• KSCE Journal of Civil and Environmental Engineering Research
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• v.40 no.5
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• pp.535-546
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• 2020

Land cover maps are derived from satellite and aerial images by the Ministry of Environment for the entire Korea since 1998. Even with their wide application in many sectors, their usage in research community is limited. The main reason for this is the map compilation cycle varies too much over the different regions. The situation requires us a new and quicker methodology for generating land cover maps. This study was conducted to automatically generate land cover map using aerial ortho-images and Landsat 8 satellite images. The input aerial and Landsat 8 image data were trained by Residual U-Net, one of the deep learning-based segmentation techniques. Study was carried out by dividing three groups. First and second group include part of level-II (medium) categories and third uses group level-III (large) classification category defined in land cover map. In the first group, the results using all 7 classes showed 86.6 % of classification accuracy The other two groups, which include level-II class, showed 71 % of classification accuracy. Based on the results of the study, the deep learning-based research for generating automatic level-III classification was presented.

• Journal of Cadastre & Land InformatiX
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• v.50 no.1
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• pp.75-85
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• 2020

This study examines machine learning cross-validation to utilized create ROI for classification of land cover. The study area located in Sejong and one KOMPSAT-3A image was used in this analysis: procedure on October 28, 2019. We used four bands(Red, Green, Blue, Near infra-red) for learning cross validation process. In this study, we used K-fold method in cross validation and used SVM kernel type with cross validation result. In addition, we used 4 kernels of SVM(Linear, Polynomial, RBF, Sigmoid) for supervised classification land cover map using extracted ROI. During the cross validation process, 1,813 data extracted from 3,500 data, and the most of the building, road and grass class data were removed about 60% during cross validation process. Based on this, the supervised SVM linear technique showed the highest classification accuracy of 91.77% compared to other kernel methods. The grass' producer accuracy showed 79.43% and identified a large mis-classification in forests. Depending on the results of the study, extraction ROI using cross validation may be effective in forest, water and agriculture areas, but it is deemed necessary to improve the distinction of built-up, grass and bare-soil area.

• Spatial Information Research
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• v.6 no.1
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• pp.65-76
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• 1998

Air contaminant density must be inferred exactly to manage air pollution. Each land use of air pollution source is duplicated in the existing air contaminant distribution because the resolution of the land use map is low. The purpose of this study is to understand how the land use map is used to determine effectively in the distribution calculation of the emission volume and the inference of air contaminant density, as it is made in a high resolution. The major findings are as follows : In this study, as to making a high resolution($28.5m{\times}28.5m$) map of land use with GIS, each air pollution source is not duplicated spatially and land use can be reflected effectively. In Seoul, each air contaminant density was inferred (using a TCM-2 model) with the existing distribution map of emission volume, whose resolution is $1km{\times}1km$, and the new distribution map of emission volume, whose resolution is $28.5km{\times}28.5km$. According to the result, the inference value of the new distribution map was more similar to the actual value of an automatic survey network.

• Journal of the Korean Association of Geographic Information Studies
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• v.14 no.2
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• pp.28-39
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• 2011

This study estimates the characteristics of flow loads by classification items of the Ministry of Environment and by land cover change using HyGIS-SWAT. The result of analyzing the land cover change using the classification items shows that the urban area and the farmland area in Mishim-cheon and Gap-cheon are expanding while the forest area is decreasing. The result of analyzing the characteristics of classification items shows that peak discharge increases and total yearly discharge decreases in Mushim-cheon. The result of analyzing the characteristics by data-construction period shows that peak discharge decreases but total discharge increases in Gap-cheon. Three land cover change scenarios are applicable to the expansion of urban area and farmland area. According to the result of application, urbanization influences and Farmland area expansion influences increase peak discharge, total yearly discharge and sediment concentration.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.19 no.2
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• pp.163-172
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• 2001

This study present the quadratic programming as the classification method of remote sensed data applying to the extraction of landcover and examine it's applicable capability by comparing the classification accuracy of quadratic programming with that of neural network and maximum likelihood method which are used in the extraction of thematic layer. As the results, as drawing the more improved classification results by 6% than maximum likelihood method, we could discern that the method of quadratic programming is appliable to classifying the remote sensed data. Also, in the classification of quadratic programming method, we could definitely indicate the results which was ignored in the previous extreme(binary) classification method by affecting the class decision with the class composition proportion.

• Proceedings of the Korea Water Resources Association Conference
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• 2005.05b
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• pp.132-137
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• 2005

재해분야에 인공위성의 활용도가 높아짐에 따라 본 연구에서는 Landsat 영상과 RADARSAT 영상을 이용하여 안성천유역을 대상으로 침수지역을 추출하고자 하였다. Landsat 영상은 침수 전과 후의 영상을 각각 선정하였으며 RADARSAT 영상은 침수 중과 침수 후 의 영상을 선정하였다. 각 영상에 대하여 전처리와 기하보정을 걸친 후 침수지역을 파악하기 위한 방법으로 토지피복분류 방법을 사용하였고, 그 중 Landsat 영상은 분광반사계를 이용하여 감독분류를 실시하였고, RADARSAT 영상은 무감독 분류를 실시하여 침수 지역을 확인할 수 있었다.

• Proceedings of the Korea Contents Association Conference
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• 2006.05a
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• pp.84-87
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• 2006

It was investigated what is the reasonable landcover classification system for the nonpoint pollution models. According to the parameters of the nonpoint pollution models, runoff curve number, crop management factor and Manning's roughness coefficient, the landcover classification system was proposed to manage the drainage basin of the Woopo wetland. Also, the rule-based classification method was adopted to extract the landcover information for this study area.

• Proceedings of the Korean Association of Geographic Inforamtion Studies Conference
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• 1999.12a
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• pp.16-20
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• 1999

우리나라 특히 서ㆍ남해안은 전형적인 리아스식 해안으로 해안선 출입이 잦아 해안의 대표적인 지형들이 골고루 발달해 있는 곳이며, 해안생물자원도 풍부한 지역이다. 종래의 생태계 조사방법은 종목록을 작성하는 수준이었기 때문에 자연자원과 생태계 보존 및 관리에 필요한 해안생태계의 서식지 유형을 분류하여 지도화하는 작업이 우선적으로 이루어져야 한다. 이 연구는 태안해안국립공원 지역을 대상으로 다음의 세가지 연구를 시행하였다. 첫째, 해안생물의 서식 기반이 되는 물리적 요소인 기질, 경사도, 파랑에너지에 입각하여 서식지 유형을 분류하였다 둘째, 해안의 생태계 보존 및 관리에 영향을 미치는 토지이용 현황을 원격탐사를 이용하여 분류하였다. 셋째, 서식지와 토지이용을 결합하여 암석해안과 갯벌(모래, 펄)의 생태계 단위를 설정하였다. 이러한 생태계 단위는 생태적으로 관리전략을 세울 수 있는 토대가 될 수 있다.

• Proceedings of the KGS Conference
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• 1999.05a
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• pp.47-52
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• 1999

• Journal of Korean Society for Geospatial Information Science
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• v.23 no.1
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• pp.15-22
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• 2015

To create a wind resource map, we need a contour map, a roughness map and wind data. We need a land cover map for the roughness map of these data. A land cover map represents the area showing similar characteristics after color indexing based on the scientific method. The features of land cover is classified by Remote sensing technique. In this study, we verified the application of the NDVI technique is reasonable after we created the wind resource map using roughness maps by unsupervised classification and NDVI technique. As a result, the wind resource map using the NDVI technique showed a 60% accordance rate and difference in class less than one. From the results, The NDVI technique is found alternative to create roughness maps by the unsupervised classification.