• Korean Journal of Remote Sensing
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• v.35 no.6_3
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• pp.1285-1298
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• 2019

Drought is one of the factors that can cause wildfires. Drought is related to not only the occurrence of wildfires but also their frequency, extent and severity. In South Korea, most wildfires occur in dry seasons (i.e. spring and autumn), which are highly correlated to drought events. In this study, we examined the relationship between wildfire occurrence and drought factors, and developed satellite-based new drought indices for assessing wildfire risk over South Korea. Drought factors used in this study were high-resolution downscaled soil moisture, Normalized Different Water Index (NDWI), Normalized Multi-band Drought Index (NMDI), Normalized Different Drought Index (NDDI), Temperature Condition Index (TCI), Precipitation Condition Index (PCI) and Vegetation Condition Index (VCI). Drought indices were then proposed through weighted linear combination and one-class support vector machine (One-class SVM) using the drought factors. We found that most drought factors, in particular, soil moisture, NDWI, and PCI were linked well to wildfire occurrence. The validation results using wildfire cases in 2018 showed that all five linear combinations produced consistently good performance (> 88% in occurrence match). In particular, the combination of soil moisture and NDWI, and the combination of soil moisture, NDWI, and precipitation were found to be appropriate for representing wildfire risk.

• Journal of the Korean Solar Energy Society
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• v.33 no.4
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• pp.46-50
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• 2013

This paper investigated wind turbine degradation quantitatively by analyzing the short-term operation records of the Shinan Wind Power Plant. Instead of a capacity factor which is needed to be normalized its variability due to monthly wind speed change, this study suggests an analysis method by taking the difference between the theoretical power output calculated from the nacelle wind speed and actual power output as the quantitative index of performance degradation. For three-year SCADA data analysis of the Shinan Wind Power Plant, it was confirmed that power output degradation rate of 0.54% per year. This value is within the average reduction rate 0.4%/year~0.9%/year of normalized capacity factor of the onshore wind power plants in U.K. and Denmark; however, lower than the rate 2%/year of Canadian wind power plants.

• Journal of Korean Society for Geospatial Information Science
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• v.23 no.4
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• pp.11-16
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• 2015

Spatial and temporal information on near-surface air temperature is important for understanding global warming and climate change. In this study, the estimation algorithm of near-surface air temperature in Korea was developed by using spatial homogeneous surface information obtained from satellite remote sensing observations. Based on LST(Land Surface Temperature), NDWI(Normalized Difference Water Index) and NDVI(Normalized Difference Vegetation Index) as independent variables, the multiple regression model was proposed for the estimation of near-surface air temperature. The different regression constants and coefficients for warm and cold seasons were calculated for considering regional climate change in Korea. The near-surface air temperature values estimated from the multiple regression algorithm showed reasonable performance for both warm and cold seasons with respect to observed values (approximately $3^{\circ}C$ root mean-square error and nearly zero mean bias). Thus;the proposed algorithm using remotely sensed surface observations and the approach based on the classified warm and cold seasons may be useful for assessment of regional climate temperature in Korea.

• 제어로봇시스템학회:학술대회논문집
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• 1999.10a
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• pp.25-28
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• 1999

In this article, a new performance index is proposed to re-duce the collision impulsive force by controlling the null motion of redundant manipulators. First, we define the normalized impact ellipsoid in the viewpoint of instantaneous velocity change. Then, we propose a new impact performance index based on velocity direction for null motion to reduce initial impulsive effects. It gives some advantage for the case of unknown environment. The optimization of this index is that the successional impact forces are reduced. The performance of the proposed index is demonstrated by simulation study.

• Structural Engineering and Mechanics
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• v.70 no.4
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• pp.457-466
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• 2019

This study develops a damage detection method based on neural networks. The performance of the method is numerically and experimentally verified using a three-story shear building model. The framework is mainly composed of two hierarchical stages to identify damage location and extent using artificial neural network (ANN). The normalized damage signature index, that is a normalized ratio of the changes in the natural frequency and mode shape caused by the damage, is used to identify the damage location. The modal parameters extracted from the numerically developed structure for multiple damage scenarios are used to train the ANN. The positive alarm from the first stage of damage detection activates the second stage of ANN to assess the damage extent. The difference in mode shape vectors between the intact and damaged structures is used to determine the extent of the related damage. The entire procedure is verified using laboratory experiments. The damage is artificially modeled by replacing the column element with a narrow section, and a stochastic subspace identification method is used to identify the modal parameters. The results verify that the proposed method can accurately detect the damage location and extent.

• Korean Journal of Remote Sensing
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• v.38 no.6_1
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• pp.1285-1300
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• 2022

Turbidity, the measure of the cloudiness of water, is used as an important index for water quality management. The turbidity can vary greatly in small river systems, which affects water quality in national rivers. Therefore, the generation of high-resolution spatial information on turbidity is very important. In this study, a turbidity retrieval model using the Korea Multi-Purpose Satellite-3 and -3A (KOMPSAT-3/3A) images was developed for high-resolution turbidity mapping of Han River system based on eXtreme Gradient Boosting (XGBoost) algorithm. To this end, the top of atmosphere (TOA) spectral reflectance was calculated from a total of 24 KOMPSAT-3/3A images and 150 Landsat-8 images. The Landsat-8 TOA spectral reflectance was cross-calibrated to the KOMPSAT-3/3A bands. The turbidity measured by the National Water Quality Monitoring Network was used as a reference dataset, and as input variables, the TOA spectral reflectance at the locations of in situ turbidity measurement, the spectral indices (the normalized difference vegetation index, normalized difference water index, and normalized difference turbidity index), and the Moderate Resolution Imaging Spectroradiometer (MODIS)-derived atmospheric products(the atmospheric optical thickness, water vapor, and ozone) were used. Furthermore, by analyzing the KOMPSAT-3/3A TOA spectral reflectance of different turbidities, a new spectral index, new normalized difference turbidity index (nNDTI), was proposed, and it was added as an input variable to the turbidity retrieval model. The XGBoost model showed excellent performance for the retrieval of turbidity with a root mean square error (RMSE) of 2.70 NTU and a normalized RMSE (NRMSE) of 14.70% compared to in situ turbidity, in which the nNDTI proposed in this study was used as the most important variable. The developed turbidity retrieval model was applied to the KOMPSAT-3/3A images to map high-resolution river turbidity, and it was possible to analyze the spatiotemporal variations of turbidity. Through this study, we could confirm that the KOMPSAT-3/3A images are very useful for retrieving high-resolution and accurate spatial information on the river turbidity.

• Korean Journal of Remote Sensing
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• v.40 no.1
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• pp.45-56
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• 2024

Faced with aging populations, declining resources, and limited agricultural productivity, rural areas in South Korea require innovative solutions. This study investigated the potential of drone-based vegetation indices (VIs) to analyze soybean growth patterns in open-field smart agriculture in Goesan-gun, Chungbuk Province, South Korea. We monitored multi-seasonal normalized difference vegetation index (NDVI) and the normalized difference red edge (NDRE) data for three soybean lots with different irrigation methods (subsurface drainage, conventional, subsurface drip irrigation) using drone remote sensing. Combining NDVI (photosynthetically active biomass, PAB) and NDRE (chlorophyll) offered a comprehensive analysis of soybean growth, capturing both overall health and stress responses. Our analysis revealed distinct growth patterns for each lot. LotA(subsurface drainage) displayed early vigor and efficient resource utilization (peaking at NDVI 0.971 and NDRE 0.686), likely due to the drainage system. Lot B (conventional cultivation) showed slower growth and potential limitations (peaking at NDVI 0.963 and NDRE 0.681), suggesting resource constraints or stress. Lot C (subsurface drip irrigation) exhibited rapid initial growth but faced later resource limitations(peaking at NDVI 0.970 and NDRE 0.695). By monitoring NDVI and NDRE variations, farmers can gain valuable insights to optimize resource allocation (reducing costs and environmental impact), improve crop yield and quality (maximizing yield potential), and address rural challenges in South Korea. This study demonstrates the promise of drone-based VIs for revitalizing open-field agriculture, boosting farm income, and attracting young talent, ultimately contributing to a more sustainable and prosperous future for rural communities. Further research integrating additional data and investigating physiological mechanisms can lead to even more effective management strategies and a deeper understanding of VI variations for optimized crop performance.

• Health Policy and Management
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• v.23 no.2
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• pp.176-187
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• 2013

Background: In 2006 Emergency Medical Services Index (EMSI), which summarizes the performance of regional emergency medical services system, was developed. This study assesses the performance of the EMSI to help determine whether EMSI can be used as evaluation tool. Methods: To build a composite score of the EMSI from predefined 24 indicators, 3 normalized values were calculated for each indicator, the normalized values of each indicator were weighted using 4 weighting methods, and the weighted values were aggregated into the final composite score using 2 aggregation schemes. The performance of EMSI was evaluated using 3 criteria: discrimination, construct validity, and sensitivity. Discrimination was the proportion of regions that did not include the overall median rank in the 5th to 95th percentiles rank interval, which was calculated from Monte Carlo simulation. Construct validity was a correlation among the alternative EMSIs. Sensitivity of EMSIs was evaluated by total shift of quartile membership and changes of 5th to 95th percentile intervals. Results: The total discrimination performance of the EMSI was 50.0%. Correlation coefficients between EMSIs using standardized values and those using rescaled values ranged from 0.621 to 0.997. Variation of the quartile membership of regions ranged from 0.0% to 75.0%. The total change in the 5th to 95th percentile intervals ranged from -19 to +17 places. Conclusion: The results suggested that the EMSI could be used as a tool for evaluating quality of regional EMS system and for identifying the areas for quality improvement.

• Weed & Turfgrass Science
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• v.4 no.4
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• pp.338-347
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• 2015

Reinforcement cells are used to aid grass growth and taken together, this serves to extend greenery beyond the conventional spaces of lawns, tree pits, gardens, and parks, and is advantageous to urban cities since space for greening is often limited. Drought has variable effects on plant life and the resilience of turf to drought resistance also varies with species. Changes in photosynthetic ability were more pronounced for media rather than grass species. The media of sand without organic matter was found to be least suited for drought resistance. Normalized difference vegetation index (NDVI) and digital image analysis (DIA) data were generally in favour of Zoysia species as oppose to A. compressus. In A. compressus, selective traits such as, a more extensive root system and lower specific leaf area (SLA) were not an underlying factor that assisted this grass with enhanced drought resistance. Generally, WUE was found to be strongly related to plant characterises such as overall biomass, photosynthetic features as well as the lushness indexes, and specific leaf area. This study found a strong relationship between WUE and a suite of plant characteristics. These traits should serve as useful selection criteria for species with the ability to resist water stress.

• Korean Journal of Remote Sensing
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• v.34 no.6_1
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• pp.1025-1032
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• 2018

Snow Cover is a form of precipitation that is defined by snow on the surface and is the single largest component of the cryosphere that plays an important role in maintaining the energy balance between the earth's surface and the atmosphere. It affects the regulation of the Earth's surface temperature. However, since snow cover is mainly distributed in area where human access is difficult, snow cover detection using satellites is actively performed, and snow cover detection in forest area is an important process as well as distinguishing between cloud and snow. In this study, we applied the Normalized Difference Snow Index (NDSI) and the Normalized Difference Vegetation Index (NDVI) to the geostationary satellites for the snow detection of forest area in existing polar orbit satellites. On the rest of the forest area, the snow cover detection using $R_{1.61{\mu}m}$ anomaly technique and NDSI was performed. As a result of the indirect validation using the snow cover data and the Visible Infrared Imaging Radiometer (VIIRS) snow cover data, the probability of detection (POD) was 99.95 % and the False Alarm Ratio (FAR) was 16.63 %. We also performed qualitative validation using the Himawari-8 Advanced Himawari Imager (AHI) RGB image. The result showed that the areas detected by the VIIRS Snow Cover miss pixel are mixed with the area detected by the research false pixel.