The environmental contribution of South Korea's domestic timber supply chains is gaining increasing attention amid efforts toward sustainable forest management and a low-carbon economy. Although undisturbed forests store more carbon in the short term, the long-term climate mitigation potential of timber utilization, especially through product substitution and carbon storage, can surpass that of preservation of forests. This study assessed how different timber utilization scenarios and by-product strategies affect environmental benefits, focusing on carbon storage and substitution effects. Using sawing simulation models and empirical stand data for larch (Larix kaempferi), the study compared environmental outcomes across scenarios involving the production of temporary construction lumber, general lumber, and structural lumber. The analysis revealed that producing high-value products such as structural lumber resulted in environmental benefits approximately 3 to 7 times greater than those of temporary construction lumber. This enhancement was primarily driven by significantly higher substitution effect of structural-grade products. Furthermore, utilizing by-products for bioplastic production enhanced environmental benefits by up to 17 to 20 times compared to conventional applications. These findings underscore the importance of integrating both primary timber and by-product utilization strategies to maximize the environmental benefits of domestic timber resources. The results will contribute to designing sustainable timber utilization strategies that support South Korea's transition to a renewable and low-carbon bioeconomy.
As a consequence of climate change, forest fires are increasingly threatening Europe's forest ecosystems, including regions where such events have historically been rare, such as Central and Northern Europe. Remote sensing technologies now offer innovative methods for analyzing and collecting data on fire activity, providing valuable insights for both researchers and practitioners. This study analyzes novel data from the Sentinel 5-P TROPOMI satellite to monitor chemical emission during the largest fire in modern Czech, which occurred in the Czech Switzerland National Park. We evaluated the responses of individual data products using the changepoint package in R and a space-time cube with change-point analysis in ArcGIS Pro. The carbon monoxide (CO) data product provided significant differences in the emission plume during the fire compared to the unaffected areas. Our analysis identified an affected area of approximately 27,000 hectares, and the infestation lasted 6 days from the fire outbreak. Satellite observations indicated a 37.6% increase in CO emissions during the first three days of the fire, and an 18.5% increase in CO during the fourth to sixth days of the fire compared to background data. This mapping approach offers a valuable tool for post-disturbance assessment and strategic planning.
Due to its diverse diet and rooting behavior, the non-native wild boar (Sus scrofa) poses significant conservation challenges worldwide. The wild boars that were introduced on Tokashiki Island, Okinawa, Japan, have caused various environmental damages, including predation on rare species and increased red soil runoff. In recent years, concerns have grown over their predation on sea turtle eggs, a key tourism resource. To mitigate this threat, effective and efficient capture methods are essential. Camera trap monitoring provides crucial insights into wild boar predation behavior, helping improve control efforts. However, manually identifying and analyzing wild boars in the vast number of images and videos-most of which are false trigger events (where no target wildlife species are captured) is highly laborintensive and time-consuming. Recently, deep learning-based object detection techniques have gained attention as promising tools for wildlife monitoring. This study evaluates the performance of YOLObased "one-stage object detection" models (GELAN, YOLOv9, and YOLOv10) using image datasets from motion-sensor camera traps set up on sea turtle nesting beaches in Tokashiki Island. The survey recorded a total of 226.6 hours of video, of which 95% (214.4 hours) consisted of empty background footage (without animals). The videos also captured goats (6.5 hours) in addition to wild boars (2.1 hours). Among the seven models tested, GELAN-C showed the highest overall performance (Precision: 0.96, Recall: 0.89, AP@0.5: 0.93). For wild boar videos (74 clips), the model correctly identified 92% (68 clips). For empty background footage (without animals) (29 clips), it correctly identified 72% (21 clips). With this empty background detection accuracy, approximately 70% of the empty footage can be pre-filtered, reducing the required video review time by about 155 hours.
Laos has experienced enormous deforestation and forest degradation since 1975. Despite years of diligent effort by the government of Laos to decrease deforestation, the trend has not reversed. Rather, there have been further increases in certain parts of the country at various degrees. This is evident by the growing concern about forestland encroachment within National Protected Areas (NPA), which have been designated as reserves for biodiversity conservation. To reduce forestland encroachment within NPAs and halt the decline of forest area in Laos, it is important to analyze the factors that contribute to forestland encroachment. This paper analyzes influencing factors of forestland encroachment by the local people within NPA, in the central part of Laos. Our study utilized both primary data from field surveys and secondary data from government official reports, as well as used previous research papers to explore the factors of forestland encroachment. Logistic regression model was then applied to analyze the factors of forestland encroachment in Phou Hin Poun NPA. The results of the study indicate that the forestland encroachment inside this NPA is strongly associated with villagers' high demand for cassava growing, power balance between villages, and the number of household members. On the other hand, other factors such as educational level, legal familiarity, economic status, and the proximity of the owner's land to forest did not significantly influence forestland encroachment on the study site. This study provides basic information to the relevant authorities who are responsible for taking effective measures against forestland encroachment inside the NPA, in Laos. The findings and knowledge gained from this study can be used by policy makers to solve the current issue of forestland encroachment as well as forest management in other countries with similar conditions.
Evaluating the progress towards global and national net-zero emissions goals requires a thorough assessment of historical emission levels and future targets. However, little attention has been paid to the actual reporting by the parties themselves. In this analysis, we examine parties reporting historical emissions and removals for Agriculture, Forestry, and Other Land Use (AFOLU) sector, as well as their commitments outlined in the Nationally Determined Contributions (NDCs) and the Long-term Low Emission Development Strategies (LT-LEDS). Our analysis reveals a worldwide decrease in historical net AFOLU emissions, spanning from 1990 to 2020. This decline primarily relates to increased removals in the LULUCF sector in non-Annex I countries. In 1990, global AFOLU emissions were recorded at 4,400 MtCO2eq, but by 2020, they had been reduced to approximately 2,200 MtCO2eq. Looking ahead, countries have committed to further reduce global net AFOLU emissions by 600-1,700 MtCO2eq by 2030 compared to 2020 levels. Moreover, fulfilment of the LT-LEDS commitment can provide an additional reduction of 2,300-3,400 MtCO2eq. By integrating these datasets, the study provides insights into the progress towards achieving climate goals, highlighting the importance of land-based mitigation strategies. The findings reveal disparities between Annex I countries and Non-Annex I countries, particularly in the ambition of the commitments and objectives. As countries begin to submit their biennial transparency reports to the United Nations Framework Convention on Climate Change (UNFCCC), our recommendation is for countries to enhance transparency in reporting and communicating their progress of implementation.
Integer programming has been extensively utilized for solving forest management planning or spatially constrained harvest scheduling problems in the past decades. In addition to determining the timing and location of harvest activities over the forest landscape, there are other environmental requirements that call for the setting aside of forest units for conservation purposes. The creation of contiguous forest stands for the protection of wildlife habitat protection can be one of those requirements. A review of existing literature on environmental management shows that a great deal of attention has been paid to nature reserve design in the selection of corridor connection among fragmented habitats. In this paper, we present a new exact optimization model which uses mixed integer programming framework to seek optimal corridor connection and the selection of suitable forage reserves from fragmented habitats, in a spatially constrained harvest scheduling problem under maximum opening size requirements, over space and time. We rely on the concept of the maximum flow problem to deal with spatial aggregation for forest units as well as corridor connection and forage reserve network. The proposed model does not need a priori enumeration and allows for multiple harvests over time. In addition to corridor connection, our novel approach takes into account forage reserves within an exact solution framework of an area restriction model.
Collisions between wildlife and vehicles are a growing conservation issue in Nepal. We examined spatial and temporal patterns of wildlife roadkill in Banke National Park and Bardia National Park. During six surveys conducted between April and June 2022, 101 animal carcasses were recorded along a 126-km stretch of the east-west national highway that runs through the two national parks. The opportunistic secondary data from national park records showed that there were 375 roadkill incidents between July 2017 and May 2022. Based on primary and secondary data (476 road fatalities from 35 different species), mammals were the most affected group (77.31%), followed by reptiles (12.61%), birds (6.93%), and amphibians (3.15%). The number of road fatalities per km was higher in Banke (0.77) than in Bardia (0.49). Poisson regression models show that road sections with high visibility (3 = -0.41) and road sections passing through human settlements (3 = -0.72) have fewer road fatalities. In contrast, mortality rates were higher on road sections that crossed water bodies (3 = 0 . 54 ), passed through the core of the national park (3 = 0 . 82 ), and near park checkpoints (3 = 1 . 03 ). Seasonal variations in traffic accidents show a higher number of fatalities in winter (chi 2 = 17.54). Overall, our results identify landscape features that may make roads more susceptible to traffic accidents, highlighting the need to consider their spatial distribution when prioritizing roadside mitigation measures. We recommend regulating vehicle speeds along wildlife concentration areas and clearing roadside vegetation to improve visibility, especially in winter, should help reduce the number of wildlife roadkill.
Tokashiki Island with convenient access from urban areas and blessed with rich marine resources, attracts numerous tourists seeking recreational activities such as diving and snorkeling. The island heavily relies on a tourism industry centered on marine resources. However, with the escape and rapid increase in the population of Japanese boars, which were initially introduced as livestock, various damages have become evident. Particularly in recent years, a major concern for local tourism industry stakeholders has been the predation of sea turtle eggs by boars because sea turtles are highly popular marine creatures among snorkelers and divers. Identifying the locations of sea turtle egg predation by wild boars and examining the conditions and situations that increase the likelihood of damage are crucial tasks in considering measures to mitigate the impact. Therefore, in this study, we analyze the factors influencing egg predation using a two-variable logistic regression model. The damage site data from on-site surveys conducted during the 2023 nesting season were utilized along with topographical, land-use, and vegetation data. Explanatory variables included in the regression analysis are average slope angle around the damage sites, predominant vegetation around the damage sites, distance to buildings, distance to roads, distance to rivers, and width of the sandy beach. Results from the regression analysis revealed that the predominant vegetation surrounding the damage sites and the width of the sandy beach significantly influence the risk of damage. Despite encountering challenges during the regression analysis, such as utilizing data from a single year's nesting season, relatively coarse geographical information, and limited explanatory variables, future endeavors should focus on collecting and accumulating more data for a more comprehensive analysis. Nevertheless, this analysis underscores the value of information provided by such studies in establishing spatial priorities for implementing measures to capture wild boars and protect sea turtle eggs in the future.
The Chinese government has set ambitious climate reduction targets: the country has pledged to reach their carbon dioxide (CO2) emission target before 2030 and achieve climate neutrality before 2060. To achieve this ambition, harvested wood products (HWP) play an essential role in offsetting the residual emission. However, knowledge gap exists in terms of the role of HWP in achieving pledged ambitions expressed in Nationally Determined contributions (NDCs) and long-term low-emission development strategies (LT-LEDS). This study projects the size of the Chinese HWP carbon pool until 2060 based on the 2019 Refinement of Intergovernmental Panel on Climate Change (IPCC) Guidelines for National Greenhouse Gas (GHG) Inventories (PA2019) and the GLOBIOM-China land use model. While the net carbon sequestration of the HWP carbon pool has increased in China over the last two decades, our assessment shows that there is a risk that the net sink of HWP carbon may have peaked as of 2020 and will saturate and decrease with time. As of 2020, the annual net GHG sink for the HWP carbon pool was estimated at-173.45 MtCO2, and under a business-as-usual (BAU) scenario, this rate would be reduced to-131.10 MtCO2 by 2060. A high bio-energy demand scenario, consistent with limiting global warming to 1.5 degrees C, leads to a greater reduction in the HWP sink, which by 2060 will amount to only-33.33 MtCO2. However, the net carbon sequestration rate of HWP could be enhanced to-136.54 MtCO2 if efforts are made to enhance the domestic consumption of semi-finished wood products. As a result, although the HWP is currently providing China with significant reductions in its economic emissions on a national level, its development over time should be fully integrated into national strategies directed at mitigating climate change and meeting international obligations.
In the present study, we aimed to develop an efficient prediction model for the site index of hinoki ( Chamaecyparis obtusa) plantations in Higashi Yoshino Village, Nara Prefecture, Japan. For this purpose, we trained a convolutional neural network (CNN) model, then investigated the accuracy of site index prediction and the reproducibility of topographic factors from digital elevation model (DEM) image data acquired using an aerial laser scanner. We also examined terrain red-green-blue (RGB) images, derived from the DEM, as an alternative form of input data. The terrain RGB images outperformed DEM images in terms of prediction accuracy and reproducibility for all evaluation indicators. Reproducibility analysis of elevation, slope, and orientation revealed particularly high accuracy (coefficient of determination > 0.95). Although further improvements to the model are needed, our results emphasize the practicality of using a CNN model combined with terrain RGB images for site index predictions. This approach has the potential to achieve prediction accuracy close to or even surpassing those of existing methods, while requiring fewer types of input data.
Taiwan has a long history of reforestation due to land degradation. However, there is a lack of understanding of how tree species grow on reclaimed lands. This study looked at tree sizes, vulnerability, and health of economically important Zelkova serrata and Quercus glauca trees planted on reclaimed agricultural lands. Thirteen former agricultural sites with trees of six to seven years old were sampled along the elevation from 107 to 2514 m above sea level. Results showed that increasing inter-tree competition reduced tree sizes and health and increased vulnerability to damage, primarily wind, for both tree species. For example, a 1 m2 ha-1 increase in inter-tree competition was associated with a 5.68 cm decrease in tree diameter, a 3.21 m decrease in tree height, a 59.31% decrease in tree health for Z. serrata. Responses of Z. serrata to inter-tree competition were generally stronger than those of Q. glauca. Elevation generally reduced tree sizes of both species and reduced health of only Z. serrata trees. Stand density has minimal effects on the tree attributes of both species. Our study suggests that Z. serrata responds strongly to inter-tree competition leading to stratification of stand structures, which agrees with past studies showing Z. serrata developing different growth strategies. Q. glauca could resist inter-tree competition so that suppressed trees could compete with its neighbors. This supports past observations that Q. glauca could persist under suppression. Our findings of the elevation caution planting both tree species outside their native habitat ranges, which was not shown before.
The trend of men's out-migration has been increasing in recent years, resulting in an additional workload on women in the rural communities of Nepal. The consequent impact of such out-migration on community-based forest management is not well known. In this context, this study attempts to determine the effect of men's out-migration on women's participation in forestry activities and the factors that affect the participation level. The study results are drawn from household surveys, focus group discussions, key informant surveys, and village meetings in the Dhodsing community forest user group of Sundarbazar municipality-8, Lamjung District of Nepal. The finding shows that despite having less technical knowledge, women's involvement in forest conservation increased after men's out-migration. The main reasons behind the increase in women's participation was the exposure of women to social and conservation works. Household income, presence of mother-in-law in the house, level of women's education, the primary occupation of households, self-employment status, having children, and livestock status are the factors that influence women's participation in forestry activities. However, age of the household head, family size, household type, migration duration, and place of migration does not affect the participation level. Men's out-migration has increased women's workload but has contributed to the household economy in the study area. We suggest that community forest user groups embrace women's participation as an opportunity to empower by providing them with appropriate practical forestry skills and building women's ownership in the community forestry decision-making process.
Growth and yield projections and current inventory assessment of a forest stand are essential tasks for successfully conducting sustainable forest management. However, much is still unknow about the amount and distribution of forest resources in subtropical island of Okinawa, Japan. Therefore, there is an urgent need to develop an effective and efficient measurement system to evaluate various forest inventory parameters such as tree diameter, tree height, and tree volume for an intricately shaped tree species common in subtropical areas. In recent years there has been rising interest in the so-called Unmanned Aerial Vehicle (UAV) -Structure-from-Motion (SfM) -Multi-View Stereo (MVS) (UAV-SfM-MVS) survey approach, which processes image data captured by UAV to develop 3D models and allows the efficient estimation of various forest inventory parameters of standing trees, without the alteration of the surrounding environment. However, the application of UAV-SfM-MVS survey approach in subtropical areas is scarce. With the aim of establishing a relatively simple but accurate forest measurement method that allows for efficient data collection of broad-leaved tree species common in a subtropical forest, we were able to conduct field experiments and acquire video data from a drone flown under tree canopy. In this paper, we developed a 3D models using SfM-MVS technique to estimate tree diameter and volume. To validate our approach, the proposed method was compared with ground truth measurements. We concluded that our approach was able to estimate tree diameters and stem volumes in our surveyed plots with a high degree of accuracy at root mean square error (RMSE) of 0.4-0.7 cm for DBH and RMSE of 0.0045-0.0147 m3 for stem volume within a shorter survey time. Although modeling of tree crown and tree parts where understory vegetation impeded camera view remains a topic for future research, we were able to demonstrate that under canopy UAV-f-MVS survey approach is relatively simple but provide highly accurate measurements of standing trees in subtropical forests without altering the surrounding environment.
Diameter-height curve is a function that defines the relationship between diameter at breast height and tree height. It is the mean trend on a scatter plot that shows the relationship between tree diameter at breast height and tree height. This curve shifts to the upper right corner as the tree ages in a uniformly aged plantation forest. Hence, it is commonly used to estimate the parameters of a model that describes the diameter-height curve by tree age. The transition in diameter-height curve with age can be interpreted as a change in the parameters of the model over time. However, the behavior of the parameters estimated independently from age-specific data may not accurately capture the features in the transition of the diameter-height curves, due to the variabilities of observations within each age of a forest stand. Against this background, in this paper, we introduce varying coefficients into the regression model for diameter-height curve to describe the transition of diameter-height curves as a function of time. The proposed method is then applied to a forest growth data of sugi (Cryptorneria japonica) stand in a village in Hoshino, Japan.
In this work we discuss possibilities and challenges in utilization of several statistical methods for assessment of forest resources related to forest inventories, especially question of dataset size where the time and resources required for data collection are often in contrast to sample size and analysis of all potential parameters of potential models. The combination of a priori knowledge of the phenomena being studied (tree number, wood volume, etc.) and understanding of behavior of individual variables provided by remote sensing instruments (different predictor variables) is crucial for production of reliable models for forest resource assessment. Using our dataset, we compared two regression techniques and one machine learning for predictor analysis for wood volume estimation. All techniques in general provided similar results in terms of variable importance and accuracy, but in more detailed analysis differences appeared, indicating that if possible biological knowledge and understanding of variables should not be neglected.
This paper focuses on the selection of height-diameter curve (HDC) which characterizes the relationship between tree height and diameter at breast height (DBH) for growth prediction. Tree height and DBH are commonly used variables in monitoring forest growth and predicting its stock. To select the appropriate HDC among multiple candidates, empirical rules or mathematical approaches based on the residual sum of squares have been applied in previous research. In this paper we apply cross-validation (CV) criterion to select an appropriate HDC for the purpose of forecasting. The CV criterion is easy to use because it is based on simple iterative process without any assumptions required on the candidate models for HDC. Not only is CV easy to use, it also evaluates forecast accuracy, which is consistent with the objectives for HDC use. In this paper, we demonstrate the results for analyzing real life data of sugi (Cryptoineria japonica) stands in Japan by preparing five candidates of HDC. We also show the results of numerical experiments for verifying the ability of the method introduced in this paper.
This study aimed to examine the relationship between the ecosystem services of pollination offered by native honey bees (Apis cerana) and the distance to natural forests (as an indicator of landscape structure) in hyuganatsu (Citrus tamurana) orchards in Aya Town, Miyazaki Prefecture, Japan. Two statistical models were developed to predict the number of visits of native honey bees to hyuganatsu tree. The area-distaince model (AD-model) considered the area of natural forests and distance from natural forests and the area model (A-model) considered the total area of the surrounding natural forests, and the results of the two models were compared. The estimated parameters of both models suggested a positive effect of natural forests on pollination services. Further, the estimated effect of the landscape structure of the AD-model was greater than that of the A-model. These results suggested that the distance to natural forests is an important landscape factor for evaluating pollination services by native honey bees.
Designing a corridor network for biodiversity concerns within forest landscape can be handled as a land-use allocation problem within an integer programming framework. In reality, a given landscape is often disturbed by manmade roads or water channels, which can transform the landscape into fragmented forest islands. In this paper, we propose a systematic modeling approach to explore the optimal corridor network for a non-contiguous forest landscape, characterized by several forest islands. Our approach first identifies if any separated forest islands exist as subgroups of landscape connection, and then looks for an optimal corridor network within each forest island or subgroup if any exist. We adapt the idea of maximum flow problems to identify forest islands, and then seek an optimal corridor in each forest island using integer programming. For demonstrative purposes, we conduct a computational experiment of our modeling approach using part of an existing forest landscape in Vietnam.
Recently, the use of forest roads for recreational activities has been increasing. High scenic quality is considered important to visitors. Therefore, the visual quality of the roadside is one of the important aspects in managing forest roads in addition to the functioning and durability for slope stabilization. However, knowledge about public’s visual preferences for different roadside management is scarce, even if it is important knowledge for designing slope greening in order to balance the function and aesthetic quality. In this study, we conducted an interview survey to explore public visual preferences towards various roadside solutions in Okinawa, Japan, which is increasingly becoming a popular tourist destination. A total of 143 survey responses were received and the non-parametric analyzes (chi-square test, gamma coefficient) were applied to examine the effect of socio-demographic characteristics (age, gender, marital status, employment status, income level, education level, birthplace, and the current residential place) on their preferences. Our results show that the public’s favorite roadside scenery is a forest vegetation without any visible man-made structures. The preferences for some slope greening and stabilization interventions depend on the socio-demographic characteristics of the respondent. Results from the responses reveal that visual quality on slopes of forest roads is highly important, but the “safety” is even more crucial.
