• Korean Journal of Remote Sensing
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• v.30 no.4
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• pp.481-491
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• 2014

Blue-ice area is a glacial ice field in ice sheet, ice shelf and glaciers where snow ablation and sublimation is larger than snowfall. As the blue-ice area has large influences on the meteorite concentration mechanism and ice mass balance, it is required to quantify the concentration of blue-ice. We analyzed spectral reflectance characteristics of blue-ice, snow and cloud by using MODIS images obtained over blue-ice areas in McMurdo Dry Valleys, East Antarctica, from 2007 to 2012. We then developed Normalized Difference Blue-ice Index (NDBI) algorithm which quantifies the concentration of blue-ice. Snow and cloud have a high reflectance in visible and near-infrared (NIR) bands. Reflectance of blue-ice is high in blue band, while that lowers in the NIR band. NDBI is calculated by dividing the difference of reflectance in the blue and NIR bands by the sum of reflectances in the two bands so that NDBI = (Blue-NIR)/(Blue + NIR). NDBI calculated from the MODIS images showed that the blue-ice areas have values ranging from 0.2 to 0.5, depending on the exposure and concentration of blue-ice. It is obviously different from that of snow and cloud that has values less than 0.2 or rocks with negative values. The change of NDBI values in the blue-ice area has higher correlation with snow depth ($R^2=0.699$) than wind speed ($R^2=0.012$) or air temperature ($R^2=0.278$), all measured at a meteorological station installed in McMurdo Dry Valleys. As the snow depth increased, the NDBI value decreased, which suggests that snow depth can be estimated from NDBI values over blue-ice areas. The NDBI algorithm developed in this study will be useful for various polar research fields such as meteorite exploration, analysis of ice mass balance as well as the snow depth estimation.

• 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.

• 한국공간정보시스템학회:학술대회논문집
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• 2007.06a
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• pp.156-161
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• 2007

도시화에 따른 도시문제발생이라는 결과로 미루어 볼 때, 지속적인 도시 성장을 위한 도시 성장 관리는 필수적이며, 이것을 위해서 도시지역을 추출하는 것은 도시의 성장 추이를 파악할 수 있게 한다는 점에서 매우 의미 있는 일이다. 본 연구에서는 도시 성장 모니터링에 있어서 정규식생지수(NDVI)와 정규시가지화지수(NDBI)를 결합한 방법의 활용성을 규명하는데 목적을 두었다. 이를 위해 토지피복분류에 일반적으로 사용되는 감독 분류기법과 도시지역추출에 이용되는 NDVI와 NDBI를 결합한 방법(식생지수결합법)으로 1988년과 2000년 두 시기의 Landsat TM 영상을 이용하여 도시지역을 추출하고 일치도를 분석하였다. 분석 결과, 1988년 식생지수결합법과 감독분류기법으로 추출한 도시지역의 일치도는 98%, 식생지수결합법 비도시지역으로 추출된 지역이 감독분류기법으로는 도시지역으로 추출될 확률은 37.35%로 나타났고, 같은 경우 2000년은 각각 99.3%와 7.7%로 나타났다. 이를 통해 식생지수결합법을 사용한 도시지역 추출 결과와 감독분류기법을 사용한 도시지역 추출 결과의 일치도가 비교적 높게 나타남을 알 수 있었다. 또, 각 기법을 통한 도시지역 추출 결과와 실제 도시 검사점과의 일치도의 분석을 통해서도 도시지역 추출 결과의 일치도가 비교적 높게 나타났다. 따라서 분류를 통한 도시지역 추출 방법에 비해 식생지수결합법을 이용한 도시지역 추출이 절차상 수월한 점을 감안하면 도시지역 추출에 있어서 식생지수결합법의 효율성을 입증할 수 있었다.

• 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).

• Korean Journal of Remote Sensing
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• v.35 no.6_4
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• pp.1403-1415
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• 2019

Urban population growth and consequent rapid urbanization involve some thermal environmental problems in the cities. Monitoring of thermal environments in urban areas such as hot spot analysis is required for effective actions to resolve these problems. This study selected 14 dongs and surrounding administrative districts of Sejong city as study areas and analyzed the characteristics of changes in surface temperature due to the urban expansion in the summer from 2013 to 2018. In the study, the surface temperature distributions in the study areas were plotted using surface temperature values from Landsat 8 and NDVI (Normalized Difference Vegetation Index) and NDBI (Normalized Difference Built-up Index) based on KOMPSAT 2/3 data, and the patterns of surface temperature changes with urban expansion were discussed using the estimated NDVI and NDBI. In particular, the distinct urbanization in the study areas were selected for case studies, and the cause of the changes in the hot spots in the regions was analyzed using high-resolution KOMPSAT images. This study results present that hot spots appeared in urbanized regions within the study areas, and it was plotted that the lower the NDVI values and the higher the NDBI values indicate the temperature values are high. The land surface temperature and satellite-based products were used to divide the study areas into continuously urbanized regions and rapidly urbanized regions and to identify the different characteristics depending on land covers. In the regions with distinct surface temperature changes by urbanization, the analysis using high-resolution KOMPSAT images as presented in this study could provide effective information for urban planning and policy utilization in the future.

• Journal of Korean Society for Geospatial Information Science
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• v.23 no.1
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• pp.113-121
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• 2015

Remote sensing technology using a multi-spectral satellite imagery can be utilized for the analysis of urban heat island effect in large area. However, weather condition of Korea mostly has a lot of clouds and it makes periodical observation using time-series of satellite images difficult. For this reason, we proposed the analysis of urban heat island effect using time-series of Landsat TM images and ATC model. To analyze vegetation condition and urbanization, NDVI and NDBI were calculated from Landsat images. In addition, land surface temperature was calculated from thermal infrared images to estimate the parameters of ATC model. Furthermore, the parameters of ATC model were compared based on the land cover map created by Korean Ministry of Environment to analyze urban heat island effect relating to the pattern of land use and land cover. As a result of a correlation analysis between calculated spectral indices and parameters of ATC model, MAST had high correlation with NDVI and NDBI (-0.76 and 0.69, respectively) and YAST also had correlation with NDVI and NDBI (-0.53 and 0.42, respectively). By comparing the parameters of ATC model based on land cover map, urban area had higher MAST and YAST than agricultural land and grassland. In particular, residential areas, industrial areas, commercial areas and transportation facilities showed higher MAST than cultural facilities and public facilities. Moreover, residential areas, industrial areas and commercial areas had higher YAST than the other urban areas.

• Advances in environmental research
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• v.9 no.1
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• pp.19-39
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• 2020

Urban heat island (UHI) is one of the most important climatic implications of urbanization and thus a matter of key concern for environmentalists of the world in the twenty-first century. The relationship between climate and urbanization has been better understood with the introduction of thermal remote sensing. So, this study is an attempt to understand the influence of urbanization on local temperature for a small developing city. The study focuses on the investigation of intensity of atmospheric and surface urban heat island for a small urbanizing district of Punjab, India. Landsat 8 OLI/TIRS satellite data and field observations were used to examine the spatial pattern of surface and atmospheric UHI effect respectively, for the month of April, 2018. The satellite data has been used to cover the larger geographical area while field observations were taken for simultaneous and daily temperature measurements for different land use types. The significant influence of land use/land cover (LULC) patterns on UHI effect was analyzed using normalized built-up and vegetation indices (NDBI, NDVI) that were derived from remote sensing satellite data. The statistical analysis carried out for land surface temperature (LST) and LULC indicators displayed negative correlation for LST and NDVI while NDBI and LST exhibited positive correlation depicting attenuation in UHI effect by abundant vegetation. The comparison of remote sensing and in-situ observations were also carried out in the study. The research concluded in finding both nocturnal and daytime UHI effect based on diurnal air temperature observations. The study recommends the urgent need to explore and impose effective UHI mitigation measures for the sustainable urban growth.

• Journal of the Korean Association of Geographic Information Studies
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• v.13 no.1
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• pp.142-154
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• 2010

The purpose of this study is to analyze the effects of various spatial characteristics on the land surface temperature and to grasp the characteristics of thermal environment by types of urban area in Changwon, Gyeongsangnam-do. The spatial data were consisted LST, normalized difference built-up index(NDBI) and normalized difference vegetation index(NDVI) obtained from Landsat 5 TM and land use and land cover map classified from high resolution digital aerial photograph($10cm{\times}10cm$). The unit space for spatial analysis was built by $500m{\times}500m$ Vector GRID. According to the results of estimation of relationship between thermal environment and spatial characteristics, LST had the highest positive correlation with NDBI by 0.929 and had high positive correlation with impervious area ratio by 0.857. In order to analysis of thermal environment on land use, types of urban area were classified by 4 of residential focus area, industrial focus area, green focus area and mixed area. According to the results of analysis, mean LST of industrial focus area was showed the highest by $21.10^{\circ}C$. But mean LST of green focus area was analyzed the lowest by $18.85^{\circ}C$. In conclusion, the results of this study investigated the effects of spatial characteristics on urban thermal environment and can provide methods and basic informations about land use planning and development density restriction for reduction of urban heat.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.34 no.3
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• pp.1033-1041
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• 2014

Rapid urbanization of Korea was an unprecedented example in the world and urban population increased significantly. As a result, unbalanced distribution of population is serious problem in Korea because approximately 50% of the population is concentrated in the capital area that is 10% of nation's territory, thereby occurring various urban problems including UHI. Hence, Sejong Special Autonomous City was inaugurated officially on 2 July 2012 in order to decentralize population of capital area and induce more balanced regional development. The Sejong City has been changed drastically over a period of years as developed practically since the late 2000's and is expected to have new problems of urbanization. The land cover change due to urbanization is the main cause of UHI that urban area is significantly warmer than its surrounding areas and UHI is not only affecting urban climate change but also natural environment. So the purpose of this research is to analyze level of urbanization and UHI effect and to provide the correlation analysis between Land Surface Temperature and spectral indices. To achieve this, satellite imagery from LANDSAT were used. NDVI, NDBI, and UI were calculated using red, near-infrared, mid-infrared ($0.63{\mu}m-1.75{\mu}m$) images and LST was retrieved utilizing thermal infrared ($10.4{\mu}m-12.5{\mu}m$) image. Based on each index and LST, Changes of NDVI, UI and UHI through TVI were analyzed in Sejong City. UHI effect increased around newly constructed multi-functional administrative city, the correlation between LST and NDVI was negative and UI was strong positive.

• Ecology and Resilient Infrastructure
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• v.3 no.4
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• pp.315-321
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• 2016

To identify the relationship between land use and thermal environment in an urban area, the air temperature was measured at different places of land use, and the changes of land use and air temperature were traced for 40 years in Incheon City. The relationship between land use and temperature was also investigated using satellite image data. The results of temperature measurements on a forest, a cropland (rice paddy), a bareland (school ground), and an urban area (asphalt road) from 19 to 21 August 2014 showed that air temperature was the highest on a pavement road. The temperature increased by about $1.4^{\circ}C$ ($0.035^{\circ}C/year$) for 40 years from 1975 to 2014 in Incheon. The changes in land use patterns of Incheon for the past 40 years showed that urban dry land, bareland and grassland have increased and cultivated land, wetland and forest land have decreased gradually. The land surface temperature (LST) was correlated with the normalized difference vegetation index (NDVI) and normalized difference built-up index (NDBI) extracted from Landsat satellite image. The land surface temperature was lower at higher NDVI, and higher at higher NDBI. Therefore, it is important to conserve and restore the land use of greenery, wetlands, and agricultural land in order to mitigate the heat island effect and improve the thermal environment in an urban area.