• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.1084-1086
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• 2003

The aim of this study is to assess the integrated use of different features extracted from spaceborne interferometric synthetic aperture radar (InSAR) data and optical data for land cover classification. Special attention is given to the discriminatory characteristics of the features derived from the multisource data sets. For the evaluation of the features , the statistical maximum likelihood decision rule and neural network classification are used and the results are compared. The performance of each method was evaluated by measuring the overall accuracy. In all cases, the performance of the first method was better than the performance of the latter one. Overall, the research indicated that multisource data sets containing different information about backscattering and reflecting properties of the selected classes of objects can significantly improve the classification of land cover types.

• Korean Journal of Remote Sensing
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• v.21 no.1
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• pp.83-89
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• 2005

This study utilized a spatial region growing segmentation and a classification using fuzzy membership vectors to detect the changes in the images observed at different dates. Consider two co-registered images of the same scene, and one image is supposed to have the class map of the scene at the observation time. The method performs the unsupervised segmentation and the fuzzy classification for the other image, and then detects the changes in the scene by examining the changes in the fuzzy membership vectors of the segmented regions in the classification procedure. The algorithm was evaluated with simulated images and then applied to a real scene of the Korean Peninsula using the KOMPSAT-l EOC images. In the expertments, the proposed method showed a great performance for detecting changes in land-cover.

• Korean Journal of Remote Sensing
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• v.31 no.4
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• pp.303-319
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• 2015

The objectives of this paper are to measure surface imperviousness using three different classification methods: per-pixel, sub-pixel, and object-oriented classification. They are tested on high-spatial resolution QuickBird data at 2.4 meters (four spectral bands and three principal component bands) as well as a medium-spatial resolution Landsat TM image at 30 meters. To measure impervious surfaces, we selected 30 sample sites with different land uses and residential densities across image representing the city of Phoenix, Arizona, USA. For per-pixel an unsupervised classification is first conducted to provide prior knowledge on the possible candidate spectral classes, and then a supervised classification is performed using the maximum-likelihood rule. For sub-pixel classification, a Linear Spectral Mixture Analysis (LSMA) is used to disentangle land cover information from mixed pixels. For object-oriented classification several different sets of scale parameters and expert decision rules are implemented, including a nearest neighbor classifier. The results from these three methods show that the object-oriented approach (accuracy of 91%) provides more accurate results than those achieved by per-pixel algorithm (accuracy of 67% and 83% using Landsat TM and QuickBird, respectively). It is also clear that sub-pixel algorithm gives more accurate results (accuracy of 87%) in case of intensive and dense urban areas using medium-resolution imagery.

• Journal of Korean Society of Rural Planning
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• v.2 no.1
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• pp.31-38
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• 1996

As a rational decision-making process of county-level area development, this study designed 3-step framework : function-giving(areal analysis) on unit planning area by decision matrix of land suitability, check of typical characteristics of each function area and formulation of its future development strategies. Two alternatives were suggested as the areal analysis method, of which one is equal ordering / valuing technique of checking indices for land suitability classfication and the other preferential weighting technique. And then, under the algorithm specially defined in this study, land suitability maps(Fig.2,3) of the case study area (Seungju-county area, Chonnam-province, Korea) were drawn from the areal analysis results. By use of land suitability classification results, unique characteristics of typical function areas were defined (on 7 types of alternative 1 , 8 types of II ) and their future development strategies were formulated in the case study area, According to the categorization criteria in this study, all the villages of the case area were classfied as a suitable type of function areas illustrated in this study.

• Journal of the Korean Institute of Landscape Architecture
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• v.26 no.3
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• pp.34-44
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• 1998

The purpose of this study is to provide the basic data for land use in the future, which result from analyzing land use, obtained after studying on the natural environment by Geographic Information System and Remote Sensing. The results of this study are as follows : ·According to the classification of land-cover, agricultural land use is relatively prominent except for overall natural covering. According to the average value of Green Vegetation Index class, the average value of GVI is 3.0, and 45% of the regions have relatively good condition of floral state. ·With a view to natural environment, the survey shows that the altitude of 90% of the total areas is below 400m, and most of them are flattened or moderately-inclined area. Therefore, this region has a good condition to be used for development. · The area for the first class in preservation degree of natural scenery of Namcheon-Myun is 2.3% of the total areas. According to the results about unstable areas on all sides, unstable districs are distributed in so small-scale units that they will be safe from some damages drawn by developing activity. But we have to consider every aspects for the future development of them. In this study, the natural environment-variables are regarded firstly, and effective designation of the land with natural environment is researched too. However, to establish more practical developing plan, ecological and human variables should be regarded.

• Korean Journal of Agricultural and Forest Meteorology
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• v.18 no.3
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• pp.127-134
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• 2016

Land suitability assessment for apples and pears was conducted with soil and climate information in South Korea. In doing so, we intended to preserve land and increase the productivity by providing valuable information regarding where more suitable areas for apples or pears are located. We used soil classification driven by soil environmental information system developed by National Institute of Agricultural Science, RDA, and also used climate classification in digital agro-climate map database for which is made by National Institute of Horticultural and Herbal Science. We combined both soil and climate classification results using a most-limiting characteristic method. The combined results showed very similar patterns with the results by classification based on soil information. Such results seem to come from the fact that the classification results by soil relatively lower than those by climate information. The results by soil classification seem to be too downgraded and checking if the final classification ranges in soil are reasonably made is strongly required. Although the most limiting characteristic method had been used widely in land suitability assessment, adapting the method based on results by soil and climate can be influenced by one downgraded factor. Therefore, alternative ways should be carefully considered for increasing the accuracy.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.24 no.1
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• pp.139-145
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• 2006

The advantage of the lidar data is in fast acquisition and process time as well as in high accuracy and high point density. However lidar data itself is difficult to classify the earth surface because lidar data is in the form of irregularly distributed point clouds. In this study, we investigated land cover classification using both lidar data and optical image through a supervised classification method. Firstly, we generated 1m grid DSM and DEM image and then nDSM was produced by using DSM and DEM. In addition, we had made intensity image using the intensity value of lidar data. As for optical images, the red, blue, green band of CCD image are used. Moreover, a NDVI image using a red band of the CCD image and infrared band of IKONOS image is generated. The experimental results showed that land cover classification with lidar data and optical image together could reach to the accuracy of 74.0%. To improve classification accuracy, we further performed re-classification of shadow area and water body as well as forest and building area. The final classification accuracy was 81.8%.

• The Journal of the Korea Contents Association
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• v.16 no.7
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• pp.31-41
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• 2016

Atmospheric correction as a preprocessing work should be performed to conduct accurately landcover/landuse classification using hyperspectral imagery. Atmospheric correction on airborne hyperspectral images was conducted and then the effect of atmospheric correction by comparing spectral reflectance characteristics before and after atmospheric correction for a few landuse classes was analyzed. In addition, land cover classification was first conducted respectively by the maximum likelihood method and the spectral angle mapper method after atmospheric correction and then the results were compared. Applying the spectral angle mapper method, the sea water area were able to be classified with the minimum of noise at the threshold angle of 4 arc degree. It is considered that object-based classification method, which take into account of scale, spectral information, shape, texture and so forth comprehensively, is more advantageous than pixel-based classification methods in conducting landcover classification of the coastal area with hyperspectral images in which even the same object represents various spectral characteristics.

• Journal of Korean Society for Geospatial Information Science
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• v.20 no.2
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• pp.73-79
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• 2012

This study attempts to analyze changes in land use patterns in a compound urban and agricultural city Kimje-si, using LANDSAT TM imagery and to forecast future changes accordingly. As a new approach to supervised classification, HSB(Hue, Saturation, Brightness)-transformed images were used to select training zones, and in doing so classification accuracy increased by more than 5 percent. Land use changes were forecasted by using a cellular automaton algorithm developed by applying Markov Chain techniques, and by taking into account classification results and GIS data, such as population of the pertinent region by area, DEMs, road networks, water systems. Upon comparing the results of the forecast of the land use changes, it appears that geographical features had the greatest influence on the changes. Moreover, a forecast of post-2030 land use change patterns demonstrates that 21.67 percent of mountain lands in Kimje-si is likely to be farmland, and 13.11 percent is likely to become city areas. The major changes are likely to occur in small mountain lands located in the heart of the city. Based on the study result, it seems certain that forecasting future land use changes can help plan land use in a compound urban and agricultural city to procure food resources.

• Korean Journal of Remote Sensing
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• v.36 no.1
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• pp.15-27
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• 2020

Accurate knowledge of land use/land cover (LULC) features and their relative changes over upon the time are essential for sustainable urban management. Urban sprawl growth has been always also a worldwide concern that needs to carefully monitor particularly in a developing country where unplanned building constriction has been expanding at a high rate. Recently, remotely sensed imageries with a very high spatial/spectral resolution and state of the art machine learning approaches sent the urban classification and growth monitoring to a higher level. In this research, we classified the Quickbird satellite imagery by object-based image analysis of Dempster-Shafer (OBIA-DS) for the years of 2002 and 2015 at Karbala-Iraq. The real LULC changes including, residential sprawl expansion, amongst these years, were identified via change detection procedure. In accordance with extracted features of LULC and detected trend of urban pattern, the future LULC dynamic was simulated by using land transformation model (LTM) in geospatial information system (GIS) platform. Both classification and prediction stages were successfully validated using ground control points (GCPs) through accuracy assessment metric of Kappa coefficient that indicated 0.87 and 0.91 for 2002 and 2015 classification as well as 0.79 for prediction part. Detail results revealed a substantial growth in building over fifteen years that mostly replaced by agriculture and orchard field. The prediction scenario of LULC sprawl development for 2030 revealed a substantial decline in green and agriculture land as well as an extensive increment in build-up area especially at the countryside of the city without following the residential pattern standard. The proposed method helps urban decision-makers to identify the detail temporal-spatial growth pattern of highly populated cities like Karbala. Additionally, the results of this study can be considered as a probable future map in order to design enough future social services and amenities for the local inhabitants.