• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.727-731
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• 2003

ASTER image has some advantages for classification such as 15 spectral bands and 15m ${\sim}$ 90m spatial resolution. However, in the classification using general remote sensing image, shadow areas are often classified into water area. It is very difficult to divide shadow and water. Because reflectance characteristics of water is similar to characteristics of shadow. Many land cover items are consisted in one pixel which is 15m spatial resolution. Nowadays, very high resolution satellite image (IKONOS, Quick Bird) and Digital Surface Model (DSM) by air borne laser scanner can also be used. In this study, mixed pixel analysis of ASTER image has carried out using IKONOS image and DSM. For mixed pixel analysis, high accurated geometric correction was required. Image matching method was applied for generating GCP datasets. IKONOS image was rectified by affine transform. After that, one pixel in ASTER image should be compared with corresponded 15×15 pixel in IKONOS image. Then, training dataset were generated for mixed pixel analysis using visual interpretation of IKONOS image. Finally, classification will be carried out based on Linear Mixture Model. Shadow extraction might be succeeded by the classification. The extracted shadow area was validated using shadow image which generated from 1m${\sim}$2m spatial resolution DSM. The result showed 17.2% error was occurred in mixed pixel. It might be limitation of ASTER image for shadow extraction because of 8bit quantization data.

• 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.

• Journal of Soil and Groundwater Environment
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• v.22 no.3
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• pp.18-26
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• 2017

The increasing frequency of droughts has been increasing the necessity of utilizing subsurface dams as reliable groundwater resources in areas where it is difficult to supply adequate agricultural water using only surface water. In this study, we analyzed the current status and actual conditions of five agricultural subsurface dams as well as the effect of obtaining additional groundwater from subsurface dams operated as one aspect of the sustainable integrated water management system. Based on the construction methods and functions of each subsurface dam, the five subsurface dams are classified into three types such as those that derive water from rivers, those that prevent seawater intrusion, and those that link to a main irrigation canal. The classification is based on various conditions including topography, reservoir location, irrigation facilities, and river and alluvial deposit distributions. Agricultural groundwater upstream of subsurface dams is obtained from four to five radial collector wells. From the study, the total amount of groundwater recovered from the subsurface dam is turned out to be about 29~44% of the total irrigation water demand, which is higher than that of general agricultural groundwater of about 4.6%.

• Journal of Korean Society of Water and Wastewater
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• v.25 no.2
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• pp.141-152
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• 2011

In this study, we extracted effective factors of pipe burst from the status data of water asset, operating data of pressure, volume and etc. and 7 years' pipe burst and repair records. The extracted factors were sorted by each attribution and then a statistical analysis was performed to generate a pipe burst probability function using the logistic regression model. As the result, material, diameter, length, laying year, pressure and road width affected to pipe burst significantly. Especially, in case of small diameter, laying year was most effective factor and in case of steel pipe, external loading was main cause of burst, and in case of cast iron, PE, PC, HP pipes, the deterioration of joint was main cause. The other side, as a result of Hosmer-Lemeshow goodness of fit test the models are turned out significant statistically. Also the classification criteria were determined to minimize the total cost from classification errors, when the predicted probability was more than 18% this pipe could have a chance of burst.

• Spatial Information Research
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• v.11 no.2
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• pp.101-117
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• 2003

High resolution satellite image analysis has been recognized as an effective technique for monitoring local land-cover and atmospheric changes. In this study, a new high resolution map for land-cover was generated using both high-resolution IKONOS image and conventional land-use mapping. Fuzzy classification method was applied to classify land-cover, with minimum operator used as a tool for joint membership functions. In separateness analysis, the values were not great for all bands due to discrepancies in spectral reflectance by seasonal variation. The land-cover map generated in this study revealed that conifer forests and farm land in the ground and tidal flat and beach in the ocean were highly changeable. The kappa coefficient was 0.94% and the overall accuracy of classification was 95.0%, thus suggesting a overall high classification accuracy. Accuracy of classification in each class was generally over 90%, whereas low classification accuracy was obtained for classes of mixed forest, river and reservoir. This may be a result of the changes in classification, e.g. reclassification of paddy field as water area after water storage or mixed use of several classification class due to similar spectral patterns. Seasonal factors should be considered to achieve higher accuracy in classification class. In conclusion, firstly, IKONOS image are used to generated a new improved high resolution land-cover map. Secondly, IKONOS image could serve as useful complementary data for decision making when combined with GIS spatial data to produce land-use map.

• Korean Journal of Remote Sensing
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• v.13 no.1
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• pp.47-56
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• 1997

In this paper, remote sensing is used to estimate trophic state which is primary concern in a lake. In using remote sensing, this study estimated trophic state not with conventional method such as regression equations but with classification methods. As europhication is caused by the extraodinary proliferation of the algae, chlorophyll a and transparency are applied to remote sensing data.. Maximum Likelihood Classification and Minimum Distance Classification which are kinds of classification methods enabled trophic state to be confirmed in a lake. These are obtained as the result of applying remote sensing to classify trophic state in a lake. Firest, when we evaluate tropic state in a large area of water body, the application of remote sensing data can obtain more than 70% accuracies just in using basic classification methods. Second, in the aspect of classification, the accuracy of Minimum Distance Classification is usually better than that of Maximum Likelihood Classification. This result is caused that samples have normal distribution, but their numbers are a few to apply statistical method. Therefore, classification method is required such as artificial neural networks which are not influenced by statistical distribution. Third, this study enables the trophic state of water body to be analyzed and evaluated rapidly, periodically and visibly. Also, this study is good for forming proper countermeasure accompanying with trophic state progress extent in a lake and is useful for basic-data.

• Journal of the Society of Naval Architects of Korea
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• v.56 no.4
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• pp.343-351
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• 2019

Green water impact may sometimes cause some structure damages on ship's bow deck. Prediction of proper design pressure against the green water impact is an essential task to prevent the possible damages on bow deck. This paper presents a computational method of the bow deck's design pressure against the green water impact. Large heave and pitch motions of ship are calculated by the time domain nonlinear strip method. Green water flow and pressure on bow deck are simulated by the predictor-corrector second kind upstream finite difference method. This green water simulation method is based on the shallow water wave equations expanded for moving bottom conditions. For various kind of ships such as container ship, VLCC, oil tanker and bulk carrier, the green water design pressures on bow deck are computed and discussed. Also, the obtained results of design pressure on bow deck are compared with those of the classification society rules and discussed.

• Korean Journal of Ecology and Environment
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• v.54 no.3
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• pp.240-246
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• 2021

The Chironomidae is a benthic macroinvertebrate commonly found in freshwater ecosystems, along with Ephemeroptera and Trichoptera, which can be used for environmental health assessments. There are approximately 15,000 species of Chironomidae worldwide, but there are limited studies on species identification of domestic Chironomidae larvae. In the present study, we carried out species classification of the Chironomidae larvae that found in Jeju's tap water purification plants using morphological characteristics and genetic identification based on cytochrome c oxidase subunit I (COI) gene of the mitochondrial DNA. Body shape, mentum, antenna, mandible in the head capsule, and claws were observed in the larvae for morphological classification. Analysis of 17 larvae collected from faucets and fire hydrants of domestic tap water purification plants revealed the presence of two species, including 14 Orthocladius tamarutilus and 3 Paratrichocladius tammaater. These results will aid the use of the criteria information about species classification of the Chironomidae for water quality management in water purification plants and diversity monitoring of freshwater environments.

• Proceedings of the Korean Society of Agricultural Engineers Conference
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• 2000.10a
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• pp.419-424
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• 2000

The land cover classification by using remotely sensed image becomes necessary and useful for hydrologic and water quality related applications. The purpose of this study is to obtain land classification map by using remotely sensed data : Landsat TM and KOMPSAT-1 EOC. The classification was conducted by maximum likelihood method with training set and Tasseled Cap Transform. The best result was obtain from the Landsat TM merged by KOMPSAT EOC, that is, similar with statistical data. This is caused by setting more precise training set with the enhanced spatial resolution by using KOMPSAT EOC(6.6m${\times}$6.6m).

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.511-512
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• 2003

The Terra/MODIS data set over Yellow River Basin, China is generated for the purpose of an input parameter into the water resource management model, which has been developed in the Research Revolution 2002 (RR2002) project. This dataset is mainly utilized for the land cover classification and radiation budget analysis. In this paper, the outline of the dataset generation, and a simple land cover classification method, which will be developed to avoid the influence of cloud contamination and missing data, are introduced.