• Korean Journal of Geomatics
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• v.3 no.1
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• pp.33-41
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• 2003

Every year, several typhoons hit the Korean peninsula and cause severe damage. For the prevention and accurate estimation of these damages, real time or almost real time flood information is essential. Because of weather conditions, images taken by optic sensors or LIDAR are sometimes not appropriate for an accurate estimation of water areas during typhoon. In this case SAR (Synthetic Aperture Radar) images which are independent of weather condition can be useful for the estimation of flood areas. To get detailed information about floods from satellite imagery, accurate classification of water areas is the most important step. A commonly- and widely-used classification methods is the ML(Maximum Likelihood) method which assumes that the distribution of brightness values of the images follows a Gaussian distribution. The distribution of brightness values of the SAR image, however, usually does not follow a Gaussian distribution. For this reason, in this study the ANN (Artificial Neural Networks) method independent of the statistical characteristics of images is applied to the SAR imagery. RADARS A TSAR images are primarily used for extraction of water areas, and DEM (Digital Elevation Model) is used as supplementary data to evaluate the ground undulation effect. Water areas are also extracted from KOMPSAT image achieved by optic sensors for comparison purpose. Both ANN and ML methods are applied to flat and mountainous areas to extract water areas. The estimated areas from satellite imagery are compared with those of manually extracted results. As a result, the ANN classifier performs better than the ML method when only the SAR image was used as input data, except for mountainous areas. When DEM was used as supplementary data for classification of SAR images, there was a 5.64% accuracy improvement for mountainous area, and a similar result of 0.24% accuracy improvement for flat areas using artificial neural networks.

• Journal of The Korean Society of Agricultural Engineers
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• v.56 no.3
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• pp.55-64
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• 2014

While utilizing high resolution satellite image for land use classification has been popularized, object-oriented classification has been adapted as an affordable classification method rather than conventional statistical classification. The aim of this study is to extract the paddy field area using object-oriented classification with time series NDVI from high-resolution satellite images, and the RapidEye satellite images of Jaeryung-gun in North Korea were used. For the implementation of object-oriented classification, creating objects by setting of scale and color factors was conducted, then 3 different land use categories including paddy field, forest and water bodies were extracted from the objects applying the variation of time-series NDVI. The unclassified objects which were not involved into the previous extraction classified into 6 categories using unsupervised classification by clustering analysis. Finally, the unsuitable paddy field area were assorted from the topographic factors such as elevation and slope. As the results, about 33.6 % of the total area (32313.1 ha) were classified to the paddy field (10847.9 ha) and 851.0 ha was classified to the unsuitable paddy field based on the topographic factors. The user accuracy of paddy field classification was calculated to 83.3 %, and among those, about 60.0 % of total paddy fields were classified from the time-series NDVI before the unsupervised classification. Other land covers were classified as to upland(5255.2 ha), forest (10961.0 ha), residential area and bare land (3309.6 ha), and lake and river (1784.4 ha) from this object-oriented classification.

• Geomechanics and Engineering
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• v.8 no.5
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• pp.631-647
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• 2015

Engineers may encounter unpredictable cavities, sinkholes and karst conduits while tunneling in karst area, and water inrush disaster frequently occurs and endanger the construction safety, resulting in huge casualties and economic loss. Therefore, an optimal classification method based on grey system theory (GST) is established and applied to accurately predict the occurrence probability of water inrush. Considering the weights of evaluation indices, an improved formula is applied to calculate the grey relational grade. Two evaluation indices systems are proposed for risk assessment of water inrush in design stage and construction stage, respectively, and the evaluation indices are quantitatively graded according to four risk grades. To verify the accuracy and feasibility of optimal classification method, comparisons of the evaluation results derived from the aforementioned method and attribute synthetic evaluation system are made. Furthermore, evaluation of engineering practice is carried through with the Xiakou Tunnel as a case study, and the evaluation result is generally in good agreement with the field-observed result. This risk assessment methodology provides a powerful tool with which engineers can systematically evaluate the risk of water inrush in karst tunnels.

• Korean Journal of Agricultural and Forest Meteorology
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• v.3 no.3
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• pp.163-170
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• 2001

The objective of this study was to evaluate land cover classification of PyeongTaeg area by Landsat Thematic Mapper Data June, 1997. This study was also to make more correct reference data using DGPS, aerophoto, and topographical chart etc.. The result of the area of paddy and upland were estimated 4,949 $\textrm{km}^2$ and 16,157 $\textrm{km}^2$, respectively. Correctness of estimation by using DGPS, aerophoto, topographical chart were shown over 90% correct in case of rice paddy field, water, and sea, while upland, vinyl house, forest, grassland, village were shown low correctness. Total average accuracy was shown to be 85.8%. Correctness of paddy field showed high value of 92%, showing that use of remote sensing data was proved to be effective methods to estimate spatial distribution and cultivation status of paddy field. Classification result of sea, water area, downtown had higher correctness, while upland, vinyl-house, grassland were proved to be relatively low correctness because of it's small area and mixed distribution.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.2 no.1
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• pp.29-37
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• 1999

Land cover classification is an essential basic information in natural environment management; however, land cover classification studies in Korea have not yet been proceeded to a sufficient level. At the present, only a limited number of the precedent studies that only cover definite city area has been conducted. Furthermore, there is almost no research conducted on the land cover classification schemes that could accurately classify the Korea's land cover conditions. This study primarily focuses on the land cover classification scheme which carries the most urgent priority in order to classify and to map out the Korean land cover conditions. In order to develop the most suitable land cover classification scheme, many foreign land cover classification cases and projects that are being carried out were reviewed in depth. The land cover classification scheme this study proposes comprises 3 levels : The first level consists of 7 different classes; the second level consists of 22 different classes; and the third level is made up of 50 classes. The land cover classification map will serve many important roles in natural environment management, such as the conjecture of natural habitats and estimation of oxygen production or carbon dioxide absorption capability of a forest. In water pollution modelling, the land cover classification data can be used to estimate and locate non-point sources of water pollution. If applied to a watershed, modelling it will allow to estimate the total amount of pollution from non-point sources of pollution in the water shed. The land cover classification data will also be good as a barometer data that determines defusion of air pollutants in air pollution modelling.

• Journal of Environmental Science International
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• v.15 no.4
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• pp.349-357
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• 2006

The purpose of this study is to evaluate the water pollution degree in the Mid-Nakdong River watershed regarding to landcover types using GIS method. As a result of landcover classification, rate of urban appeared highly on Daegu Metropolitan city. Also, rate of agriculture showed highly in the riparian zones of the Nakdong and Guemho River and rate of forest appeared highly in the borders of the study area. To identify the groups of watershed with similar landcover patterns using the Cluster Analysis. According to the cluster analysis, the fifty sub-watersheds were grouped in three clusters, 'Urban watershed', 'Agriculture watershed', 'Forest watershed'. The proportion of urban area in each cluster had a positive correlation with water pollution degree. Otherwise, the proportion of agriculture in the Agriculture watershed had a high positive correlation with water pollution degree. Therefore, it is necessary to estimate environmental capacity of water duality considering ecological and environmental characteristics of watershed ecosystem and expand water duality monitoring systems to small stream.

• Spatial Information Research
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• v.23 no.2
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• pp.31-38
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• 2015

Remote sensing data is used to increasing efficiency on benthic cover type survey. Satellite and aerial imagery has variance of reflectance by water column effect even if bottom is consisted with same cover type and condition. This study tried to analyze advances of surveying extent and accuracy through water column correction of CASI-1500 hyperspectral image. Study area is coast of Gangneung city, South Korea where benthic environment is rapidly changing with bleaching of coral reef. Water column correction coefficient was estimated using regression models between water reflectance ($R_W$) and depth for sand bottom then the coefficients were applied to whole image. The results shows that expanded interpretable depth from 6-7m to 15m and decreased variation of reflectance by depth. Additionally, water column corrected reflectance image shows 13%p increased accuracy on benthic cover type classification.

• Journal of Korean Society on Water Environment
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• v.29 no.4
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• pp.504-513
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• 2013

Water quality trends for major tributaries (66 sites) in the Yeongsan River basin of Korea were examined for 12 parameters based on water quality data collected every month over a period of 12 months. The complex data matrix was treated with multivariate analysis such as PCA, FA and CA. PCA/FA identified four factors, which are responsible for the structure explaining 78.2% of the total variance. The first factor accounting 27.3% of the total variance was correlated with BOD, TN, TP, and TOC, and weighting values were allowed to these parameters for grade classification. CA rendered a dendrogram, where monitoring sites were grouped into 5 clusters. Cluster 2 corresponds to high pollution from domestic wastewater, wastewater treatment and run-off from livestock farms. For grade classification of tributaries, scores to 10 indexes were calculated considering the weighting values to 3 parameters as BOD, TN and TP which were categorized as the first factor after FA. The highest-polluted group included 10 tributaries such as Gwangjucheon, Jangsucheon, Daejeoncheon, Gamjungcheon, Yeongsancheon. The results indicate that grade classification method suggested in this study is useful in reliable classification of tributaries in the study area.

• Korean Journal of Remote Sensing
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• v.2 no.1
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• pp.13-21
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• 1986

The study was emphasized on the applicability of Landsat data for vegetation classification of touch as small natural swamp areas Yujeon natural swamp in Haman-gun through image processing system. The image processing technique which was applied is maximum likelihood method. The classified types on the Landsat image are water, nelumbo, grass, agricultural field and conifer. The computer processed classification was compared the existed data for evaluating the result, but there are some difficulties on the exact comparison between them because of discordance of the temporal resolution. The result, anyhow, is quite remarkable that Landsat MSS data can be used for the quantitative estimates of vegetation type classification in such small area.

• Korean Journal of Remote Sensing
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• v.37 no.6_1
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• pp.1599-1610
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• 2021

The natural environment and local ecosystem change depending on various factors, but among them, the change in water temperature is one of the major factors affecting the surrounding environment in the river ecosystem. However, research on water temperature change have not been actively conducted to date compared to the effect of water temperature on the river environment. Therefore, this study intends to study the change in water temperature from 2015 to 2021 through the change in the area of winter ice in the Hongcheon River. Optical satellite images were classified by referring to the field survey results, and the SAR satellite imagestried to overcome the limitations of the input data by using the GLCM texture analysis method. After verifying the accuracy of all images used, the calculated monthly average ice area was compared with the temperature data of the adjacent AWS. It was found that there is a correlation between water temperature and ice area, and the results of this study can be used to study environmental changes in small-scale rivers that are difficult to access or do not have systems in place.