• Environmental Sciences Bulletin of The Korean Environmental Sciences Society
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• v.4 no.3
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• pp.149-156
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• 2000

This study presents an investigation of the changes that have occurred in the coastal ocean area of Kwangyang Bay located in the South Coastal region of Korea using remote sensing data based on Landsat Thematic Mapper (TM) multispectral digital data from 1988 and 1996. The coastal changes were detected using the digital histogram method and vector trace method. All the images were preprocessed, i.e. geometrically corrected, before the training set selection. when comparing the histograms of 7-band TM data, it was found that the band 5 image exhibited two critical Digital Number(DN) peaks, thereby indicating new coastal water and coastal land data. Based on this information, the coastal ocean area of the band 5 image was calculated using the vector tracing method supported by a CAD program. The result shows that the coastal ocean area decreased by about 5 % between 1988 to 1994. Accordingly, this gives a strong indication that the continuing land development will have a serious impact on the ecosystem of Kwangyang Bay.

• Korean Journal of Remote Sensing
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• v.28 no.6
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• pp.597-609
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• 2012

Leaf area index (LAI) is important in explaining the ability of the crop to intercept solar energy for biomass production and in understanding the impact of crop management practices. This paper describes a procedure for estimating LAI as a function of image-derived vegetation indices from temporal series of IKONOS, Landsat TM, and MODIS satellite images using empirical models and demonstrates its use with data collected at Missouri field sites. LAI data were obtained several times during the 2002 growing season at monitoring sites established in two central Missouri experimental fields, one planted to soybean (Glycine max L.) and the other planted to corn (Zea mays L.). Satellite images at varying spatial and spectral resolutions were acquired and the data were extracted to calculate normalized difference vegetation index (NDVI) after geometric and atmospheric correction. Linear, exponential, and expolinear models were developed to relate temporal NDVI to measured LAI data. Models using IKONOS NDVI estimated LAI of both soybean and corn better than those using Landsat TM or MODIS NDVI. Expolinear models provided more accurate results than linear or exponential models.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.21 no.3
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• pp.261-267
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• 2003

We are able to simultaneously extract the land surface change information, as we input each information extracted from images classified during the two periods, as the attribute information of geographic information, and then use it a parameter of GIS. Hence, this research sought to present basic data far efficient management and development of land surface, together with land use trends, by using the remote-sensing technique enabling the acquisition of the land surface covering information, as well as the benefits of GIS. The research conducted a study on the extraction of land surface change information, and made it possible to treat image information easily compared to the existing image classification methods, thereby making it easy to know the land surface change process for each pixel.

• Journal of The Korean Society of Agricultural Engineers
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• v.58 no.2
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• pp.53-63
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• 2016

Monitoring of cyanobacteria bloom in reservoir systems is important for water managers responsible of water supply system. Cyanobacteria affect the taste and smell of water and pose considerable filtration problems at water use places. Harmful cyanobacteria bloom in reservoir have significant economic impacts. We develop a new method for estimating the cyanobacteria bloom using Landsat TM and ETM+ data. Developed model was calibrated and cross-validated with existing in situ measurements from Daecheong Reservoir's Water Quality Monitoring Program and Algae Alarm System. Measurements data of three stations taken from 2004 to 2012 were matched with radiometrically converted reflectance data from the Landsat TM and ETM+ sensor. Stepwise multiple linear regression was used to select wavelengths in the Landsat TM and ETM+ bands 1, 2 and 4 that were most significant for predicting cyanobacteria cell number and bio-volume. Based on statistical analysis, the linear models were that included visible band ratios slightly outperformed single band models. The final monitoring models captured the extents of cyanobacteria blooms throughout the 2004-2012 study period. The results serve as an added broad area monitoring tool for water resource managers and present new insight into the initiation and propagation of cyanobacteria blooms in Daecheong reservoir.

• Journal of Environmental Impact Assessment
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• v.6 no.1
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• pp.55-66
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• 1997

This study summarizes the use of satellite data to detect the change of southwestern coastal wetlands in Korea. The images used for this study were two Landsat Thematic Mapper(TM) images (June 12, 1984 & June 2, 1992). TM images were used to classify such different types of wetlands as aquatic bed, nonaquatic bed and other land use in the region. Then it, was possible to a) determine the status of wetlands using image classification products, and b) detect the changes of various types of wetlands influenced by both human and nature. The results from spatiotemporal analysis showed that approximately 120 lad of coastal wetlands were lost from the year of 1984 to 1992. 71 % of the lost wetlands were converted to the reclaimed land. This loss of wetlands has been causing the profound environmental impacts. It has been successfully proved that satellite data are very effective for spatiatemporal change analysis, especially for that of coastal wetlands.

• Korean Journal of Remote Sensing
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• v.3 no.1
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• pp.1-10
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• 1987

The study was performed to recognize the most preferable spectral chennels for discriminating geological materials using the portable radiometer. The portable radiometer covers the visible and short infrared regions from approximately 0.4 to 2.5 microns which are coincided with Landsat TM, and the rock samples used for the study are pyrophylites, andesites, granite, granodiorite and silicified sedimentary rocks which are collected in Yangsan-Dongrae fault area. The analysis of the rock sample provides a preliminary basis for determining the wavelength regions showing diagnostic spectral features and for discriminating hydrothermal altered rocks from the unaltered rocks. The measurement of spectral of spectral reflectance for the rock samples was carried out in the laboratory which environment condition such as temperature, light sources, and humidity are constant. The analysis of the measured data was based on correlation between the reflectance value of the rock samples, and the follow discriptions are output of the study. 1) Pyrophyllite shows absorption at 0.83 $\mu\textrm{m}$ due to the oxidation of pyrite, and absorption at 2.22 $\mu\textrm{m}$ due to OH. 2) The altered rocks have generally higher reflectance than the unaltered rocks. 3) The ratio mesurement of pyrophyllites shows strong absorption at band 5/6 and band 6/4(in Landsat TM 5/7, 7/4). The ratio 1/5(Landsat TM 1/5) may be useful to discriminate andesite from the granite.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.31B no.8
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• pp.147-159
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• 1994

The performance of the improvement method of spatial resolution for satellite images based on the generalized inverse matrix is superior to the conventional methods. But, this method calculates the coefficient values for extracting the spatial information from the relation between a small pixel and large pixels. Accordingly it has the problem of remaining the blocky patterns at the result image. In this paper, a new generalized inverse matrix method is proposed which is different in the calculation method of coefficient values for extracting the spatial information. In this proposed metod, it calculates the coefficient values for extracting the spatial information from the relation between a small pixel and small pixels. Consequently it can improve the spatial resolution more efficiently without remaining the blocky patterns at the result image. The effectiveness of the proposed method is varified by simulation experiments with real TM image data.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.11 no.1
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• pp.55-60
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• 1993

Forest occupied a part of natural ecosystem carries out a role of purifying air, preserving water resource, prevention of the breeding and extermination, recreation areas and etc that preserve and for me one's living environment. In this study, the classification for management of this forest is performed with Landsat TM Image. The classes are decided needle-leaf trees, broad-leaf trees, farming land and grass land, and water. When the TM digital images are classified on computer, water is represented in 7∼13 D.N. of 4th band. But the others is appeared similar mostly specific values so that must be done image processing. When the images compounded 2ed band and 3ed band are processed with ratio of enhancement. Needle-leaf treas is represented in l18∼136 D.N. of 1st band, broad-leaf trees in 72∼91 D.N. of 3ed band, farm land and glass land in 96∼120 of 3ed band. Forest Information is classified with M.L.C, an image classification method. The errors of needle-leaf trees, broad-leaf trees, farm land and grass land, and water are appeared each －7.43, ＋1.89,＋7.58 and －2.04 as compared the digital image with investigation on the scene. Finally, these results are useful for classification of forest vegetation with Landsat TM Image.

• Proceedings of the Korean Environmental Sciences Society Conference
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• 2003.11a
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• pp.33-36
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• 2003

Principal component analysis (PCA) was used to improve image classification by the unsupervised classification techniques, the K-means. To do this, I selected a Landsat TM scene of Jeju Island, Korea and proposed two methods for PCA： unstandardized PCA (UPCA) and standardized PCA (SPCA). The estimated accuracy of the image classification of Jeju area was computed by error matrix. The error matrix was derived from three unsupervised classification methods. Error matrices indicated that classifications done on the first three principal components for UPCA and SPCA of the scene were more accurate than those done on the seven bands of TM data and that also the results of UPCA and SPCA were better than those of the raw Landsat TM data. The classification of TM data by the K-means algorithm was particularly poor at distinguishing different land covers on the island. From the classification results, we also found that the principal component based classifications had characteristics independent of the unsupervised techniques (numerical algorithms) while the TM data based classifications were very dependent upon the techniques. This means that PCA data has uniform characteristics for image classification that are less affected by choice of classification scheme. In the results, we also found that UPCA results are better than SPCA since UPCA has wider range of digital number of an image.

• Journal of the Korean Association of Geographic Information Studies
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• v.17 no.4
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• pp.1-9
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• 2014

The interest group and corporation in Korea have cultivated Suaeda grass in the source area every year as a plan to prevent the yellow dust due to Chinese desertification. It needs the afforestation analysis about the research area to plan the greenbelt environment development in the region of yellow dust origin. Thus, this research analyzed the greenbelt environment based on Landsat 5 TM satellite image and Landsat 8 image to grasp and analyze the present of greenbelt environment development. And this research analyzed the inside of the salt desert to understand the detailed greenbelt environment and vegetation index. As a result, it represents that the afforestation was accomplished efficiently between 2009 and 2011, while the greenbelt area was decreased rapidly and bare soil was increased between 2011 and 2013. Through these results, we could recognize that it is in trouble about the greenbelt environment development after 2011 and it needs the project implementation using satellite image when the next afforestation project is planned henceforth.