Journal of Korean Society for Geospatial Information Science
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v.20
no.3
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pp.11-18
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2012
Increasing concern about biodiversity has lead to a rise in demand on the spatial assessment of biological resources such as biodiversity assessment, protected area selection, habitat management and restoration in Korea. The purpose of this study is to create species richness map through data collection and modeling techniques for wildlife habitat assessment. The GAM (Generalized Additive Model) is easy to interpret and shows better relationship between environmental variables and a response variable than an existing overlap analysis and GLM (Generalized Linear Model). The study area delineated by a large watershed contains Jirisan national park, Mt. Baekun and Sumjin river with three kinds of protected areas (a national park, a landscape ecology protected area and an otter protected area). We collected the presence-absence data for wildlife (mammals and birds) using a stratified random sampling based on a land cover in the study area and implemented natural and socio-environmental data affecting wildlife habitats. After doing a habitat use analysis and specifying significant factors for each species, we built habitat suitability models using a presence-absence model and created habitat suitability maps for each species. Biodiversity maps were generated by taxa and all species using habitat suitability maps. Significant factors affecting each species habitat were different according to their habitat selection. Although some species like a water deer or a great tit were distributed at the low elevation, most potential habitats for mammals and birds were found at the edge of a national park boundary or near a forest around the medium elevation of a mountain range. This study will be used for a basis on biodiversity assessment and proected area selection carried out by Ministry of Environment.
Using multi-temporal KOMPSAT-3/3A high-resolution satellite images, the Normalized Difference Vegetation Index (NDVI) for the area around the Fukushima daiichi nuclear power plant was determined, and the pattern of vegetation changes was analyzed. To calculate the NDVI, surface reflectance from the KOMPSAT-3/3A satellite image was used. Satellite images from four years were used, and the zones where the images overlap was designated as the area of interest (AOI) for the study, and by setting a profile passing through highly vegetated area as a data analysis method, the changes by year were examined. In addition, random points were extracted within the AOI and displayed as a box plot to quantitatively indicate change of NDVI distribution pattern. The main results of this study showed that the NDVI in 2014 was low within AOI in the vicinity of the nuclear power plant, but vegetated area continued to expand until 2021. These results were also confirmed in the change monitoring results shown in a profile or box plot. In disaster areas where access is restricted, such as the Fukushima nuclear power plant area, where it is difficult to collect field data, obtaining land cover classification products with high accuracy using satellite images is challenging, so it is appropriate to analyze them using primary outputs such as vegetation indices obtained from high-resolution satellite imagery. It is necessary to establish an international cooperation system for jointly utilizing satellite images. Meanwhile, to periodically monitor environmental changes in neighboring countries that may affect the Korean peninsula, it is necessary to establish utilization models and systems using high-resolution satellite images.
Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB) data help to enable rapid emergency responses through detection of the artificial and natural disasters occurring at night. The DNB data without correction of lunar irradiance effect distributed by Korea Ocean Science Center (KOSC) has advantage for rapid change detection because of direct receiving. In this study, radiance differences according to the phase of the moon was analyzed for urban and mountain areas in Korean Peninsula using the DNB data directly receiving to KOSC. Lunar irradiance correction algorithm was proposed for the change detection. Relative correction was performed by regression analysis between the selected pixels considering the land cover classification in the reference DNB image during the new moon and the input DNB image. As a result of daily difference image analysis, the brightness value change in urban area and mountain area was ${\pm}30$ radiance and below ${\pm}1$ radiance respectively. The object based change detection was performed after the extraction of the main object of interest based on the average image of time series data in order to reduce the matching and geometric error between DNB images. The changes in brightness occurring in mountainous areas were effectively detected after the calibration of lunar irradiance effect, and it showed that the developed technology could be used for real time change detection.
Min, Kyeong Seo;Chung, Se Woong;Kim, Sung Jin;Kim, Dong Kyun
Journal of Korea Water Resources Association
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v.55
no.10
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pp.785-799
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2022
The aim of this study was to characterize the emission pathways and the footprint of greenhouse gases (GHG) in Daecheong Reservoir using the G-res Tool, and to evaluate the GHG emission intensity (EI) compared to other energy sources. In addition, the change in GHG emissions was assessed in response to the total phosphorus (TP) concentration. The GHG flux in post-impoundment was found to be 262 gCO2eq/m2/yr, of which CO2 and CH4 were 45.7% and 54.2%, respectively. Diffusion of CO2 contributed the most, followed by diffusion, degassing, and bubbling of CH4. The net GHG flux increased to 510 gCO2eq/m2/yr because the forest (as CO2 sink) was lost after dam construction. The EI of Daecheong Reservoir was 86.8 gCO2eq/kWh, which is 3.7 times higher than the global EI of hydroelectric power, due to its low power density. However, it was remarkable to highlight the value to be 9.5 times less than that of coal, a fossil fuel. We also found that a decrease in TP concentration in the reservoir leads to a decrease in GHG emissions. The results can be used to improve understanding of the GHG emission characteristics and to reduce uncertainty of the national GHG inventory of dam reservoirs.
As part of the next-generation Compact Advanced Satellite 500 (CAS500) project, CAS500-4 is scheduled to be launched in 2025 focusing on the remote sensing of agriculture and forestry. To obtain quantitative information on vegetation from satellite images, it is necessary to acquire surface reflectance through atmospheric correction. Thus, it is essential to develop an atmospheric correction method suitable for CAS500-4. Since the absorption and scattering characteristics in the atmosphere vary depending on the wavelength, it is needed to analyze the sensitivity of atmospheric correction parameters such as aerosol optical depth (AOD) and water vapor (WV) considering the wavelengths of CAS500-4. In addition, as CAS500-4 has only five channels (blue, green, red, red edge, and near-infrared), making it difficult to directly calculate key parameters for atmospheric correction, external parameter data should be used. Therefore, thisstudy performed a sensitivity analysis of the key parameters (AOD, WV, and O3) using the simulated images based on Sentinel-2 satellite data, which has similar wavelength specifications to CAS500-4, and examined the possibility of using the products of GEO-KOMPSAT-2A (GK2A) as atmospheric parameters. The sensitivity analysisshowed that AOD wasthe most important parameter with greater sensitivity in visible channels than in the near-infrared region. In particular, since AOD change of 20% causes about a 100% error rate in the blue channel surface reflectance in forests, a highly reliable AOD is needed to obtain accurate surface reflectance. The atmospherically corrected surface reflectance based on the GK2A AOD and WV was compared with the Sentinel-2 L2A reflectance data through the separability index of the known land cover pixels. The result showed that two corrected surface reflectance had similar Seperability index (SI) values, the atmospheric corrected surface reflectance based on the GK2A AOD showed higher SI than the Sentinel-2 L2A reflectance data in short-wavelength channels. Thus, it is judged that the parameters provided by GK2A can be fully utilized for atmospheric correction of the CAS500-4. The research findings will provide a basis for atmospheric correction of the CAS500-4 in the future.
Flash drought (FD), characterized by the rapid onset and intensification, can significantly impact ecosystems and induce immediate water stress. A more comprehensive understanding of the causes and characteristics of FD events is required to enhance drought monitoring. Therefore, we investigated the FD events took place over the Korean peninsula using Global Land Data Assimilation System (GLDAS) data from 2012 to 2022. We first detected FD events using the stress-based method (Standardized Evaporative Stress Ratio, SESR), and analyzed the frequency and duration of FDs. The FD events were classified into three cases based on the variations in Actual Evapotranspiration (AET) and potential Evapotranspiration (PET), and spatially analyzed. Results revealed that there are regional disparities in frequency and duration of FDs, with a mean frequency of 6.4 and duration of 31 days. When classified into Case 1 (normal condition), Case 2 (AET-driven), and Case 3 (PET-driven), we found that Case 2 FDs emerged approximately 1.5 times more frequently than those driven by PET (Case 3) across the Korean peninsula. Case 2 FDs were found to be induced under water-limited conditions, and led both AET and PET to be decreased. Conversely, Case 3 FDs occurred under energy-limited conditions, with increase in both. Case 2 FDs predominantly affected the northwestern and central-southern agricultural regions, while Case 3 occurred in the eastern region, characterized by forested land cover. These findings offers insights into our understanding of FDs over the Korean peninsula, considering climate factors, land cover, and water availability.
To reveal and classify site characteristics in densely populated areas in Chuncheon, Korea, Rayleigh-waves were recorded at 50 sites including four sites in the forest area using four 1-Hz velocity sensors and 24 4.5-Hz vertical geophones during the period of January 2011 to May 2013. Dispersion curves of the Rayleigh waves obtained by the extended spatial autocorrelation method were inverted to derive shear-wave velocity ($v_s$) models comprising 40 horizontal layers of 1-m thickness. Depths to weathered rocks ($D_b$), shear wave velocities of these basement rocks ($v_s^b$), average velocities of the overburden layer ($\bar{v}_s^s$), and the average velocity to a depth of 30 m ($v_s30$), were then derived from those models. The estimated values of $D_b$, $v_s^b$, $\bar{v}_s^s$, and $v_s30$ for 46 sites at lower altitudes were in the ranges of 5 to 29 m, 404 to 561 m/s, 208 to 375 ms/s, and 226 to 583 m/s, respectively. According to the Korean building code for seismic design, the estimated $v_s30$ indicates that the lower altitude areas in Chuncheon are classified as $S_C$ (very dense soil and soft rock) or $S_D$ (stiff soil). To determine adequate proxies for $v_s30$, we compared the computed values with land cover, lithology, topographic slope, and surface elevation at each of the measurement sites. Due to a weak correlation (r = 0.41) between $v_s30$ and elevation, the best proxy of them, applications of this proxy to Chuncheon of a relatively small area seem to be limited.
Journal of the Korean Association of Geographic Information Studies
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v.20
no.2
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pp.1-16
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2017
This study was conducted to analyze the urban heat island(UHI) intensity of South Korea by using Moderate Resolution Imaging Spectroradiometer(MODIS) satellite imagery. For this purpose, the metropolitan area was spatially divided according to land cover classification into urban and non-urban land. From the analysis of land surface temperature(LST) in South Korea in the summer of 2009 which was calculated from MODIS satellite imagery it was determined that the highest temperature recorded nationwide was $36.0^{\circ}C$, lowest $16.2^{\circ}C$, and that the mean was $24.3^{\circ}C$, with a standard deviation of $2.4^{\circ}C$. In order to analyze UHI by cities and counties, UHI intensity was defined as the difference in average temperature between urban and non-urban land, and was calculated through RST1 and RST2. The RST1 calculation showed scattered distribution in areas of high UHI intensity, whereas the RST2 calculation showed that areas of high UHI intensity were concentrated around major cities. In order to find an effective method for analyzing UHI by cities and counties, analysis was conducted of the correlation between the urbanization ratio, number of tropical heat nights, and number of heat-wave days. Although UHI intensity derived through RST1 showed barely any correlation, that derived through RST2 showed significant correlation. The RST2 method is deemed as a more suitable analytical method for measuring the UHI of urban land in cities and counties across the country. In cities and counties with an urbanization ratio of < 20%, the rate of increase for UHI intensity in proportion to increases in urbanization ratio, was very high; whereas this rate gradually declined when the urbanization ratio was > 20%. With an increase of $1^{\circ}C$ in RST2 UHI intensity, the number of tropical heat nights and heat wave days was predicted to increase by approximately five and 0.5, respectively. These results can be used for reference when predicting the effects of increased urbanization on UHI intensity.
Government regulations and policies are important means of restraining the indiscreet expansion of urban areas. According to the standards from those means, it is clear that the fluctuation of forest green proportion encroached by the increase of urban space is obvious. In this study, we interpreted the changes of urban areas as well as the green ones owing to the urban expansion by the decades from 1996, with focusing on the cities of Seoul and Daegu highly developed in South Korea. The purpose of this study is to analyze the spatial expansion and morphological characteristics of urban land cover using not only satellite imageries (1996, 2006, 2016). but also the urban expansion intensity index (UEII) and GUIDOS program. Ultimately, this study is to compare the changes in the rate of forests due to urban expansions annually analyzed based on areas of forest elevation, slope, and the area of single forest patch. In Seoul, the expansion begun from urban space to suburban areas was comparatively rapid, which led the forest fragmentation and the gradual decline of the single patch. However, when it comes to DEM (Digital elevation model) and slope above a certain standard, by the development regulations, there was little decrease in area by anthropogenic developments. The city of Daegu has increased at a slow speed since 1996 in urban and suburban areas, whereas green forests have greatly increased through green forest conservation campaigns. In this way, as to the government policies and regulations, the quantitative and morphological expansion of cities owing to development could be controlled and forest spaces could be preserved as well. Therefore, regulations and policies by the government should be appropriately utilized for sustainable cities.
The sigma naught (${\sigma}^0$) equation is essential to calculate geo-physical properties from Synthetic Aperture Radar (SAR) images for the applications such as ground target identification,surface classification, sea wind speed calculation, and soil moisture estimation. In this paper, we are suggesting new Kompsat-5 (K5) Radar Cross Section (RCS) and ${\sigma}^0$ equations reflecting the final SAR processor update and absolute radiometric calibration in order to increase the application of K5 SAR images. Firstly, we analyzed the accuracy of the K5 RCS equation by using trihedral corner reflectors installed in the Kompsat calibration site in Mongolia. The average difference between the calculated values using RCS equation and the measured values with K5 SAR processor was about $0.2dBm^2$ for Spotlight and Stripmap imaging modes. In addition, the verification of the K5 ${\sigma}^0$ equation was carried out using the TerraSAR-X (TSX) and Sentinel-1A (S-1A) SAR images over Amazon rainforest, where the backscattering characteristics are not significantly affected by the seasonal change. The calculated ${\sigma}^0$ difference between K5 and TSX/S-1A was less than 0.6 dB. Considering the K5 absolute radiometric accuracy requirement, which is 2.0 dB ($1{\sigma}$), the average difference of $0.2dBm^2$ for RCS equation and the maximum difference of 0.6 dB for ${\sigma}^0$ equation show that the accuracies of the suggested equations are relatively high. In the future, the validity of the suggested RCS and ${\sigma}^0$ equations is expected to be verified through the application such as sea wind speed calculation, where quantitative analysis is possible.
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