• Journal of Korea Water Resources Association
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• v.52 no.6
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• pp.441-449
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• 2019

Drought is a longer lasting and more extensive disaster than other natural disasters, resulting in significant socioeconomic damage. Even though drought events have same severity, their damage vary from region to region because of spatial, technical, economic, and social circumstances. In this study, drought vulnerability was assessed considering socioeconomic factors. Preliminary factors were identified from the case study for Chungcheong province, and evaluative factors were selected by applying the principal component analysis. The entropy method was applied to determine the weights of evaluative factors. As a result, in Chungcheong province, farm population, number of recipient of basic living, water fare gap indicator, area of industrial complex, amount of underground water usage, amount of water available per capita, water supply ratio, financial soundness for water resources, amount of domestic water usage, amount of agricultural water usage and agricultural land area were chosen as the evaluative factors. Among them, the factors associated with agriculture had larger weights. The overall assessment of vulnerability indicated that Cheongju, Dangjin and Seosan were the most vulnerable to drought.

• Korean Journal of Remote Sensing
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• v.34 no.6_1
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• pp.905-923
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• 2018

In order to generate high-resolution products using UAV images, it is necessary to analyze the sharpness of the themselves measured through image analysis. When images that have unclear sharpness of UAV are used in the production, they can have a great influence on operations such as acquisition and mapping of accurate three-dimensional information using UAV. GRD (Ground Resolved Distance) has been used as an indicator of image clarity. GRD is defined as the minimum distance between two identifiable objects in an image and is used as a concept against the GSD (Ground Sampling Distance), which is a spatial sample interval. In this study, GRD is extracted by analyzing the edge target without visual analysis. In particular, GRD to GSD ratio (GRD/GSD), or GRD expressed in pixels, is used as an index for evaluation the relative image sharpness. In this paper, GRD is calculated by analyzing edge targets at various altitudes in various shooting environments using a rotary wing. Using GRD/GSD, it was possible to identify images whose sharpness was significantly lowered, and the appropriateness of the image as an image clarity index was confirmed.

• Atmosphere
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• v.30 no.4
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• pp.439-454
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• 2020

This study investigates the change in the fine particulate matter (PM2.5) concentration and World Health Organization (WHO) air quality index (AQI) in East Asia (EA) under Shared Socioeconomic Pathways (SSPs). AQI is an indicator of increasing levels about health concern, divided into six categories based on PM2.5 annual concentrations. Here, we utilized the ensemble results of UKESM1, the climate model operated in Met Office, UK, for the analysis of long-term variation during the historical (1950~2014) and future (2015~2100) period. The results show that the spatial distributions of simulated PM2.5 concentrations in present-day (1995~2014) are comparable to observations. It is found that most regions in EA exceeded the WHO air quality guideline except for Japan, Mongolia regions, and the far seas during the historical period. In future scenarios containing strong air quality (SSP1-2.6, SSP5-8.5) and medium air quality (SSP2-4.5) controls, PM2.5 concentrations are substantially reduced, resulting in significant improvement in AQI until the mid-21st century. On the other hand, the mild air pollution controls in SSP3-7.0 tend to lead poor AQI in China and Korea. This study also examines impact of increased in PM2.5 concentrations on downward shortwave energy at the surface. As a result, strong air pollution controls can improve air quality through reduced PM2.5 concentrations, but lead to an additional warming in both the near and mid-term future climate over EA.

• Korean Journal of Agricultural and Forest Meteorology
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• v.23 no.4
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• pp.424-433
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• 2021

Recent climate change has caused abnormal weather phenomena all over the world and a lot of damage in many fields of society. Particularly, a lot of recent damages were due to extreme precipitation, such as torrential downpour or drought. The objective of this study was to analyze the temporal and spatial changes in the precipitation pattern in South Korea. To achieve this objective, this study selected some of the precipitation indices suggested in previous studies to compare the temporal characteristics of precipitation induced by climate change. This study selected ten ASOS observatories of the Korea Meteorological Administration to understand the change over time for each location with considering regional distribution. This study also collected daily cumulative precipitation from 1951 to 2020 for each point. Additionally, this study generated high-resolution national daily precipitation distribution maps using an orographic precipitation model from 1981 to 2020 and analyzed them. Temporal analysis showed that although annual cumulative precipitation revealed an increasing trend from the past to the present. The number of precipitation days showed a decreasing trend at most observation points, but the number of torrential downpour days revealed an increasing trend. Spatially, the number of precipitation days and the number of torrential downpour days decreased in many areas over time, and this pattern was prominent in the central region. The precipitation pattern of South Korea can be summarized as the fewer precipitation days and larger daily precipitation over time.

• Journal of Korean Society of Forest Science
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• v.112 no.1
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• pp.11-22
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• 2023

This study was established to provide basic information necessary for ecological management to restore the naturalness of black locust (Robinia pseudoacacia L.) plantations located in the mountains of Gyeongbuk, Korea. Using vegetation data collected from 200 black locust stands, vegetation types were classified using the TWINSPAN method, the spatial arrangement status according to the environmental gradient was identified through DCA analysis, and a synoptic table of communities was prepared based on the diagnostic species determined by determining community fidelity (Φ) for each vegetation type. The vegetation types were classified into seven types, namely, Quercus mongolica-Polygonatum odoratum var. pluriflorum type, Castanea crenata-Smilax china type, Clematis apiifolia-Lonicera japonica type, Rosa multiflora-Artemisia indica type, Quercus variabilis-Lindera glauca type, Ulmus parvifolia-Celtis sinensis type, and Prunus padus-Celastrus flagellaris type. These types usually reflected differences in complex factors such as altitude, moisture regime, successional stage, and disturbance regime. The mean relative importance value of the constituent species was highest for black locust(39.7), but oaks such as Quercus variabilis, Q. serrata, Q. mongolica, Q. acutissima, and Q. aliena were also identified as important constituent species with high relative importance values, indicating their potential for successional trends. In addition, the total percent cover of constituent species by vegetation type, life form composition, species diversity index, and indicator species were compared.

• Journal of the Korean Society of Marine Environment & Safety
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• v.29 no.1
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• pp.24-35
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• 2023

Rapid industrialization and urbanization have led to severe marine pollution. A Water Quality Index (WQI) has been developed to allow the effective management of marine pollution. However, the WQI suffers from problems with loss of information due to the complex calculations involved, changes in standards, calculation errors by practitioners, and statistical errors. Consequently, research on the use of artificial intelligence techniques to predict the marine and coastal WQI is being conducted both locally and internationally. In this study, six techniques (RF, XGBoost, KNN, Ext, SVM, and LR) were studied using marine environmental measurement data (2000-2020) to determine the most appropriate artificial intelligence technique to estimate the WOI of five ecoregions in the Korean seas. Our results show that the random forest method offers the best performance as compared to the other methods studied. The residual analysis of the WQI predicted score and actual score using the random forest method shows that the temporal and spatial prediction performance was exceptional for all ecoregions. In conclusion, the RF model of WQI prediction developed in this study is considered to be applicable to Korean seas with high accuracy.

• Journal of the Korean Association of Geographic Information Studies
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• v.26 no.4
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• pp.173-187
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• 2023

In an era of population decline, depopulated regions facing challenges in attracting inbound population migration must enhance urban vitality through the attraction of living populations. This study focuses on Busan, a city experiencing population decline, comparing the spatiotemporal distribution characteristics of registered residents and living populations in various administrative districts (Eup-Myeon-Dong) using mobile communication big data. Administrative districts are typified based on population change patterns, and regional characteristics are analyzed using indicators related to urban decline and vitality. Spatiotemporal distribution analysis reveals generally similar density patterns between registered residents and living populations; however, a distinctive feature is observed in the city center areas where the density of registered residents is low, while the density of living populations is high. Divergent trends in spatial patterns of change between registered residents and living populations show clusters of registered population decline in low-density areas and clusters of living population decline in high-density areas. Areas adjacent to declining living populations exhibit large clusters of population changes, indicating a spillover effect from high-density to neighboring areas. Typification results reveal that, even in areas with a decline in registered residents, there is active population influx due to commuting or visiting. These areas sustain an increase in the number of businesses, confirming the presence of industrial and economic growth. However, approximately 47% of administrative districts in Busan are experiencing a decline in both registered residents and living populations, indicating ongoing regional decline. Urgent measures are needed for enhancing urban vitality. The study emphasizes the necessity of utilizing living population data as an urban planning indicator, considering the increasing limit distance of urban activities and growing interregional interaction due to advancements in transportation and communication.

• Journal of Korean Society of Rural Planning
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• v.30 no.1
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• pp.57-66
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• 2024

This study aimed to develop a precise vegetation cover classification model for small streams using the combination of drone remote sensing and support vector machine (SVM) techniques. The chosen study area was the Idong stream, nestled within Geosan-gun, Chunbuk, South Korea. The initial stage involved image acquisition through a fixed-wing drone named ebee. This drone carried two sensors: the S.O.D.A visible camera for capturing detailed visuals and the Sequoia+ multispectral sensor for gathering rich spectral data. The survey meticulously captured the stream's features on August 18, 2023. Leveraging the multispectral images, a range of vegetation indices were calculated. These included the widely used normalized difference vegetation index (NDVI), the soil-adjusted vegetation index (SAVI) that factors in soil background, and the normalized difference water index (NDWI) for identifying water bodies. The third stage saw the development of an SVM model based on the calculated vegetation indices. The RBF kernel was chosen as the SVM algorithm, and optimal values for the cost (C) and gamma hyperparameters were determined. The results are as follows: (a) High-Resolution Imaging: The drone-based image acquisition delivered results, providing high-resolution images (1 cm/pixel) of the Idong stream. These detailed visuals effectively captured the stream's morphology, including its width, variations in the streambed, and the intricate vegetation cover patterns adorning the stream banks and bed. (b) Vegetation Insights through Indices: The calculated vegetation indices revealed distinct spatial patterns in vegetation cover and moisture content. NDVI emerged as the strongest indicator of vegetation cover, while SAVI and NDWI provided insights into moisture variations. (c) Accurate Classification with SVM: The SVM model, fueled by the combination of NDVI, SAVI, and NDWI, achieved an outstanding accuracy of 0.903, which was calculated based on the confusion matrix. This performance translated to precise classification of vegetation, soil, and water within the stream area. The study's findings demonstrate the effectiveness of drone remote sensing and SVM techniques in developing accurate vegetation cover classification models for small streams. These models hold immense potential for various applications, including stream monitoring, informed management practices, and effective stream restoration efforts. By incorporating images and additional details about the specific drone and sensors technology, we can gain a deeper understanding of small streams and develop effective strategies for stream protection and management.

• Korean Journal of Ecology and Environment
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• v.43 no.3
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• pp.385-399
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• 2010

The objective of this study was to analyze long term temporal trends of water chemistry and spatial heterogeneity for 83 sampling sites of Geum-River watershed using water quality dataset during 2003~2007 (obtained from the Ministry of Environment, Korea). The water quality, based on multi-parameters of temperature, dissolved oxygen (DO), biochemical oxygen demand (BOD), chemical oxygen demand (COD), suspended solids (SS), total nitrogen (TN), total phosphorus (TP), and electric conductivity (EC), largely varied depending on the landuse patterns, years and seasons. The watershed was classified into three different landuse types: forest stream (Fo), agricultural stream (Ag), and urban stream (Ur). Largest seasonal variabilities in most parameters occurred during the two months of July to August and these were closely associated with large spate of summer monsoon rain. Conductivity, used as a key indicator for an ionic dilution during rainy season, and nutrients of TN and TP had inverse functions of precipitation. BOD, COD decrease during the rainy season. Minimum values in the conductivity, TN, and TP were observed during the summer monsoon, indicating an ionic and nutrient dilution of river water by the rainwater. In contrast, major inputs of suspended solids (SS) occurred during the period of summer monsoon. The landuse patterns analyses, based on the variables of BOD, COD, TN, TP and SS, showed that the values were greater in the agricultural stream (Ag) than in the forest stream (Fo) and urban stream (Ur) and that water quality was worst in the urban stream (Ur). The overall dataset suggest that efficient water quality management, especially in Gap-Stream and Miho-Stream, which showed worst water quality is required along with some of urban stream (Ur), based on the analysis of landuse patterns.

• Korean Journal of Ecology and Environment
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• v.39 no.1 s.115
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• pp.73-84
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• 2006

The objective of this study was to analyze long-term temporal trends of water chemistry and spatial heterogeneity for 10 sampling sites of the Yeongsan River watershed using water quality dataset during 1995 to 2004 (obtained from the Ministry of Environment, Korea). The water quality, based on multi-parameters of biological oxygen demand (BOD), chemical oxygen demand (COD), conductivity, dissolved oxygen (Do), total phosphorus (TP), total nitrogen (TN) and total suspended solids (TSS), largely varied depending on the sampling sites, seasons and years. Largest seasonal variabilities in most parameters occurred during the two months of July to August and these were closely associated with large spate of summmer monsoon rain. Conductivity, used as a key indicator for a ionic dilution during rainy season, and nutrients of TN and TP had an inverse function of precipitation (absolute r values> 0.32, P< 0.01, n= 119), whereas BOD and COD had no significant relations(P> 0.05, n= 119) with rainfall. Minimum values in conductivity, TN, and TP were observed during the summer monsoon, indicating an ionic and nutrient dilution of river water by the rainwater. In contrast, major inputs of total suspended solids (TSS) occurred during the period of summer monsoon. BOD values varied with seasons and the values was closely associated (r=0.592: P< 0.01) with COD, while variations of TN were had high correlations (r=0.529 : P< 0.01) with TP. Seasonal fluctuations of DO showed that maximum values were in the cold winter season and minimum values were in the summer seasons, indicating an inverse relation with water temperature. The spatial trend analyses of TP, TN, BOD, COD and TSS, except for conductivity, showed that the values were greater in the mid-river reach than in the headwater and down-river reaches. Conductivity was greater in the down-river sites than any other sites. Overall data of BOD, COD, and nutrients (TN, TP) showed that water quality was worst in the Site 4, compared to those of others sites. This was due to continuous effluents from the wastewater treatment plants within the urban area of Gwangju city. Based on the overall dataset, efficient water quality management is required in the urban area for better water quality.