• Geophysics and Geophysical Exploration
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• v.23 no.4
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• pp.230-242
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• 2020

We performed a magnetotelluric (MT) survey to delineate the geological structures below the depth of 20 km in the Gyeongju area where an earthquake with a magnitude of 5.8 occurred in September 2016. The measured MT data were severely distorted by electrical noise caused by subways, power lines, factories, houses, and farmlands, and by vehicle noise from passing trains and large trucks. Using machine-learning methods, we classified the MT time series data obtained near the railway and highway into two groups according to the inclusion of traffic noise. We applied three schemes, stochastic gradient descent, support vector machine, and random forest, to the time series data for the highspeed train noise. We formulated three datasets, Hx, Hy, and Hx & Hy, for the time series data of the large truck noise and applied the random forest method to each dataset. To evaluate the effect of removing the traffic noise, we compared the time series data, amplitude spectra, and apparent resistivity curves before and after removing the traffic noise from the time series data. We also examined the frequency range affected by traffic noise and whether artifact noise occurred during the traffic noise removal process as a result of the residual difference.

• Journal of Environmental Science International
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• v.17 no.2
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• pp.257-267
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• 2008

Groundwater recharge characteristics in a fractured granite area, Mt. Geumjeong, Korea. was interpreted using bedrock groundwater and wet-land water data. Time series analysis using autocorreclation, cross-correlation and spectral density was conducted for characterizing water level variation and recharge rate in low water and high water seasons. Autocorrelation analysis using water levels resulted in short delay time with weak linearity and memory. Cross-correlation function from cross-correlation analysis was lower in the low water season than the high water season for the bedrock groundwater. The result of water level decline analysis identified groundwater recharge rate of about 11% in the study area.

• Journal of the Korean Geographical Society
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• v.47 no.6
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• pp.825-835
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• 2012

The aim of this research is to develope technique and mapping for detecting distribution of vegetation dieback areas in the subalpine zone of Mt. Baekdu. A detection technique developed the rule-based model using MODIS images. Dieback areas could be classified as 4 categories of initial dieback, middle dieback, and end dieback by pruning stages of leaves. Dieback area was $28km^2$ from year 2001 to year 2006, intial dieback was $16km^2$, middle dieback was $10km^2$, and end dieback was $2km^2$ by the each stage. Dieback area was $35km^2$ from year 2006 to year 2011. Total area was $35km^2$ from year 2001 to year 2011, areas of middle dieback and end dieback were increased. The research method for this study may help to support in application with preliminary detection of dieback areas in the mountains by the global warming.

• The Journal of Engineering Geology
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• v.23 no.4
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• pp.409-425
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• 2013

This study presents a prediction of a time-series of the area inundated by effluent from Heavenly Lake caused by ground behavior prior to a volcanic eruption. A GIS-based hydrological algorithm that considers the multi-flow direction of effluent, the absorption and storage capacity of the ground soil, the storage volume of the basin or the depression terrain, was developed. To analyze the propagation pattern, four hypothetical collapse zones on the cheonji ground were set, considering the topographical characteristics and distributions of volcanic rocks at Mt. Baekdu. The results indicate that at 3 hours after collapse, for both scenarios 1 and 2 (collapses of the entire/southern boundary of cheonji), a flood hazard exists for villages in China, but not for those on the North Korean side of the mountain, due to the topographical characteristics of Mt. Baekdu. It is predicted that villages in both North Korea and China would be significantly damaged by flood inundation at 3 hours elapsed time for both scenarios 3 and 4 (collapses on the southern boundary of cheonji and on the southeastern-peak area).

• Journal of Korean Society of Forest Science
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• v.77 no.3
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• pp.322-330
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• 1988

This study was analyzed the behavior of recreational use through interviewing visitors with the questionnaire (1986.11-1987.9) in Mt. Kyeryong National Park. The number of visitors have been forecasted by tune series data of the past number of visitors, population, GNP, and number of cars (1974-1986) in korea. The results of the study can be summarized as follows : 1) Visitor's subjective evaluation about recreational environment evaluated to be fair in Mt. Kyeryong National Park. 2) They preferred natural forest resources to historic remains, tourist facility, etc.. 3) Number of participation was mostly once or five times over. 4) Visitors were affirmative to re-visit to the Mt. Kyeryong National Park. 5) Most of visitors stay for one day. 6) The most suitable estimated user regression model was : Y=-5753.7350+0.1726 Pop. -0.6564 NO. of Car. According to this equation, the total number of visitors will he increased by 3% per year from 1,023 thousands people in 1987 to 1,698 thousands in 2000.

• Korean Journal of Ecology and Environment
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• v.47 no.3
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• pp.186-193
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• 2014

This study aimed to analyze spatio-temporal trends of phenological characteristics in South Korea by using MODIS EVI. For the phenology analysis, we had applied double logistic function to MODIS time-series data. Our results showed that starting date of phenology seems to have a tendency along with latitudinal trends. Starting date of phenology of Jeju Island and Mt. Sobeak went back for 0.38, 0.174 days per year, respectively whereas, Mt. Jiri and Mt. Seolak went forward for 0.32 days, 0.239 days and 0.119 days, respectively. Our results exhibited the fluctuation of plant phonological season rather than the change of phonological timing and season. Starting date of plant phenology by spatial distribution revealed tendency that starting date of mountain area was late, and basin and south foot of mountain was fast. In urban ares such as Seoul metropolitan, Masan, Changwon, Milyang, Daegu and Jeju, the phonological starting date went forward quickly. Pheonoligcal attributes such as starting date and leaf fall in urban areas likely being affected from heat island effect and related warming. Our study expressed that local and regional monitoring on phonological events and changes in Korea would be possible through MODIS data.

• Korean Journal of Agricultural and Forest Meteorology
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• v.9 no.4
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• pp.247-259
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• 2007

In this study, we examined the flux of carbon and water using an eco-hydrological model, Regional Hydro-Ecologic Simulation System (RHESSys). Our purposes were to develop a set of parameters optimized for a well-designed experimental watershed (Gwangneung Research Watershed, GN) and then, to test suitability of the parameters for predicting carbon and water fluxes of other watershed with different regimes of climate, topography, and vegetation structure (i.e Gangseonry Watershed in Mt. Jumbong, GS). Field datasets of stream flow, soil water content (SWC), and wood biomass product (WBP) were utilized for model parameterization and validation. After laborious parameterization processes, RHESSys was validated with the field observations from the GN watershed. The parameter set identified at the GN watershed was then applied to the GS watershed in Mt. Jumbong, which resulted in good agreement for SWC but poor predictability for WBP. Our study showed that RHESSys simulated reliable SWC at the GS by adjusting site-specific porosity only. In contrast, vegetation productivity would require more rigorous site-specific parameterization and hence, further study is necessary to identify primary field ecophysiological variables for enhancing model parameterization and application to multiple watersheds.

• Korean Journal of Remote Sensing
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• v.28 no.4
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• pp.409-420
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• 2012

Since the Industrial Revolution, CO2 levels have been increasing with climate change. In this study, Analyze time-series changes in snow cover quantitatively and predict the vanishing point of snow cover statistically using remote sensing. The study area is Mt. Kilimanjaro, Tanzania. 23 image data of Landsat-5 TM and Landsat-7 ETM+, spanning the 27 years from June 1984 to July 2011, were acquired. For this study, first, atmospheric correction was performed on each image using the COST atmospheric correction model. Second, the snow cover area was extracted using the NDSI (Normalized Difference Snow Index) algorithm. Third, the minimum height of snow cover was determined using SRTM DEM. Finally, the vanishing point of snow cover was predicted using the trend line of a linear function. Analysis was divided using a total of 23 images and 17 images during the dry season. Results show that snow cover area decreased by approximately $6.47km^2$ from $9.01km^2$ to $2.54km^2$, equivalent to a 73% reduction. The minimum height of snow cover increased by approximately 290 m, from 4,603 m to 4,893 m. Using the trend line result shows that the snow cover area decreased by approximately $0.342km^2$ in the dry season and $0.421km^2$ overall each year. In contrast, the annual increase in the minimum height of snow cover was approximately 9.848 m in the dry season and 11.251 m overall. Based on this analysis of vanishing point, there will be no snow cover 2020 at 95% confidence interval. This study can be used to monitor global climate change by providing the change in snow cover area and reference data when studying this area or similar areas in future research.