• Korean Journal of Remote Sensing
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• v.30 no.2
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• pp.275-292
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• 2014

The objective of this study is to determine Tasseled Cap Transformation (TCT) coefficients for the Geostationary Ocean Color Imager (GOCI). TCT is traditional method of analyzing the characteristics of the land area from multi spectral sensor data. TCT coefficients for a new sensor must be estimated individually because of different sensor characteristics of each sensor. Although the primary objective of the GOCI is for ocean color study, one half of the scene covers land area with typical land observing channels in Visible-Near InfraRed (VNIR). The GOCI has a unique capability to acquire eight scenes per day. This advantage of high temporal resolution can be utilized for detecting daily variation of land surface. The GOCI TCT offers a great potential for application in near-real time analysis and interpretation of land cover characteristics. TCT generally represents information of "Brightness", "Greenness" and "Wetness". However, in the case of the GOCI is not able to provide "Wetness" due to lack of ShortWave InfraRed (SWIR) band. To maximize the utilization of high temporal resolution, "Wetness" should be provided. In order to obtain "Wetness", the linear regression method was used to align the GOCI Principal Component Analysis (PCA) space with the MODIS TCT space. The GOCI TCT coefficients obtained by this method have different values according to observation time due to the characteristics of geostationary earth orbit. To examine these differences, the correlation between the GOCI TCT and the MODIS TCT were compared. As a result, while the GOCI TCT coefficients of "Brightness" and "Greenness" were selected at 4h, the GOCI TCT coefficient of "Wetness" was selected at 2h. To assess the adequacy of the resulting GOCI TCT coefficients, the GOCI TCT data were compared to the MODIS TCT image and several land parameters. The land cover classification of the GOCI TCT image was expressed more precisely than the MODIS TCT image. The distribution of land cover classification of the GOCI TCT space showed meaningful results. Also, "Brightness", "Greenness", and "Wetness" of the GOCI TCT data showed a relatively high correlation with Albedo ($R^2$ = 0.75), Normalized Difference Vegetation Index (NDVI) ($R^2$ = 0.97), and Normalized Difference Moisture Index (NDMI) ($R^2$ = 0.77), respectively. These results indicate the suitability of the GOCI TCT coefficients.

• Proceedings of the Korean Geotechical Society Conference
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• 2004.03b
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• pp.592-599
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• 2004

Talus topography is that weathered rock clasts were accumulated dropping in steep slope to action of gravity. Rock fall talus is formed by the accumulation of rock debris falling as individual particles from a cliff. If the collapse is produced in talus slope. it will be possible the loss of manpower and country. Despite correct access about talus is required, domestic research was scientific access about talus short because of short resolution of aerophoto and difficulties of research about huge talus. In this Study, Our research team analgize the wetness index using the geomorphogical data. Lineament through wetness index is simillar with distribution of the talus. And, the aim of the present study is to review and compare fabric data derived from rock fall talus about orientation, distribution and morphology. These deposits tend to have approximately equal amounts of clasts oriented parallel and perpendicular to the dip direction of the slope. And, platy- shaped clasts dominate the proximal and intermediate parts of the talus, wheres blocky-shaped clasts is more common in the distal part. we carry out Rock Fall Simulation. And, We install criteria of the rockfall protective barrier using talus and geomorphological characteristics.

• Korean Journal of Remote Sensing
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• v.30 no.5
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• pp.587-596
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• 2014

Soil moisture is one of the most important interests in hydrological response and the interaction between the land surface and atmosphere. Estimation of Antecedent Wetness Conditions (AWC) which is soil moisture condition prior to a rainfall in the basin should be considered for rainfall-runoff prediction. In this study, Soil Wetness Index (SWI), Antecedent Precipitation Index ($API_5$), remotely sensed Soil Moisture ($SM_{rs}$), and 5 days ground Soil Moisture ($SM_{g5}$) were selected to estimate the AWC at four study area in the Korean Peninsula. The remotely sensed soil moisture data were taken from the AMSR-E soil moisture archive. The maximum potential retention ($S_{obs}$) was obtained from direct runoff and rainfall using Soil Conservation Service-Curve Number (SCS-CN) method by rainfall data of 2011 for each study area. Results showed the great correlations between the maximum potential retention and SWI with a mean correlation coefficient which is equal to -0.73. The results of time length representing the time scale of soil moisture showed a gap from region to region. It was due to the differences of soil types and the characteristics of study area. Since the remotely sensed soil moisture has been proved as reasonable hydrological variables to predict a wetness in the basin, it should be continuously monitored.

• Journal of Korea Water Resources Association
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• v.36 no.2
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• pp.251-261
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• 2003

The resolution issue of various soil moisture prediction parameters such as wetness index and curvatures is addressed. The sensitivities of various index are discussed on the base of the statistical aspects. The statistical analysis of three flow determination algorithms on the DEM is performed. The upslope area associated with SFD algorithm appear to more sensitive than the parameters of the other algorithms(MFD, DEMON). The wetness index shows relatively less variation both in resolution and the calculation Procedures.

• Proceedings of the Korea Water Resources Association Conference
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• 2010.05a
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• pp.1640-1644
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• 2010

In this study the wetness/dryness in Geum river basin are classified by dint of cumulative probability density function of monthly moisture index and the long term changes of them are analyzed based on climatic water balance concept. The drought events in Geum river basin are selected through evaluation of monthly moisture index and the various hydrological properties of them are investigated in detail. Also the trends of time-series of climatic water balance components are examined by Seasonal Kendall test and the variability of hydrological cycle in Geum river basin during the recent decade is inquired. It is judged that the results of this study can be contributed to establishment of the counter plan against the future drought events as the fundamental information.

• Journal of Forest and Environmental Science
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• v.30 no.1
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• pp.91-100
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• 2014

This research was conducted on the conservation area of Baekdudaegan, Kangwondo under the purpose of evaluating bio-diversity according to the changes of spatial scale, using GIS data and spatial filtering method. The diversity index was calculated based on the information of species of The $5^{th}$ forest type map using Shannon-weaver index (H'), evenness index ($E_i$) and richness index ($R_i$). The diversity index was analyzed and compared according to the changes of 12 spatial scales from Kernel size $3{\times}3$ to $73{\times}73$ and basin unit. As for H' and $R_i$, spatial scale increased as diversity index decreased, while $E_i$ decreases gradually. H' and $R_i$ was highest; each 1.1 and 0.6, when the Kernel size was $73{\times}73$, while $E_i$ was 0.2, the lowest. When you look at according to the basin unit, for large basin unit, 'YeongDong' region shows higher diversity index than 'YeongSeo' region. For middle basin unit, 'Gangneung Namdaecheon' region, and for small basin unit, 'Gangneung Namdaecheon' and 'Gangneung Ohbongdaem' region shows high diversity index. When you look at the relationship between diversity index and Geographic factors, H' shows positive relation to curvature and sunshine factor while shows negative to elevation, slope, hillshade, and wetness index. Also $E_i$ was similar to the relationship between H' and Geographic factor. Meanwhile, $R_i$ shows positive relationship to curvature and sunshine factor, while negative to elevation, slope, hillshade, and wetness index. macro unit diversity index evaluation was possible through the GIS data and spatial filtering, and it can be a good source for local biosphere conservation policy making.

• Korean Journal of Remote Sensing
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• v.24 no.6
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• pp.583-590
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• 2008

This study presents a correlation between timber Age, image bands and vegetation indices for timber age estimation. Basically, this study used Landsat TM images of three difference years (1994, 1994, 1998) and difference between Shuttle Radar Topography Mission (SRTM) and National Elevation Dataset (NED). Bands of 4, 5 and 7, Normalized Difference Vegetation Index (NDVI), Infrared Index (II), Vegetation Condition Index (VCI) and Soil Adjusted Vegetation Index (SA VI) were obtained from Landsat TM images. Tasseled cap - greenness and wetness images were also made by Tasseled cap transformation. Finally, analysis of correlation between timber age, difference between Shuttle Radar Topography Mission (SRTM) and National Elevation Dataset (NED), individual TM bands (4, 5, 7), Normalized Difference Vegetation Index (NDVI), Tasseled cap-Greenness, Wetness, Infrared Index (II), Vegetation Condition Index (VCI) and Soil Adjusted Vegetation Index (SAVI) using regression model. In this study about 1,992 datasets were analyzed. The Tasseled cap - Wetness, Infrared Index (II) and Vegetation Condition Index (VCI) showed close correlation for timber age estimation.

• Journal of the Korean earth science society
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• v.35 no.1
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• pp.54-68
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• 2014

In order to analyze the land surface properties in Seoul and its surrounding metropolitan area, several indices and land surface temperature were calculated by the Landsat satellites (e.g., Landsat 5, Landsat 7, and Landsat 8). The Landsat data came from only in the fall season with Landsat 5 on October 21, 1985, Landsat 7 on September 29, 2003, and Landsat 8 on September 16, 2013. The land surface properties used are the indices that represented Soil Adjusted Vegetation Index (SAVI), Modified Normalized Difference Wetness Index (MNDWI), Normalized Difference Wetness Index (NDWI), Tasseled cap Brightness, Tasseled cap Greenness, Tasseled cap Wetness Index, Normalized Difference Vegetation Index (NDVI) and Normalized Difference Built-up Index (NDBI) and the land surface temperature of the area in and around Seoul. Most indices distinguish very well between urban, rural, mountain, building, river and road. In particular, most of the urbanization is represented in the new city (e.g., Ilsan) around Seoul. According to NDVI, NDBI and land surface temperature, urban expansion is displayed in the surrounding area of Seoul. The land surface temperature and surface elevation have a strong relationship with the distribution and structure of the vegetation/built-up indices such as NDVI and NDBI. While the NDVI is positively correlated with the land surface temperature and is also negatively correlated with the surface elevation, the NDBI have just the opposite correlations, respectively. The NDVI and NDBI index is closely associated with the characteristics of the metropolitan area. Landsat 8 and Landsat 5 have very strong correlations (more than -0.6) but Landsat 7 has a weak one (lower than -0.5).

• Journal of the Korean earth science society
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• v.25 no.8
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• pp.754-763
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• 2004

This study was aimed to examine the Landsat Enhanced Thematic Mapper Plus (ETM+) index, which matches well with the field survey data in the wildfire area of Gangneung, Gangweon Province, Korea. In the wildfire area NDVI (Normalized Difference Vegetation Index), SAVI (Soil Adjusted Vegetation Index), and Tasseled Cap Transformation Index (Brightness, Wetness, Greenness) were compared with field survey data. NDVI and SAVI were very useful in detecting the difference between the wildfire and non-wildfire area, but not so in classify the soil types in the wildfire area. The soil plane based on the Tasseled Cap Transformation showed a better result in classifying the soil types in the wildfire areas than NDVI and SAVI, and corresponded well with field survey data. Using a linear function based on greenness and wetness in the Tasseled Cap Transformation is expected to provide a more efficient and quicker method to classify wildfire areas.

• Spatial Information Research
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• v.10 no.3
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• pp.481-492
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• 2002

The severity and spatial Patterns of spring drought on the croplands arc investigated using satellite imagery(Landsat ETM+). It is necessary to analyze the area droughty conditions in order to decrease the damage and make the efficient policies. In this context, the information about soil moisture levels, which were fatal factors to the crop growth, was acquired from wetness calculated from Tasseled cap transformation. We confirmed that the wetness values have a strong correlation with NDVI and the principal components. The result showed that the intensity of vegetation covering the surface could be understood as the index of the impacts of drought on croplands and these relationships were effective to classify dry areas in satellite imagery.