• Korean Journal of Remote Sensing
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• v.37 no.5_1
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• pp.903-916
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• 2021

An area's topography refers to the shape of the earth's surface, described by its elevation, slope, and aspect, among other features. The topographical conditions determine energy flowsthat move water and energy from higher to lower elevations, such as how much solar energy will be received and how much wind or rain will affect it. Another common factor, the topographic wetness index (TWI), is a calculation in digital elevation models of the tendency to accumulate water per slope and unit area, and is one of the most widely referenced hydrologic topographic factors, which helps explain the location of forest vegetation. Analyses of topographical factors can be calculated using a geographic information system (GIS) program based on digital elevation model (DEM) data. Recently, a large number of free open source software (FOSS) GIS programs are available and developed for researchers, industries, and governments. FOSS GIS programs provide opportunitiesfor flexible algorithms customized forspecific user needs. The majority of biodiversity in island areas exists at about 20% higher elevations than in land ecosystems, playing an important role in ecological processes and therefore of high ecological value. However, island areas are vulnerable to disturbances and damage, such as through climate change, environmental pollution, development, and human intervention, and lacks systematic investigation due to geographical limitations (e.g. remoteness; difficulty to access). More than 4,000 of Korea's islands are within a few hours of its coast, and 88% are uninhabited, with 52% of them forested. The forest ecosystems of islands have fewer encounters with human interaction than on land, and therefore most of the topographical conditions are formed naturally and affected more directly by weather conditions or the environment. Therefore, the analysis of forest topography in island areas can be done more precisely than on its land counterparts, and therefore has become a major focus of attention in Korea. This study is focused on calculating the performance of different topographical factors using FOSS GIS programs. The test area is the island forests in Korea's south and the DEM of the target area was processed with GRASS GIS and SAGA GIS. The final slopes and TWI maps were produced as comparisons of the differences between topographic factor calculations of each respective FOSS GIS program. Finally, the merits of each FOSS GIS program used to calculate the topographic factors is discussed.

• Journal of the Korean association of regional geographers
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• v.18 no.4
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• pp.473-493
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• 2012

Malaria infection is sensitively influenced by regional meteorological conditions along with global climate change. Remote sensing techniques have become an important tool for extraction of climatic and environmental factors, including rainfall, temperature, surface water, soil moisture, and land use, which are directly linked to the habitat qualities of malaria mosquitoes. Improvement of sensor fidelity with higher spatial and spectral resolution, new multinational sensor development, and decreased data cost have nurtured diverse remote sensing applications in malaria research. In 1984, eradication of endemic malaria was declared in Korea, but reemergence of malaria was reported in mid-1990s. Considering constant changes in malaria cases since 2000, the epidemiological management of the disease needs careful monitoring. Geographically, northmost counties neighboring North Korea have been ranked high in the number of malaria cases. High infection rates in these areas drew special attention and led to a hypothesis that malaria dispersion in these border counties might be caused by north-origin, malaria-bearing adult mosquitoes. Habitat conditions of malaria mosquitoes are important parameters for prediction of the vector abundance. However, it should be realized that malaria infection and transmission is a complex mechanism, where non-environmental factors, including human behavior, demographic structure, landscape structure, and spatial relationships between human residence and the vector habitats, are also significant considerations in the framework of medical geography.

• Journal of the Korean Association of Geographic Information Studies
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• v.23 no.3
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• pp.26-67
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• 2020

Synthetic Aperture Radar(SAR) is able to photograph the earth's surface regardless of weather conditions, day and night. Because of its possibility to search for hydrological factors such as soil moisture and groundwater, and its importance is gradually increasing in the field of water resources. SAR began to be mounted on satellites in the 1970s, and about 15 or more satellites were launched as of 2020, which around 10 satellites will be launched within the next 5 years. Recently, various types of SAR technologies such as enhancement of observation width and resolution, multiple polarization and multiple frequencies, and diversification of observation angles were being developed and utilized. In this paper, a brief history of the SAR system, as well as studies for estimating soil moisture and hydrological components were investigated. Up to now hydrological components that can be estimated using SAR satellites include soil moisture, subsurface groundwater discharge, precipitation, snow cover area, leaf area index(LAI), and normalized difference vegetation index(NDVI) and among them, soil moisture is being studied in 17 countries in South Korea, North America, Europe, and India by using the physical model, the IEM(Integral Equation Model) and the artificial intelligence-based ANN(Artificial Neural Network). RADARSAT-1, ENVISAT, ASAR, and ERS-1/2 were the most widely used satellite, but the operation has ended, and utilization of RADARSAT-2, Sentinel-1, and SMAP, which are currently in operation, is gradually increasing. Since Korea is developing a medium-sized satellite for water resources and water disasters equipped with C-band SAR with the goal of launching in 2025, various hydrological components estimation researches using SAR are expected to be active.

• Proceedings of the Korea Water Resources Association Conference
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• 2006.05a
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• pp.333-337
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• 2006

The purpose of this paper is to present a strategic approach to selecting prior areas of soil erosion to be examined for effective soil conservation planning and management, in conjunction with remote sensing data and GIS skill for surface characteristics. To do this, two basins are selected: Andong and Imha basin. Geographically one is in the vicinity of the other but turbidity in the main reservoir of each basin is quite different. it is important to clarify general behavior of soil erosion driven by rainfall event for both basins for further understanding and effective soil conservation planning and management. Also, Both basins are divided into several sub-basins and the severity of soil loss is intensively investigated to identify areas with high erosion potential for each sub-basin so that the efficiency of soil conservation program may increase. Especially, this study analyzed soil erodibility factor(K), topographic factor(LS), cover management factor(C) and soil erosion; 3 sub-basins for Andong basin (up-, mid-, downstream) and 6 sub-basins for Imha basin (up-, mid-, and downstream for two tributaries) because Imha basin consists of two tributaries (Banyeon and Yongjeon river). The approach suggested herein will provide a guideline for choosing prior areas to be examined and managed for soil conservation planning.

• Proceedings of the Korea Water Resources Association Conference
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• 2015.05a
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• pp.294-294
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• 2015

하천과 해양에서 발생한 수질오염은 특성상 유속의 흐름에 따라 광범위하며 급속도로 퍼져나가기 때문에 이를 효율적으로 유지, 관리하기 위해서는 오염인자들에 대한 모니터링이 수행되어야 한다. 원격탐사 기술을 이용한 하천의 수질측정은 대규모지역으로 분포해있는 수질농도의 변화양상을 시 공간적으로 모니터링 하는 것이 가능하게 할 뿐 아니라, 사람이 접근하기 어려운 지역에는 직접취수를 하지 않음으로써 기존의 수질측정방법들에 비해 편의성을 높여 시간적, 경제적 측면에서 효율적이다. 이에 본 연구에서는 최근 수질오염이 심화되고 있는 낙동강유역을 대상으로 인공위성 이미지영상을 이용하여 수질인자들의 농도측정을 수행하였다. 연구를 위해 사용된 인공위성은 NASA와 USGS가 공동으로 운용중인 Landsat 8 인공위성이다. Landsat 8의 11개 band 중 band2(Blue), band3(Green), band4(Red), band5(Near Infrared)를 사용하여 실제로 측정된 지점자료와 인공위성자료간의 상관관계를 규명하였다. 사용된 인공위성자료는 지점자료 날짜를 포함하는 총 4개의 연구날짜(2013/10/27, 2013/11/12, 2014/04/14, 2014/05/16)에 해당하는 위성이미지영상이다. Pearson상관계수를 통한 밴드와 수질인자간의 상관 결과, 본 연구지역에서는 $0.85-0.88{\mu}m$(band5)의 파장영역에서 클로로필-a와 부유물질이 가장 민감하게 반응함을 알 수 있었다. 두 수질인자들은 band2, band3, band4에서도 비교적 높은 상관성을 보였으며, 이를 근거로 band combination, band ratio를 통해 클로로필-a와 부유물질의 회귀모델식을 유도하였다. 각각의 회귀모델식은 실제 측정된 데이터들과 비교 검증을 통해 4개의 연구기간 중 2013년 10월 27일, 2014년 5월 16일에 대해서 클로로필-a와 부유물질의 공간적인 분포양상을 시각적으로 도시화하였다.

• Proceedings of the Korea Water Resources Association Conference
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• 2020.06a
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• pp.248-248
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• 2020

토양수분은 증발산, 유출, 침투 등 물수지 요소들과 밀접한 연관이 있는 주요한 변수 중에 하나이다. 토양수분의 정도는 토양의 특성, 토지이용 형태, 기상 상태 등에 따라 공간적으로 상이하며, 특히 기상 상태에 따라 시간적 변동성을 보이고 있다. 기존 토양수분 측정은 토양시료 채취를 통한 실내 실험 측정과 측정 장비를 통한 현장 조사 방법이 있으나 시간적, 경제적 한계점이 있으며, 원격탐사 기법은 공간적으로 넓은 범위를 포함하지만 시간 해상도가 낮은 단점이 있다. 또한, 모델링을 통한 토양수분 예측 기술은 전문적인 지식이 요구되며, 복잡한 입력자료의 구축이 요구된다. 최근 머신러닝 기법은 수많은 자료 학습을 통해 사용자가 원하는 출력값을 도출하는데 널리 활용되고 있다. 이에 본 연구에서는 토양수분과 연관된 다양한 기상 인자들(강수량, 풍속, 습도 등)을 활용하여 머신러닝기법의 반복학습을 통한 토양수분의 예측 가능성을 분석하고자 한다. 이를 위해 시공간적으로 토양수분 실측 자료가 잘 구축되어 있는 청미천과 설마천 유역을 대상으로 머신러닝 기법을 적용하였다. 두 대상지에서 2008년~2012년 수문자료를 확보하였으며, 기상자료는 기상자료개방포털과 WAMIS를 통해 자료를 확보하였다. 토양수분 자료와 기상자료를 머신러닝 알고리즘을 통해 학습하고 2012년 기상 자료를 바탕으로 토양수분을 예측하였다. 사용되는 머신러닝 기법은 의사결정 나무(Decision Tree), 신경망(Multi Layer Perceptron, MLP), K-최근접 이웃(K-Nearest Neighbors, KNN), 서포트 벡터 머신(Support Vector Machine, SVM), 랜덤 포레스트(Random Forest), 그래디언트 부스팅 (Gradient Boosting)이다. 토양수분과 기상인자 간의 상관관계를 분석하기 위해 히트맵(Heat Map)을 이용하였다. 히트맵 분석 결과 토양수분의 시간적 변동은 다양한 기상 자료 중 강수량과 상대습도가 가장 큰 영향력을 보여주었다. 또한 다양한 기상 인자 기반 머신러닝 기법 적용 결과에서는 두 지역 모두 신경망(MLP) 기법을 제외한 모든 기법이 전반적으로 실측값과 유사한 형태를 보였으며 비교 그래프에서도 실측값과 예측 값이 유사한 추세를 나타냈다. 따라서 상관관계있는 과거 기상자료를 통해 머신러닝 기법 기반 토양수분의 시간적 변동 예측이 가능할 것으로 판단된다.

• Korean Journal of Remote Sensing
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• v.27 no.2
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• pp.191-201
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• 2011

Soybean has widely grown for its edible bean which has numerous uses. Microwave remote sensing has a great potential over the conventional remote sensing with the visible and infrared spectra due to its all-weather day-and-night imaging capabilities. In this investigation, a ground-based polarimetric scatterometer operating at multiple frequencies was used to continuously monitor the crop conditions of a soybean field. Polarimetric backscatter data at L, C, and X-bands were acquired every 10 minutes on the microwave observations at various soybean stages. The polarimetric scatterometer consists of a vector network analyzer, a microwave switch, radio frequency cables, power unit and a personal computer. The polarimetric scatterometer components were installed inside an air-conditioned shelter to maintain constant temperature and humidity during the data acquisition period. The backscattering coefficients were calculated from the measured data at incidence angle $40^{\circ}$ and full polarization (HH, VV, HV, VH) by applying the radar equation. The soybean growth data such as leaf area index (LAI), plant height, fresh and dry weight, vegetation water content and pod weight were measured periodically throughout the growth season. We measured the temporal variations of backscattering coefficients of the soybean crop at L, C, and X-bands during a soybean growth period. In the three bands, VV-polarized backscattering coefficients were higher than HH-polarized backscattering coefficients until mid-June, and thereafter HH-polarized backscattering coefficients were higher than VV-, HV-polarized back scattering coefficients. However, the cross-over stage (HH > VV) was different for each frequency: DOY 200 for L-band and DOY 210 for both C and X-bands. The temporal trend of the backscattering coefficients for all bands agreed with the soybean growth data such as LAI, dry weight and plant height; i.e., increased until about DOY 271 and decreased afterward. We plotted the relationship between the backscattering coefficients with three bands and soybean growth parameters. The growth parameters were highly correlated with HH-polarization at L-band (over r=0.92).

• The Journal of Engineering Geology
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• v.25 no.3
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• pp.349-356
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• 2015

Although geophysical methods are useful and generally provide valuable information about the subsurface, it is important to recognize their limitations. A common limitation is the lack of sufficient contrast in physical properties between different layers. Thus, multiple methods are commonly used to best constrain the physical properties of different layers and interpret each section individually. Ground penetrating radar (GPR) and shallow seismic reflection (SSR) methods, used for shallow and very shallow subsurface imaging, respond to dielectric and velocity contrasts between layers, respectively. In this study, we merged GPR and SSR data from a test site within the Cheongui granitic mass, where the water table is ~3 m deep all year. We interpreted the data in combination with field observations and existing data from drill cores and well logs. GPR and SSR reflections from the tops of the sand layer, water table, and weathered and soft rocks are successfully mapped in a single section, and they correlate well with electrical resistivity data and SPS (suspension PS) well-logging profiles. In addition, subsurface interfaces in the integrated section correlate well with S-wave velocity structures from multi-channel analysis shear wave (MASW) data, a method that was recently developed to enhance lateral resolution on the basis of CMP (common midpoint) cross-correlation (CMPCC) analysis.

• Geophysics and Geophysical Exploration
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• v.26 no.4
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• pp.185-198
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• 2023

In marine seismic surveys, the inevitable occurrence of trace gaps in the near offset resulting from geometrical differences between sources and receivers adversely affects subsequent seismic data processing and imaging. The absence of data in the near-offset region hinders accurate seismic imaging. Therefore, reconstructing the missing near-offset information is crucial for mitigating the influence of seismic multiples, particularly in the case of offshore surveys where the impact of multiple reflections is relatively more pronounced. Conventionally, various interpolation methods based on the Radon transform have been proposed to address the issue of the nearoffset data gap. However, these methods have several limitations, leading to the recent emergence of deep-learning (DL)-based approaches as alternatives. In this study, we conducted an in-depth analysis of two representative DL-based studies to scrutinize the challenges that future studies on near-offset interpolation must address. Furthermore, through field data experiments, we precisely analyze the limitations encountered when applying previous DL-based trace interpolation techniques to near-offset situations. Consequently, we suggest that near-offset data gaps must be approached by extrapolation rather than interpolation.

• Proceedings of the Korea Water Resources Association Conference
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• 2005.05b
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• pp.60-64
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• 2005

강우-유출 모형을 수행하기 위해서는 지표면의 성질을 나타내는 보조 자료로서 식생피복자료, 토양분포, DEM 등이 주어지는데, 이러한 인자들이 흐름을 지배하게 되므로 중요하다. 그러므로 강우-유출 모형에서는 지표에서의 식생을 적절하게 나타내 줄 필요가 있는데, 최근의 원격 탐사 기술이 하나의 대안으로 제시되어 왔으며 식생 정보는 2차원 디지털 지도로 작성되어 수치모형에서는 이 디지털지도로부터 조도계수의 값을 전환하는 다단계 방법을 이용하여 오고 있다. 그러나 강우-유출 모형의 격자 간격이 식생 피복을 나타내기 위한 디지털 지도의 격자 간격과 항상 일치하는 것이 아니고 디지털 지도의 격자 간격이 일반적으로 더 세밀하므로 강우-유출 모형에서 디지털 지도를 이용하기 위해서는 적당한 방법의 스케일 확장 (up-scaling)이 필요하며, 이 연구에서는 질량 보존의 법칙을 사용하여 이론적 기반에 근거한 하나의 전략적인 방법이 제시된다. 이 연구의 목적은 강우-유출 모형의 수행 시 이질성을 보이는 지표면에서 식생과 관련된 매개변수를 통합/확장 처리하는 하나의 기법을 대기모형에서 이미 제시된 방법을 모사하여 제시함으로써 이질성을 보이는 지표면의 처리할 때 방향을 설정해 주고자함이다.