• Journal of Korea Water Resources Association
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• v.36 no.4
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• pp.609-621
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• 2003

The purpose of this study is to evaluate the hydrological impact due to temporal land cover change by gradual urbanization of upstream watershed of Pyeongtaek gauging station of Anseong -cheon. WMS HEC-1 was adopted, and DEM with 200$\times$200m resolution and hydrologic soil group from 1:50,000 soil map were prepared. Land covers of 1986, 1990, 1994 and 1999 Landsat TM images were classified by maximum likelihood method. The watershed showed a trend that forest ＆ paddy areas decreased and urban/residential area gradually increased for the period of 14 years. The model was calibrated at 2 locations (Pyeongtaek and Gongdo) by comparing observed with simulated discharge results for 5 summer storm events from 1998 to 2001. The watershed average CN values varied from 61.7 to 62.3 for the 4 selected years. To identify the impact of streamflow by temporal area change of a target land use, a simple evaluation method that the CN values of areas except the target land use are unified as one representative CN value was suggested. By applying the method, watershed average CN value was affected in the order of paddy, forest and urban/residential, respectively.

• Korean Journal of Remote Sensing
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• v.38 no.5_2
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• pp.695-706
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• 2022

Medium and high-resolution optical satellites have proven their effectiveness in detecting wildfire areas. However, smoke plumes generated by wildfire scatter visible light incidents on the surface, thereby interrupting accurate monitoring of the area where wildfire occurs. Therefore, a technology to extract smoke in advance is required. Deep learning technology is expected to improve the accuracy of smoke extraction, but the lack of training datasets limits the application. However, for clouds, which have a similar property of scattering visible light, a large amount of training datasets has been accumulated. The purpose of this study is to develop a smoke extraction technique using deep learning, and the limits due to the lack of datasets were overcome by using a cloud dataset on transfer learning. To check the effectiveness of transfer learning, a small-scale smoke extraction training set was made, and the smoke extraction performance was compared before and after applying transfer learning using a public cloud dataset. As a result, not only the performance in the visible light wavelength band was enhanced but also in the near infrared (NIR) and short-wave infrared (SWIR). Through the results of this study, it is expected that the lack of datasets, which is a critical limit for using deep learning on smoke extraction, can be solved, and therefore, through the advancement of smoke extraction technology, it will be possible to present an advantage in monitoring wildfires.

• Journal of Environmental Impact Assessment
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• v.6 no.2
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• pp.181-188
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• 1997

Vegetation is an important factor in EIA(Environmental Impact Assessment) and it is assessed according to DGN(Degree of Green Naturality) in EIS(Environmental Impact Statement) preparation. But DGN has room for improvement of assessing vegetation Status. This study introduced NDVI(Normalized Difference Vegetation Index) for improving status assessment method that subjects to DGN. For the application of NDVI, Landsat TM data of Chunchon on May 2, 1989 and June 1, 1994, and data of Ulsan on November 5, 1984, November 2, 1992 and May 9, 1994 were used. It compared NDVI with DGN according to season and location. The correlation coefficient value for the spring image (1994, 0.7, p=0.01) was proved to be higher than that of autumn (1984, 0.5, p=0.01).

• 한국신재생에너지학회:학술대회논문집
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• 2005.06a
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• pp.693-696
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• 2005

KIGAM (Korea Institute of Geoscience and Mineral Resources) launched a new project to develop the low-temperature geothermal water in the area showing high geothermal anomaly, north of Pohang city, for large-scale space heating. Surface geologic and geophysical surveys including Landsat 1M image analysis, gravity, magnetic, Magnetotelluric (MT) and controlled-source audio-frequency MT (CSAMT), and self-potential (SP) methods have been conducted and the possible fracture zone was found that would serve as deeply connected geothermal water conduit. In 2004, two test wells of 1.1km and 1.5km depths have been drilled and various kinds of borehole survey including geophysical logging, pumping test, SP monitoring, core logging and sample analysis have followed. Temperature of geothermal water at the bottom of 1.5km borehole reached over $70^{\circ}C$ and the pumping test showed that the reservoir contained huge amount of geothermal water. Drilling for the production well of 2 km depth is on going. After test utilization and the feasibility study, geothermal water developed from the production well is going to be provided to nearby apartments.

• Journal of Korean Society of Rural Planning
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• v.12 no.1 s.30
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• pp.75-83
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• 2006

본 연구에서는 최대우도법과 인공신경망 모형에 의해 카테고리 분류를 수행하고 각각의 분류 성능을 비교 평가하였다. 인공신경망 모형은 오류역전파 알고리즘을 이용한 것으로서 학습을 통한 은닉층의 최적노드수를 결정하여 카테고리 분류를 수행하도록 하였다. 인공신경망 최적 모형은 입력층의 노드수가 7개, 은닉층의 최적노드수가 18개, 그리고 출력층의 노드수가 5개인 것으로 구성하였다. 위성영상은 1996년에 촬영된 Landsat TM-5 영상을 사용하였고, 최대우도법과 인공신경망 모형에 의한 카테고리 분류를 위하여 각각의 카테고리에 대한 분광특성을 대표하는 지역을 절취하였다. 분류 정확도는 인공신경망 모형에 의한 방법이 90%, 최대우도법이 83%로서, 인공신경망 모형의 분류 성능이 뛰어난 것으로 나타났다. 카테고리 분류 항목인 토지 피복 상태에 따른 분류는 두 가지 방법에서 밭과 주거지의 분류오차가 큰 것으로 나타났다. 특히, 최대우도법에 의한 밭에서의 태만오차는 62.6%로서 매우 큰 값을 보였다. 이는 밭이나 주거지의 특성이 위성영상 촬영시기에 따라 나지의 형태로 분류되거나 산림, 또는 논으로도 분류되는 경향이 있기 때문인 것으로 보인다. 차후에 카테고리 분류를 위한 각각의 클래스의 보조적인 정보를 추가한다면, 카테고리 분류 향상이 이루어질 것으로 기대된다.

• Economic and Environmental Geology
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• v.29 no.5
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• pp.615-622
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• 1996

Slope stability analysis was conducted using remote sensing and Geoscientific Information System (GIS) as a part of natural hazard assessment around Chungju area. Landsat TM band 5 and 7 which contain more information about geological structure and geography are chosen and processed to analyse regional geological structure. Through image processing technique such as PCA, HFF, edge detection and enhancement, regional lineament can be mapped and identified. The lineament density map is constructed based on summed length of lineaments per unit area and the study area can be divided into 7 structural domains. Various factors of slope stability analysis such as geology, slope aspect, degree of slope, landcover, water shed as well as characterized structural domain are constructed as a database of GIS. Rating and weighting of each factor for slope stability analysis is decided by considering environmental geological characteristics of study area. Spatial analysis of regional slope stability is examined through overlaying technique of the GIS. The result of areal distribution of slope stability shows that the most unstable area is all over Jaeogae-ni, Hyangsan-ni and Mt. Daedun.

• Proceedings of the Korean Association of Geographic Inforamtion Studies Conference
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• 2004.03a
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• pp.487-492
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• 2004

본 연구에서는 인공신경망 기법을 이용하여 산사태 발생원인에 대한 가중치를 구하였다. 여름철 집중호우시 산사태가 많이 발생하는 강원도 강릉시 사천면 사기막리 와 주문진읍 삼교리에 해당한다. 산사태가 발생할 수 있는 요인으로 지형도로부터 경사, 경사방향, 곡률, 수계추출을, 정밀토양도로부터 토질, 모재, 배수, 유효토심, 지형을, 임상도로부터 임상, 경급, 영급, 밀도를, 지질도로부터 암상을, Landsat TM 영상으로부터 토지이용도와 추출하여 격자화 하였으며, 아리랑1호 영상으로부터 선구조를 추출하여 l00m 간격으로 버퍼링 한 후 격자화 하였다. 이렇게 구축된 산사태 발생 위치 및 발생요인 데이터 베이스를 이용하여 인공신경망 기법을 적용하여 산사태 발생 원인에 대한 상대적인 가중치를 구하였다. 인공신경망의 역전파 알고리즘을 이용한 사기막리 지역과 삼교리 지역의 산사태 가중치를 보면 GPS를 이용한 현장조사와 위성영상을 이용한 변화탐지 기법모두의 경우모두와 훈련지역을 실제 산사태 발생 지역과 경사도가 0°인 지역, 실제 산사태 발생 지역과 Frequence ratio를 이용하여 작성한 취약성도에서 산사태 발생이 낮을 것으로 예상되는 지역, Frequence ratio를 이용한 취약성도에서 산사태 발생이 높을 것으로 예상되는 지역 과 낮을 것으로 예상되는 지역의 경우에서도 경사도는 1.5～2.5배정도 높은 상대적 가중치를 나타냈다. 이러한 가중치는 산사태 취약성도를 작성하는데 활용될 수 있다.

• Korean Journal of Remote Sensing
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• v.5 no.2
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• pp.109-122
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• 1989

The ISODATA method, which is one of the most famous of the square-error clustering methos, has been applied to two Chungju multivariate data sets in order to evaluate the effectiveness of the regional lithological mapping. One is an airborne radiometric data set and the other is a mixed data set of the airborne radiometric and Landsat TM data. In both cases, the classification of the Bulguksa granite and the Kyemyongsan biotite-quartz gneiss are the most successful. Hyangsanni dolomitic limestone and neighboring Daehyangsan quartzite are also classified by their typical lowness of the radioactive intensities, though it is still confused with some others such as water-covered areas and nearby alluvials, and unaltered limestone areas. Topographically rugged valleys are also classified as the same cluster as above. This could be due to unavoidable variations of flight height and the attitude of the airborne system in such rugged terrains. The regional geological mapping of sedimentary rock units of the Ockchun System is in general confused. This might be due to similarities between different sediments. Considarable discrepancies occurred in mapping some lithological boundaries might also be due to secondary effects such as contamination or smoothing in digitizing process. Further study should be continued in the variable selection scheme as no absolutely superior method claims to exist yet since it seems somewhat to be rather data dependent. Study could also be made on the data preprocessing in order to reduce the erratic effects as mentioned above, and thus hoprfully draw much better result in regional geological mapping.

• Korean Journal of Remote Sensing
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• v.2 no.2
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• pp.79-94
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• 1986

TM data, which have better resolution in spatial and spectral than MSS data, were used for this study, and several Image Processing Techniques (IPT) were examined for finding the best IPT to fit to lineament extraction and mineralized zone mapping. The Ryeongnam area was selected as test area, because the area is one of major mineralized zones in Korea and its hydrothermal alteration zone is wider and deeper than other areas. The spatial filtering method is most optimum one for limeament extraction: that is, the directional spatial filtering is most efficient to detect N-S, E-W direction lineaments on the image, and the high boost filtering can be applied for mapping all direction lineaments. The ratio method was selected for detecting altered zone. It is possible to make several tens combinations in ratio with 7 bands of TM data, but considering spectral characteristics of each band of TM to the geological meterials and vegetation, the band 4/band 3(A), band 5/band 7(B), and B/A ratio methods were chosen among them. The 5/7 ratio image did not show clearly the altered area due to noise from vegetation cover, so the 4/3 ratio imae was used for trying to decrease the effect of vegetation. As a result the B/A ratio image showed quite nicely the altered zone of the test area. In conclusion, the spatial filtering is the best image processing techniques for lineament mapping, and the B/A ratio image in TM data is useful for the mineralized zone mapping.

• Korean Journal of Remote Sensing
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• v.6 no.2
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• pp.115-133
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• 1990

Study was made on the spectral characteristics of rock samples including bentonites collected from the northern Ulsan area. The geology of the area consists mainly of sediments of the Kyongsang Series and Bulguksa granite, the Tertiary volcanics, andesites and tuffs. Relative reflectances of meshed samples(2.5~10mm) to BaSO$_4$ are measured at 6 Landsat TM spectral windows (excluding the thermal band) with HHRR, and their reflection charactristics were analysed. In addition, three different data selection schemes including the Eulidean distance, multiple regression, and PCA weight methods were applied to the 30 TM ratio channels, derived from the above 6 bands. The selected data sets were subject to two unsupervised classification techniques(FA and ISODATA) in order to compare the effectiveness for classification of particularly bentonite from others. As a result, in ISODATA analysis the multiple regression model shows the best, followed by the Euliean distances one. The PCA weight model seems to show some confusion. In FA, though difficult for quantitative analysis, the best still seems to be the regression model. Among ratio bands, rations of band 7 or 5 against other bands represent the best contribution in classification of bentonites from others.