• Korean Journal of Ecology and Environment
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• v.57 no.2
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• pp.102-110
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• 2024

This study presents a fine scale distribution of the endangered species, Odontobutis obscura, through field surveys and literature reviews. Using the mark-recapture method, the population size in major habitats was determined. Field surveys conducted on 18 streams in Geoje Island revealed that the O. obscura was only found in the main streams and tributaries of the Sanyang, Gucheon, and Buchun Streams, which are part of the Sanyang Stream watershed. The O. obscura exhibited relative abundances ranging from 0.5% to 35.3% at different locations, with certain spots showing higher relative abundances (18.8% to 35.3%), indicating major habitat areas. A review of six literature studies confirmed the presence of the O. obscura, although there were differences in occurrence status depending on the purpose, scope, and duration of the studies. Combining the results of field and literature surveys, it was found that the O. obscura inhabits the main and tributary streams of the Sanyang, Gucheon, and Buchun Streams, from the upper to lower reaches. Currently, the O. obscura population in the Sanyang Stream watershed maintains a stable habitat, but its limited distribution range suggests potential issues such as genetic diversity deficiency in the long term. The population size of the O. obscura was confirmed at two specific locations, with densities of 0.5 to 1.5 individuals per m². The average movement distance from the release point was 13.1 m, indicating the limited mobility characteristic of ambush predators. Understanding the distribution and population size of endangered species is the first step towards their conservation and protection. Based on this information, further research could significantly contribute to the conservation of the O. obscura.

• Journal of Korea Water Resources Association
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• v.53 no.10
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• pp.845-859
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• 2020

Climate change and recent heat waves have drawn public attention toward other environmental issues, such as water pollution in the form of algal blooms, chemical leaks, and oil spills. Water pollution by the leakage of chemicals may severely affect human health as well as contaminate the air, water, and soil and cause discoloration or death of crops that come in contact with these chemicals. Chemicals that may spill into water streams are often colorless and water-soluble, which makes it difficult to determine whether the water is polluted using the naked eye. When a chemical spill occurs, it is usually detected through a simple contact detection device by installing sensors at locations where leakage is likely to occur. The drawback with the approach using contact detection sensors is that it relies heavily on the skill of field workers. Moreover, these sensors are installed at a limited number of locations, so spill detection is not possible in areas where they are not installed. Recently hyperspectral images have been used to identify land cover and vegetation and to determine water quality by analyzing the inherent spectral characteristics of these materials. While hyperspectral sensors can potentially be used to detect chemical substances, there is currently a lack of research on the detection of chemicals in water streams using hyperspectral sensors. Therefore, this study utilized remote sensing techniques and the latest sensor technology to overcome the limitations of contact detection technology in detecting the leakage of hazardous chemical into aquatic systems. In this study, we aimed to determine whether 18 types of hazardous chemicals could be individually classified using hyperspectral image. To this end, we obtained hyperspectral images of each chemical to establish a spectral library. We expect that future studies will expand the spectral library database for hazardous chemicals and that verification of its application in water streams will be conducted so that it can be applied to real-time monitoring to facilitate rapid detection and response when a chemical spill has occurred.

• Korean Journal of Remote Sensing
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• v.34 no.6_1
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• pp.1041-1053
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• 2018

In this study, a real-time storage level and capacity monitoring and forecasting system for Yongdam Dam watershed was developed using high resolution satellite image. The drought indices such as Standardized Precipitation Index (SPI) from satellite data were used for storage level monitoring in case of drought. Moreover, to predict storage volume we used a statistical method based on Principle Component Analysis (PCA) of Singular Spectrum Analysis (SSA). According to this study, correlation coefficient between storage level and SPI (3) was highly calculated with CC=0.78, and the monitoring and predictability of storage level was diagnosed using the drought index calculated from satellite data. As a result of analysis of principal component analysis by SSA, correlation between SPI (3) and each Reconstructed Components (RCs) data were highly correlated with CC=0.87 to 0.99. And also, the correlations of RC data with Normalized Water Surface Level (N-W.S.L.) were confirmed that has highly correlated with CC=0.83 to 0.97. In terms of high resolution satellite image we developed a water detection algorithm by applying an exponential method to monitor the change of storage level by using Multi-Spectral Instrument (MSI) sensor of Sentinel-2 satellite. The materials of satellite image for water surface area detection in Yongdam dam watershed was considered from 2016 to 2018, respectively. Based on this, we proposed the possibility of real-time drought monitoring system using high resolution water surface area detection by Sentinel-2 satellite image. The results of this study can be applied to estimate of the reservoir volume calculated from various satellite observations, which can be used for monitoring and estimating hydrological droughts in an unmeasured area.

• Journal of the Korean Association of Geographic Information Studies
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• v.5 no.2
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• pp.16-24
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• 2002

Classification of the land cover characteristics is a major application of remote sensing. The goal of this study is to propose an optimal classification process for electro-optical camera(EOC) of Korea Multi-Purpose Satellite(KOMPSAT). The study was carried out on Landsat TM, high spectral resolution image and KOMPSAT EOC, high spatial resolution image of Miho river basin, Korea. The study was conducted in two stages: one was image fusion of TM and EOC to gain high spectral and spatial resolution image, the other was land cover classification on fused image. Four fusion techniques were applied and compared for its topographic interpretation such as IHS, HPF, CN and wavelet transform. The fused images were classified by radial basis function neural network(RBF-NN) and artificial neural network(ANN) classification model. The proposed RBF-NN was validated for the study area and the optimal model structure and parameter were respectively identified for different input band combinations. The results of the study propose an optimal classification process of KOMPSAT EOC to improve the thematic mapping and extraction of environmental information.

• Journal of Korean Society for Geospatial Information Science
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• v.13 no.2 s.32
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• pp.13-20
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• 2005

Upon the request of balanced development of the country and making inroads into the continent of China the development of the west coast was begun in the late 1980s, which has been being continued till recently under the blueprint of making the western part of the capital region to be the hub of northeastern Asia. As more lively development is expected to continue in the area, there are many occurrences of change in topology and terrain in the west coast. This study was done to detect the topographic and terrain change of the vicinity of the west coast. To make the basic map of the change in topology and terrain, the mosaic images were made using landsat images. The accuracy of the images was examined by comparing them with GCP through 1:25,000's digital map. After that, among the resultant images of the 1970s and 2000s, those of Sihwa, Hwaong and Ansan, the lands reclaimed by drainage were compared to observe the change in the area. From the results, it was concluded that, in case of the land the topological change was not so big due to the development in the reclaimed land or the bare land, and the area of agriculture and downtown increased, the drainage and bare land area decreased by comparing the change of land use.

• Korean Journal of Remote Sensing
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• v.39 no.5_1
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• pp.655-667
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• 2023

Algal bloom outbreaks are frequently reported around the world, and serious water pollution problems arise every year in Korea. It is necessary to protect the aquatic ecosystem through continuous management and rapid response. Many studies using satellite images are being conducted to estimate the concentration of chlorophyll-a (Chl-a), an indicator of algal bloom occurrence. However, machine learning models have recently been used because it is difficult to accurately calculate Chl-a due to the spectral characteristics and atmospheric correction errors that change depending on the water system. It is necessary to consider the factors affecting algal bloom as well as the satellite spectral index. Therefore, this study constructed a dataset by considering water quality, hydrological and meteorological factors, and sentinel-2 images in combination. Representative ensemble models random forest and extreme gradient boosting (XGBoost) were used to predict the concentration of Chl-a in eight weirs located on the Nakdong river over the past five years. R-squared score (R²), root mean square errors (RMSE), and mean absolute errors (MAE) were used as model evaluation indicators, and it was confirmed that R² of XGBoost was 0.80, RMSE was 6.612, and MAE was 4.457. Shapley additive expansion analysis showed that water quality factors, suspended solids, biochemical oxygen demand, dissolved oxygen, and the band ratio using red edge bands were of high importance in both models. Various input data were confirmed to help improve model performance, and it seems that it can be applied to domestic and international algal bloom detection.