• Journal of Korean Society of Forest Science
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• v.100 no.3
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• pp.455-465
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• 2011

In order to assess and mitigate climate change, the role of forest biomass as carbon sink has to be understood spatially and quantitatively. Since existing forest statistics can not provide spatial information about forest resources, it is needed to predict spatial distribution of forest biomass under an alternative scheme. This study focuses on developing an upscaling method that expands forest variables from plot to landscape scale to estimate spatially explicit aboveground biomass(AGB). For this, forest stand variables were extracted from National Forest Inventory(NFI) data and used to develop AGB regression models by tree species. Dominant/codominant height and crown density were used as explanatory variables of AGB regression models. Spatial distribution of AGB could be estimated using AGB models, forest type map and the stand height map that was developed by forest type map and height regression models. Finally, it was estimated that total amount of forest AGB in Danyang was 6,606,324 ton. This estimate was within standard error of AGB statistics calculated by sample-based estimator, which was 6,518,178 ton. This AGB upscaling method can provide the means that can easily estimate biomass in large area. But because forest type map used as base map was produced using categorical data, this method has limits to improve a precision of AGB map.

• Journal of The Geomorphological Association of Korea
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• v.25 no.3
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• pp.71-87
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• 2018

As part of further study on Gwangseungri coastal deposits which occurred at 10 ~ 15m above sea level and was analyzed as palaeo-coastal flood-type sediments, six burial ages of six additional samples from the two cross sections (KST1 and, KST2) near to the points of the past study were estimated and the geochemical analysis was performed. Further investigation on the cross section KST1 revealed a reversal of the burial age at the bottom of the section which was identified as palaeo-flooding sediments and supposed to have been buried about 350 years ago. At the lower part of the KST1, the burial age of the sediment layer was estimated to be 3,800 years. The lower part of KST2 sediments was identified as sediments that was formed about 6,600 years ago and about 20,000 years ago. Considering the inclination of the sediment layers, the coastal flooding sedimentsreported to have formed 700 years ago in the previousstudy are located at the top and the KST1 section analyzed in thisstudy seemed to be connected to the lower part. The chemical analysis showed that the relationship between these layers was not continuous but had a discontinuous characteristic influenced by a specific event, and the chemical composition also showed a rapid change. If we judge these together, the lowest part of Gwangseungrisediment layerseemed to have formed during the last glacial period but it was hard to find its origins clearly. On top of this layer, a fine sediment layer containing gravels was also formed.Itseemed thatsedimentation did not occur continuously, but was affected by temporary events in such a way that after a sediment layer was formed, it stopped. Since then, a coastal flooding event occurred about 700 years ago, and part of flooded sediments accumulated in the rear slope. After that, when a flood layer including additional granular materials about 350 years ago was formed, sedimentation along the slope seemed to have occurred.

• Fisheries and Aquatic Sciences
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• v.25 no.9
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• pp.473-488
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• 2022

To have a better understanding of the impact of the PETRONAS oil and gas platform on commercial fisheries activities, Universiti Malaysia Terengganu (UMT) examined two approaches which are data collection from satellite and data collection from fishermen and anglers. By profiling the anglers who utilize reefed oil and gas structures for fishing, it can determine if the design and location of the reef platforms will benefit or negatively impacts those anglers and fisherman. Furthermore, this assessment will be contributing to the knowledge regarding the value of offshore oil and gas platforms as fisheries resources. Collectively, the apparent fishing activity data included, combined with the findings in the reefing viability index will help to inform PETRONAS's future decommissioning decisions and may help determine if the design and proposed locations for future rigs-to-reefs candidates would benefit commercial fishing groups, further qualifying them as appropriate artificial reef candidates. The method applied in this study is approaching by using a data satellite known as Google's Global Fishing Watch technology, which is one of the applications to measure commercial fishing efforts around the globe. The apparent commercial fishing effort around the selected twelve PETRONAS platforms was analyzed from January 2012 to December 2018. Using the data collection from fishermen which is the total estimation of commercial fish value cost (in Malaysia ringgit, MYR [RM]) in Peninsular Malaysia Asset, Sabah Asset, and Sarawak Operation region. The data were extracted every month from 2016 to 2018 from the National Oceanic and Atmospheric Administration database. Most of the selected platforms that show a high frequency of vessels around the year are platform KP-A, platform BG-A and platform PL-B. The estimated values of commercial fishes varied between platforms, with ranged from RM 10,209.92 to RM 89,023.78. Thus, platforms with high commercial fish value are selected for reefing in-situ and will serve multi-purposes and benefit the locals as well as the country. The current study has successfully assessed the potential reefing area of the Malaysian offshore environment with greater representativeness and this paper focused on its potential as a new fishing ground.

• Economic and Environmental Geology
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• v.55 no.1
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• pp.29-43
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• 2022

The groundwater quality in Boseong area was characterized and their background concentrations were estimated based on the chemical data obtained from 200 groundwater samples collected during July 2019. Groundwater data were classified into two groups: Group 1 with NO₃^- < 44.3 mg/L and Group 2 with NO₃^- ≥ 44.3 mg/L. Results of t-tests indicate that groundwaters in Group 2 are significantly higher in water temperature and Ca concentration and significantly lower in F, As, Pb and Zn concentrations. It was also revealed that groundwaters Group 2 are closely linked to low pH, alkalinity, NH₄, NO₂, F, As, Fe, Mn, Pb and Zn levels, and high EC and water temperature. Background concentrations were estimated using the the BRIDGE method. The results depicted the higher levels in NO₃, NH₄, PO₄, As, Fe, Mn than the ones estimated by MOE and KECO (2018; 2019), which were prepared for the three catchment units in Boseong. The results of this study are believed to have more reliability because more data were used.

• Journal of Korean Society of Forest Science
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• v.111 no.3
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• pp.428-434
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• 2022

This study was conducted to derive the site index of forest productivity of Robinia pseudoacacia (honey plant) to characterize suitable planting sites and to investigate the effect of the site environmental factors on the site index using the quantification theory I method. The data used in the analysis were growth factors (stand age, dominant height, etc.) of the 6th national forest resources survey and various site environmental factors of a forest soil map (1:5,000). The average site index value of the R. pseudoacacia stand in Korea was 14 (range, 8 to 18). The environmental factors affecting the site index were parent rock, climatic zone, soil texture, local topography, and altitude. The accuracy of the estimation model using quantification theory I was only 33%. However, the correlation between the site index and the site environmental factors was statistically significant at the 1% level. Results of quantification analysis between site index and site environmental factors revealed that metamorphic and igneous rocks received high grades as parent rocks, climate zones received higher grades than central temperate zone, clay loam and silt loam received high grades in soil texture, and hillside received a high grade in local topography. Analysis of the partial correlation between site topographical factors and forest productivity (site index) found that soil class and altitude were partially correlated to x by 0.4129 and 0.4023, respectively, indicating that these factors are the most influential variables.

• Tunnel and Underground Space
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• v.33 no.1
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• pp.29-41
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• 2023

The brittleness of rocks plays an important role in determining the fragmentation and failure behavior of rock. However, there is still no standard method to evaluate the brittleness of rock, and previous studies have suggested the several definitions for estimation of brittleness of rock. Even in the process of mechanical rock excavation and drilling, the brittleness of rock is considered as an important property for evaluating the excavation efficiency of mechanical excavators or boreability of rock. The previous studies have been carried out to investigate the correlation between different brittleness of rock and cutting efficiency and boreability of rock. This study introduced a method for calculating the brittleness of rock from punch penetration test, and analyzed the correlation between the brittleness of rock calculated by the uniaxial compressive and Brazilian tensile strengths and that from punch penetration test. From the results of correlation analysis, the relationship between various brittleness was confirmed, and it was found that PSI and BI₃ showed a good correlation with the strength-based brittleness index. In addition, the results indicated that B₃ and B₄ are suitable to represent the brittleness of rock in the field of mechanical rock excavation.

• Journal of Internet Computing and Services
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• v.24 no.1
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• pp.61-69
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• 2023

Recently, as the fishery resources are depleted, expectations for productivity improvement by 'rearing fishery' in land farms are greatly rising. In the case of land farms, unlike ocean environments, it is easy to control and manage environmental and breeding factors, and has the advantage of being able to adjust production according to the production plan. On the other hand, unlike in the natural environment, there is a disadvantage in that operation costs may significantly increase due to the artificial management for fish growth. Therefore, profit maximization can be pursued by efficiently operating the farm in accordance with the planned target shipment. In order to operate such an efficient farm and nurture fish, an accurate growth prediction model according to the target fish species is absolutely required. Most of the growth prediction models are mainly numerical results based on statistical analysis using farm data. In this paper, we present a growth prediction model from a stochastic point of view to overcome the difficulties in securing data and the difficulty in providing quantitative expected values for inaccuracies that existing growth prediction models from a statistical point of view may have. For a stochastic approach, modeling is performed by introducing a Gaussian process regression method based on water temperature, which is the most important factor in positive growth. From the corresponding results, it is expected that it will be able to provide reference values for more efficient farm operation by simultaneously providing the average value of the predicted growth value at a specific point in time and the confidence interval for that value.

• Journal of Korean Society of Forest Science
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• v.112 no.3
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• pp.340-351
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• 2023

Landslides are major natural geological hazards that cause enormous property damage and human casualties annually. The vulnerability of mountainous areas to landslides is further exacerbated by the impacts of climate change. Soil depth is a crucial parameter in landslide and debris flow analysis, and plays an important role in the evaluation of watershed hydrological processes that affect slope stability. An accurate method of estimating soil depth is to directly investigate the soil strata in the field. However, this requires significant amounts of time and money; thus, numerous models for predicting soil depth have been proposed. However, they still have limitations in terms of practicality and accuracy. In this study, 71 seismic survey results were collected from domestic mountainous areas to estimate soil depth on hill slopes. Soil depth was estimated on the basis of a shear wave velocity of 700 m/s, and a database was established for slope angle, elevation, and soil depth. Consequently, the statistical characteristics of soil depth were analyzed, and the correlations between slope angle and soil depth, and between elevation and soil depth were investigated. Moreover, various soil depth prediction models based on slope angle were investigated, and corrected linear and exponential soil depth prediction models were proposed.

• International conference on construction engineering and project management
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• 2022.06a
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• pp.1243-1244
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• 2022

In recent years, the growing interest in off-site construction has led to factories scaling up their manufacturing and production processes in the construction sector. Consequently, continuous large-scale site monitoring in low-variability environments, such as prefabricated components production plants (precast concrete production), has gained increasing importance. Although many studies on computer vision-based site monitoring have been conducted, challenges for deploying this technology for large-scale field applications still remain. One of the issues is collecting and transmitting vast amounts of video data. Continuous site monitoring systems are based on real-time video data collection and analysis, which requires excessive computational resources and network traffic. In addition, it is difficult to integrate various object information with different sizes and scales into a single scene. Various sizes and types of objects (e.g., workers, heavy equipment, and materials) exist in a plant production environment, and these objects should be detected simultaneously for effective site monitoring. However, with the existing object detection algorithms, it is difficult to simultaneously detect objects with significant differences in size because collecting and training massive amounts of object image data with various scales is necessary. This study thus developed a large-scale site monitoring system using edge computing and a small-object detection system to solve these problems. Edge computing is a distributed information technology architecture wherein the image or video data is processed near the originating source, not on a centralized server or cloud. By inferring information from the AI computing module equipped with CCTVs and communicating only the processed information with the server, it is possible to reduce excessive network traffic. Small-object detection is an innovative method to detect different-sized objects by cropping the raw image and setting the appropriate number of rows and columns for image splitting based on the target object size. This enables the detection of small objects from cropped and magnified images. The detected small objects can then be expressed in the original image. In the inference process, this study used the YOLO-v5 algorithm, known for its fast processing speed and widely used for real-time object detection. This method could effectively detect large and even small objects that were difficult to detect with the existing object detection algorithms. When the large-scale site monitoring system was tested, it performed well in detecting small objects, such as workers in a large-scale view of construction sites, which were inaccurately detected by the existing algorithms. Our next goal is to incorporate various safety monitoring and risk analysis algorithms into this system, such as collision risk estimation, based on the time-to-collision concept, enabling the optimization of safety routes by accumulating workers' paths and inferring the risky areas based on workers' trajectory patterns. Through such developments, this continuous large-scale site monitoring system can guide a construction plant's safety management system more effectively.

• The Journal of Engineering Geology
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• v.32 no.1
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• pp.59-72
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• 2022

This study employed a 3-D numerical analysis based on the distinct element method to estimate the strength and deformability of a Cretaceous biotite granitic rock mass at Gijang, Busan, Korea. A workflow was proposed to evaluate the scale effect and the representative elementary volume (REV) of mechanical properties for fractured rock masses. Directional strength and deformability parameters such as block strength, deformation modulus, shear modulus, and bulk modulus were estimated for a discrete fracture network (DFN) in a cubic block the size of the REV. The size of the mechanical REV for fractured rock masses in the study area was determined to be a 15 m cube. The mean block strength and mean deformation modulus of the DFN cube block were found to be 52.8% and 57.7% of the intact rock's strength and Young's modulus, respectively. A constitutive model was derived for the study area that describes the linear-elastic and orthotropic mechanical behavior of the rock mass. The model is expected to help evaluate the stability of tunnels and underground spaces through equivalent continuum analysis.