• Korean Journal of Ecology and Environment
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• v.45 no.4
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• pp.392-402
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• 2012

In this study, to analyze the stream health using fish assemblage and make effective management, we conducted fish monitoring in the Hoeya River basin and neighboring streams. A total of 33 species classified into 12 families were collected from 29 sites in 2007. Dominant species was Zacco platypus (Relative abundance, RA: 24.8%), and subdominant species was Rhynchocypris oxycephalus (RA: 16.2%). Eight Korean endemic species and 4 exotic species were identified. Moreover, two species (Opsariichthys uncirostris amurensis and Hemiculter eigenmanni) were translocated from other basin. To evaluate stream health of the study sites, Index of Biological Integrity (IBI) was applied, based on fish assemblages. Overall, IBI values were "C (Fair)" or "D (Poor)" condition, according to the grade except two sites which recorded "B (Good)". The correlation between land use pattern of surrounding watershed and IBI was analyzed to verify impact of development on stream health using fish assemblage. As a result, when percentage of the developmental groups increased, IBI values were decreased (Pearson correlation, r=-0.425, p=0.022). In contrast, increment of percent forest and grass land was positively correlated with IBI (r=0.556, p=0.002). The agricultural group and IBI did not significantly correlate with each other (r=-0.231, p=0.333). In this study, we identified a relationship between land use of surrounding watershed and stream health using fish data (i.e. IBI). These results could be provided useful fundamental information to establish management and restoration plan in the Hoeya River basin and other rivers distributed in Korea.

• Journal of IKEEE
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• v.23 no.4
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• pp.1273-1279
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• 2019

Though sleep polysomnography (PSG) is considered as a golden rule for medical diagnosis of sleep disorder, it is essential to find alternative diagnosis methods due to its cost and time constraints. Recently, as the popularity of wearable health devices, there are many research trials to replace conventional actigraphy to consumer grade devices. However, these devices are very limited in their use due to the accessibility of the data and algorithms. In this paper, we showed the predictive model for sleep stages classified by American Academy of Sleep Medicine (AASM) standard and we proposed the estimation of sleep cycle by comparing sensor data and power spectrums of δ wave and θ wave. The sleep stage prediction for 31 subjects showed an accuracy of 85.26%. Also, we showed the possibility that proposed algorithm can find the sleep cycle of REM sleep and NREM sleep.

• Korean Journal of Agricultural Science
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• v.47 no.1
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• pp.59-66
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• 2020

Particulate matter (PM) is regarded as a major threat to public health and safety in urban areas. Despite a variety of efforts to systemically monitor the distribution of PM, the limited amount of sampling sites may not provide sufficient coverage over the areas where the monitoring stations are not located in close proximity. This study examined the capacity of using remotely sensed data to estimate the PM10 and PM2.5 concentrations in Seoul, South Korea. Multiple linear regression models were developed using the multispectral band data from the Moderate-resolution imaging spectro-radiometer equipped on Terra (MODIS) and Operational Land Imager equipped on Landsat 8 (Landsat 8) and meteorological parameters. Compared to MODIS-derived models (r² = 0.25 for PM10, r² = 0.30 for PM2.5), the Landsat 8-derived models showed improved model reliabilities (r² = 0.17 to 0.57 for PM10, r² = 0.47 to 0.71 for PM2.5). Landsat 8 model-derived PM concentration and ground-truth PM measurements were cross-validated to each other to examine the capability of the models for estimating the PM concentration. The modeled PM concentrations showed a stronger correlation to PM10 (r = 0.41 to 0.75) than to PM2.5 (r = 0.14 to 0.82). Overall, the results indicate that Landsat 8-derived models were more suitable in estimating the PM concentrations. Despite the day-to-day fluctuation in the model reliability, several models showed strong correspondences of the modeled PM concentrations to the PM measurements.

• Korean Journal of Remote Sensing
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• v.37 no.2
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• pp.275-290
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• 2021

Sulfur dioxide (SO2) in the atmosphere is mainly generated from anthropogenic emission sources. It forms ultra-fine particulate matter through chemical reaction and has harmful effect on both the environment and human health. In particular, ground-level SO2 concentrations are closely related to human activities. Satellite observations such as TROPOMI (TROPOspheric Monitoring Instrument)-derived column density data can provide spatially continuous monitoring of ground-level SO2 concentrations. This study aims to propose a 2-step residual corrected model to estimate ground-level SO2 concentrations through the synergistic use of satellite data and numerical model output. Random forest machine learning was adopted in the 2-step residual corrected model. The proposed model was evaluated through three cross-validations (i.e., random, spatial and temporal). The results showed that the model produced slopes of 1.14-1.25, R values of 0.55-0.65, and relative root-mean-square-error of 58-63%, which were improved by 10% for slopes and 3% for R and rRMSE when compared to the model without residual correction. The model performance by country was slightly reduced in Japan, often resulting in overestimation, where the sample size was small, and the concentration level was relatively low. The spatial and temporal distributions of SO2 produced by the model agreed with those of the in-situ measurements, especially over Yangtze River Delta in China and Seoul Metropolitan Area in South Korea, which are highly dependent on the characteristics of anthropogenic emission sources. The model proposed in this study can be used for long-term monitoring of ground-level SO2 concentrations on both the spatial and temporal domains.

• Korean Journal of Remote Sensing
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• v.35 no.5_1
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• pp.751-761
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• 2019

Mt. Baekdu, Mt. Aso, Mt. Sakurajima, Mt. Kikai and etc are distributed around the Korean Peninsula. Recently signs of eruption of Mt. Baekdu are increasing, raising concerns over possible damage to volcanic ash from seasonal winds during the winter eruption. Therefore, detailed procedures for investigation and countermeasures for volcanic ash spread and damage are required. But the standards for the warning and alarm signal of volcanic ash presented by Korea Ministry of Government Legislation are vague, with "when damage is expected" and "when serious damage is expected". In this study, to analyze the damage threshold and to apply the cases of overseas damage to the country, a survey was conducted on the establishment of domestic spatial information by public institutions with public confidence. As a result of the investigation of damage from volcanic ash overseas, the details of the damage cases were different depending on the type of life or income sources of each country. Therefore, instead of applying the volcanic ash damage cases abroad in Korea, spatial information analysis was performed to reflect domestic social and natural characteristics. In addition, we selected the areas to be considered in the event of volcanic ash damage in Korea. Finally, domestic volcanic ash damages should be classified as health, residential, road, railroad, aviation, power, water, agriculture, livestock, forest, and soil. When establishing the volcanic ash alarm optimized for Korea in the future, overseas volcanic ash damage cases and domestic spatial information construction in this study will be helpful in policy establishment.