• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.36 no.4
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• pp.263-270
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• 2018

Land surface temperature is known to be an important factor in understanding the interactions of the ground-atmosphere. However, because of the large spatio-temporal variability, regular observation is rarely made. The existing land surface temperature is observed using satellite images, but due to the nature of satellite, it has the limit of long revisit period and low accuracy. In this study, in order to confirm the possibility of replacing land surface temperature observation using satellite imagery, images acquired by TIR (Thermal Infrared) sensor mounted on UAV (Unmanned Aerial Vehicle) are used. The acquired images were transformed from JPEG (Joint Photographic Experts Group) to TIFF (Tagged Image File Format) format and orthophoto was then generated. The DN (Digital Number) value of orthophoto was used to calculate the actual land surface temperature. In order to evaluate the accuracy of the calculated land surface temperature, the land surface temperature was compared with the land surface temperature directly observed with an infrared thermometer at the same time. When comparing the observed land surface temperatures in two ways, the accuracy of all the land covers was below the measure accuracy of the TIR sensor. Therefore, the possibility of replacing the satellite image, which is a conventional land surface temperature observation method, is confirmed by using the TIR sensor mounted on UAV.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.1230-1232
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• 2003

This study aims to extract maximum geological information using the ASTER (Advanced Spaceborne Thermal Emission and Reflection radiometer) images of a part of south India. The area chosen for this study is characterized by rock types such as Migmatite, Magnetite Quartzite, Charnockite, Granite, dykes, Granitoid gneiss and Ultramafic rocks, and minerals such as Bauxite, Magnesite, Iron ores, Calcite etc. Advantage was taken of the characteristic reflectance and absorption phenomenon in the VNIR, SWIR and TIR bands for these rocks and minerals, and they were mapped in detail. Image processing methods such as contrast stretching, PC analysis, band ratios and fusion were used in this study. The results of the processing matched with the field details and showed additional details, thus demonstrating the usefulness of ASTER (especially the SWIR bands) data for better geological mapping.

• The Journal of the Korea Contents Association
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• v.12 no.11
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• pp.482-491
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• 2012

Because the satellite imagery can detect the radiative heat from the surface using the thermal IR (TIR) channel, there have been many efforts to verify the relationship between the land surface temperature (LST) and urban heat island. However, the relationship between geomorphological characteristics like surface aspects and LST is relatively less studied. Therefore, the geomorphological elements, for example, surface aspects and surface slopes, are considered to evaluate their effects on the change of the surface temperature distribution using the Landsat 7 ETM+ TIR channel and the possibility of the image to detect anthropogenic heat from the surface. We found that the surface aspect is ignorable but the surface slope with the sun elevation influences on the surface temperature distribution. Also, the radiative heat from the surface to the atmosphere could not be accurately recorded by the satellite image due to the surface slope but the slope correction process used in this study could correct the surface temperature under slope condition and the slope correction, in fact, was not influenced on the average temperature of the surface. The possibility of the anthropogenic heat detection from the surface from the satellite imagery was verified as well.

• Journal of the Korean Association of Geographic Information Studies
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• v.21 no.3
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• pp.162-175
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• 2018

The purpose of this study was to analyze the surface temperature of surface fabrics using UAV TIR images, to mitigate problems in the thermal environment of urban areas. Surface temperature values derived from UAV images were compared with those measured in-situ during the similar period as when the images were taken. The difference in the in-situ measured and UAV image derived surface temperatures is the highest for gray colored concrete roof fabrics, at $17^{\circ}C$, and urethane fabrics show the lowest difference, at $0.3^{\circ}C$. The experiment power of the scatter plot of in-situ measured and UAV image derived surface temperatures was 63.75%, indicating that the correlation between the two is high. The surface fabrics with high temperature are metal roofs($48.9^{\circ}C$), urethane($43.4^{\circ}C$), and gray colored concrete roofs($42.9^{\circ}C$), and those with low temperature are barren land($30.2^{\circ}C$), area with trees and lawns($30.2^{\circ}C$), and white colored concrete roofs($34.9^{\circ}C$). These results show that accurate analysis of the thermal characteristics of surface fabrics is possible using UAV images. In future, it will be necessary to increase the usability of UAV images via comparison with in-situ data and linkage to satellite imagery.

• Proceedings of the KSRS Conference
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• 2006.03a
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• pp.240-245
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• 2006

오늘날 도시인구집중화 현상에 따른 대규모 도시개발과 도시역의 확대로 지표면의 피복 변화가 극심하게 이루어지고 있는 한편 이러한 현상으로 인해 도시의 내 외적 경관변화 뿐만 아니라 지형 및 기온상승, 바람장의 변화 등 복합적인 국지기후 변화를 초래하게 되었다. 본 연구에서는 이러한 도시의 기후 변화에 따라 청계천 복원 전의 도시 지표 열 환경 특성을 분석을 수행하고자 한다 도시지역의 열환경 분석을 위하여 기존에는 주로 Landsat TM/ETM+ 위성영상 자료를 사용하였으나 2003년 5월 위성 센서의 고장으로 위성영상 자료의 사용이 불가피하게 되었다. 이에 대체 방안으로 ASTER 영상 열적외 센서에서 취득한 지표온도 값과 현장에서 취득한 AWS자료와의 상관성 분석을 실시하였으며, 이를 기반으로 청계천 주변의 근접성 분석 및 토지이용별 지표온도 분포 패턴 등 도시 열 환경 변화 탐지 및 분석을 위하여 GIS 및 RS 분석을 실시하였다.

• Korean Journal of Remote Sensing
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• v.35 no.1
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• pp.39-55
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• 2019

In this study, we have proposed an improved method to detect forest fires by correcting the reflected signals of day images using the middle-wavelength infrared (MWIR) channel. The proposed method is allowed to remove the reflected signals only using the image itself without an existing data source such as a land-cover map or atmospheric data. It includes the processing steps for calculating a solar-reflected signal such as 1) a simple correction model of the atmospheric transmittance for the MWIR channel and 2) calculating the image-based reflectance. We tested the performance of the method using the MODIS product. When compared to the conventional MODIS fire detection algorithm (MOD14 collection 6), the total number of detected fires was improved by approximately 17%. Most of all, the detection of fires improved by approximately 30% in the high reflection areas of the images. Moreover, the false alarm caused by artificial objects was clearly reduced and a confidence level analysis of the undetected fires showed that the proposed method had much better performance. The proposed method would be applicable to most satellite sensors with MWIR and thermal infrared channels. Especially for geostationary satellites such as GOES-R, HIMAWARI-8/9 and GeoKompsat-2A, the short acquisition time would greatly improve the performance of the proposed fire detection algorithm because reflected signals in the geostationary satellite images frequently vary according to solar zenith angle.

• 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.

• Journal of the Korean Association of Geographic Information Studies
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• v.15 no.2
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• pp.113-125
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• 2012

This research is aimed at evaluating the land surface characterization of KOMPSAT-3A middle infrared (MIR) data. Airborne Hyperspectral Scanner (AHS) data, which has MIR bands with high spatial resolution, were used to assess land surface temperature (LST) retrieval and classification accuracy of MIR bands. Firstly, LST values for daytime and nighttime, which were calculated with AHS thermal infrared (TIR) bands, were compared to digital number of AHS MIR bands. The determination coefficient of AHS band 68 (center wavelength $4.64{\mu}m$) was over 0.74, and was higher than other MIR bands. Secondly, The land cover maps were generated by unsupervised classification methods using the AHS MIR bands. Each class of land cover maps for daytime, such as water, trees, green grass, roads, roofs, was distinguished well. But some classes of land cover maps for nighttime, such as trees versus green grass, roads versus roofs, were not separated. The image classification using the difference images between daytime AHS MIR bands and nighttime AHS MIR bands were conducted to enhance the discrimination ability of land surface for AHS MIR imagery. The classification accuracy of the land cover map for zone 1 and zone 2 was 67.5%, 64.3%, respectively. It was improved by 10% compared to land cover map of daytime AHS MIR bands and night AHS MIR bands. Consequently, new algorithm based on land surface characteristics is required for temperature retrieval of high resolution MIR imagery, and the difference images between daytime and nighttime was considered to enhance the ability of land surface characterization using high resolution MIR data.