• Proceedings of the Korean Society of Agricultural Engineers Conference
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• 2003.10a
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• pp.103-106
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• 2003

A research was made to describe the characteristics of alpine agricultural areas in Hongcheon, Pyeongchang and Jeongsun municipalities that comprise large alpine belts. Analysis of current land-use status, pattern classification of agricultural areas, and land-use suitability evaluation were made to describe the characteristics using GIS.

• Journal of Climate Change Research
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• v.8 no.4
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• pp.329-337
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• 2017

In this study, land-use changes from 1990 to 2010 in Jeju Island by different approaches were produced and compared to suggest a more efficient approach. In a sample-based method, land-use changes were analyzed with different sampling intensities of 2 km and 4 km grids, which were distributed by the fifth National Forest Inventory (NFI5), and their uncertainty was assessed. When comparing the uncertainty for different sampling intensities, the one with the grid of 2 km provided more precise information; ranged from 6.6 to 31.3% of the relative standard error for remaining land-use categories for 20 years. On the other hand, land-cover maps by a wall-to-wall approach were produced by using time-series Landsat imageries. Forest land increased from 34,194 ha to 44,154 ha for 20 years, where about 69% of total forest land were remained as forest land and 19% and 8% within forest lands were converted to grassland and cropland, respectively. In the case of grassland, only about 40% of which were remained as grassland and most of the area were converted to forest land and cropland. When comparing land-cover area by land-use categories with land-use statistics, forest areas were underestimated while areas of cropland and grassland were overestimated. In order to analyze land use change, it is necessary to establish a clear and consistent definition on the six land use classification.

• 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 the Korean Institute of Landscape Architecture
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• v.33 no.4 s.111
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• pp.1-10
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• 2005

Recently, as urban landscape is growing in importance, urban landscape planning is being actively performed. for this purpose, classification of the urban landscape is definitely required. Therefore, this research focuses on classifying urban landscape in Daejeon metropolis by dividing the urban land use pattern. This results are as follows. 1. Urban land use pattern is divided into 20 classes. The residential, commercial and industrial areas, the old market and the bus terminal are evaluated negatively, whereas the areas of school, water reservoir, neighborhood park and train station are appreciated as being positive in landscape characters. 2. As a result of a cluster analysis, urban landscape has five different landscape types. These are: landscapes of medium diversity lacking green area, landscapes of high diversity lacking green area, landscapes rich in green area and with medium diversity, landscapes rich in green area and with high diversity, and landscapes rich in green area and with low diversity. 3. In landscape characters of beauty and harmony, landscapes rich in green area and with medium diversity are more positively evaluated than those rich in green area and with low diversity. This point should be taken into account for planning the urban landscape.

• Proceedings of the KSRS Conference
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• 1999.11a
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• pp.386-389
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• 1999

In this study, visible and near infrared channels of NOAA/AVHRR data were used to classify land use and vegetation types over Korean peninsula. Analyzing forest stand structures and prediction of forest productivity using satellite data were also reviewed. Land use and land cover classification was made by unsupervised clustering methods. After monthly Normalized Difference Vegetation Index (NDVI) composite images were derived from April to November 1998, the derived composite images were used as temporal feature vector's in this clustering analysis. Visually interpreted, the classification result was satisfactory in overall for it matched well with the general land cover patterns. But subclassification of forests into coniferous, deciduous, and mixed forests were much confused due to the effects of low ground resolution of AVHRR data and without defined classification scheme. To investigate into the forest stand structures, digital forest type maps were used as an ancillary data. Forest type maps, which were compiled and digitalized by Forestry Research Institute, were registered to AVHRR image coordinates. Two data sets were compared and percent forest cover over whole region was estimated by multiple regression analysis. Using this method, other forest stand structure characteristics within the primary data pixels are expected to be extracted and estimated.

• Korean Journal of Remote Sensing
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• v.11 no.3
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• pp.83-100
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• 1995

The land-use has changed rapidly during the last two decades in accordance with urbanization in the Seoul Metropolitan Region. As a result of these changes, the local climate has undergone changes as well. This study intends to define the land-use changes, and then to show how they have brought in significant changes in the local climates. Land-use changes in the study area so repidly that up-to date maps and documents are not available at present. Therefore, Landsat data for land-use classification and NOAA AVHRR thermal data for the temperature fields were analyzed. Additionary, to visualize the effect of the land-use on the local climate, computer-enhanced brightness temperatures, Green Belt and city boundaries were overlaid on land-use patterns obtained from satellite images using GIS techniques. The results of analysis demonstrate that Green Space in the Seoul Metropolitan Region decreased from 94% to 62% while urban land-use increased ten times, from 4% to 39% for the period of 1972-1992. The resulting disappearance of biomass caused by land-use changes may have implications for the local-and micro-climate. The results show that the local climate of the study area became drier and warmer. This study also suggests a need for further studies of man's effects on local climate to minimize adverse influences and hazardous pollution and efficacious ways for urban planning.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.1464-1466
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• 2003

classification of the satellite images provides information about land cover and/or land use. Quality of the classification result depends mainly on the spatial and spectral resolutions of the images. In this study, image fusion in terms of resolution merging, and band integration with multi-source of the satellite images; Landsat ETM+ and Ikonos were carried out to improve classification. Resolution merging and band integration could generate imagery of high resolution with more spectral bands. Precise image co-registration is required to remove geometric distortion between different sources of images. Combination of unsupervised and supervised classification of the fused imagery was implemented to improve classification. 3D display of the results was possible by combining DEM with the classification result so that interpretability could be improved.

• Proceedings of the KSRS Conference
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• v.2
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• pp.647-650
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• 2006

Compact Airborne Spectrographic Imager (CASI) hyperspectral imagery was acquired over the Little Miami River Watershed (1756 square miles) in Ohio, U.S.A., which is one of the largest hyperspectral image acquisition. For the development of a 4m-resolution land cover dataset, a hierarchical approach was employed using two different classification algorithms: 'Image Object Segmentation' for level-1 and 'Spectral Angle Mapper' for level-2. This classification scheme was developed to overcome the spectral inseparability of urban and rural features and to deal with radiometric distortions due to cross-track illumination. The land cover class members were lentic, lotic, forest, corn, soybean, wheat, dry herbaceous, grass, urban barren, rural barren, urban/built, and unclassified. The final phase of processing was completed after an extensive Quality Assurance and Quality Control (QA/QC) phase. With respect to the eleven land cover class members, the overall accuracy with a total of 902 reference points was 83.9% at 4m resolution. The dataset is available for public research, and applications of this product will represent an improvement over more commonly utilized data of coarser spatial resolution such as National Land Cover Data (NLCD).

• Korean Journal of Remote Sensing
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• v.20 no.4
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• pp.253-259
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• 2004

The purpose of this study is to present a standardized scheme for providing agriculture-related information at various spatial resolutions of satellite images including Landsat + ETM, KOMPSAT-1 EOC, ASTER VNIR, and IKONOS panchromatic and multi-spectral images. The satellite images were interpreted especially for identifying agricultural areas, crop types, agricultural facilities and structures. The results were compared with the land cover/land use classification system suggested by National Geographic Information based on aerial photograph and Ministry of Environment based on satellite remote sensing data. As a result, high-resolution agricultural land cover map from IKONOS imageries was made out. The classification result by IKONOS image will be provided to KOMPSAT-2 project for agricultural application.

• Journal of the Korean Association of Geographic Information Studies
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• v.17 no.1
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• pp.24-34
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• 2014

To understand urban space and make appropriate plans, the integrative analyses considering road and land use simultaneously are required. In addition, studies that involve both horizontal and vertical spaces must be taken into consideration. Therefore, the purpose of this study is to conduct a classification analysis of road network-based land use considering spatial structure. The methods of this study were as follows; first, a space syntax theory considering the structure of road network was introduced for roads. For land use, to consider both horizontal and vertical development densities of residential and commercial buildings were used. And the explanatory power of three variables-Euclidean distance, global integration and length-reflected global integration-were compared. Third, based on road as an appropriate variable, modified-IPA was conducted with land use and the results were categorized into four areas. The proposed method was applied to Gangnam-gu, a CBD area in Seoul, and results were analyzed and visualized using GIS.