• Journal of Korean Society of Forest Science
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• v.96 no.5
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• pp.591-601
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• 2007

Spatial information on forest biomass is an important factor to evaluate the capability of forest as a carbon sequestrator and is a core independent variable required to drive models which describe ecological processes such as carbon budget, hydrological budget, and energy flow. The objective of this study is to understand the relationship between satellite image and field data, and to quantitatively estimate and map the spatial distribution of forest biomass. Landsat Enhanced Thematic Mapper (ETM+) derived vegetation indices and field survey data were applied to estimate the biomass distribution of mountainous forest located in Gwangneung Experimental Forest (230 ha). Field survey data collected from the ground plots were used as the dependent variable, forest biomass, while satellite image reflectance data (Band 1~5 and Band 7), Normalized Difference Vegetation Index (NDVI), Soil-Adjusted Vegetation Index (SAVI), and RVI (Ratio Vegetation Index) were used as the independent variables. The mean and total biomass of Gwangneung catchment area were estimated to be about 229.5 ton/ha and $52.8{\times}10^3$ tons respectively. Regression analysis revealed significant relationships between the measured biomass and Landsat derived variables in both of deciduous forest ($R^2=0.76$, P < 0.05) and coniferous forest ($R^2=0.75$, P < 0.05). However, there still exist many uncertainties in the estimation of forest ecosystem parameters based on vegetation remote sensing. Developing remote sensing techniques with adequate filed survey data over a long period are expected to increase the estimation accuracy of spatial information of the forest ecosystem.

• Journal of Environmental Impact Assessment
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• v.15 no.2
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• pp.121-127
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• 2006

The conventional water quality measurements by point sampling provide only site specific temporal water quality information but not the synoptic geographic coverage of water quality distribution. To circumvent these limitations in temporal and spatial measurements, the use of remote sensing is increasingly involved in the water quality monitoring research. In other to assess a trophic state of Yongdam reservoir using satellite imagery data, I obtained Landsat ETM data and water quality data on 16th September and 18th October 2001. The approach involved acquisition of water quality samples from boats at 33 sites on 16th September and 30 sites on 18th October 2001, simultaneous with Landsat-7 satellite overpass. The correlation coefficients between the DN values of the imagery and the concentrations of chlorophyll-a were analyzed. The visible bands(band 1,2,3) and near infrared band(band 4) data of September image showed the correlation coefficient values higher than 0.9. The October image showed the correlation coefficient values about 0.7 due to the atmospheric effect and low variation of chlorophyll-a concentration. Regression models between the chrophyll-a concentration and DN values of the Landsat imagery data have been developed for each image. The regression model was determined based on the spectral characteristics of chlorophyll, so the green band(band 2) and near infrared band(band 4) were selected to generate a trophic state map. The coefficient of determination(R2) of the regression model for 16th September was 0.95 and that of the regression model for 18th October was 0.55. According to the trophic state map made based on Aizaki's TSI and chlorophyll-a concentration, the trophic state of Yongdam reservoir was mostly eutrophic state during this study.

• Journal of Korean Society for Geospatial Information Science
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• v.11 no.2 s.25
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• pp.47-52
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• 2003

Change detection and analysis is a powerful application of remote sensing, in that the spectral resolution of multi-band sensors can be used to advantage in monitoring both significant and subtle land cover changes over time. In this study, the LANDSAT TM data was used to detect the change areas affected by flood from a heavy rainfall. The study area is the Nakdong River located in the Korea peninsular. Among the several change detection techniques, change vector analysis(CVA), principle component analysis(PCA) and image difference approach are utilized in this paper. CVA uses any number of spectral bands from multi-date satellite data to produce change image that yield information of the magnitude and direction of differences pixel values. And accuracy assessment was carried out with a change image produced from three techniques. In result, CVA was found to be the most accurate for detecting areas affected by flood. CVA with the overall accuracy and Kappa coefficient of 97.27 percent and 94.45 percent, respectively.

• Journal of the Korean Association of Geographic Information Studies
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• v.3 no.4
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• pp.73-86
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• 2000

This study focused on extracting an efficient method in the fruit cropping distribution mapping with various classification methods using multi-temporal satellite images and Geographic Information Systems(GIS). For this study, multi-temporal Landsat TM images, in observation data and existing fruit cropping area statistics were used to compare and analyze the properties of fruit cropping and seasonal distribution per classification method. As a result, this study concludes that Maximum Likelihood Method with earlier autumn satellite image was most efficient for the fruit cropping mapping using Landsat TM image. In addition, it was clarified that cropping area per administrative boundary was prepared and distribution pattern was identified efficiently using GIS spatial analysis.

• Korean Journal of Remote Sensing
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• v.15 no.3
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• pp.217-225
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• 1999

Through the ISODATA method of unsupervised classification, the micro-landform of Julpo-Bay tidal flat was classified into mudflat, mixedflat, and sandflat using Landsat TM image. Each showed an apparent differences in its topographical characteristics and grain size composition. Mudflat occupied innermost part of the tidal flat, sandflat located closest to the entrance of the bay and mixed flat in the center is. For example, mudlflats are formed with flat faces and tidal channel. Topographically, mudflat consist of tidal channels and flat intermediate surface. Its average relief of them is about 2 meter. Meanwhile, sandflat comprised very flat landform with well-developed ripple marks of less than 10cm average relief. And Mixed flat stood in between. In addition, Out of 7 bands of Landsat TM images, band 5 and 7 provided the highest power level for discrimination between micro-landforms of the tidal flat. Band 4 showed a clear boundary between the land and tidal flat, and band 3 did its share by showing well a boundary between the sea surface and the tidal flat.

• Spatial Information Research
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• v.18 no.2
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• pp.1-12
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• 2010

At present, monitoring programmes for green network have been mainly based on field sampling, which relies on attributes of an area at one point in time, reflecting an emphasis on the small number of in-situ data. One of the major disadvantages of traditional field monitoring is that it is costly, laborious and time consuming due to the large number of samples required. The aim of this research was to evaluate green network based on pixel of Landsat TM satellite image. An empirical study for a case study site was conducted to demonstrate how a standard remote sensing technology can be used to assist in monitoring the green network based on pixel. The pixel-based analysis made it possible to identify area-wide patterns of green network subject to many different type of artificial structures, which cannot be acquired by traditional field sampling. It was demonstrated that the degradation trends of green network could be used effectively as an indicator to restrict further development of the sites since the quantitative data generated from remote sensing can present area-wide visual evidences by permanent record. It is anticipated that this research output could be used as a valuable reference to support more scientific and objective decision-making in monitoring green network.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.28 no.4
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• pp.471-476
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• 2010

This study aimed at monitoring, mapping, and assessing the land degradation in the desert area. In this research, the Landsat TM and ETM+ imageries to assess the extent of land degradation for study area during the period from 1991 to 2007. Were used to study supervized, unsupervized classfication and NDVI land cover changes in the desert area in Mongolia. The classified map consists of five classes of water, vegetation, slight desertification, middle desertification and sever desertification. It shows that for determination classfication methods and NDVI, desertification map of the study area are prepared. The result showed accounting for a clear deterioration in vegetative cover, an increase of sever desertification and a decrease in middle desertification and slight desertification respectively of the total study area.

• Journal of Korean Society for Geospatial Information Science
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• v.10 no.1 s.19
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• pp.101-108
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• 2002

The studies using satellite data have been progressed in many area. Especially, Landsat data have been widely used due to its wide coverage. To establish a plan for preserving fishing and environment preservation, measurement of sea temperature is important. The measurement of the sea temperature was made on the ship. However, the measurement on the ship could not provide sufficient information due to the poor timing with relatively longer measurement, and point-based data acquisition. Thus remote sensing technique is required because satellite image data offer more wide coverage in sea temperature monitoring. The purpose of this paper was to study on the sea temperature monitoring with thermal band information of Landsat ETM+. From this study, sea temperature map of Cheju island has been made efficiently.

• Korean Journal of Remote Sensing
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• v.14 no.2
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• pp.149-164
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• 1998

The main object of this study was to prove the effectiveness of different merging methods by using the high resolution IRS(Indian Remote Sensing Satellite)-1C panchromatic data and the multispectral Landsat TM data. The five methods used to merging the information contents of each of the satellite data were the intensity-hue-saturation(IHS), principal component analysis(PCA), high pass filter(HPF), ratio enhancement method and look-up-table(LUT) procedures. Two measures are used to evaluate the merging method. These measures include visual inspection and comparisons of the mean, standard deviation and root mean square error between merged image and original image data values of each band. The ratio enhancement method was well preserved the spectral characteristics of the data. From visual inspection, PCA method provide the best result, HPF next, ratio enhancement, IHS and LUT method the worst for the preservation of spatial resolution.

• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
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• 2006.04a
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• pp.281-286
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• 2006

In this study, acquired time series Landsat TM/ETM+ image to extract land surface temperature for wide-area region and executed geometric correction and radiometric correction. And extracted land surface temperature using NASA Model, and I achieved the first correction by perform land coverage category for study region and applies characteristic emission rate. Land surface temperature that acquire by the first correction analyzed correlation with Meteorological Administration's temperature data by regression analysis, and established correction formula. And I wished to improve accuracy of land surface temperature extraction using satellite image by second correcting deviations between two datas using establishing correction formula. As a result, land surface temperature that acquire by 1,2th correction could correct in mean deviation of about ${\pm}3.0^{\circ}C$ with Meteorological Administration data. Also, could acquire land surface temperature about study region by relative high accuracy by applying to other Landsat image for re-verification of study result.