• Proceedings of the KSRS Conference
• /
• 2000.04a
• /
• pp.108-113
• /
• 2000

1991년 5월 31일, 1996년 9월 1일 그리고 1997년 6월 17일의 Landsat TM 위성화상자료를 Tasseled Cap 변환을 통하여 추출되는 4번째 특징을 연무지수농도로 사용할 수 있는가를 알아보고 또한 이렇게 산출된 연무지수를 사용하여 각 일자별 대기오염측정인자와 기상측정인자와 비교하고 서울시 각 행정구별 연무현상에 대하여 알아보았다. 동일화상내에서 연무가 있는 삼림지역과 연무가 없는 삼림지역을 표본조사하여 검정한 결과 Tasseled Cap 변환의 4번째 특징이 연무지수와 관련되어 있음을 밝혔고 기상측정 인자의 시정거리와 연무지수사이의 역상관의 경향이 있음을 확인하엿다. 대기오염측정 인자의 경우 정확한 연관성을 파악하지는 못하였는데 이는 연무현상이 대기오염 물질의 2차 생성물에 의한 것으로 추정된다. 각 날자에서 1991년 5월 31일과 1997년 6월 17일 서울에 연무현상이 있음을 확인하였고 대체적으로 날짜에 상관 없이 중구, 종로구, 강남구, 서초구에서 높은 연무지수를 나타내었다.

• Journal of the Korean earth science society
• /
• v.25 no.8
• /
• pp.754-763
• /
• 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
• /
• v.10 no.3
• /
• pp.481-492
• /
• 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.

• Economic and Environmental Geology
• /
• v.28 no.4
• /
• pp.405-414
• /
• 1995

An algorithm for monitoring geomorphological change using remote sensing data is investigated and tested using two LANDSAT TM data sets acquired over the Kyunggi Bay on April 15 1986 and September 22 1992, respectively. The algorithm exploits change vector analysis and tasseled cap transform. Although change vector analysis is effective for change detection, efficiency is decreased as the number of variables are increased. In this algorithm, we overcome the problem by utilizing the tasseled cap transform which can reduce six bands of LANDSAT TM data into only two components called Brightness and Greenness. The test results demonstrate that the algorithm is very effective in monitoring small-scaled changes over coastal area as well as significant changes in geomorphology. The resulting change vector image, however, is more sensitive to the changes occurred by human activities than by pure geological processes mainly because of relatively short time interval between two LANDSAT TM data sets.

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

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

• Proceedings of the KSRS Conference
• /
• 2009.03a
• /
• pp.3-6
• /
• 2009

본 연구에서는 경주시 내남면 일대를 대상으로 KOMPSAT MSC(Multi Spectral Camera) 영상(2007.06.12)을 이용하여 TCT(Tasseled-Cap Transformation), NDVI(Normalized Difference Vegetation Index) 알고리즘을 적용하여 분포도를 작성 하였으며 TCT DN 값을 기초로 영상 강조 및 변환을 통한 임상분류에 적합한 밴드 추출과 NDVI 분포도에서의 DN값을 기초로 산림현장 조사 결과에서 취득된 결과와의 비교 분석을 통하여 알고리즘에 대한 임상분류에 있어서의 변별력 분석을 수행하였다. 본 연구를 통하여 KOMPSAT MSC 영상에서의 임상분류를 위한 식생 알고리즘 적용 가능성을 검토하고자 한다.

• Korean Journal of Remote Sensing
• /
• v.30 no.2
• /
• pp.275-292
• /
• 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 Association of Geographic Inforamtion Studies Conference
• /
• 2004.03a
• /
• pp.295-300
• /
• 2004

변화탐지를 위하여 기존에 사용하던 화소차 혹은 화소비 변화탐지 기법은 단밴드의 영상을 사용하므로 다중분광 자료를 활용하기 어렵고 변화지역의 유형을 추출하는데 적절하지 못하다는 단점이 있다. 후분류 변화탐지 기법은 다중분팡 영상의 활용이 가능하고 변화지역의 변화 유형을 파악할 수 있지만 변화탐지 성과가 분류 결과의 정확도에 의해 크게 영향을 받는다. 이에 반해 CVA(Change Vector Analysis) 변화탐지는 여러 밴드의 다중분광 영상을 이용하여 변화지역을 탐지할 뿐 아니라 피복 변화의 경향을 파악할 수 있어 보다 효율적인 기법으로 평가받고 있다. 기본적인 CVA 변화탐지는 일반적으로 다중분광 영상의 red 밴드와 infrared 밴드의 영상을 조합하여 변화탐지를 수행하여 식생 및 인공물의 변화를 탐지한다. 본 연구에서는 단순한 red/NIR 밴드간의 조합 외에 식생의 특성을 계수로 변환하는 PDA(Pattern Decomposition Analysis) 변환 및 Tasseled Cap 변환 결과를 이용한 CVA 변화탐지를 수행하고 각 결과의 정확도를 비교하여 보다 효율적인 식생 변화탐지 기법을 제안하고자 하였다.

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