• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.423-423
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• 2023

습지 생태계는 탄소저장고, 대기 온·습도 조절 등의 기능을 수행하는 만큼 면밀한 관리가 요구된다. 습지의 규모와 생태계는 밀접한 연관성을 가지므로 그 규모를 우선적으로 파악할 필요가 있으며, 이를 위해 지표면의 상태를 산지, 습지, 수역 등의 항목으로 구분한 토지피복지도가 고려될 수 있다. 현재, 환경부에서 운영 중인 환경공간정보서비스(https://egis.me.go.kr/)에서는 각각 30 m, 5 m, 1 m의 공간 해상도와 7, 22, 41가지 분류 항목을 갖는 대분류, 중분류, 세분류로 구분된 토지피복지도를 제공하며 이러한 자료들은 모두 1년 이상의 시간 해상도를 갖는다. 습지의 경우, 계절에 따른 환경 변화로 인한 규모의 변동성이 크게 나타날 수 있기 때문에 1년 이하의 시간 해상도를 갖는 고품질 토지피복 분류 정보가 요구된다. 따라서 본 연구에서는 기존 자료의 낮은 시간 해상도 보완을 목표로, 1개월과 30 m의 시·공간 해상도를 갖는 토지피복지도를 구축하기 위한 방법론을 제안하고자 한다. 이를 위해 Landsat-8 등과 같은 다양한 인공위성 자료를 수집하고, Support Vector Machine 등과 같은 머신러닝 기법을 적용하였다. 최종적으로 습지보전법에서 지정한 습지보호지역 중 내륙습지 26개소를 대상으로, 본 연구로부터 산출된 토지피복지도를 기존 환경공간정보서비스 내 대분류 토지피복지도와 비교·평가하였다.

• Korean Journal of Remote Sensing
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• v.25 no.1
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• pp.71-83
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• 2009

This paper is written to analyze possible problems encountered with the existing data for the monitoring of land use and land cover change over the Korean peninsula and, further, to provide technical alternatives for the future land monitoring over the area. The oldest type of non-spatial data related to the land use change are cadastral statistics obtained since 1911. Annual statistics of cadastral data in early years (before 1942) can be used to assess land use change over the area. However, the cadastral statistics after the Korean War are not very appropriate for land use monitoring since the land class in cadastral data does not always correspond with actual land cover status. Majority of spatial data available for land monitoring over the area are land cover maps classified from satellite imagery since early 1970's. To analyze the suitability of land cover maps that were produced by two separate institutes with about 10 years interval, we conducted simple change detection analysis using these maps. These maps were not quite ready to be compared each other, in which they did not have the same class definition, classification method, and geometric registration. To achieve continuous and effective monitoring of land use and land cover change, particularly over North Korea, we should have a standard scheme in type and season of satellite imagery, image classification procedure, and class definition, which also should correspond to international standards.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2001.12a
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• pp.1-4
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• 2001

The classification performance of Artificial Neural Network (ANN) and RBF-NN was compared for Landsat TM image. The RBF-NN was validated for three unique landuse types (e.g. Mixed landuse area, Cultivated area, Urban area), different input band combinations and classification class. The bootstrap resampling technique was employed to estimate the confidence intervals and distribution for unit load, The pollutant generation was varied significantly according to the classification accuracy and percentile unit load applied. Especially in urban area, where mixed landuse is dominant, the difference of estimated pollutant load is largely varied.

• Journal of Korean Society for Geospatial Information Science
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• v.19 no.1
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• pp.105-113
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• 2011

Since existing thematic maps have been made with medium- to low-resolution satellite images, they have several shortcomings including low positional accuracy and low precision of presented thematic information. Digital aerial photo image taken recently can express panchromatic and color bands as well as NIR (Near Infrared) bands which can be used in interpreting forest areas. High resolution images are also available, so it would be possible to conduct precision land cover classification. In this context, this paper implemented object-based land cover classification by using digital aerial photos with 0.12m GSD (Ground Sample Distance) resolution and IKONOS satellite images with 1m GSD resolution, both of which were taken on the same area, and also executed qualitative analysis with ortho images and existing land cover maps to check the possibility of object-based land cover classification using digital aerial photos and to present usability of digital aerial photos. Also, the accuracy of such classification was analyzed by generating TTA(Training and Test Area) masks and also analyzed their accuracy through comparison of classified areas using screen digitizing. The result showed that it was possible to make a land cover map with digital aerial photos, which allows more detailed classification compared to satellite images.

• Spatial Information Research
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• v.22 no.1
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• pp.27-33
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• 2014

Considering the wide coverage, the transparency from climate condition, Interferometric Synthetic Aperture Radar (InSAR) possesses a great potential for the landcover classification as shown in many precedent researches. In addition to the merits of InSAR products for the landcover classification, the time series analysis of InSAR pairs can provide a highly reliable basis to interpret landcover. We applied such idea with the test site in Mountain Baekdu located on the border between North Korea and China. Since it is recently noted as the potential volcanic activation site, the landcover especially the vegetation distribution information is highly essential to validate the reliability of Differential Interferometric Synthetic Aperture Radar (DInSAR) over Mt. Baekdu. The algorithms combining the auxiliary information from Moderate Resolution Imaging Spectroradiometer (MODIS) to analyze the phase coherence and backscatter coefficient of Observing Satellite (ALOS) Phased Array type L-band Synthetic Aperture Radar (PALSAR) was established. The results using InSAR signatures from two polarization modes of ALOS PALSAR showed high reliability for mining landcover and spatial distribution.

• Journal of the Korean Geographical Society
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• v.39 no.5 s.104
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• pp.786-803
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• 2004

These days, interests on land cover classification using not only multi-sensor data but also thematic GIS information, are increasing. Often, although we have useful GIS information for the classification, the traditional classification method like maximum likelihood estimation technique (MLE) does not allow us to use the information due to the fact that the MLE and the existing computer programs cannot handle GIS data properly. We proposed a new method for updating the image classification using multi-spectral and multi-temporal images. In this study, we have simultaneously extended the MLE to accommodate both multi-spectral images data and land cover data for land cover classification. In addition to the extended MLE method, we also have extended the empirical likelihood ratio estimation technique (LRE), which is one of non-parametric techniques, to handle simultaneously both multi-spectral images data and land cover data. The proposed procedures were evaluated using land cover map based on Landsat ETM+ images in the Anmyeon-do area in South Korea. As a result, the proposed methods showed considerable improvements in classification accuracy when compared with other single-spectral data. Improved classification images showed that the overall accuracy indicated an improvement in classification accuracy of $6.2\%$ when using MLE, and $9.2\%$ for the LRE, respectively. The case study also showed that the proposed methods enable the extraction of the area with land cover change. In conclusion, land cover classification produced through the combination of various GIS spatial data and multi-spectral images will be useful to involve complementary data to make more accurate decisions.

• Journal of Korean Society for Geospatial Information Science
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• v.22 no.3
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• pp.155-160
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• 2014

This paper deals with comparison of classification accuracy between three land cover classification results having difference in resolution and they were classified with eight classes including building, road, forest, etc. Airborne hyperspectral image used in this study was acquired at 1000m, 2000m, 3000m elevation and had 24 bands(0.5m spatial resolution), 48 bands(1.0m), 96 bands(1.5m). Assessment of classification accuracy showed that the classification using 48 bands hyperspectral image had outstanding result as compared with other images. For using hyperspectral image, it was verified that 1m spatial resolution image having 48 bands was appropriate to classify land cover and qualitative improvement is expected in thematic map creation using airborne hyperspectral image.

• Journal of the Korean Association of Geographic Information Studies
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• v.21 no.3
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• pp.205-213
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• 2018

Hyperspectral image is more increasing spectral resolution that Multi-spectral image. Because of that, each pixel of the hyperspectral image includes much more information and it is considered the most appropriate technic for land cover classification. but recent research of hyperspectral image is stayed land cover classification of general level. therefore we classified land cover of detail level using ED, SAM, SSS method and made Decision Tree from result of that. As a result, the overall accuracy of general level was improved by 1.68% and the overall accuracy of detail level was improved by 5.56%.

• Korean Journal of Remote Sensing
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• v.30 no.2
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• pp.207-218
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• 2014

The information on the land cover along stream corridor is important for stream restoration and maintenance activities. This study aims to review the different classification methods for mapping the status of stream corridors in Seom River using airborne RGB and CIR digital ortho imagery with a ground pixel resolution of 0.2m. The maximum likelihood classification, minimum distance classification, parallelepiped classification, mahalanobis distance classification algorithms were performed with regard to the improvement methods, the skewed data for training classifiers and filtering technique. From these results follows that, in aerial image classification, Maximum likelihood classification gave results the highest classification accuracy and the CIR image showed comparatively high precision.

• Journal of Cadastre & Land InformatiX
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• v.48 no.2
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• pp.171-183
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• 2018

Despite the rapid expansion of satellite images supply, the application of imagery is often restricted due to unautomated image processing. This paper presents the automated process for the selection of training areas which are essential to conducting supervised image classification. The training areas were selected based on the prior and cover information. After the selection, the training data were used to classify land cover in an urban area with the latest image and the classification accuracy was valuated. The automatic selection of training area was processed with following steps, 1) to redraw inner areas of prior land cover polygon with negative buffer (-15m) 2) to select the polygons with proper size of area ($2,000{\sim}200,000m^2$) 3) to calculate the mean and standard deviation of reflectance and NDVI of the polygons 4) to select the polygons having characteristic mean value of each land cover type with minimum standard deviation. The supervised image classification was conducted using the automatically selected training data with Sentinel-2 images in 2017. The accuracy of land cover classification was 86.9% ($\hat{K}=0.81$). The result shows that the process of automatic selection is effective in image processing and able to contribute to solving the bottleneck in the application of imagery.