignificant fire occurrences were observed in young forests and shrub lands. On a seasonal scale, temperature was found to have a greater impact on BA compared with precipitation and wind speed. [ABSTRACT FROM AUTHOR]

Compared to more developed countries, the use of land resources is less efficient in China. China's vast forest land area gives it a rich and underutilized carbon sink. This is an important way for China to achieve the goals of "carbon peaking" and "carbon neutrality", which is of great significance to China's sustainable development. In the past 20 years, China has designed a series of policies to serve the development of forestry carbon sinks, namely the forestry carbon sink mechanism (FCSM). However, the questions of which policy is the most important, and what is the socio-economic value it generates, have not been fully investigated. Accordingly, this paper studied 30 provincial-level regions in China from 2005 to 2020 using the difference-in-differences (DID) model. The conclusions show that: (1) the FCSM does increase the socio-economic value of land resources, thus improving the sustainability of land resources; (2) the FCSM helps to increase forest coverage, forest stock volume and the forest coverage rate, which increases the social value of land resources from the greening path; (3) the FCSM helps to increase the gross forestry product, which increases the economic value of land resources through the path of increasing production value. [ABSTRACT FROM AUTHOR]

Blockchain applications have received a lot of attention in recent years. They provide enormous benefits and advantages to many different sectors. To date, there have not been any systematic studies comprehensively reviewing current blockchain-based applications in the forestry sector. This paper examines published work on blockchain-based applications in the forestry sector. A systematic review was conducted to identify, analyze, and discuss current literature on current blockchain applications deployed (and/or proposed) in the forestry sector, grouping results into three domains of forest management, traceability of forest-based products, and forest fire detection based on content analysis. The analyses highlight reported benefits, opportunities, and challenges of blockchain applications in the forestry sector. The study results show that blockchain has great potential in sustainable forestry, minimizing illegal logging, conserving biodiversity, and many other areas in forestry. It also shows that blockchain in forestry is still immature and complex, since it requires specialists to adopt. This paper contributes towards filling the existing research gap through this systematic review on blockchain applications in forestry. This review offers insights into a deep understanding of blockchain applications for managers, practitioners, and consultants interested in forestry. The paper identifies existing research gaps on related topics of blockchain applications in forestry and makes recommendations on potential future directions for research into blockchain in forestry. [ABSTRACT FROM AUTHOR]

To address the problem that traditional deep learning algorithms cannot fully utilize the correlation properties between spectral sequence information and the feature differences between different spectra, this paper proposes a parallel network architecture land-use classification based on a combined multi-head attention mechanism and multiscale residual cascade called MARC-Net. This parallel framework is firstly implemented by deeply mining the features generated by grouped spectral embedding for information among spectral features by adding a multi-head attention mechanism, which allows semantic features to have expressions from more subspaces while fully considering all spatial location interrelationships. Secondly, a multiscale residual cascade CNN (convolutional neural network) is designed to fully utilize the fused feature information at different scales, thereby improving the network's ability to represent different levels of information. Lastly, the features obtained by the multi-head attention mechanism are fused with those obtained by the CNN, and the merged resultant features are downgraded through the fully connected layer to obtain the classification results and achieve pixel-level multispectral image classification. The findings show that the algorithm proposed in this paper has an aggregate precision of 97.22%, compared to that of the Vision Transformer (ViT) with 95.08%; its performance on the Sentinel-2 dataset shows a huge improvement. Moreover, this article mainly focuses on the change rate of forest land in the study area. The Forest land area was 125.1143 km 2  in 2017, 105.6089 km 2  in 2019, and 76.3699 km 2  in 2021, with an increase of  15.59 % , an decrease of  0.97 % , and increase of  14.76 %  in 2017–2019, 2019–2021 and 2017–2021, respectively. [ABSTRACT FROM AUTHOR]

Forest bathing was proposed in Japan in 1982 for the purpose of reducing stress and health management, and research on the health promotion and disease prevention effects of forest bathing began in Japan in 2004. Since then, the author's research team has published many papers and, in 2012, established Forest Medicine as a new preventive medicine. Imagine a new medical science that could let you know how to be more active, more relaxed, healthier, and happier, with reduced stress and a reduced risk of lifestyle-related diseases. This new medical science is Forest Medicine. Forest Medicine studies the effects of forest environments on human health and is a new interdisciplinary science that belongs to the categories of environmental and preventive medicine. On the other hand, more than 10 years have passed since then, and a lot of evidence has been obtained and many articles regarding Forest Medicine have been published. Therefore, in this review, the author proposes a new concept of Forest Medicine, based on the new evidence and published papers, with the psycho-neuro-endocrino-immune network in mind. [ABSTRACT FROM AUTHOR]

Resource depletion, land-use change, and population growth triggered by the industrial revolution represent serious issues shared globally that have altered the structure, processes, and functions of ecosystems and had significant impacts on human well-being and survival security. This paper assesses changes in ecosystem service values (ESVs) in karst areas based on the perspective of land-use change. Guizhou province, which is typical of the South China Karst (SCK) ecologically fragile areas, was taken as a research subject. The past, current, and future spatial-temporal evolution of karst ESVs were assessed, using equivalence factors and CA-Markov modelling methods. The results show that: (1) from 1980 to 2040, arable land, woodland, and grassland occupy the main land types in the study area; at the same time, the water and built-up areas show a steady growth trend, with dramatic land use shifts occurring in the western, middle, and northern parts. (2) The overall ESVs increased by US$ 31.12 million during the study period, showing a temporal change trend of first decreasing and then increasing; forest land, grassland, and arable land area shift are the main factors of ESVs change; soil conservation, biodiversity conservation, and gas regulation functions are enhanced and play a vital role in the single ESVs increase; ESVs show a basin-type spatial distribution pattern. (3) The sensitivity index is <1, the ESV is inelastic to the VC factor adjustment, and the results are true and valid. This paper found that using quantitative methods to predict land use change of the South China Karst can provide accurate assessment of the differences in supply and demand for natural and social ecosystem services (ES) in a region, clarifying the trade-offs and synergies of ecosystem service functions, contributing to the achievement of sustainable development, and providing a practical reference for global land landscape optimization and land resource planners. [ABSTRACT FROM AUTHOR]