• Fisheries and Aquatic Sciences
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• v.26 no.12
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• pp.738-751
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• 2023

The Sembilan Archipelago is famous for its great biodiversity, in which the humphead wrasse (Cheilinus undulatus) (locally named Napoleon fish) is the primary commodity (economically important), and currently, the environmental degradation occurs due to anthropogenic activities. This study aimed to examine the eco-environmental parameters and assess their influence on the abundance of humphead wrasse and other coral reef fish compositions in the Sembilan Archipelago. Direct field monitoring was performed using a visual census throughout an approximately one km transect. Coral cover data collection and assessment were also carried out. A coastal water quality index (CWQI) was used to assess the water quality status. Furthermore, statistical-based analyses [hierarchical clustering, Pearson's correlation, principal component analysis (PCA), and canonical correspondence analysis (CCA)] were performed to examine the correlation between eco-environmental parameters. The Napoleon fish was only found at stations 1 and 2, with a density of about 3.8 Ind/ha, aligning with the dominant composition of the family Serranidae (covering more than 15% of the total community) and coinciding with the higher coral mortality and lower reef fish abundance. The coral reef conditions were generally ideal for supporting marine life, with a living coral percentage of about > 50% in all stations. Based on CWQI, the study area is categorized as good and excellent water quality. Of the 60 parameter values examined, the phytoplankton abundance, Napoleon fish, and temperature are highly correlated, with a correlation coefficient value greater than 0.7, and statistically significant (F < 0.05). Although the adaptation of reef fish to water quality parameters varies greatly, the most influential parameters in shaping their composition in the study area are living corals, nitrites, ammonia, larval abundance, and temperature.

• Korean Journal of Remote Sensing
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• v.37 no.5_1
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• pp.1029-1042
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• 2021

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.

• Journal of the Korean Association of Geographic Information Studies
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• v.21 no.4
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• pp.64-80
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• 2018

In Korea, citizens can only know general information about crime. Thus it is difficult to know how much they are exposed to crime. If the police can predict the crime risky area, it will be possible to cope with the crime efficiently even though insufficient police and enforcement resources. However, there is no prediction system in Korea and the related researches are very much poor. From these backgrounds, the final goal of this study is to develop an automated crime prediction system. However, for the first step, we build a big data set which consists of local real crime information and urban physical or non-physical data. Then, we developed a crime prediction model through machine learning method. Finally, we assumed several possible scenarios and calculated the probability of crime and visualized the results in a map so as to increase the people's understanding. Among the factors affecting the crime occurrence revealed in previous and case studies, data was processed in the form of a big data for machine learning: real crime information, weather information (temperature, rainfall, wind speed, humidity, sunshine, insolation, snowfall, cloud cover) and local information (average building coverage, average floor area ratio, average building height, number of buildings, average appraised land value, average area of residential building, average number of ground floor). Among the supervised machine learning algorithms, the decision tree model, the random forest model, and the SVM model, which are known to be powerful and accurate in various fields were utilized to construct crime prevention model. As a result, decision tree model with the lowest RMSE was selected as an optimal prediction model. Based on this model, several scenarios were set for theft and violence cases which are the most frequent in the case city J, and the probability of crime was estimated by $250{\times}250m$ grid. As a result, we could find that the high crime risky area is occurring in three patterns in case city J. The probability of crime was divided into three classes and visualized in map by $250{\times}250m$ grid. Finally, we could develop a crime prediction model using machine learning algorithm and visualized the crime risky areas in a map which can recalculate the model and visualize the result simultaneously as time and urban conditions change.

• Korean Journal of Remote Sensing
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• v.38 no.6_2
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• pp.1607-1622
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• 2022

The purpose of this study is to determine the actual performance of cool roof in preventing absorbed solar radiation. The spatial correlation between surface temperature and absorbed solar radiation is the method by which the performance of a cool roof can be understood and evaluated. The research area of this study is the vicinity of Jangyu Mugye-dong, Gimhae-si, Gyeongsangnam-do, where an actual cool roof is applied. FLIR Vue Pro R thermal infrared sensor, Micasense Red-Edge multi-spectral sensor and DJI H20T visible spectral sensor was used for aerial photography, with attached to the drone DJI Matrice 300 RTK. To perform the spatial correlation analysis, thermal infrared orthomosaics, absorbed solar radiation distribution maps were constructed, and land cover features of roof were extracted based on the drone aerial photographs. The temporal scope of this research ranged over 9 points of time at intervals of about 1 hour and 30 minutes from 7:15 to 19:15 on July 27, 2021. The correlation coefficient values of 0.550 for the normal roof and 0.387 for the cool roof were obtained on a daily average basis. However, at 11:30 and 13:00, when the Solar altitude was high on the date of analysis, the difference in correlation coefficient values between the normal roof and the cool roof was 0.022, 0.024, showing similar correlations. In other time series, the values of the correlation coefficient of the normal roof are about 0.1 higher than that of the cool roof. This study assessed and evaluated the potential of an actual cool roof to prevent solar radiation heating a rooftop through correlation comparison with a normal roof, which serves as a control group, by using high-resolution drone images. The results of this research can be used as reference data when local governments or communities seek to adopt strategies to eliminate the phenomenon of urban heat islands.

• Korean Journal of Remote Sensing
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• v.36 no.5_3
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• pp.1109-1123
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• 2020

In South Korea with forest as a major land cover class (over 60% of the country), many wildfires occur every year. Wildfires weaken the shear strength of the soil, forming a layer of soil that is vulnerable to landslides. It is important to identify the severity of a wildfire as well as the burned area to sustainably manage the forest. Although satellite remote sensing has been widely used to map wildfire severity, it is often difficult to determine the severity using only the temporal change of satellite-derived indices such as Normalized Difference Vegetation Index (NDVI) and Normalized Burn Ratio (NBR). In this study, we proposed an approach for determining wildfire severity based on machine learning through the synergistic use of Sentinel-1A Synthetic Aperture Radar-C data and Sentinel-2A Multi Spectral Instrument data. Three wildfire cases-Samcheok in May 2017, Gangreung·Donghae in April 2019, and Gosung·Sokcho in April 2019-were used for developing wildfire severity mapping models with three machine learning algorithms (i.e., Random Forest, Logistic Regression, and Support Vector Machine). The results showed that the random forest model yielded the best performance, resulting in an overall accuracy of 82.3%. The cross-site validation to examine the spatiotemporal transferability of the machine learning models showed that the models were highly sensitive to temporal differences between the training and validation sites, especially in the early growing season. This implies that a more robust model with high spatiotemporal transferability can be developed when more wildfire cases with different seasons and areas are added in the future.

• Korean Journal of Environment and Ecology
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• v.35 no.1
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• pp.24-36
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• 2021

Countries around the world, including the Republic of Korea, are participating in efforts to preserve biodiversity. Concerning species, in particular, studies that aim to find potential habitats and establish conservation plans by conducting habitat suitability analysis for specific species are actively ongoing. However, few studies on mid- to long-term changes in suitable habitat areas are based on accumulated information. Therefore, this study aimed to analyze the time-series changes in the habitat suitable area and examine the otters' changing pattern (Lutra lutra) designated as Level 1 endangered wildlife in Gangwon-do. The time-series change analysis used the data on otter species' presence points from the 2nd, 3rd, and 4th national natural environment surveys conducted for about 20 years. Moreover, it utilized the land cover map consistent with the survey period to create environmental variables to reflect each survey period's habitat environment. The suitable habitat area analysis used the MaxEnt model that can run based only on the species presence information, and it has been proven to be reliable by previous studies. The study derived the habitat suitability map for otters in each survey period, and it showed a tendency that habitats were distributed around rivers. Comparing the response curves of the environmental variables derived from the modeling identified the characteristics of the habitat favored by otters. The examination of habitats' change by survey period showed that the habitats based on the 2nd National Natural Environment Survey had the widest distribution. The habitats of the 3rd and 4th surveys showed a tendency of decrease in area. Moreover, the study aggregated the analysis results of the three survey periods and analyzed and categorized the habitat's changing pattern. The type of change proposed different conservation plans, such as field surveys, monitoring, protected area establishment, and restoration plan. This study is significant because it produced a comprehensive analysis map that showed the time-series changes of the location and area of the otter habitat and proposed a conservation plan that is necessary according to the type of habitat change by region. We believe that the method proposed in this study and its results can be used as reference data for establishing a habitat conservation and management plan in the future.

• Proceedings of the Korean Environmental Health Society Conference
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• 2005.06a
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• pp.45-72
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• 2005

C. neoformans-associated cryptococcosis is primarily a disease of immunocompromised persons, has a world-wide distribution, and is often spread by pigeons in the urban environment. In contrast, C. gattii causes infection in normal hosts, has only been described in tropical and semi-tropical areas of the world, and has a unique niche in river gum Eucalyptus trees. Cryptococcosis is acquired through inhalation of the yeast propagules from the environment. C. gattii has been identified as the cause of an emerging infectious disease centered on Vancouver Island, British Columbia, Canada. No cases of C. gattii-disease were diagnosed prior to 1999; the current incidence rate is 36 cases per million population. A search was initiated in 2001 to find the ecological niche of this basidiomycetous yeast. C. gaftii was found in the environment in treed areas of Vancouver Island. The highest percentage of colonized-tree clusters were found around central Vancouver Island, with decreasing rates of colonization to the north and south. Climate, soil and vegetation cover of this area, called the Coastal Douglas fir biogeoclimatic zone, is unique to British Columbia and Canada. The concentration of airborne C. gattii was highest in the dry summer months, and lowest during late fall, winter, and early spring, months which have heavy rainfall. The study of the emerging colonization of this organism and subsequent cases of environmentally acquired disease will be informative in planning public health management of new routes of exposure to exotic agents in areas impacted by changing climate and land use patterns. Cryptococcosis is an infection associated with an encapsulated, basidiomycetous yeast Cryptococcus neoformans. The route of entry for this organism is through the lungs, with possible systemic spread via the circulatory system to the brain and meninges. There are four cryptococcal serogroups associated with disease in humans and animals, distinguished by capsular polysaccharide antigens. Cryptococcus neoformans: variety grubii (serotype A), variety neoformans (serotype D), and variety gattii (serotypes B and C) (Franzot et at. 1999). C. neoformans variety gattii has recently been elevated to species status, C. gattii. C. neoformans val. grubii and var. neoformans have a world-wide distribution, and are particularly associated with soil and weathered bird droppings. In contrast, C. gattii (CG) is not associated with bird excrement, is primarily found in tropical and subtropical climates, and has a restricted environmental niche associated with specific tree species. (Ellis & Pfiffer 1990) Ellis and Pfeiffer theorize that, as a basidiomycete, CG requires an association with a tree in order to become pathogenic to mammals. In Australia, CG has been found to be associated with five species of Eucalypts, Eucalyptus camaldulensis, E. tereticornis, E. blakelyi, E. gomphocephala, and E. rudis. Eucalypts, although originally native to Australia, now have a world-wide distribution. CG has been found associated with imported eucalypts in India, California, Brazil, and Egypt. In addition, in Brazil and Columbia, where eucalypts have been naturalized, native trees have been shown to harbour CG (Callejas et al. 1998; Montenegro et al. 2000). In British Columbia, Canada, since the beginning of 1999, there have been 120 confirmed cases of cryptococcal mycoses associated with CG in humans, including 4 fatalities (data from British Columbia Centre for Disease Control), and over 200 cases in animal pets in BC (data from Central Laboratory for Veterinarians). What is remarkable about the BC outbreak of C. gattii-cryptococcosis is that all of the cases have been residents of, or visitors to, a narrow area along the eastern coast of Vancouver Island, BC, from the tip of the island in the south (Victoria) to Courtenay on the north-central island as illustrated in Figure 1. Of the first 38 human cases, 58% were male with a mean age of 59.7 years (range 20 - 82): 36 cases (95%) were Caucasian. Ten cases (26%) presented with meningitis, the remainder presented with respiratory symptoms. Cultures recovered from cases of cryptococcosis associated with the outbreak were typed as serogroup B, which is specific to CG (Bartlett et al. 2003). This was the first reported outbreak of CVG in Canada, or indeed, the world. Where infection with CG is endemic, for example, Australia, the incidence of cryptococcosis ranges from 1.8 - 4.7 per million between the southern and northern states (Sorrell 2001). However, the overall incidence of cryptococcosis in immunocompenent individuals has been estimated at 0.2 per million population per year (Kwon-Chung et al. 1984). The population of Vancouver Island is approximately 720,000,consequently, even if the organism were endemic, one would expect a maximum of 0.15 cases of cryptococcal disease annually.

• Korean Journal of Soil Science and Fertilizer
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• v.37 no.4
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• pp.199-206
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• 2004

Factors of universal soil loss equation, USLE, and its revised version, RUSLE for Korean soils were reevaluated to estimate the national scale of soil loss based on digital soil maps. Rainfall erosivity factor, R, of 158 locations of cities and counties were spacially interpolated by the inverse distance weight method. Soil erodibility factor, K, of 1321 soil phases of 390 soil series were calculated using the data of soil survey and agri-environmental quality monitoring. Topographic factor, LS, was estimated using soil map of 1:25,000 scale with soil phase and land use type. Cover management factor, C, of major crops and support practice factor, P, were summarized by analyzing the data of lysimeter and field experiments for 27 years (1975-2001) in the National Institute of Agricultural Science and Technology. R factor varied between 2322 and 6408 MJ mm $ha^{-1}$ $yr^{-1}$ $hr^{-1}$ and the average value was 4276 MJ mm $ha^{-1}$ $yr^{-1}$ $hr^{-1}$. The average K value was evaluated as 0.027 MT hr $MJ^{-1}$ $mm^{-1}$. The highest K factor was found in paddy rice fields, 0.034 MT hr $MJ^{-1}$ $mm^{-1}$, and K factors in upland fields, grassland, and forest were 0.026, 0.019, and 0.020 MT hr $MJ^{-1}$ $mm^{-1}$, respectively. C factors of upland crops ranged from 0.06 to 0.45 and that of grassland was 0.003. P factor varied between 0.01 and 0.85.

• Economic and Environmental Geology
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• v.53 no.4
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• pp.413-423
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• 2020

Over the wide area, King Sejong Station and the nearby land are uncovered with snow and ice conditions. Therefore, the active layer on the permafrost has been formed to be much thicker than the other Antarctica region. Electrical resistivity survey of Wenner and dipole-dipole arrays was undertaken at a series of time in the freezing season at the King Sejong Station to delineate subsurface structure and to monitor active layer in permafrost terrain. Time-lapse resistivity structures are well in terms of the vegetation distribution, ground surface temperature, and snow depth. Horizontal high resistivity belt(>1826 Ωm) at very shallow depth is thickening with the lapse of time, probably caused by the freezing of the water in the pore spaces with decrease of ground temperature. Subsurface structures for the area of low snow-cover and vegetated zone area are comprised of 0~0.5 m deep high-resistive gravel-rich soil, 0.5~3 m deep low-resistive active layer, and the underlying permafrost. In contrast, the unvegetated area and high snow-buildup is characterized with high resistivities larger than approximately 2000 Ωm due to freezing of the soil throughout the year. Data interpretation and correlation schemes explored in this paper can be applied to confirm the active layer, which is expected to get thinner in additional survey during the thawing season.

• Korean Journal of Remote Sensing
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• v.37 no.2
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• pp.321-335
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• 2021

Particulate matter (PM10 and PM2.5 with a diameter less than 10 and 2.5 ㎛, respectively) can be absorbed by the human body and adversely affect human health. Although most of the PM monitoring are based on ground-based observations, they are limited to point-based measurement sites, which leads to uncertainty in PM estimation for regions without observation sites. It is possible to overcome their spatial limitation by using satellite data. In this study, we developed machine learning-based retrieval algorithm for ground-level PM10 and PM2.5 concentrations using aerosol parameters from Geostationary Ocean Color Imager (GOCI) satellite and various meteorological parameters from a numerical weather prediction model during January to December of 2019. Gradient Boosted Regression Trees (GBRT) and Light Gradient Boosting Machine (LightGBM) were used to estimate PM concentrations. The model performances were examined for two types of feature sets-all input parameters (Feature set 1) and a subset of input parameters without meteorological and land-cover parameters (Feature set 2). Both models showed higher accuracy (about 10 % higher in R2) by using the Feature set 1 than the Feature set 2. The GBRT model using Feature set 1 was chosen as the final model for further analysis(PM10: R2 = 0.82, nRMSE = 34.9 %, PM2.5: R2 = 0.75, nRMSE = 35.6 %). The spatial distribution of the seasonal and annual-averaged PM concentrations was similar with in-situ observations, except for the northeastern part of China with bright surface reflectance. Their spatial distribution and seasonal changes were well matched with in-situ measurements.