• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.26 no.3
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• pp.219-225
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• 2008

There are many problems in monitering environmental change around of nuclear power station, because interesting area is coastal and relatively large. The ground resolution of Landsat ETM+ imagery is high (30 m), but this imagery does not have enough informations for conducting atmospheric correction in evaluating sea surface temperatures. On the other hand, while it is possible to conduct atmospheric correction using MODIS imagery with it's two infrared bands, it's resolution is relatively low (1 km). Therefore, atmospheric corrected high resolution temperature information can be obtained from these two satellite images. In this study, digital numbers of Landsat ETM+ data in interesting area are georeferenced, converted to effective temperatures based on radiance value, and then the atmospheric correction is conducted using MODIS data. As a result, about $3.5^{\circ}C$ temperature differences were detected in comparing sea surface temperature of the surrounding area of Uljin nuclear power station with it of the same area located 5km far east.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.32 no.4_1
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• pp.327-333
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• 2014

To improve thermal environments in urban area, the structural characteristic analysis of thermal environments of the certain area should be preceded to analyze and supplement its problems. With Landsat-8, we measured the centrality estimation, the distribution map, and the spatial statistical analysis of Daegu Metropolitan City in January and August, which of data applied in analyzing the structure of thermal environments following to its spatial property. The thermal infrared band of satellite images has been used to analyze the standard normal deviated scores, which extract the centrality, while the cluster map, based upon Local Local Moran's I, has composed for understanding the autocorrelation of local spatial within environment space structure. Understanding the distribution features as well as the pivot center of thermal environments with satellite images provides principle database for updating urban thermal environments' policies and plans; because those are reference materials that should have precedence over for diverse thermal environment policies.

• Journal of Korean Society for Geospatial Information Science
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• v.22 no.4
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• pp.53-61
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• 2014

Landsat OLI satellite imagery can be applied to various remote sensing applications, such as generation of land cover map, urban area analysis, extraction of vegetation index and change detection, because it includes various multispectral bands. In addition, land cover map is an important information to monitor and analyze land cover using GIS. In this paper, land cover map is generated by using Landsat OLI and existing land cover map. First, training dataset is obtained using correlation between existing land cover map and unsupervised classification result by K-means, automatically. And then, spectral signatures corresponding to each class are determined based on training data. Finally, abundance map and land cover map are generated by using iterative spectral mixture model. The experiment is accomplished by Landsat OLI of Cheongju area. It shows that result by our method can produce land cover map without manual training dataset, compared to existing land cover map and result by supervised classification result by SVM, quantitatively and visually.

• Journal of the Korean Association of Geographic Information Studies
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• v.21 no.2
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• pp.68-79
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• 2018

In general, Top Of Atmosphere(TOA) brightness temperature is closely related to air temperature. Brightness temperature can be derived from the Thermal Infra-Red Sensors (TIRS) of the earth observation satellites such as the Landsat series. The TIRS instrument of the Landsat-8 satellite collects the two spectral bands (Bands 10 and 11) that measure brightness temperature. In this research, the relationship between the air temperature data measured by the weather stations in Seoul, South Korea and the brightness temperature data separately derived from Bands 10 and 11 of the Landsat-8 satellite were assessed in the different seasons through the correlation analysis. The statistical results led to the following conclusions. First, brightness temperature is closely related to air temperature in order of Spring, Autumn, Winter and Summer. Second, when air temperature increases, brightness temperature also increases in Spring, Autumn and Winter but decreases in Summer. Third, Band 10 has a closer relationship to air temperature than Band 11.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.8
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• pp.34-40
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• 2017

In recent years, global warming has caused abnormal weather phenomena. Unusually cold climates have occurred all around the world, including cold waves in the Northeastern United States, Beijing, China, Southern India, and Pakistan, as well as floods in Chile, Kazakhstan, and Vietnam. China has been experiencing a nationwide heat wave annually since the year 2013, especially in the southern region. In this study, we used Landsat 8 OLI TIRS sensor images from four periods to analyze the characteristics of abnormal high temperature phenomena in Cixi-si, China. Land cover classification was performed using 10 bands of satellite imagery, and the surface temperature was extracted using the 10th thermal band. The results of the land cover classification of the fourth period show the changes of the time series quantitatively. The results of the surface temperature calculation provided both the average overall temperature and the average temperature of individual items. The temperature was found to be highest for buildings, followed by grassland, forest, agricultural land, water systems, and tidal flats in the same period.

• Journal of the Korean Association of Geographic Information Studies
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• v.2 no.2
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• pp.59-68
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• 1999

The study for detecting suspended sediment distribution in Lake Sihwa, which has a large surface area and coastal area, using remote sensing technique was carried out with development of satellite data collected since 1970. The research, however, analysis of spatial distribution and quantity, is not common in domestic study and useful algorithms have not been proposed. In this study, a suspended sediment algorithm was composed with in-situ data obtained in study area and remote sensing reflectance obtained in-water optical instrument, which has SeaWiFS wavelength bands. However, when the algorithm was applied to Landsat TM data, including an in-situ data set, and some problems arose. The composition of the algorithm which was structured with band difference and band ratio showed the correlation of $R^2$=0.7649 with concentration of suspended sediments. And, between calculated and observed concentration of suspended sediments there was a correlation of $R^2$=0.6959. However, remote sensing reflectance obtained from Landsat TM is not good for the estimation of concentration of suspended sediments, because of high concentration of chlorophyll and CDOM(colored dissolved organic matter).

• Journal of the Korean Association of Geographic Information Studies
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• v.23 no.1
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• pp.30-40
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• 2020

Monitoring the urban environmental changes caused by the urban regeneration project is necessary for evaluating the effect of the various types of urban regeneration projects that have been carried out in Seoul, South Korea. However, there is few available data and professional expert for evaluating the effect of these urban regeneration projects. This research evaluated the effect of the construction of rainwater village in Jangwi-dong area, constructed through the Seoul urban regeneration project, by utilizing the land surface temperatures derived from the multi-temporal Landsat-8 satellite images through the following steps. In the first step, the land surface temperature images were generated using the multispectral bands of the Landsat-8 satellite images. In the final step, the effect of constructing the rainwater villages was assessed by calculating the seasonal LST statistics for Jangwi-dong area, its neighbor area and entire Seoul area. The experimental results led the following conclusion: the construction of rainwater villages did not have the significant effect on the land surface temperature changes in Jangwi-dong area.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.1
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• pp.434-439
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• 2019

Recently, interest in urban temperature change and ground surface temperature change has been increasing due to weather phenomenon due to global warming, heat island phenomenon caused by urbanization in urban areas. In Korea, weather data such as temperature and precipitation have been collected since 1904. In recent years, there are 96 ASOS stations and 494 AWS weather observation stations. However, in the case of terrestrial networks, terrestrial meteorological data except measurement points are predicted through interpolation because they provide point data for each installation point. In this study, to improve the resolution of ground surface temperature measurement, the surface temperature using satellite image was calculated and its applicability was analyzed. For this purpose, the satellite images of Landsat 8 OLI TIRS were obtained for Seoul Metropolitan City by seasons and transformed to surface temperature by applying NASA equation to the thermal bands. The ground measurement data was based on the temperature data measured by AWS. Since the AWS temperature data is station based point data, interpolation is performed by Kriging interpolation method for comparison with Landsat image. As a result of comparing the satellite image base surface temperature with the AWS temperature data, the temperature difference according to the season was calculated as fall, winter, summer, based on the RMSE value, Spring, in order of applicability of Landsat satellite image. The use of that attribute and AWS support starts at $2.11^{\circ}C$ and RMSE ${\pm}3.84^{\circ}C$, which reflects information from the extended NASA.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2001.12a
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• pp.1-4
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• 2001

The classification performance of Artificial Neural Network (ANN) and RBF-NN was compared for Landsat TM image. The RBF-NN was validated for three unique landuse types (e.g. Mixed landuse area, Cultivated area, Urban area), different input band combinations and classification class. The bootstrap resampling technique was employed to estimate the confidence intervals and distribution for unit load, The pollutant generation was varied significantly according to the classification accuracy and percentile unit load applied. Especially in urban area, where mixed landuse is dominant, the difference of estimated pollutant load is largely varied.

• The Journal of the Korea institute of electronic communication sciences
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• v.9 no.7
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• pp.811-818
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• 2014

This study was aimed to detect Submarine Groundwater Discharge (SGD) distribution image of Sea Surface Temperature (SST) using infrared band of Landsat-7 ETM+ around Jeju island. It is used to analyze SST distribution that DN value of satellite images converted into temperature. The estimation of SGD location is that extracting range of $15{\sim}17^{\circ}C$ from SST. The summer season images(July 28. 2006, Aug. 29. 2006 and Sep. 19. 2008) were used to analyze big difference between SST and temperature of SGD. The results, estimated SGD locations were occurred part of coastal area in northeastern of Jeju island.