• Journal of the Korean Society of Environmental Restoration Technology
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• v.10 no.5
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• pp.20-30
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• 2007

Environmental Conservation Value Assessment Map (ECVAM) is the class map to divide the national land into conservation areas and development areas based on legal and ecological assessment criteria. It contributes to enhancements of the efficiency and the scientificity when framing a policy in various fields including the environment. However, it is impossible to understand the multiphase vegetation structure as data on judging the national forest class in ECVAM are restricted to areal information of Ecological Nature Status, Degree of Green Naturality and Forest Map. This point drops the reliability of ECVAM. Therefore we constructed vegetation information using LiDAR (Light Detection And Raging) technology. We generated Biomass Class Maps as final results of this study, to introduce the new forest assessment criterion in ECVAM that alternates or makes up for existing forest assessment criteria. And then, we compared these with Forest Map and Landsat TM NDVI image. As a result, biomass classes are generally higher than stand age classes and DBH classes of Vegetation Map, and lower than NDVI of Landsat TM image because of the difference of time on data construction. However distributions between these classes are mostly similar. Therefore we estimates that it is possible to apply the biomass item to the new forest assessment criterion of ECVAM. The introduction of the biomass in ECVAM makes it useful to detect the vegetation succession, to adjust the class of the changed zone since the production of Vegetation Map and to rectify the class error of Vegetation Map because variations on tree heights, forest area, gaps between trees, vegetation vitality and so on are acquired as interim findings in process of computing biomass.

• Journal of the Korean Association of Geographic Information Studies
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• v.3 no.1
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• pp.44-56
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• 2000

Through the ISODATA method of unsupervised classification, the micro-landform of Gomso-Bay tidal flat was classified into mud, mixed, and sand flats by using Landsat TM image. Each tidal flat shows on apparent differences in its topographical characteristics and grain size compositions. Mud flat is occupied the innermost part of the tidal flat. Sand flat is distributed adjacent to the entrance of the bay, while the mixed one is located in the central part of the bay. Mud flat deposits have fine grain size, more than 4 in average mean phi, bad sorting, more than 1 phi in standard devation, and positive skewness. Mixed and sand flat deposits have coarse grain size, less than 4 average mean phi, good sorting, less than 1 phi in standard daviation, and negative skewness. Topographically, the mud flat consists of flat surfaces and dissected channels. The average depth of dissected channels is about 2 meters. Meanwhile, sand flat has a very flat landform with well-developed ripple marks of less than 10 centimeters in average relief. And the mixed one shows the intermediate topographical characteristics of those of mud and sand flats.

• Journal of the Institute of Convergence Signal Processing
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• v.8 no.3
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• pp.185-191
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• 2007

Remote sensing images are multispectral image data collected from several band divided by wavelength ranges. The classification of remote sensing images is the method of classifying what has similar spectral characteristics together among each pixel composing an image as the important algorithm in this field. This paper presents a pattern classification method of remote sensing images by applying a possibilistic fuzzy c-means (PFCM) algorithm. The PFCM algorithm is a hybridization of a FCM algorithm, which adopts membership degree depending on the distance between data and the center of a certain cluster, combined with a PCM algorithm, which considers class typicality of the pattern sets. In this proposed method, we select the training data for each class and perform supervised classification using the PFCM algorithm with spectral signatures of the training data. The application of the PFCM algorithm is tested and verified by using Landsat TM and IKONOS remote sensing satellite images. As a result, the overall accuracy showed a better results than the FCM, PCM algorithm or conventional maximum likelihood classification(MLC) algorithm.

• Journal of Korean Society for Geospatial Information Science
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• v.24 no.3
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• pp.39-47
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• 2016

In this study analysis the status of land cover change and land degradation of Tuul-Basin in Mongolia by using the Landsat satellite images that was taken in year of 1990, 2001 and 2011 respectively in the summer at the time of great growth of green plants. Analysis of the land cover change during time series data in Tuul-Basin, Mongolia and NDVI (Normalized Difference Vegetation Index), SAVI (Soil-Adjusted Vegetation Index) and LST (Land Surface Temperature) algorithm are used respectively. As a result shows, there was a decrease of forest and green area and increase of dry and fallow land in the study area. It was be considered as trends to be a land degradation. In addition, there was high correlation between LST and vegetation index. The land cover change or vitality of vegetation which is taken in study area can be closely related to the temperature of the surface.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.1337-1339
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• 2003

In this article, a new remote sensing image fusion model based on modified Kohonen networks is given. And a new fusion rule based on modified voting rule was established. Select Shaoxing City as the study site, located at Zhejiang Province, P.R.China. The fusion experiment between Landsat TM data (30m) and IRS-C Pan data (5.8m) was performed using the given fusion method. The fusion results show that the new method can gain better result in apply ing to the lower hill area, and the whole classification accuracy was 10% higher than the basic Kohonen method. The confusion between the woodlands and the waterbodies was also diminished.

• Journal of the Korean Institute of Navigation
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• v.21 no.1
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• pp.119-128
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• 1997

Nowadays, many satellites regularly produce digital multispectral images of the earth's surface. Multispectral images may be displayed as color pictures by selecting three components for assignment to the primary colors. It is desired to enhance these images to generate a display picture that are representativde of their features. in this paper, a false color image processing algorithm is proposed for the purpose of enchancement of the multispectral images based on the human perception. The mean of each primary component is transformed to equalo. Intensity and saturation are enhanced by modified piecewise linear contrast strectching and saturation enhancement method. The proposed method has been successfully applied the LANDSAT TM image and shows good enhancement.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.33B no.8
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• pp.42-49
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• 1996

In this paper, we propose a satellite multispectral image data compression method using classified vector quantization. This method classifies each pixel vector considering band characteristics of multispectral images. For each class, we perform both intraband and interband vector quantization to romove spatial and spectral redundancy, respectively. And residual vector quantization for error images is performed to reduce error of interband vector quantization. Thus, this method improves compression efficiency because of removing both intraband(spatial) and interband (spectral) redundancy in multispectral images, effectively. Experiments on landsat TM multispectral image show that compression efficiency of proposed method is better than that of conventional method.

• Korean Journal of Remote Sensing
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• v.12 no.2
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• pp.139-154
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• 1996

In order to evaluate the sensor`s look direction bias in the Landsat TM image and to estimate trends of primary geological lineaments, we have attempted to systematically compare lineaments in TM image, relief shadowed DEM's, and actual lineaments of geologic and topographic map through the Hough transform technique. Hough transform is known to be very effective to estimate the trend of geological lineaments, and help us to obtain the true trends of lineaments. It is often necessary to compensate the preferential enhancements of terrain lineaments in a TM image occurred by to look direction bias, and that can be achieved by utilizing an auxiliary data. In this study, we have successfully adopted the relief shadowed DEM in which the illuminating azimuth angle is perpendicular to look direction of a TM image for assessing true trends of geological lineaments. The results also show that the sum of four relief shadowed DEM's directional components can possibly be used as an alternative. In Euiseong-gun area where Sindong Group and Mayans Group are mainly distributed, geological lineaments trending $N5^{\circ}$~$10^{\circ}$W are dominant, while those of $N55^{\circ}$~$65^{\circ}$ W are major trends in Cheongsong-gun area where Hayang Group, Yucheon Group and Bulguksa Granite are distributed. Using relief shadowed DEM as an auxiliary data, we found the $N55^{\circ}$~$65^{\circ}$ W lineaments which are not cleanly observed in TM image over Euiseong-gun area. Compared with the trend of Gumchon and Gaum strike-slip faults, these lineaments are considered to be an extension of the faults. Therefore these strike-slip faults possibly extend up to Sindong Group in the northwest parts in the study area.

• Korean Journal of Remote Sensing
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• v.8 no.2
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• pp.105-123
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• 1992

A study was made on the feature extraction for non-metallic one deposits and their related geology using the Remote Sensing and Airborne Radiometric data. The area chosen is around the Haenam area, where dickite and Quarzite mines are distributed in. The geology of the area consists mainly of Cretaceous volcanics and PreCambrian metamorphic. The methods applied are study on the reflectance characteristics of minerals and rocks sampled in the study area, and the feature extraction extraction of histogram normalized images for Landsat TM and Airborne Radiometric data, and finally evaluation of applicability of some useful pattern recognition techniques for regional lithological mapping. As a result, reflectances of non-metallic minerals are much higher than rock samples in the area. However, low grade dickites are slightly higher than rock samples, probably due to their greyish colour and also their textural features which may scatter the reflectance and may be capable of capturing much hychoryl ions. The reflectances of rock samples may depend on the degree of whiteness of samples. The outcrops or mine dumps in the study area were most effectively extracted on the histogram normalized image of TM Band 1, 2 and 3, due to their high reflectivity. The Masking technique using the above bands may be the most effective and the natural colour composite may provide some success as well. The colour composite image of PCA may also be effective in extracting geological features, and airborne radiometric data may be useful to some degree as an complementary tool.

• Journal of Korean Society for Geospatial Information Science
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• v.19 no.3
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• pp.23-32
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• 2011

This research uses a distributed model, Vflo which can devide subwater shed into square grids and interpret diverse topographic elements which are obtained through GIS processing. To use the distributed model, soil wetness information was extracted through Tasseled Cap transformation from LANDSAT 7 $ETM^+$ satellite data and then they were applied to each cell of the test area, unlike previous studies in which have applied average soil condition of river basin uniformly regardless of space-difference in subwater shed. As a resut of the research, it was ascertained the spatial change of soil wetness is suited to the distributed model in a subwater shed. In addition, we derived out a relation between soil wetness of image collection time and 10 days-preceded rainfall and improved the feasibility of weights obtained by the relation equation.