• Title/Summary/Keyword: Landsat-8

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The Comparison of Thermal Infrared Satellite Observation for Plume Assessment of Thermal Discharge (온배수 확산 평가를 위한 열적외선 위성관측 비교)

  • Jeong, Jong-Chul
    • Journal of Environmental Impact Assessment
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    • v.24 no.4
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    • pp.367-374
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    • 2015
  • To examine the effect of thermal discharge from nuclear power plants, Sea Surface Temperature (SST) is one of the most important variables measured by satellite remote sensing. However, the study was not much comparison of field data and satellite SST from operational Landsat 8 Thermal Infrared Sensor(TIRS) and Landsat 7 ETM+. The Landsat 8 TIRS have 2 spilt Thermal Infrared channels but ETM+ uses one channel for extracting of SST. In spite of that this research carried out that Landsat 7 ETM+ have more profitable for correction of SST than Landsat 8 TIRS. The used 15 Landsat 7 and 8 Thermal Infrared data of path/row 114-36 were processed by SST algorithm of ENVI and IDL. The in-situ SST data from KHOA(Korea Hydrographic and Oceanographic Administration) compared with satellite SST and the accuracy of extracted SST were assessed by each field sites in-situ point data with time series satellite SST.

Conjugation of Landsat Data for Analysis of the Land Surface Properties in Capital Area (수도권 지표특성 분석을 위한 Landsat 자료의 활용)

  • Jee, Joon-Bum;Choi, Young-Jean
    • 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).

Analysis of Land Surface Temperature from MODIS and Landsat Satellites using by AWS Temperature in Capital Area (수도권 AWS 기온을 이용한 MODIS, Landsat 위성의 지표면 온도 분석)

  • Jee, Joon-Bum;Lee, Kyu-Tae;Choi, Young-Jean
    • Korean Journal of Remote Sensing
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    • v.30 no.2
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    • pp.315-329
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    • 2014
  • In order to analyze the Land Surface Temperature (LST) in metropolitan area including Seoul, Landsat and MODIS land surface temperature, Automatic Weather Station (AWS) temperature, digital elevation model and landuse are used. Analysis method among the Landsat and MODIS LST and AWS temperature is basic statistics using by correlation coefficient, root-mean-square error and linear regression etc. Statistics of Landsat and MODIS LST are a correlation coefficient of 0.32 and Root Mean Squared Error (RMSE) of 4.61 K, respectively. And statistics of Landsat and MODIS LST and AWS temperature have the correlations of 0.83 and 0.96 and the RMSE of 3.28 K and 2.25 K, respectively. Landsat and MODIS LST have relatively high correlation with AWS temperature, and the slope of the linear regression function have 0.45 (Landsat) and 1.02 (MODIS), respectively. Especially, Landsat 5 has lower correlation about 0.5 or less in entire station, but Landsat 8 have a higher correlation of 0.5 or more despite of lower match point than other satellites. Landsat 7 have highly correlation of more than 0.8 in the center of Seoul. Correlation between satellite LSTs and AWS temperature with landuse (urban and rural) have 0.8 or higher. Landsat LST have correlation of 0.84 and RMSE of more than 3.1 K, while MODIS LST have correlation of more than 0.96 and RMSE of 2.6 K. Consequently, the difference between the LSTs by two satellites have due to the difference in the optical observation and detection the radiation generated by the difference in the area resolution.

Landsat 8-based High Resolution Surface Broadband Albedo Retrieval (Landsat 8 위성 기반 고해상도 지표면 광대역 알베도 산출)

  • Lee, Darae;Seo, Minji;Lee, Kyeong-sang;Choi, Sungwon;sung, Noh-hun;Kim, Honghee;Jin, Donghyun;Kwon, Chaeyoung;Huh, Morang;Han, Kyung-Soo
    • Korean Journal of Remote Sensing
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    • v.32 no.6
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    • pp.741-746
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    • 2016
  • Albedo is one of the climate variables that modulate absorption of solar energy, and its retrieval is important process for climate change study. High spatial resolution and long-term consistent periods are important considerations in order to efficiently use the retrieved albedo data. This study retrieved surface broadband albedo based on Landsat 8 as high resolution which is consistent with Landsat 7. First of all, we analyzed consistency of Landsat 7 channel and Landsat 8 channel. As a result, correlation coefficient(R) on all channels is average 0.96. Based on this analysis, we used multiple linear regression model using Landsat 7 albedo, which is being used in many studies, and Landsat 8 reflectance channel data. The regression coefficients of each channel calculated by regression analysis were used to derive a formula for converting the Landsat 8 reflectance channel data to broadband albedo. After Landsat 8 albedo calculated using the derived formula is compared with Landsat 7 albedo data, we confirmed consistency of two satellite using Root Mean Square Error (RMSE), R-square ($R^2$) and bias. As a result, $R^2$ is 0.89 and RMSE is 0.003 between Landsat 7 albedo and Landsat 8 albedo.

Comparison of Surface Temperatures between Thermal Infrared Image and Landsat 8 Satellite (열적외 영상과 Landsat 8 위성으로부터 관측된 지표면 온도 비교)

  • Cho, Chaeyoon;Jee, Joon-Bum;Park, Moon-Soo;Park, Sung-Hwa;Choi, Young-Jean
    • Journal of Korean Society for Atmospheric Environment
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    • v.32 no.1
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    • pp.46-56
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    • 2016
  • In order to analyze the surface temperature in accordance with the surface material, surface temperatures between Thermal InfraRed Image (TIRI) and Landsat 8 satellite observed at the commercial area (Gwanghwamun) and residential area (Jungnang) are compared. The surface temperature from TIRI had applied atmospheric correction and compared with that from Landsat 8. The surface temperatures from Landsat 8 at Gwanghwamun and Jungnang are underestimated in comparison with that from TIRI. The difference of surface temperature between the two methods is greater in summer than in winter. When the analysis area was divided into detailed regions, depending on the material and the position of the surface, correlation of surface temperature between TIRI with Landsat 8 is as low as 0.29 (Gwanghwamun) and 0.18 (Jungnang), respectively. The results were caused from the resolution difference between the two methods. While the surface temperatures of each zone from Landsat 8 were observed almost constant, high-resolution TIRI observed relatively precise surface temperatures. When the each area was averaged as one space, correlation of surface temperature between TIRIs and Landsat 8 is more than 0.95. The spatially averaged surface temperature is higher at Jungnang, representing residential areas, than at Gwanghwamun, representing commercial areas. As a result, the observation of high resolution is required in order to observe the precise surface temperature. This is because it appears that the spatial distribution of the various surface temperature in the range of micro-scale according to the conditions of the ground surface.

Analysis on the Effect of Spectral Index Images on Improvement of Classification Accuracy of Landsat-8 OLI Image

  • Magpantay, Abraham T.;Adao, Rossana T.;Bombasi, Joferson L.;Lagman, Ace C.;Malasaga, Elisa V.;Ye, Chul-Soo
    • Korean Journal of Remote Sensing
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    • v.35 no.4
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    • pp.561-571
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    • 2019
  • In this paper, we analyze the effect of the representative spectral indices, normalized difference vegetation index (NDVI), normalized difference water index (NDWI) and normalized difference built-up index (NDBI) on classification accuracies of Landsat-8 OLI image.After creating these spectral index images, we propose five methods to select the spectral index images as classification features together with Landsat-8 OLI bands from 1 to 7. From the experiments we observed that when the spectral index image of NDVI or NDWI is used as one of the classification features together with the Landsat-8 OLI bands from 1 to 7, we can obtain higher overall accuracy and kappa coefficient than the method using only Landsat-8 OLI 7 bands. In contrast, the classification method, which selected only NDBI as classification feature together with Landsat-8 OLI 7 bands did not show the improvement in classification accuracies.

Retrieval of Land SurfaceTemperature based on High Resolution Landsat 8 Satellite Data (고해상도 Landsat 8 위성자료기반의 지표면 온도 산출)

  • Jee, Joon-Bum;Kim, Bu-Yo;Zo, Il-Sung;Lee, Kyu-Tae;Choi, Young-Jean
    • Korean Journal of Remote Sensing
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    • v.32 no.2
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    • pp.171-183
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    • 2016
  • Land Surface Temperature (LST) retrieved from Landsat 8 measured from 2013 to 2014 and it is corrected by surface temperature observed from ground. LST maps are retrieved from Landsat 8 calculate using the linear regression function between raw Landsat 8 LST and ground surface temperature. Seasonal and annual LST maps developed an average LST from season to annual, respectively. While the higher LSTs distribute on the industrial and commercial area in urban, lower LSTs locate in surrounding rural, sea, river and high altitude mountain area over Seoul and surrounding area. In order to correct the LST, linear regression function calculate between Landsat 8 LST and ground surface temperature observed 3 Korea Meteorological Administration (KMA) synoptic stations (Seoul(ID: 108), Incheon(ID: 112) and Suwon(ID: 119)) on the Seoul and surrounding area. The slopes of regression function are 0.78 with all data and 0.88 with clear sky except 5 cloudy pixel data. And the original Landsat 8 LST have a correlation coefficient with 0.88 and Root Mean Square Error (RMSE) with $5.33^{\circ}C$. After LST correction, the LST have correlation coefficient with 0.98 and RMSE with $2.34^{\circ}C$ and the slope of regression equation improve the 0.95. Seasonal and annual LST maps represent from urban to rural area and from commercial to industrial region clearly. As a result, the Landsat 8 LST is more similar to the real state when corrected by surface temperature observed ground.

Availability of Land Surface Temperature Using Landsat 8 OLI/TIRS Science Products (Landsat 8 OLI/TIRS Science Product를 활용한 지표면 온도 유용성 평가)

  • Park, SeongWook;Kim, MinSik
    • Korean Journal of Remote Sensing
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    • v.37 no.3
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    • pp.463-473
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    • 2021
  • Recently, United States Geological Survey (USGS) distributed Landsat 8 Collection 2 Level 2 Science Product (L2SP). This paper aims to derive land surface temperature from L2SP and to validate it. Validation is made by comparing the land surface temperature with the one calculated from Landsat 8 Collection 1 Level 1 Terrain Precision (L1TP) and the one from Automated Synoptic Observing System (ASOS). L2SP is calculated from Landsat 8 Collection 2 Level 1 data and it provides land surface temperature to users without processing surface reflectance data. Landsat 8 data from 2018 to 2020 is collected and ground sensor data from eight sites of ASOS are used to evaluate L2SP land surface temperature data. To compare ground sensor data with remotely sensed data, 3×3 grid area data near ASOS station is used. As a result of analysis with ASOS data, L2SP and L1TP land surface temperature shows Pearson correlation coefficient of 0.971 and 0.964, respectively. RMSE (Root Mean Square Error) of two results with ASOS data is 4.029℃, 5.247℃ respectively. This result suggests that L2SP data is more adequate to acquire land surface temperature than L1TP. If seasonal difference and geometric features such as slope are considered, the result would improve.

Fully Automated Generation of Cloud-free Imagery Using Landsat-8 (Landsat-8을 이용한 자동화된 구름 제거 영상 생성)

  • Kim, Byeong Hee;Kim, Yong;Han, You Kyung;Choi, Won Seok;Kim, Yong
    • Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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    • v.32 no.2
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    • pp.133-142
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    • 2014
  • Landsat is one of the popular satellites for observing land surface that is used in various areas including monitoring, detecting and classifying changes in land surface. However, shades, which cloud itself and its shadow, interrupted often clear observation and analysis of ground surface. For this reason, the process of removing shades and restoring original ground surfaces are critical for geospatial users. This study is planned to recommend a methodology for more accurate and clear images of Landsat-8 sensor, which provided two additional bands of costal/aerosol and cirrus. In fact, those bands are known as functioned effectively in detecting and restoring shades. Otsu's thresholding technique to detect clouds, we replaced those detective shades by using experimental and reference images. In accurate assessment, the overall accuracy and kappa coefficients were about 85% and 0.7128, respectively. This indicates that the proposed technique is effective for recovering the original land surface.

Active Fire Detection Using Landsat 8 OLI Images: A Case of 2019 Australia Fires (Landsat 8 OLI 영상을 이용한 산불탐지: 2019년 호주 산불을 사례로)

  • Kim, Nari;Lee, Yangwon
    • Korean Journal of Remote Sensing
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    • v.36 no.5_1
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    • pp.775-784
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    • 2020
  • Recent global warming and anthropogenic activities have caused more frequent and massive wildfires with longer durations and more significant damages. MODIS has been monitoring global wildfires for almost 20 years, and GK2A and Himawari-8 are observing the wildfires in East Asia 144 times a day. However, the spatial resolution of 1 to 2 km is not sufficient for the detection of small and medium-size active fires, and therefore the studies on the active fire detection using high-resolution images are essential. However, there is no official product for the high-resolution active fire detection. Hence, we implemented the active fire detection algorithm of Landsat 8 and carried out a high-resolution-based detection of active fires in Australia in 2019, followed by the comparisons with the products of Himawari-8 and MODIS. Regarding the intense fires, the three satellites showed similar results, whereas the weak igniting and extinguishing fires or the fires in narrow areas were detected by only Landsat 8 with a 30m resolution. Small-sized fires, which are the majority in Korea, can be detected by the high-resolution satellites such as Landsat 8, Sentinel-2, Kompsat-3A, and the forthcoming Kompsat-7. Also, a comprehensive analysis together with the geostationary satellites in East Asia such as GK2A, Himawari-8, and Fengyun-3 will help the interoperability and the improvement of spatial and temporal resolutions.