• 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.

• Proceedings of the KSRS Conference
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• 2008.10a
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• pp.358-360
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• 2008

Recently there are many pilot studies for advanced application of first Korea national high resolution satellite image, which is called as KOMPSAT-MSC (Korean Multi-purpose Satellite-Multi-Spectral Camera), in Korea. In this study the forest type classification methodology is developed and its distribution map was constructed by applying high resolution satellite image, KOMPSAT-MSC, based on Tasseled Cap Transformation, especially through comparing the result of detailed filed surveying such as forest type, tree species, tree diameter, tree age and tree crown density in pilot study area.

• 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.

• Journal of Environmental Impact Assessment
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• v.19 no.3
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• pp.297-305
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• 2010

This study has intended to analyze the nature of the errors that occur as a result of shadows during the process of NDVI calculation using high-resolution satellite images of Cheongju City, in order to calibrate such errors, and to verify the results. This study has calibrated the shadow errors by utilizing the relationship between the Greenness above Bare Soil (GRABS) calculated through Tasseled-Cap transformation and the original NDVI. To verify the accuracy of the results, this study has compared the shadow area extracted by the difference between before and after calibration of NDVI, with the original shadow area. The NDVI value converged on the value of -1.0, representing water, because shadow areas could not accept the reflection value from each band. However, after performing Tasseled-Cap transformation, the NDVI of shadow areas that had converged on -1.0 prior to calibration had increased to a level similar to the NDVI of neighboring areas. In addition, the average NDVI in general had increased from -0.08 to -0.01. Finally, the shadow area drawn out was almost matched to the original one, meaning that the NDVI calibration method employed turned out to be highly accurate in extracting shadow areas.

• Korean Journal of Remote Sensing
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• v.19 no.2
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• pp.107-115
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• 2003

This research investigated the 2001 spring drought on croplands in South Korea using satellite imagery. South Korea has suffered from spring droughts almost every year. Meteorological indices have been used for monitoring droughts, however they don't tell the local severity of drought. Therefore, this research aimed at detecting the local, spatial pattern of drought severity at a cropland level. This research analyzed the agricultural drought using the wetness of remotely sensed pixels that affects the growth of early crops significantly in the spring. This research, specifically, analyzed the spatial distribution and severity of drought using the tasseled cap transformation and topographical factors. The wetness index from the tasseled cap transformation of Landsat 7 ETM/sub ＋/ imagery was very useful for detecting the 2001 spring drought susceptibility in agricultural croplands. Especially, the wetness values smaller than -0.2 were identified as the croplands that were suffering from serious water deficit. Using the water deficit pixels, drought severity was modeled finally.

• Journal of Korean Society for Geospatial Information Science
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• v.17 no.2
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• pp.55-60
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• 2009

By applying NDVI(Normalized Difference Vegetation Index) and TCT(Tasseled-Cap Transformation) image algorithm on the basis of KOMSAP-2 MSC(Multi Spectral Camera) image(Jun. 12, 2007) for Naenam-myeon, Gyeongju city in this study, DN distribution map was drawn up. Discrimination analysis of image algorithm for the accuracy improvement of forest type classification was conducted through the comparative analysis between the distribution maps of NDVI and TCT DN, and forest field surveying data, and finally, the accuracy of the forest type classification was verified through the overlay analysis with the forest field surveying data. Through this study, it is thought that low cost and high efficiency will be able to be expected in the process of the examination for the automation practicality of the forest type classification and of the production of the accurate forest type classification map by using KOMPSAT-2 MSC image.

• Proceedings of the KSRS Conference
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• 2008.10a
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• pp.378-381
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• 2008

The regulating and relaxing targets in the Land Use Regulation and Total Maximum Daily Loads are influenced by Land cover information. For the providing more accurate land information, this study attempted to generate an impervious surface map using KOMPSAT-2 image which a Korea manufactured high resolution satellite image. The classification progress of this study carried out by tasseled-cap spectral enhancement through each class extraction technique neither existing classification method. KOMPSAT-2 image of this study is enhanced by Soil Brightness Index(SBI), Green vegetation Index(GVI), None-Such wetness Index(NWI). Then ranges of extracted each index in enhanced image are determined. And then, Confidence Interval of classes was determined through the calculating Non-exceedance Probability. Spectral distributions of each class are changed according to changing of Control coefficient(${\alpha}$) at the calculated Non-exceedance Probability. Previously, Land cover classification map was generated based on established ranges of classes, and then, pervious and impervious surface was reclassified. Finally, impervious ratio of reclassified impervious surface map was calculated with blocks in the study area.

• Proceedings of the KSRS Conference
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• v.1
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• pp.94-97
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• 2006

Timber stand age information of timber in industrialized plantation forest is generally collected by field surveying which is labor-intensive, time-consuming, and very costly. It is also inconsistent in analyses perspective. As an alternative, The objective of this research is to present a practical solution for estimating timber age of loblolly pine plantation using Landsat thematic mapper (TM) images, shuttle radar topography mission (SRTM), and national elevation dataset (NED). A multivariate regression model was developed based upon satellite image-based information (i.e.normalized difference vegetation index (NDVI), tasseled cap (TC) transformation, and derived tree heights). A residual studentized technique was applied to remove potential outliers. After that, a refined age estimation model with a correlation coefficient R-square of 84.6% was obtained. Finally, the feasibility test of estimated model was performed by comparing estimated and measured stand ages of timber plantations using test datasets of plantation stands (2,032 stands). The result shows that the proposed method of this study can estimate loblolly pine stand age within an error of $2{\sim}3$ years in an effective and consistent way in terms of time and cost.

• Korean Journal of Remote Sensing
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• v.33 no.1
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• pp.79-88
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• 2017

The RapidEye satellite sensor has various spectral wavelength bands, and it can capture large areas with high temporal resolution. Therefore, it affords advantages in generating various types of thematic maps, including land cover maps. In this study, we applied a supervised classification scheme to generate high-resolution land cover maps using RapidEye images. To improve the classification accuracy, object-based classification was performed by adding brightness, yellowness, and greenness bands by Tasseled Cap Transformation (TCT) and Normalized Difference Water Index (NDWI) bands. It was experimentally confirmed that the classification results obtained by adding TCT and NDWI bands as input data showed high classification accuracy compared with the land cover map generated using the original RapidEye images.

• 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.