• 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.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 Forest and Environmental Science
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• v.15 no.1
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• pp.65-70
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• 1999

The study analyze Conifer and Hardwood area and their distribution to seek an efficient survey for Experimental Forest of Kangwon National University using Landsat-5 TM data. Through forest cover types, we try to study on the possibility to use Satellite Data. development of Satellite Interpretation Skill and Digital Mapping Method. As analysis tools we use IMAGINE 8.3 and ArcView 3.0 software. The result showed that Hardwood took approximately 73%, 2,224ha of the total 3,058ha and Conifer takes 27%, 832ha.

• Journal of the Korean earth science society
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• v.25 no.8
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• pp.764-773
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• 2004

The soil in wildfire area Sacheon-myeon, Gangneung, Gangweon Province, Korea, were investigated to clarify pattern of the soils. The soils were classified into 5 types on the basis of vegetation, types of organic matter. thickness of soil horizons, and completeness of soil profile. Each type showed different erosion pattern and Landsat ETM image. Coverage of plant leaves, litter, root, ash and other organic matter was an important component that affected soil color and reflectance of Landsat image (digital number). Although the NDVI (Normalized Distribution Vegetation Index) method in the wildfire area did not show much difference in soil types, the applied supervised classification method showed characteristic pattern of Landsat ETM image of soil types. This study showed that the applied supervised Landsat TM image classification in wildfire area is an effective way to estimate the distribution of erosion pattern of soil in wildfire area.

• Korean Journal of Remote Sensing
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• v.33 no.5_2
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• pp.677-687
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• 2017

In this paper, we propose the use of hierarchical classification for winter crop mapping based on satellite imagery. A hierarchical classification is a classifier that maps input data into defined subsumptive output categories. This classification method can reduce mixed pixel effects and improve classification performance. The methodology are illustrated focus on winter cropsin Gimje city, Jeonbuk with Landsat-8 imagery. First, agriculture fields were extracted from Landsat-8 imagery using Smart Farm Map. And then winter crop fields were extracted from agriculture fields using temporal Normalized Difference Vegetation Index (NDVI). Finally, winter crop fields were then classified into wheat, barley, IRG, whole crop barley and mixed crop fields using signature from Unmanned Aerial Vehicle (UAV). The results indicate that hierarchical classifier could effectively identify winter crop fields with an overall classification accuracy of 98.99%. Thus, it is expected that the proposed classification method would be effectively used for crop mapping.

• Journal of the Korean Association of Geographic Information Studies
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• v.17 no.4
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• pp.1-9
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• 2014

The interest group and corporation in Korea have cultivated Suaeda grass in the source area every year as a plan to prevent the yellow dust due to Chinese desertification. It needs the afforestation analysis about the research area to plan the greenbelt environment development in the region of yellow dust origin. Thus, this research analyzed the greenbelt environment based on Landsat 5 TM satellite image and Landsat 8 image to grasp and analyze the present of greenbelt environment development. And this research analyzed the inside of the salt desert to understand the detailed greenbelt environment and vegetation index. As a result, it represents that the afforestation was accomplished efficiently between 2009 and 2011, while the greenbelt area was decreased rapidly and bare soil was increased between 2011 and 2013. Through these results, we could recognize that it is in trouble about the greenbelt environment development after 2011 and it needs the project implementation using satellite image when the next afforestation project is planned henceforth.

• Korean Journal of Remote Sensing
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• v.37 no.3
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• pp.449-461
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• 2021

Analysis Ready Data (ARD) for optical satellite images represents a pre-processed product by applying spectral characteristics and viewing parameters for each sensor. The atmospheric correction is one of the fundamental and complicated topics, which helps to produce Top-of-Atmosphere (TOA) and Top-of-Canopy (TOC) reflectance from multi-spectral image sets. Most remote sensing software provides algorithms or processing schemes dedicated to those corrections of the Landsat-8 OLI sensors. Furthermore, Google Earth Engine (GEE), provides direct access to Landsat reflectance products, USGS-based ARD (USGS-ARD), on the cloud environment. We implemented the Orfeo ToolBox (OTB) atmospheric correction extension, an open-source remote sensing software for manipulating and analyzing high-resolution satellite images. This is the first tool because OTB has not provided calibration modules for any Landsat sensors. Using this extension software, we conducted the absolute atmospheric correction on the Landsat-8 OLI images of Railroad Valley, United States (RVUS) to validate their reflectance products using reflectance data sets of RVUS in the RadCalNet portal. The results showed that the reflectance products using the OTB extension for Landsat revealed a difference by less than 5% compared to RadCalNet RVUS data. In addition, we performed a comparative analysis with reflectance products obtained from other open-source tools such as a QGIS semi-automatic classification plugin and SAGA, besides USGS-ARD products. The reflectance products by the OTB extension showed a high consistency to those of USGS-ARD within the acceptable level in the measurement data range of the RadCalNet RVUS, compared to those of the other two open-source tools. In this study, the verification of the atmospheric calibration processor in OTB extension was carried out, and it proved the application possibility for other satellite sensors in the Compact Advanced Satellite (CAS)-500 or new optical satellites.

• Journal of the Korean Association of Geographic Information Studies
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• v.22 no.1
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• pp.129-139
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• 2019

Mapping urban areas using the earth observation satellites is useful for monitoring urban expansions and measuring urban developments. In this research, the different thresholds for detecting the urban areas separately from the remote-sensing index images (normalized-difference built-up index(NDBI) and urban index(UI) images) generated from the Landsat-8 image acquired in Daegu, South Korea were evaluated through the following steps: (1) the NDBI and UI images were separately generated from the given Landsat-8 image; (2) the different thresholds (-0.4, -0.2, and 0) for detecting the urban areas separately from the NDBI and UI images were evaluated; and (3) the accuracy of each detected urban area was assessed. The experiment results showed that the threshold -0.2 had the best performance for detecting the urban areas from the NDBI image, while the threshold -0.4 had the best performance for detecting the urban areas from the UI image. Some misclassification errors, however, occurred in the areas where the bare soil areas were classified into urban areas or where the high-rise apartments were classified into other areas. In the future research, a robust methodology for detecting urban areas, including the various types of urban features, with less misclassification errors will be proposed using the satellite images. In addition, research on analyzing the pattern of urban expansion will be carried out using the urban areas detected from the multi-temporal satellite images.

• Proceedings of the KSRS Conference
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• 2001.03a
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• pp.77-81
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• 2001

2000년 4월, 강원도 삼척일대에 크게 발생한 산불지역에 대해서 Landsat TM 인공위성 영상자료를 이용하여 산불의 피해지역을 조사분석하였다. 산불발생 전과 후의 2시기 위성영상을 이용하여 변화탐지 기법의 하나인 화상간차이법을 적용하였다. 분석결과 산불 발생지역의 탐지에는 NDVI를 유도하고 그 차이를 이용하는 것이 가장 탁월한 것으로 나타났다. 산불 피해지역을 구분하는 임계값을 표준편차$\times$0.9로 하였을 때, 현지조사 결과에 대한 전체정확도는 93.8%, 카파계수는 0.82로 매우 높았다.

• Korean Journal of Remote Sensing
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• v.36 no.5_4
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• pp.1231-1244
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• 2020

Landsat satellites are representative optical satellites that have observed the Earth's surface for a long-term, and are suitable for long-term changes such as disaster preparedness/recovery monitoring, land use change, change detection, and time series monitoring. In this paper, clouds and cloud shadows were detected using QA bands to detect and remove clouds simply and efficiently. Then, the missing area of the experimantal image is restorated through the SSG algorithm, which does not directly refer to the pixel value of the reference image, but performs restoration to the pixel value in the Experimental image. Through this study, we presented the possibility of utilizing the modified SSG algorithm by quantitatively and qualitatively evaluating information on variousl and cover conditions in the thermal wavelength band as well as the visible wavelength band observing the surface.