• Journal of the Korean Institute of Landscape Architecture
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• v.46 no.2
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• pp.52-61
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• 2018

Urban heat islands occur due to increases in the extent of artificial surfaces such as concrete, asphalt and high-rise buildings. In this regard, research into the use of satellite thermal infrared images for thermal environment analysis of urban areas is being carried out. However, such analysis of the characteristics of individual land cover with low-resolution satellite images suffers from limitations because land cover patterns in urban areas are complicated. Recently, UAV has been widely used, which can compensate for this limitation as it is able to acquire high-resolution images. In this paper, the accuracy of UAV infrared images is verified and the applicability of UAV in urban thermal environment analysis is examined by comparing the results with land surface temperatures from Landsat 8 thermal images. The results show a high positive correlation of temperature values at 0.95, and no statistically significant difference between the two groups. Comparisons of land surface temperature according to land cover showed that the largest difference observed was $4.63^{\circ}C$ in the Used area, and UAV images with small cell units reflected various surface temperatures. Furthermore, it was possible to analyze the surface temperatures of various green spaces such as wetlands and street tree areas, which can lower surface temperatures in urban areas, with street tree shadows reducing surface temperatures by about $4-6^{\circ}C$. UAV can easily and rapidly measure the surface temperature of urban areas and is able to analyze various types of green spaces. Thus, this is an effective tool for thermal environment analysis in urban areas to aid in the design or management of urban green spaces, as it can allow for land cover and the effects of the various green spaces.

• Journal of the Korean earth science society
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• v.32 no.3
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• pp.276-293
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• 2011

In this study a modeling system consisting of Weather Research and Forecasting (WRF), Sparse Matrix Operator Kernel Emissions (SMOKE), the Community Multiscale Air Quality (CMAQ) model, and the CMAQ-Model of Aerosol Dynamics, Reaction, Ionization, and Dissolution (MADRID) model has been applied to estimate enhancements of $PM_{10}$ during Asian dust events in Korea. In particular, 5 experimental formulas were applied to the WRF-SMOKE-CMAQ (MADRID) model to estimate Asian dust emissions from source locations for major Asian dust events in China and Mongolia: the US Environmental Protection Agency (EPA) model, the Goddard Global Ozone Chemistry Aerosol Radiation and Transport (GOCART) model, and the Dust Entrainment and Deposition (DEAD) model, as well as formulas by Park and In (2003), and Wang et al. (2000). According to the weather map, backward trajectory and satellite image analyses, Asian dust is generated by a strong downwind associated with the upper trough from a stagnation wave due to development of the upper jet stream, and transport of Asian dust to Korea shows up behind a surface front related to the cut-off low (known as comma type cloud) in satellite images. In the WRF-SMOKE-CMAQ modeling to estimate the PM10 concentration, Wang et al.'s experimental formula was depicted well in the temporal and spatial distribution of Asian dusts, and the GOCART model was low in mean bias errors and root mean square errors. Also, in the vertical profile analysis of Asian dusts using Wang et al's experimental formula, strong Asian dust with a concentration of more than $800\;{\mu}g/m^3$ for the period of March 31 to April 1, 2007 was transported under the boundary layer (about 1 km high), and weak Asian dust with a concentration of less than $400\;{\mu}g/m^3$ for the period of 16-17 March 2009 was transported above the boundary layer (about 1-3 km high). Furthermore, the difference between the CMAQ model and the CMAQ-MADRID model for the period of March 31 to April 1, 2007, in terms of PM10 concentration, was seen to be large in the East Asia area: the CMAQ-MADRID model showed the concentration to be about $25\;{\mu}g/m^3$ higher than the CMAQ model. In addition, the $PM_{10}$ concentration removed by the cloud liquid phase mechanism within the CMAQ-MADRID model was shown in the maximum $15\;{\mu}g/m^3$ in the Eastern Asia area.

• Journal of the Korean Institute of Landscape Architecture
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• v.37 no.3
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• pp.46-53
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• 2009

Urbanization brings several changes to the natural environment. Its consequences can have a direct effect on climatic features, as in the Urban Heat Island Effect. One factor that directly affects the urban climate is the green area. In urban areas, vegetation is suppressed in order to accommodate manmade buildings and streets. In this paper we analyze the effect of green areas on the urban temperature in Seoul. The period selected for analysis was July 30th, 2007. The ground temperature was measured using Landsat TM satellite imagery. Land cover was calculated in terms of city area, water, bare soil, wet lands, grass lands, forest, and farmland. We extracted the surface temperature using the Linear Regression Model. Then, we did a regression analysis between air temperature at the Automatic Weather Station and surface temperature. Finally, we calculated the temperature decrease area and the population benefits from the green areas. Consequently, we determined that a green area with a radius of 500m will have a temperature reduction area of $67.33km^2$, in terms of urban area. This is 11.12% of Seoul's metropolitan area and 18.09% of the Seoul urban area. We can assume that about 1,892,000 people would be affected by this green area's temperature reduction. Also, we randomly chose 50 places to analysis a cross section of temperature reduction area. Temperature differences between the boundaries of green and urban areas are an average of $0.78^{\circ}C$. The highest temperature difference is $1.7^{\circ}C$, and the lowest temperature difference is $0.3^{\circ}C$. This study has demonstrated that we can understand how green areas truly affect air temperature.

• Korean Journal of Environment and Ecology
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• v.35 no.5
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• pp.558-568
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• 2021

Poor supervision and tourism activities have resulted in forest degradation in islands in Korea. Since the southern coastal region of the Korean peninsula was originally dominated by warm-temperate evergreen broad-leaved forests, it is desirable to restore forests in this region to their original vegetation. In this study, we identified suitable areas to be restored as evergreen broad-leaved forests by analyzing the environmental factors of existing evergreen broad-leaved forests in the islands of Jeollanam-do. We classified forest lands in the study area into six vegetation types from Sentinel-2 satellite images using a deep learning algorithm and analyzed the tolerance ranges of existing evergreen broad-leaved forests by measuring the locational, topographic, and climatic attributes of the classified vegetation types. Results showed that evergreen broad-leaved forests were distributed more in areas with a high altitudes and steep slope, where human intervention was relatively low. The human intervention has led to a higher distribution of evergreen broad-leaved forests in areas with lower annual average temperature, which was an unexpected but understandable result because an area with higher altitude has a lower temperature. Of the environmental factors, latitude and average temperature in the coldest month (January) were relatively less contaminated by the effects of human intervention, thus enabling the identification of suitable restoration areas of the evergreen broad-leaved forests. The tolerance range analysis of evergreen broad-leaved forests showed that they mainly grew in areas south of the latitude of 34.7° and a monthly average temperature of 1.7℃ or higher in the coldest month. Therefore, we predicted the areas meeting these criteria to be suitable for restoring evergreen broad-leaved forests. The suitable areas cover 614.5 km², which occupies 59.0% of the total forest lands on the islands of Jeollanamdo, and 73% of actual forests that exclude agricultural and other non-restorable forest lands. The findings of this study can help forest managers prepare a restoration plan and budget for island forests.

• Korean Journal of Remote Sensing
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• v.39 no.5_3
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• pp.1009-1029
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• 2023

Urban trees play a vital role in urban ecosystems,significantly reducing impervious surfaces and impacting carbon cycling within the city. Although previous research has demonstrated the efficacy of employing artificial intelligence in conjunction with airborne light detection and ranging (LiDAR) data to generate urban tree information, the availability and cost constraints associated with LiDAR data pose limitations. Consequently, this study employed freely accessible, high-resolution multispectral satellite imagery (i.e., Sentinel-2 data) to estimate fractional tree canopy cover (FTC) within the urban confines of Suwon, South Korea, employing machine learning techniques. This study leveraged a median composite image derived from a time series of Sentinel-2 images. In order to account for the diverse land cover found in urban areas, the model incorporated three types of input variables: average (mean) and standard deviation (std) values within a 30-meter grid from 10 m resolution of optical indices from Sentinel-2, and fractional coverage for distinct land cover classes within 30 m grids from the existing level 3 land cover map. Four schemes with different combinations of input variables were compared. Notably, when all three factors (i.e., mean, std, and fractional cover) were used to consider the variation of landcover in urban areas(Scheme 4, S4), the machine learning model exhibited improved performance compared to using only the mean of optical indices (Scheme 1). Of the various models proposed, the random forest (RF) model with S4 demonstrated the most remarkable performance, achieving R² of 0.8196, and mean absolute error (MAE) of 0.0749, and a root mean squared error (RMSE) of 0.1022. The std variable exhibited the highest impact on model outputs within the heterogeneous land covers based on the variable importance analysis. This trained RF model with S4 was then applied to the entire Suwon region, consistently delivering robust results with an R² of 0.8702, MAE of 0.0873, and RMSE of 0.1335. The FTC estimation method developed in this study is expected to offer advantages for application in various regions, providing fundamental data for a better understanding of carbon dynamics in urban ecosystems in the future.

• Korean Journal of Environment and Ecology
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• v.38 no.3
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• pp.277-292
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• 2024

In order to manage large-scale forest fires concentrated in Gangwon-do and Gyeongsangbuk-do with severe topographical heterogeneity, a decision-making process through efficient and rapid damage assessment using satellite images is essential. Accordingly, this study targets a large-scale forest fire that ignited in Gangneung and the Donghae, Gangwon-do on March 5, 2022, and was extinguished around 19:00 on March 8, to estimate the fire severity using dNBR and derive environmental factors that affect the grade. As environmental factors, we quantified the regular vegetation index representing vegetation or fuel type, the forest index that classifies tree species, the regular moisture index representing moisture content, and DEM in relation to topography, and then analyzed the correlation with the fire severity. In terms of fire severity, the widest range was 'Unbured' at 52.4%, followed by low severity at 42.9%, medium-low severity at 4.3%, and medium-high severity at 0.4%. Environmental factors showed a negative correlation with dNDVI and dNDWI, and a positive correlation with slope. Regarding vegetation, the differences between coniferous, broad-leaved, and other groups in dNDVI, dNIWI, and slope, which were analyzed to affect the fire severity, were analyzed to be significant with p-value < 2.2e-16. In particular, the difference between coniferous and broad-leaved forests was clear, and it was confirmed that coniferous forest suffered more damage than broad-leaved forest due to the higher fire severity in the Gangwon-do region, including Pinus densiflora, which are dominant species, as well as P. koraiensis, P. rigida and P. thunbergii.

• Journal of the Korean Vacuum Society
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• v.18 no.2
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• pp.108-115
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• 2009

The superlattice infrared photodetector (SLIP) with an active layer of 8/8-ML InAs/GaSb type-II strained-layer superlattice (SLS) of 150 periods was grown by MBE technique, and the proto-type discrete device was defined with an aperture of $200-{\mu}m$ diameter. The contrast profile of the transmission electron microscope (TEM) image and the satellite peak in the x-ray diffraction (XRD) rocking curve show that the SLS active layer keeps abrupt interfaces with a uniform thickness and a periodic strain. The wavelength and the bias-voltage dependences of responsivity (R) and detectivity ($D^*$) measured by a blackbody radiation source give that the cutoff wavelength is ${\sim}5{\mu}m$, and the maximum Rand $D^*$ ($\lambda=3.25{\mu}m$) are ${\sim}10^3mA/W$ (-0.6 V/13 K) and ${\sim}10^9cm.Hz^{1/2}/W$ (0 V/13 K), respectively. The activation energy of 275 meV analyzed from the temperature dependent responsivity is in good agreement with the energy difference between two SLS subblevels of conduction and valence bands (HH1-C) involving in the photoresponse process.

• Korean Journal of Environment and Ecology
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• v.19 no.4
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• pp.375-384
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• 2005

This study was conducted selecting 44 places with a block unit subject to urban area in Gangnam-gu, to analyze a temperature change according to land use and green structure. In this study, it was used the broad-wide urban temperature, supported by Landset TM and ETM+ satellite image 6scene(1999${\~}$2002). The result of the research, the land use pattern has slightly influence on a temperature change of urban area. The result from correlation analysis between temperature and the factors affected by land cover type, such as building-to-land ratio(A correlation coefficient is 0.368${\~}$0.709) have positive correlation and green area ratio(a correlation coefficient is -0.551${\~}$-0.860) have negative correlation. The result from correlation analysis between temperature and green capacity of the land, crown projection area ratio, each factor have negative correlation with temperature, as showing that a correlation coefficient of green capacity of the land is -0.577(June 2006)${\~}$-0.882(June 1999) and crown projection area ratio's is -0.549(June 2001)${\~}$-0.817(June 1999). The result of the regression analysis for establishing urban area temperature change prediction model showed that green capacity of the land of the explanation variable was accepted.

• Publications of The Korean Astronomical Society
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• v.27 no.4
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• pp.111-116
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• 2012

Far-infrared observations provide crucial data for the investigation and characterisation of the properties of dusty material in the Interstellar Medium (ISM), since most of its energy is emitted between ~ 100 and $200{\mu}m$. We present the first all-sky image from a sensitive all-sky survey using the Japanese AKARI satellite, in the wavelength range $50-180{\mu}m$. Covering > 99% of the sky in four photometric bands with four filters centred at $65{\mu}m$, $90{\mu}m$, $140{\mu}m$, and $160{\mu}m$ wavelengths, this achieved spatial resolutions from 1 to 2 arcmin and a detection limit of < 10 MJy $sr^{-1}$, with absolute and relative photometric accuracies of < 20%. All-sky images of the Galactic dust continuum emission enable astronomers to map the large-scale distribution of the diffuse ISM cirrus, to study its thermal dust temperature, emissivity and column density, and to measure the interaction of the Galactic radiation field and embedded objects with the surrounding ISM. In addition to the point source population of stars, protostars, star-forming regions, and galaxies, the high Galactic latitude sky is shown to be covered with a diffuse filamentary-web of dusty emission that traces the potential sites of high latitude star formation. We show that the temperature of dust particles in thermal equilibrium with the ambient interstellar radiation field can be estimated by using $90{\mu}m$, $140{\mu}m$, and $160{\mu}m$ data. The FIR AKARI full-sky maps provide a rich new data set within which astronomers can investigate the distribution of interstellar matter throughout our Galaxy, and beyond.

• Journal of the Korean Association of Geographic Information Studies
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• v.9 no.4
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• pp.12-21
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• 2006

Recently, Very large and high resolution geological data from aerial or satellite imagery are available. Many researches and applications require to do realtime visualization of interest geological area or entire planet. Important operation of wide-spreaded terrain realtime visualization technique is the appropriate model resolution selection from pre-processed multi-resolution model hierarchy depend upon participant's view. For embodying such realtime rendering system with large geometric data, Preprocessing multi-resolution hierarchy from large scale geological information of interest area is required. In this research, recent Cubic multiresolution 4-8 tile hierarchy is selected for global planetary applications. Based upon the tile hierarchy, It constructs the selective terminal level tile mesh for original geological information area and starts to sample individual generated tiles for terminal level tiles. It completes the hierarchy by constructing intermediate tiles with low pass filtering in bottom-up direction. This research embodies series of efficient cubic 4-8 tile hierarchy construction mechanism with out-of-core storage. The planetary scale Mars' geographical altitude data and image data were selected for the experiment.