• The Journal of Engineering Geology
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• v.33 no.3
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• pp.371-388
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• 2023

A stratum with a complex composition and a distributed low-permeability soil layer is difficult to remediate quickly because the soil remediation does not proceed easily. For efficient purification, the permeability should be improved and the soil remediation agent (H₂O₂) should be injected into the contaminated section to make sufficient contact with the TPH (Total petroleum hydrocarbons). This study analyzed a method for crack formation and effective delivery of the soil remediation agent based on pneumatic fracturing, plasma blasting, and vacuum suction (the PPV method) and compared its improvement effect relative to chemical oxidation. A demonstration test confirmed the effective delivery of the soil remediation agent to a site contaminated with TPH. The injection amount and injection time were monitored to calculate the delivery characteristics and the range of influence, and electrical resistivity surveying qualitatively confirmed changes in the underground environment. Permeability tests also evaluated and compared the permeability changes for each method. The amount of soil remediation agent injected was increased by about 4.74 to 7.48 times in the experimental group (PPV method) compared with the control group (chemical oxidation); the PPV method allowed injection rates per unit time (L/min) about 5.00 to 7.54 times quicker than the control method. Electrical resistivity measurements assessed that in the PPV method, the diffusion of H₂O₂2 and other fluids to the surface soil layer reduced the low resistivity change ratio: the horizontal change ratio between the injection well and the extraction well decreased the resistivity by about 1.12 to 2.38 times. Quantitative evaluation of hydraulic conductivity at the end of the test found that the control group had 21.1% of the original hydraulic conductivity and the experimental group retained 81.3% of the initial value, close to the initial permeability coefficient. Calculated radii of influence based on the survey results showed that the results of the PPV method were improved by 220% on average compared with those of the control group.

• Korean Journal of Remote Sensing
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• v.39 no.6_1
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• pp.1341-1352
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• 2023

The normalized difference vegetation index (NDVI) derived from satellite images is a crucial tool to monitor forests and agriculture for broad areas because the periodic acquisition of the data is ensured. However, optical sensor-based vegetation indices(VI) are not accessible in some areas covered by clouds. This paper presented a synthetic aperture radar (SAR) based approach to retrieval of the optical sensor-based NDVI using machine learning. SAR system can observe the land surface day and night in all weather conditions. Radar vegetation indices (RVI) from the Sentinel-1 vertical-vertical (VV) and vertical-horizontal (VH) polarizations, surface elevation, and air temperature are used as the input features for an automated machine learning (AutoML) model to conduct the gap-filling of the Sentinel-2 NDVI. The mean bias error (MAE) was 7.214E-05, and the correlation coefficient (CC) was 0.878, demonstrating the feasibility of the proposed method. This approach can be applied to gap-free nationwide NDVI construction using Sentinel-1 and Sentinel-2 images for environmental monitoring and resource management.

• Korean Journal of Remote Sensing
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• v.39 no.5_3
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• pp.949-965
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• 2023

In this study, we propose a method to monitor the surface area of agricultural reservoirs in South Korea using Sentinel-1 synthetic aperture radar images and the deep learning model, Swin Transformer. Utilizing the Google Earth Engine platform, datasets from 2017 to 2021 were constructed for seven agricultural reservoirs, categorized into 700 K-ton, 900 K-ton, and 1.5 M-ton capacities. For four of the reservoirs, a total of 1,283 images were used for model training through shuffling and 5-fold cross-validation techniques. Upon evaluation, the Swin Transformer Large model, configured with a window size of 12, demonstrated superior semantic segmentation performance, showing an average accuracy of 99.54% and a mean intersection over union (mIoU) of 95.15% for all folds. When the best-performing model was applied to the datasets of the remaining three reservoirsfor validation, it achieved an accuracy of over 99% and mIoU of over 94% for all reservoirs. These results indicate that the Swin Transformer model can effectively monitor the surface area of agricultural reservoirs in South Korea.

• Korean Journal of Remote Sensing
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• v.39 no.5_3
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• pp.979-995
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• 2023

As wildfires are difficult to predict, real-time monitoring is crucial for a timely response. Geostationary satellite images are very useful for active fire detection because they can monitor a vast area with high temporal resolution (e.g., 2 min). Existing satellite-based active fire detection algorithms detect thermal outliers using threshold values based on the statistical analysis of brightness temperature. However, the difficulty in establishing suitable thresholds for such threshold-based methods hinders their ability to detect fires with low intensity and achieve generalized performance. In light of these challenges, machine learning has emerged as a potential-solution. Until now, relatively simple techniques such as random forest, Vanilla convolutional neural network (CNN), and U-net have been applied for active fire detection. Therefore, this study proposed an active fire detection algorithm using state-of-the-art (SOTA) deep learning techniques using data from the Advanced Himawari Imager and evaluated it over East Asia and Australia. The SOTA model was developed by applying EfficientNet and lion optimizer, and the results were compared with the model using the Vanilla CNN structure. EfficientNet outperformed CNN with F1-scores of 0.88 and 0.83 in East Asia and Australia, respectively. The performance was better after using weighted loss, equal sampling, and image augmentation techniques to fix data imbalance issues compared to before the techniques were used, resulting in F1-scores of 0.92 in East Asia and 0.84 in Australia. It is anticipated that timely responses facilitated by the SOTA deep learning-based approach for active fire detection will effectively mitigate the damage caused by wildfires.

• Geophysics and Geophysical Exploration
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• v.16 no.3
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• pp.171-179
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• 2013

An automated ultrasonic velocity measurement system adopting pulse-echo-overlap (PEO) method has been constructed, which is known to be a precise and versatile method. It has been applied to velocity measurements for 5 kinds of engineering plastic cores and compared to first arrival picking (FAP) method. Because it needs multiple reflected waves and waves travel at least 4 times longer than FAP, PEO has basic restriction on sample length measurable. Velocities measured by PEO showed slightly lower than that by FAP, which comes from damping and diffusive characteristics of the samples as the wave travels longer distance in PEO. PEO, however, can measure velocities automatically by cross-correlating the first echo to the second or third echo, so that it can exclude the operator-oriented errors. Once measurable, PEO shows essentially higher repeatability and reproducibility than FAP. PEO system can diminish random noises by stacking multiple measurements. If it changes the experimental conditions such as temperature, saturation and so forth, the automated PEO system in this study can be applied to monitoring the velocity changes with respect to the parameter changes.

• Korean Journal of Remote Sensing
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• v.28 no.5
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• pp.501-508
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• 2012

Albedo is known as a factor that directly impacts on the surface energy balance one of the elements of earth radiation balance. The change of albedo includes the change of soil moisture, vegetation, solar zenith angle, snow, and so on. In addition, it operates as a crucial path to understanding feedback mechanisms between radiation balance and its influence on climate and vegetation dynamics and therefore, observing the variation of albedo is a one of the essential procedures for anticipating climate change. In this study, we used MODIS 16-Day composited Albedo data from 2001 to 2011 years with the purpose of observing the change of albedo over Northeast Asia. According to the tendency of albedo for 11 years, albedo in the area of an active vegetation has increased in near-infrared (NIR) domain and decreased in visible (VIS) domain. On the basis of local changes in vegetation in 2002, the both area of the Gobi Desert and the Manchuria was enormously changed and chosen the research area and furthermore, the vegetation of both regions had deteriorated due to the change of the minimum value since 2010.

• Korean Journal of Remote Sensing
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• v.28 no.5
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• pp.531-548
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• 2012

To identify Cochlodinium polykrikoides red tide from non-red tide water (satellite high chlorophyll waters) in the South Sea of Korea (SSK), we improved a spectral classification method proposed by Son et al.(2011) for the world first Geostationary Ocean Color Imager (GOCI). C. polykrikoides blooms and non-red tide waters were classified based on four different criteria. The first step revealed that the radiance peaks of potential red tide water occurred at 555 and 680 nm (fluorescence peak). The second step separated optically different waters that were influenced by relatively low and high contributions of colored dissolved organic matter (CDOM) (including detritus) to chlorophyll. The third and fourth steps discriminated red tide water from non-red tide water based on the blue-to-green ratio, respectively. After applying the red tide classification, the spectral response of C. polykrikoides red tide water, which is influenced by pigment concentration as well as CDOM (detritus), showed different slopes for the blue and green bands (lower slope at blue bands and higher slope at green bands). The opposite result was found for non-red tide water. This modified spectral classification method for GOCI led to increase user accuracy for C. polykrikoides and non-red tide blooms and provided a more reliable and robust identification of red tides over a wide range of oceanic environments than was possible using chlorophyll a concentration, or proposed red tide detection algorithms. Maps of C. polykrikoides red tide in SSK outlined patches of red tide covering the area near Naro-do and Tongyeong during the end of July and early of August, 2012 and extending into from Wan-do and Geoje-do during the middle of August, 2012.

• Korean Journal of Remote Sensing
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• v.35 no.6_3
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• pp.1251-1260
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• 2019

Existing environmental spatial information, which has been concentrated on spatial resolution, has limitations in solving realistic environmental problems that must be accompanied by physical and chemical characterization. Accordingly, there is a need for an image radar capable of identifying physical characteristics of an object regardless of weather conditions, day and night, and sunlight. Image radar is used in various fields in the United States and Europe. The next generation of medium-sized satellite No. 5 in Korea, which is under development with the aim of monitoring water disasters, is also looking for ways to expand the scope to various applications based on the existing application range. To this end, we analyzed domestic and international papers (100 works) using image radar, and reviewed KEI 2016 report, domestic papers, and foreign papers. Based on this, various environmental issues were summarized and the effects of when the image radar was used were analyzed and land cover was selected as an environmental issue. In the future, we will embody the technology to improve the accuracy of the land cover map, which is the environmental issue selected in this study, and build the foundation system for the stable use of the land cover map.

• Journal of the Korean Geographical Society
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• v.35 no.1
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• pp.3-15
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• 2000

산업화와 더불어 심각해지고 있는 수질오염 문제를 해결하기 위해서는 여러 가지 수질관리 방안이 요구된다. 수질오염이 과거에는 국지적이었으나 점차 광범한 지역으로 확장됨에 다라 지속적인 수질 모니터링에 어려움이 따른다. 본 연구에서는 위성영상을 사용한 원격탐사 기법으로 수역의 수질환경 인자를 추출하고자 한다. 사용된 영상은 Landasat TM이며, 연구지역은 한강하류 지역이다. 수질분석 인자는 클로로필-a, 부유물질, 투명도 등을 선정하였으며, 수면분광반사율의 특징 및 수질인자별 처리기법을 개발하는데 목적을 두었다. 분광특성 분석결과를 요약하면, 첫 번째 스펙트럼 반사율 분석결과 클로로필-a의 농도는 0.4~0.5$\mu\textrm{m}$ 파장대역에서 낮은 반사치 경향을 보이며, 녹색파장대인 0.57$\mu\textrm{m}$ 부근에서 반사율이 높아진다. 두 번째 부유물질의 반사도는 농도가 증가할수록 0.8$\mu\textrm{m}$ 부근에서 상대적으로 낮은 반사율이 나타난다. 마지막으로 투명도가 낮은 수면은 0.55$\mu\textrm{m}$에서 높은 반사율 경향을 보인다. Landsat TM영상을 이용하여 주성분분석 및 비연산처리를 실시하여 수질분석을 시도한 결과를 보면 클로로필-a와 투명도는 제1주성분 영상 및 제2주성분 영상에서 현장 실측자료와 유사한 결과를 얻을 수 있었으며, 부유물질은 밴드 2와 밴드 4의 비연산처리를 통하여 분포도를 작성할 수 있었다. 이상의 결과들은 계절적 및 시간적 변화에 따라 파장대역이 달라질 수 있다. 그러므로 위성자료를 이용하여 보다 정확한 수질환경 인자를 추출하기 위해서는 현장실측 및 수역의 분광반사 특성을 지속적으로 조사하여야 한다.때문으로 경주 산사태와 포함-구릉포간 국도면의 산사태가 이 종류의 산사태에 속한다.열 인식의 신뢰도를 향상시킬수 있는 방법을 제안하였다.작성하여 최신 의료영상 처리 기법을 쉽게 임상에 적용하고 실험할 수 있는 장점이 있다. 지대에서 가능하였고, 파종기는 중생종보다 이르게 나타났다. 등숙만한출수기 기준의 안전작기는 조생종과 중생종은 태백고냉지대와 태백준고냉지대, 소백산간지대 일부지역을 제외한 다른 지역에서 설정되었고, 중만생종은 태백고냉지대, 태백준고냉지대, 동해안북부지대, 소백산간지대, 노령소백산간지대의 일부 지역은 벼 담수직파가 불가능하게 판단되었다. information on the regular basis of time and provide it when the users query over the Web-database gateway. The other approach is a shopping agent mechanism, which stores information on "how to shop" and the shopping agent collects the information of product items just after users query about the product and provide the information in real time or notify them by alerting service. Thirty nine shopping information services are compared and classified in this paper and they are extracted from "Naver" and "Yahoo! Korea". The final result shows that most services are just a

• Korean Journal of Remote Sensing
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• v.29 no.1
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• pp.35-43
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• 2013

We analyzed the relationships between backscattering coefficients of wheat measured by COSMO-SkyMed SAR and biophysical measurements such as biomass, vegetation water content, and soil moisture over an entire wheat growth period. Backscattering coefficients increased until DOY 129 and then decreased along with fresh weight, dry weight, and vegetation water content. Correlation analysis between backscattering and wheat growth parameters revealed that backscatter correlated well with fresh weight (r=0.88), vegetation water content (r=0.87), and dry weight (r=0.80), while backscatter did not correlated with soil moisture (r=0.18). Prediction equations for estimation of wheat growth parameters from the backscattering coefficients were developed.