• Magazine of the Korean Society of Agricultural Engineers
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• v.7 no.2
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• pp.935-943
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• 1965

1. Nation wide soil surver is going of from Oct't by Unkup. According to the sever years program of national high hueld of food production campaign. 2. About eighry of new soil surveryors wee assigned to provincial office of Unkup. 3. Land capability classifieation comes from U.S.D.A method. Bur we fells most adequate land classification should be studied and set uo of the real Korean Natural situation. 4. This theory has been studied by the Unkup soil survey staffs.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.29 no.4
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• pp.429-438
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• 2011

LiDAR technology is a combination of laser ranging, satellite positioning technology and digital image technology for study and determination with high accuracy of the true earth surface features in 3 D. Laser scanning data is typically a points cloud on the ground, including coordinates, altitude and intensity of laser from the object on the ground to the sensor (Wehr & Lohr, 1999). Data from laser scanning can produce products such as digital elevation model (DEM), digital surface model (DSM) and the intensity data. In Vietnam, the LiDAR technology has been applied since 2005. However, the application of LiDAR in Vietnam is mostly for topological mapping and DEM establishment using point cloud 3D coordinate. In this study, another application of LiDAR data are present. The study use the intensity image combine with some other data sets (elevation data, Panchromatic image, RGB image) in Bacgiang City to perform land cover classification using neural network method. The results show that it is possible to obtain land cover classes from LiDAR data. However, the highest accurate classification can be obtained using LiDAR data with other data set and the neural network classification is more appropriate approach to conventional method such as maximum likelyhood classification.

• Journal of the Korean Institute of Landscape Architecture
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• v.13 no.1
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• pp.43-66
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• 1985

Korea needs proper and rational public policies on conservation and use of forest land and other natural resources because of the accelerating expansion of national land developments in recent years. Unfortunately, there is no systematic planning system to support the needs. Generally, forest land use planning needs suitability analysis based on efficient land classification system. The goal of this study was to classify a forest land using multivariate satistical methods. A case study was carried out in winter of 1983 on a mountainous area higher than 100m above sea level located at Mt. Kwanak in Anyang -city, Kyung-gi-do (province). The study area was 19.80 km$^2$wide and was divided into 1, 383 Operational Taxonomic Units (OTU's) by a 120m$\times$120m grid. Fourteen descriptors were identified and quantified for each OTU from existing national land data : elevation, slope, aspect, terrain form, geologic material, surface soil permeability, topsoil type, depth of the solum, soil acidity, forest cover type, stand size class, stand age class, stand density class, and simple forest soil capability class. For this study, a FORTRAN IV program was written for input and output map data, and the computer statistics packages, SPSS and BMD, were used to perform the multivariate statistical analysis. Fourteen variables were analyzed to investigate the characteristics of their fire quench distribution and to estimate the correlation coefficients among them. Principal component analysis was executed to find the dimensions of forest land characteristics, and factor scores were used for proper samples of OTU throughout the study area. In order to develop the classes of forest land classification based on 102 surrogates, cluster and discriminant analyses of principal descriptor variable matrix were undertaken. Results obtained through a series of multivariate statistical analyses were as follows ; 1) Principal component analysis was proved to be a useful tool for data selection and identification of principal descriptor variables which represented the characteristics of forest land and facilitated the selection of samples.

• Korean Journal of Remote Sensing
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• v.33 no.6_2
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• pp.1089-1100
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• 2017

This paper presented a method using past land cover maps in image segmentation and training set collection for updating land cover maps. In this method, the object boundaries in past land cover maps were used for segmenting image clearly. Also, the classes of past land cover maps were used to extract additional informative training set from the initial classification result using a small number of initial training set. To evaluate the applicability of proposed method, a case study for updating land cover maps was carried out using middle-level land cover maps and WorldView-2 image in the Taean-gun, South Korea. As a result of the case study, the confusions between urban and barren, paddy/dry field and grassland in the initial classification result were reduced by adding training set. In addition, the object segmentation using boundaries of past land cover map cleared land cover boundaries and improved classification accuracy. Based on the result of case study, the proposed method using past land cover maps is expected to be useful for updating land cover maps.

• Journal of Cadastre & Land InformatiX
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• v.51 no.2
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• pp.169-184
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• 2021

Recently, land cover spectral libraries have been widely used in studies to classify land cover based on hyperspectral images. Overseas, various institutions have built and provided land cover spectral libraries, but in Korea, the building and provision of land cover spectral libraries is insufficient. Against this background, the purpose of this study is to suggest the possibility of using domestic and foreign spectral libraries in the classification studies of domestic land cover. Band matching is required for comparative analysis of the spectral libraries and land cover classification using the spectral libraries, and in this study, an automation logic to automatically perform this is presented. In addition, the directly constructed domestic land cover spectral library and the existing overseas land cover spectral library were comparatively analyzed. As a result, the directly constructed land cover spectral library had the highest correlation coefficient of 0.974. Finally, for the accuracy evaluation, aerial hyperspectral images of the study area were supervised and classified using the domestic and foreign land cover spectral libraries using the SAM technique. As a result of the accuracy evaluation, it is judged that Soils, Artificial Materials, and Coatings among the classification items of the foreign land cover spectral library can be sufficiently applied to classify the cover in Korea.

• 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.

• Journal of the Korean Association of Geographic Information Studies
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• v.19 no.2
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• pp.98-106
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• 2016

Remote sensing is an efficient technology for observing and monitoring the land surfaces inaccessible to humans. This research proposes a methodology for improving the accuracy of the land cover classification using the Landsat-8 operational land imager(OLI) image. The proposed methodology consists of the following steps. First, the normalized difference vegetation index(NDVI) and normalized difference water index(NDWI) images are generated from the given Landsat-8 OLI image. Then, a new image is generated by adding both NDVI and NDWI images to the original Landsat-8 OLI image using the layer-stacking method. Finally, the maximum likelihood classification(MLC), and support vector machine(SVM) methods are separately applied to the original Landsat-8 OLI image and new image to identify the five classes namely water, forest, cropland, bare soil, and artificial structure. The comparison of the results shows that the utilization of the layer-stacking method improves the accuracy of the land cover classification by 8% for the MLC method and by 1.6% for the SVM method. This research proposes a methodology for improving the accuracy of the land cover classification by using the layer-stacking method.

• Journal of the Korea Institute of Military Science and Technology
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• v.13 no.1
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• pp.147-155
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• 2010

Landcover information of access-denied area was extracted from low-medium and high resolution satellite image. Training for supervised classification was performed to refer visually by landcover map which is made and distributed from The Ministry of Environment. The classification result was compared by relating data of FACC land classification system. As we rasterize digital military map with same pixel size of satellite classification, the accuracy test was performed by image to image method. In vegetation case, ancillary data such as NDVI and image for seasons are going to improve accuracy. FACC code of FDB need to recognize the properties which can be automated.

• Korean Journal of Remote Sensing
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• v.38 no.5_1
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• pp.461-469
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• 2022

This paper presents a novel training data extraction approach using semi-supervised learning (SSL)-based classification without the analyst intervention for time-series land-cover mapping. The SSL-based approach first performs initial classification using initial training data obtained from past images including land-cover characteristics similar to the image to be classified. Reliable training data from the initial classification result are then extracted from SSL-based iterative classification using classification uncertainty information and class labels of neighboring pixels as constraints. The potential of the SSL-based training data extraction approach was evaluated from a classification experiment using unmanned aerial vehicle images in croplands. The use of new training data automatically extracted by the proposed SSL approach could significantly alleviate the misclassification in the initial classification result. In particular, isolated pixels were substantially reduced by considering spatial contextual information from adjacent pixels. Consequently, the classification accuracy of the proposed approach was similar to that of classification using manually extracted training data. These results indicate that the SSL-based iterative classification presented in this study could be effectively applied to automatically extract reliable training data for time-series land-cover mapping.

• International Journal of Knowledge Content Development & Technology
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• v.7 no.1
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• pp.57-78
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• 2017

There are several statistical classification algorithms available for land use/land cover classification. However, each has a certain bias or compromise. Some methods like the parallel piped approach in supervised classification, cannot classify continuous regions within a feature. On the other hand, while unsupervised classification method takes maximum advantage of spectral variability in an image, the maximally separable clusters in spectral space may not do much for our perception of important classes in a given study area. In this research, the output of an ANN algorithm was compared with the Possibilistic c-Means an improvement of the fuzzy c-Means on both moderate resolutions Landsat8 and a high resolution Formosat 2 images. The Formosat 2 image comes with an 8m spectral resolution on the multispectral data. This multispectral image data was resampled to 10m in order to maintain a uniform ratio of 1:3 against Landsat 8 image. Six classes were chosen for analysis including: Dense forest, eucalyptus, water, grassland, wheat and riverine sand. Using a standard false color composite (FCC), the six features reflected differently in the infrared region with wheat producing the brightest pixel values. Signature collection per class was therefore easily obtained for all classifications. The output of both ANN and FCM, were analyzed separately for accuracy and an error matrix generated to assess the quality and accuracy of the classification algorithms. When you compare the results of the two methods on a per-class-basis, ANN had a crisper output compared to PCM which yielded clusters with pixels especially on the moderate resolution Landsat 8 imagery.