• Journal of the Korean Association of Geographic Information Studies
• /
• v.24 no.4
• /
• pp.99-112
• /
• 2021

This study was performed to predict spatial change of future forestland area in South Korea at regional level for supporting forest-related plans established by local governments. In the study, land use was classified to three types which are forestland, agricultural land, and urban and other lands. A logistic regression model was developed using transitional interaction between each land use type and topographical factors, land use restriction factors, socioeconomic indices, and development infrastructures. In this model, change probability from a target land use type to other land use types was estimated using raster dataset(30m×30m) for each variable. With priority order map based on the probability of land use change, the total annual amount of land use change was allocated to the cells in the order of the highest transition potential for the spatial analysis. In results, it was found that slope degree and slope standard value by the local government were the main factors affecting the probability of change from forestland to urban and other land. Also, forestland was more likely to change to urban and other land in the conditions of a more gentle slope, lower slope criterion allowed to developed, and higher land price and population density. Consequently, it was predicted that forestland area would decrease by 2027 due to the change from forestland to urban and others, especially in metropolitan and major cities, and that forestland area would increase between 2028 and 2050 in the most local provincial cities except Seoul, Gyeonggi-do, and Jeju Island due to locality extinction with decline in population. Thus, local government is required to set an adequate forestland use criterion for balanced development, reasonable use and conservation, and to establish the regional forest strategies and policies considering the future land use change trends.

• Korean Journal of Remote Sensing
• /
• v.39 no.6_1
• /
• pp.1255-1272
• /
• 2023

Satellite-based chlorophyll-a concentration, produced as a long-term time series, is crucial for global climate change research. The production of data without gaps through the merging of time-synthesized or multi-satellite data is essential. However, studies related to satellite-based chlorophyll-a concentration in the waters around the Korean Peninsula have mainly focused on evaluating seasonal characteristics or proposing algorithms suitable for research areas using a single ocean color sensor. In this study, a merging dataset of remote sensing reflectance from the geostationary sensor GOCI-II and polar-orbiting sensors (MODIS, VIIRS, OLCI) was utilized to achieve high spatial coverage of chlorophyll-a concentration in the waters around the Korean Peninsula. The spatial coverage in the results of this study increased by approximately 30% compared to polar-orbiting sensor data, effectively compensating for gaps caused by clouds. Additionally, we aimed to quantitatively assess accuracy through comparison with global chlorophyll-a composite data provided by Ocean Colour Climate Change Initiative (OC-CCI) and GlobColour, along with in-situ observation data. However, due to the limited number of in-situ observation data, we could not provide statistically significant results. Nevertheless, we observed a tendency for underestimation compared to global data. Furthermore, for the evaluation of practical applications in response to marine disasters such as red tides, we qualitatively compared our results with a case of a red tide in the East Sea in 2013. The results showed similarities to OC-CCI rather than standalone geostationary sensor results. Through this study, we plan to use the generated data for future research in artificial intelligence models for prediction and anomaly utilization. It is anticipated that the results will be beneficial for monitoring chlorophyll-a events in the coastal waters around Korea.

• Journal of the Korean Society of Radiology
• /
• v.82 no.3
• /
• pp.670-681
• /
• 2021

Purpose This study aimed to investigate the optimal threshold value in Hounsfield units (HU) on CT to detect the solid components of pulmonary subsolid nodules using pathologic invasive foci as reference. Materials and Methods Thin-section non-enhanced chest CT scans of 25 patients with pathologically confirmed minimally invasive adenocarcinoma were retrospectively reviewed. On CT images, the solid portion was defined as the area with higher attenuation than various HU thresholds ranging from -600 to -100 HU in 50-HU intervals. The solid portion was measured as the largest diameter on axial images and as the maximum diameter on multiplanar reconstruction images. A linear mixed model was used to evaluate bias in each threshold by using the pathological size of invasive foci as reference. Results At a threshold of -400 HU, the biases were lowest between the largest/maximum diameter of the solid portion of subsolid nodule and the size of invasive foci of the pathological specimen, with 0.388 and -0.0176, respectively. They showed insignificant difference (p = 0.2682, p = 0.963, respectively) at a threshold of -400 HU. Conclusion For quantitative analysis, -400 HU may be the optimal threshold to define the solid portion of subsolid nodules as a surrogate marker of invasive foci.

• Journal of the Korean Association of Geographic Information Studies
• /
• v.12 no.1
• /
• pp.73-80
• /
• 2009

Recently in order to mange better life quality much effort was spent for environmental-friendly urban development project and environmental restoration project. During these projects, there should be deep understanding about atmospheric environment change analysis and long term monitoring so that it would be helpful for better environment promotion such as heat island mitigation effect and wind way construction. In this study, the surface temperature environment change between before and after Cheonggye Stream Restoration Project was mapped and analyzed by using ASTER(Advanced Spaceborne Thermal Emission Reflection Radiometer) TIR(Thermal Infrared) satellite imagery and finally the fact, that the heat island effect was mitigated, was clarified. For this study, the correlation analysis was conducted through comparing the difference between atmosphere temperature of AWS(Automatic Weather System) and surface temperature of ASTER. Furthermore, this study will be the infrastructure of urban meteorology model development by understanding surface temperature pattern change and executing quantitative analysis of heat island.

• Korean Journal of Weed Science
• /
• v.30 no.4
• /
• pp.390-395
• /
• 2010

This study was conducted to predict reduction of soybean yield as affected by different densities of Cuscuta pentagona. All data were fitted to Cousens' rectangular hyperbola model to estimate parameters for predicting soybean yield loss. The yield of soybean in the various densities (1 to 48 plants $m^{-2}$) of C. pentagona reduced by 80.3 to 99.7%, respectively. Among yield components, number of pods was the most significantly influenced by weed interferences. The prediction model for soybean yield as affected by weed competition was as follows: Y= 274.6783/(1+4.3522X), $r^2$=0.999 in C. pentagona. Economic threshold levels calculated using cousens' equation was 0.004 plants $m^{-2}$ in C. pentagona.

• Journal of Korean Society of Coastal and Ocean Engineers
• /
• v.27 no.5
• /
• pp.304-314
• /
• 2015

We have reviewed the current status of coastal vulnerability index(CVI) to be guided into an appropriate CVI development for Korean coast and applied a methodology into the east coast of Korea to quantify coastal vulnerability by future sea_level rise. The CVIs reviewed includes USGS CVI, sea_level rise CVI, compound CVI, and multi scale CVI. The USGS CVI, expressed into the external forcing of sea_level rise, wave and tide, and adaptive capacity of morphology, erosion and slope, is adopted here for CVI quantification. The range of CVI is 1.826~22.361 with a mean of 7.085 for present condition and increases into 2.887~30.619 with a mean of 12.361 for the year of 2100(1 m sea_level rise). The index "VERY HIGH" is currently 8.57% of the coast and occupies 35.56% in 2100. The pattern of CVI change by sea_level rise is different to different local areas, and Gangneung, Yangyang and Goseong show the highest increase. The land use pattern in the "VERY HIGH" index is dominated by both human system of housing complex, road, cropland, etc, and natural system of sand, wetland, forestry, etc., which suggests existing land utilization should be reframed in the era of climate change. Though CVI approach is highly efficient to deal with a large set of climate scenarios entailed in climate impact assessment due to uncertainties, we also propose three_level assessment for the application of CVI methodology in the site specific adaptation such as first screening assessment by CVI, second scoping assessment by impact model, and final risk quantification with the result of impact model.

• Journal of Aquaculture
• /
• v.19 no.3
• /
• pp.157-165
• /
• 2006

Expression of major antioxidant enzyme (AOE) including Cu/Zn superoxide dismutase (Cu/Zn-SOD), catalase (CAT), glutathione-S-transferase (GST) and 3 glutathione peroxidase isotypes (GPXs) at mRNA levels during heat stress was examined in mud loach (Misgurnus mizolepis) liver. Based on the semi-quantitative RT-PCR, real-time RT-PCR and/or northern dot blot hybridization, the antioxidant enzyme genes were generally up-regulated during elevation of water temperature from $23^{\circ}C$ up to $32^{\circ}C$. GPXs and SOD displayed the most significant elevation of mRNA levels (up to 3 and 2 folds, respectively) while CAT showed the steady-state expression irrespective of thermal conditions. GST represented the relatively moderate response (1.3-fold increase) in its transcription to thermal stress. The transcriptional activation of AOE genes was not significant at the treatment temperature lower than $29^{\circ}C$. Increased mRNA levels of GPX (extracellular form) and SOD genes in the fish exposed to $32^{\circ}C$ was readily detectable 1 day after exposure to heat stress.

• Korean Journal of Remote Sensing
• /
• v.28 no.4
• /
• pp.409-420
• /
• 2012

Since the Industrial Revolution, CO2 levels have been increasing with climate change. In this study, Analyze time-series changes in snow cover quantitatively and predict the vanishing point of snow cover statistically using remote sensing. The study area is Mt. Kilimanjaro, Tanzania. 23 image data of Landsat-5 TM and Landsat-7 ETM+, spanning the 27 years from June 1984 to July 2011, were acquired. For this study, first, atmospheric correction was performed on each image using the COST atmospheric correction model. Second, the snow cover area was extracted using the NDSI (Normalized Difference Snow Index) algorithm. Third, the minimum height of snow cover was determined using SRTM DEM. Finally, the vanishing point of snow cover was predicted using the trend line of a linear function. Analysis was divided using a total of 23 images and 17 images during the dry season. Results show that snow cover area decreased by approximately $6.47km^2$ from $9.01km^2$ to $2.54km^2$, equivalent to a 73% reduction. The minimum height of snow cover increased by approximately 290 m, from 4,603 m to 4,893 m. Using the trend line result shows that the snow cover area decreased by approximately $0.342km^2$ in the dry season and $0.421km^2$ overall each year. In contrast, the annual increase in the minimum height of snow cover was approximately 9.848 m in the dry season and 11.251 m overall. Based on this analysis of vanishing point, there will be no snow cover 2020 at 95% confidence interval. This study can be used to monitor global climate change by providing the change in snow cover area and reference data when studying this area or similar areas in future research.

• Journal of the Korean Association of Geographic Information Studies
• /
• v.19 no.3
• /
• pp.61-74
• /
• 2016

The purpose of this paper is to analyze the relationship between the location of the epicenter of a medium-sized earthquake(magnitude 4.8) that occurred on January 20, 2007 in the Odaesan area with lineament features using a shaded relief map(1/25,000 scale) and satellite images from LANDSAT-8 and KOMPSAT-2. Previous studies have analyzed lineament features in tectonic settings primarily by examining two-dimensional satellite images and shaded relief maps. These methods, however, limit the application of the visual interpretation of relief features long considered as the major component of lineament extraction. To overcome some existing limitations of two-dimensional images, this study examined three-dimensional images, produced from a Digital Elevation Model and drainage network map, for lineament extraction. This approach reduces mapping errors introduced by visual interpretation. In addition, spline interpolation was conducted to produce density maps of lineament frequency, intersection, and length required to estimate the density of lineament at the epicenter of the earthquake. An algorithm was developed to compute the Value of the Relative Density(VRD) representing the relative density of lineament from the map. The VRD is the lineament density of each map grid divided by the maximum density value from the map. As such, it is a quantified value that indicates the concentration level of the lineament density across the area impacted by the earthquake. Using this algorithm, the VRD calculated at the earthquake epicenter using the lineament's frequency, intersection, and length density maps ranged from approximately 0.60(min) to 0.90(max). However, because there were differences in mapped images such as those for solar altitude and azimuth, the mean of VRD was used rather than those categorized by the images. The results show that the average frequency of VRD was approximately 0.85, which was 21% higher than the intersection and length of VRD, demonstrating the close relationship that exists between lineament and the epicenter. Therefore, it is concluded that the density map analysis described in this study, based on lineament extraction, is valid and can be used as a primary data analysis tool for earthquake research in the future.

• Korean Journal of Agricultural and Forest Meteorology
• /
• v.8 no.4
• /
• pp.209-221
• /
• 2006

The purpose of this study is to analyze the effects of climate change on the nonpoint source pollution in a small watershed using a mid-range model. The study area is a basin in a rural area that covers 384 ha with a composition of 50% forest and 19% paddy. The hydrologic and water quality data were monitored from 1996 to 2004, and the feasibility of the GWLF (Generalized Watershed Loading function) model was examined in the agricultural small watershed using the data obtained from the study area. As one of the studies on climate change, KEI (Korea Environment Institute) has presented the monthly variation ratio of rainfall in Korea based on the climate change scenario for rainfall and temperature. These values and observed daily rainfall data of forty-one years from 1964 to 2004 in Suwon were used to generate daily weather data using the stochastic weather generator model (WGEN). Stream runoff was calibrated by the data of $1996{\sim}1999$ and was verified in $2002{\sim}2004$. The results were determination coeff, ($R^2$) of $0.70{\sim}0.91$ and root mean square error (RMSE) of $2.11{\sim}5.71$. Water quality simulation for SS, TN and TP showed $R^2$ values of 0.58, 0.47 and 0.62, respectively, The results for the impact of climate change on nonpoint source pollution show that if the factors of watershed are maintained as in the present circumstances, pollutant TN loads and TP would be expected to increase remarkably for the rainy season in the next fifty years.