• Parasites, Hosts and Diseases
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• v.59 no.1
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• pp.103-108
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• 2021

To date, there is no report on the genetic diversity of ticks in these regions. A total of 370 representative ticks from the south and east regions of Kazakhstan (SERK) and Xinjiang Uygur Autonomous Region (XUAR) were selected for molecular comparison. A fragment of the mitochondrial cytochrome c oxidase subunit I (cox1) gene, ranging from 631 bp to 889 bp, was used to analyze genetic diversity among these ticks. Phylogenetic analyses indicated 7 tick species including Hyalomma asiaticum, Hyalomma detritum, Hyalomma anatolicum, Dermacentor marginatus, Rhipicephalus sanguineus, Rhipicephalus turanicus and Haemaphysalis erinacei from the SERK clustered together with conspecific ticks from the XUAR. The network diagram of haplotypes showed that i) Hy. asiaticum from Almaty and Kyzylorda Oblasts together with that from Yuli County of XUAR constituted haplogroup H-2, and the lineage from Chimkent City of South Kazakhstan was newly evolved; and ii) the R. turanicus ticks sampled in Israel, Almaty, South Kazakhstan, Usu City, Ulugqat and Baicheng Counties of XUAR were derivated from an old lineage in Alataw City of XUAR. These findings indicate that: i) Hy. asiaticum, R. turanicus and Ha. erinacei shared genetic similarities between the SERK and XUAR; and ii) Hy. marginatum and D. reticulatus show differences in their evolution.

• Korean Journal of Remote Sensing
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• v.38 no.6_1
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• pp.1007-1014
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• 2022

Compact Advanced Satellite 500 (CAS500) can be used for various purposes, including vegetation, forestry, and agriculture fields. It is expected that it will be possible to acquire satellite images of various areas quickly. In order to use satellite images acquired through CAS500 in the agricultural field, it is necessary to develop a satellite image-based extraction technique for crop-cultivated areas.In particular, as research in the field of deep learning has become active in recent years, research on developing a deep learning model for extracting crop cultivation areas and generating training data is necessary. This manuscript classified the onion and garlic cultivation areas in Hapcheon-gun using PlanetScope satellite images and farm maps. In particular, for effective model learning, the model performance was analyzed according to the proportion of crop-cultivated areas. For the deep learning model used in the experiment, Fully Convolutional Densely Connected Convolutional Network (FC-DenseNet) was reconstructed to fit the purpose of crop cultivation area classification and utilized. As a result of the experiment, the ratio of crop cultivation areas in the training data affected the performance of the deep learning model.

• The Journal of Engineering Geology
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• v.32 no.4
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• pp.547-558
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• 2022

In August 2020, a debris flow occurred in Gokseon, Jeollanam-do, that resulted in the death of five residents. In this study area, high-resolution 0.03 m topographic information was generated through photogrammetry, and the amount of soil movement/loss was measured. In addition, sensitivity analysis was performed for flow depth, flow velocity, and debris flow area with the program Flo-2D using the difference in simulation parameter that discharge and topographic information. Wth increasing debris flow input discharge, increases were seen in flow depth, flow velocity, and debris flow area, as ell as in the gap in results from high-resolution topographic information and low-resolution topographic information. Also, when high-resolution topographic information was used, the results were similar to the actual (measured) flow direction of the debris flow. Therefore, the application of high-resolution topographic information increases the accuracy of the debris flow analysis results compared with low-resolution information. Results could be further imporved in the future by considering geological information such as yield stress and viscosity.

• International conference on construction engineering and project management
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• 2022.06a
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• pp.443-449
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• 2022

With climate change and the global population growth, the frequency and scope of wildfires are constantly increasing, which threatened people's lives and property. For example, according to California Department of Forestry and Fire Protection, in 2020, a total of 9,917 incidents related to wildfires were reported in California, with an estimated burned area of 4,257,863 acres, resulting in 33 fatalities and 10,488 structures damaged or destroyed. At the same time, the ongoing development of technology provides new tools to simulate and analyze the spread of wildfires. How to use new technology to reduce the losses caused by wildfire is an important research topic. A potentially feasible strategy is to simulate and analyze the spread of wildfires through computing technology to explore the impact of different factors (such as weather, terrain, etc.) on the spread of wildfires, figure out how to take preemptive/responsive measures to minimize potential losses caused by wildfires, and as a result achieve better management support of wildfires. In preparation for pursuing these goals, the authors used a powerful computing framework, Spark, developed by the Commonwealth Scientific and Industrial Research Organization (CSIRO), to study the effects of different weather factors (wind speed, wind direction, air temperature, and relative humidity) on the spread of wildfires. The test results showed that wind is a key factor in determining the spread of wildfires. A stable weather condition (stable wind and air conditions) is beneficial to limit the spread of wildfires. Joint consideration of weather factors and environmental obstacles can help limit the threat of wildfires.

• Asian-Australasian Journal of Animal Sciences
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• v.28 no.2
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• pp.280-289
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• 2015

Anaerobic digestion is an efficient and renewable energy technology that can produce biogas from a variety of biomasses such as animal manure, food waste and plant residues. In developing countries this technology is widely used for the production of biogas using local biomasses, but there is little information about the value of these biomasses for energy production. This study was therefore carried out with the objective of estimating the biogas production potential of typical Vietnamese biomasses such as animal manure, slaughterhouse waste and plant residues, and developing a model that relates methane ($CH_4$) production to the chemical characteristics of the biomass. The biochemical methane potential (BMP) and biomass characteristics were measured. Results showed that piglet manure produced the highest $CH_4$ yield of 443 normal litter (NL) $CH_4kg^{-1}$ volatile solids (VS) compared to 222 from cows, 177 from sows, 172 from rabbits, 169 from goats and 153 from buffaloes. Methane production from duckweed (Spirodela polyrrhiza) was higher than from lawn grass and water spinach at 340, 220, and 110.6 NL $CH_4kg^{-1}$ VS, respectively. The BMP experiment also demonstrated that the $CH_4$ production was inhibited with chicken manure, slaughterhouse waste, cassava residue and shoe-making waste. Statistical analysis showed that lipid and lignin are the most significant predictors of BMP. The model was developed from knowledge that the BMP was related to biomass content of lipid, lignin and protein from manure and plant residues as a percentage of VS with coefficient of determination (R-square) at 0.95.This model was applied to calculate the $CH_4$ yield for a household with 17 fattening pigs in the highlands and lowlands of northern Vietnam.

• Korean Journal of Remote Sensing
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• v.37 no.5_1
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• pp.1029-1042
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• 2021

As part of the next-generation Compact Advanced Satellite 500 (CAS500) project, CAS500-4 is scheduled to be launched in 2025 focusing on the remote sensing of agriculture and forestry. To obtain quantitative information on vegetation from satellite images, it is necessary to acquire surface reflectance through atmospheric correction. Thus, it is essential to develop an atmospheric correction method suitable for CAS500-4. Since the absorption and scattering characteristics in the atmosphere vary depending on the wavelength, it is needed to analyze the sensitivity of atmospheric correction parameters such as aerosol optical depth (AOD) and water vapor (WV) considering the wavelengths of CAS500-4. In addition, as CAS500-4 has only five channels (blue, green, red, red edge, and near-infrared), making it difficult to directly calculate key parameters for atmospheric correction, external parameter data should be used. Therefore, thisstudy performed a sensitivity analysis of the key parameters (AOD, WV, and O₃) using the simulated images based on Sentinel-2 satellite data, which has similar wavelength specifications to CAS500-4, and examined the possibility of using the products of GEO-KOMPSAT-2A (GK2A) as atmospheric parameters. The sensitivity analysisshowed that AOD wasthe most important parameter with greater sensitivity in visible channels than in the near-infrared region. In particular, since AOD change of 20% causes about a 100% error rate in the blue channel surface reflectance in forests, a highly reliable AOD is needed to obtain accurate surface reflectance. The atmospherically corrected surface reflectance based on the GK2A AOD and WV was compared with the Sentinel-2 L2A reflectance data through the separability index of the known land cover pixels. The result showed that two corrected surface reflectance had similar Seperability index (SI) values, the atmospheric corrected surface reflectance based on the GK2A AOD showed higher SI than the Sentinel-2 L2A reflectance data in short-wavelength channels. Thus, it is judged that the parameters provided by GK2A can be fully utilized for atmospheric correction of the CAS500-4. The research findings will provide a basis for atmospheric correction of the CAS500-4 in the future.

• The Journal of Engineering Geology
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• v.28 no.1
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• pp.47-59
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• 2018

At present, there has been a wide range of studies on debris flow in Korea, more specifically, on rainfall characteristics that trigger debris flow including rainfall intensity, rainfall duration, and preceding rainfall. the prediction of landslide / debris flow relies on the criteria for landslide watch and warning by the Korea Forest Service (KFS, 2012). Despite this, it has been found that most incidents of debris flow were caused by rainfall above the level of landslide watch, maximum hourly rainfall, extensive damage was caused even under the watch level. Under these circumstances, we calculated a rainfall triggering index (RTI) using the main factors that trigger debris flow-rainfall, rainfall intensity, and cumulative rainfall-to design a more sophisticated watch / warning criteria than those by the KFS. The RTI was classified into attention, caution, alert, and evacuation, and was assessed through the application of two debris flow incidents that occurred in Umyeon Mountain, Seoul, and Cheongju, Inje, causing serious damage and casualties. Moreover, we reviewed the feasibility of the RTI by comparing it with the KFS's landslide watch / warning criteria (KFS, 2012).

• Korean Journal of Remote Sensing
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• v.38 no.6_2
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• pp.1743-1747
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• 2022

Sentinel-2 can be used as proxy data for the Korean Compact Advanced Satellite 500-4 (CAS500-4), also known as Agriculture and Forestry Satellite, in terms of spectral wavelengths and spatial resolution. This letter examined cloud detection for later use in the CAS500-4 based on deep learning technologies. DeepLabV3+, a traditional Convolutional Neural Network (CNN) model, and Shifted Windows (Swin) Transformer, a state-of-the-art (SOTA) Transformer model, were compared using 22,728 images provided by Radiant Earth Foundation (REF). Swin Transformer showed a better performance with a precision of 0.886 and a recall of 0.875, which is a balanced result, unbiased between over- and under-estimation. Deep learning-based cloud detection is expected to be a future operational module for CAS500-4 through optimization for the Korean Peninsula.

• The Journal of Engineering Geology
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• v.30 no.3
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• pp.315-325
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• 2020

This study was performed to develop a model to predict landslides and determine the variable importance of landslides susceptibility factors based on the probabilistic prediction of landslides occurring on slopes along the road. Field survey data of 30,615 slopes from 2007 to 2020 in Korea were analyzed to develop a landslide prediction model. Of the total 131 variable factors, 17 topographic factors and 114 geological factors (including 89 bedrocks) were used to predict landslides. Automated machine learning (AutoML) was used to classify landslides and non-landslides. The verification results revealed that the best model, an extremely randomized tree (XRT) with excellent predictive performance, yielded 83.977% of prediction rates on test data. As a result of the analysis to determine the variable importance of the landslide susceptibility factors, it was composed of 10 topographic factors and 9 geological factors, which was presented as a percentage for each factor. This model was evaluated probabilistically and quantitatively for the likelihood of landslide occurrence by deriving the ranking of variable importance using only on-site survey data. It is considered that this model can provide a reliable basis for slope safety assessment through field surveys to decision-makers in the future.

• Journal of Korea Foresty Energy
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• v.25 no.2
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• pp.1-8
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• 2006

The physical, mechanical and chemical properties of old and new Korean red pine (Pinus densiflora) were analyzed. The old woods were from dismantled timbers of Bonjungsa temple. The crystallized resin in the latewood was observed by microscopic analysis. Also, reduction of specific gravity, occurrence of microscopic cleavage of tracheid was observed in the old wood. The angle of microscopic cleavage of tracheid is estimated with the same angle of micro-fibril angle of 52 layer. The bending, compression and shear strength of old world were decreased about 35-27% than those of new wood. Dynamic modulus of elasticity measured by ultrasonic nondestructive test has the tendency of reducing by the time elapse of the wood usage. Therefore, deterioration of wood could be measured by reduction of specific gravity and dynamic MOE. The static MOE and mechanical properties of old wood could be predictable by measuring dynamic MOE in the longitudinal direction. Extractives of the old wood in 1-% NaOH solution are larger quantity than new wood. Therefore the decay of the wood could be evaluated by analyzing the chemical compound, especially 1-% NaOH solution. The results of this research could be used for understanding and prediction of the changing properties with elapsing time of wood.