• Korean Journal of Remote Sensing
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• v.15 no.2
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• pp.147-158
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• 1999

Remote sensing technique has offered better understanding of our environment for the decades by providing useful level of information on the landcover. In many applications using the remotely sensed data, digital image processing methodology has been usefully employed to characterize the features in the data and develop the models. Random field models, especially Markov Random Field (MRF) models exploiting spatial relationships, are successfully utilized in many problems such as texture modeling, region labeling and so on. Usually, remotely sensed imagery are very large in nature and the data increase greatly in the problem requiring temporal data over time period. The time required to process increasing larger images is not linear. In this study, the methodology to reduce the computational cost is investigated in the utilization of the Markov Random Field. For this, multiresolution framework is explored which provides convenient and efficient structures for the transition between the local and global features. The computational requirements for parameter estimation of the MRF model also become excessive as image size increases. A Bayesian approach is investigated as an alternative estimation method to reduce the computational burden in estimation of the parameters of large images.

• Journal of the Korea Convergence Society
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• v.12 no.10
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• pp.189-201
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• 2021

The art of dynamic images has experienced three development stages, including experimental films, recording art, and new media image. By introducing all kinds of new materials, new media to the art, and the art of dynamic images has created more freedom for art creation. With the development of digital information technology, dynamic image works have put forward an increasingly high requirement of visual art. The combination of dynamic images and visual illusion can give rise to different forms and expression methods, thus endowing artworks with more vigor. This paper provides an overview by sorting out the lineage and development of dynamic images in the background, as well as understanding the application and performance of contrasted visual illusion. Based on the understanding of the characteristics of visual illusion, we discuss the new characteristics of applying the theory of visual illusion to new media dynamic images in relation to the technical approach of dynamic images. Through the analysis of specific works of Telematic Vision, we search for its reasonable combination and find the appropriate technical means of implementation. We discuss how to use digital multimedia technology and spatial optical illusion to make the design more novel and impactful, and consider how the combination of digital dynamic image technology and visual illusion should be interpreted and applied.

• Korean Journal of Remote Sensing
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• v.37 no.5_3
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• pp.1405-1423
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• 2021

Precipitation is one of the main factors that affect water and energy cycles, and its estimation plays a very important role in securing water resources and timely responding to water disasters. Satellite-based quantitative precipitation estimation (QPE) has the advantage of covering large areas at high spatiotemporal resolution. In this study, machine learning-based rainfall intensity models were developed using Himawari-8 Advanced Himawari Imager (AHI) water vapor channel (6.7 ㎛), infrared channel (10.8 ㎛), and weather radar Column Max (CMAX) composite data based on random forest (RF). The target variables were weather radar reflectivity (dBZ) and rainfall intensity (mm/hr) converted by the Z-R relationship. The results showed that the model which learned CMAX reflectivity produced the Critical Success Index (CSI) of 0.34 and the Mean-Absolute-Error (MAE) of 4.82 mm/hr. When compared to the GeoKompsat-2 and Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks (PERSIANN)-Cloud Classification System (CCS) rainfall intensity products, the accuracies improved by 21.73% and 10.81% for CSI, and 31.33% and 23.49% for MAE, respectively. The spatial distribution of the estimated rainfall intensity was much more similar to the radar data than the existing products.

• Ecology and Resilient Infrastructure
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• v.8 no.3
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• pp.154-163
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• 2021

This study was carried out to identify the factors affecting the distribution of suitable habitats for Geranium carolinianum, which was naturalized in South Korea, and to predict the changes of distribution in the future. We collected occurrence data of G. carolinianum at 68 sites in South Korea, and applied the MaxEnt model under climate change scenarios (SSP2-4.5, and SSP5-8.5). Precipitation seasonality (bio15), mean temperature of warmest quarter (bio10), and mean temperature of driest quarter (bio09) had high contribution for potential distribution of G. carolinianum. According to climate change scenarios, high suitable habitats of G. carolinianum occupied 6.43% of the land of South Korea in historical period (1981~2010), and 92.60% under SSP2-4.5, and 98.36% undr SSP5-8.5 in far future (2071~2100).

• Korea Science and Art Forum
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• v.37 no.2
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• pp.275-292
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• 2019

This study was initiated with the attention of demanding new and diverse approaches, we're talking familiar with imitations in the design process like a way to draw a image. So I studied a convergence of humanities and visual arts with the understanding and conceptual approach of the formless. The purpose of this study is to develop formless languages and to organize practical courses which are to enable deeper research and design expression on theoretical approaches and explanations of outcomes required before and after the process when we practice in connection with the formless. The method of this study is to draw detailed items from selected words through advanced researches, work and author researches and practice teaching. The results of the study I proposed the formless language that is related to the horizontality in spatial positioning system, and pulse in the separation of space and time, and entropy in structural orders of the system, and base materialism in the limitation of matter as the operating mechanism and parent item of formless. And those elements are related with shape, size, shading, color, texture, space, structure as visual elements of formative elements and those have various adjectival meanings as the subordinate concept. So I presented an education materials of basic design which is to enable understanding and expressing the formless language in the overall process of formless visual art(theoretical approach, practice course, presentation, etc.). Based on these study results, I hope that this educational materials will be used as educational contents that makes them express and understand different new beauties, and a role that reveals social identity, and a reference for research on a formless visual arts.

• Journal of Korea Water Resources Association
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• v.54 no.12
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• pp.1255-1263
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• 2021

While it could become an alternative water resource, fog could undermine traffic safety and operational performance of infrastructures. To reduce such adverse impacts, it is necessary to have spatially continuous fog risk information. In this work, tree-based machine-learning models were developed in order to quantify fog risks with routine meteorological observations alone. The Extreme Gradient Boosting (XGB), Light Gradient Boosting (LGB), and Random Forests (RF) were chosen for the regional fog models using operational weather and visibility observations within the Jeollabuk-do province. Results showed that RF seemed to show the most robust performance to categorize between fog and non-fog situations during the training and evaluation period of 2017-2019. While the LGB performed better than in predicting fog occurrences than the others, its false alarm ratio was the highest (0.695) among the three models. The predictability of the three models considerably declined when applying them for an independent period of 2020, potentially due to the distinctively enhanced air quality in the year under the global lockdown. Nonetheless, even in 2020, the three models were all able to produce fog risk information consistent with the spatial variation of observed fog occurrences. This work suggests that the tree-based machine learning models could be used as tools to find locations with relatively high fog risks.

• Journal of the Korean Association of Geographic Information Studies
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• v.22 no.3
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• pp.1-9
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• 2019

In the recent marine sector, the development and standardization regarding S-100, which is the universal hydrographical data model standard for development of marine space information, was progressed, and for the effectiveness of marine chart production work and the multi-purpose use of water level data in S-100, S-102(Bathymetric Surface grid) standard development and various studies of BAG formats combined with water level and uncertainty, property information is being progressed. Since the water level information that is important in the operation of the ship is provided based on S-102, the calibration method of the location information when producing S-102 is an important factor in deciding the water level. In this study, the hydrographical surveying was conducted by piloting the standardized method for the production of S-102 in Korea, and have compared the accuracy of water level information according to the GNSS post treatment calibration method. As a result of comparing the water level in 2 places in the rocky terrain of the study area, the northern water level of Namu-do was shown as DL 0.79~0.83m, the eastern water level of Daeho-do was DL 12.63~12.91m, and the horizontal position errors of the intermittent sunshine water level were confirmed to be within 1m. As a result, the intermittent sunshine water level according to the location calibration method when producing the BAG was confirmed that it was in the available range for a ship's safe voyage. However, the accuracy verification for the location of the ship when conducting hydrographical surveying was judged that there is a need for a various additional study about regional characteristics and environment factor.

• Journal of Climate Change Research
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• v.1 no.2
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• pp.109-120
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• 2010

Heat wave is a disaster, which increases morbidity and mortality in temperate regions. Climate model results indicate that both intensity and frequency of heat wave in the future will be increased. This study shows the result about relationship between excess mortality and offensive airmass in 7 metropolitan cities, and an operational Heat-Health Warning System (HHWS) in Korea. Using meteorological observations, the Spatial Synoptic Classification (SSC) has been used to classify each summer day from 1982 to 2007 into specific airmass categories for each city. Through the comparative study analysis of the daily airmass type and the corresponding daily mortality rate, Dry Tropical (DT), and Moist Tropical plus (MT+) were identified as the most offensive airmasses with the highest rates of mortality. Therefore, using the multiple linear regression, forecast algorithm was produced to predict the number of the excess deaths that will occur with each occurrence of the DT and MT+ days. Moreover, each excess death forecast algorithm was implemented for the system warning criteria based on the regional acclimatization differences. HHWS will give warnings to the city's residents under offensive weather situations which can lead to deterioration in public health, under the climate change.

• Journal of Ecology and Environment
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• v.43 no.1
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• pp.43-60
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• 2019

Background: Tropical montane forests played an important role in the provision of ecosystem services. The intense degradation and deforestation for the need of agricultural land expansion result in a significant decline of forest cover. However, the expansion of agricultural land did not completely destruct natural forests. There remain forests inaccessible for agricultural and grazing purpose. Studies on these forests remained scant, motivating to investigate biomass and soil carbon stocks. Data of biomass and soils were collected in 80 quadrats ($400m^2$) systematically in 5 forests. Biomass and disturbance gradients were determined using allometric equation and disturbance index, respectively. The regression modeling is employed to explore the spatial distribution of carbon stock along disturbance and environmental gradients. Correlation analysis is also employed to identify the relation between site factors and carbon stocks. Results: The result revealed that a total of 1655 individuals with a diameter of ${\geq}5cm$, representing 38 species, were measured in 5 forests. The mean aboveground biomass carbon stocks (AGB CS) and soil organic carbon (SOC) stocks at 5 forests were $191.6{\pm}19.7$ and $149.32{\pm}6.8Mg\;C\;ha^{-1}$, respectively. The AGB CS exhibited significant (P < 0.05) positive correlation with SOC and total nitrogen (TN) stocks, reflecting that biomass seems to be a general predictor of SOCs. AGB CS between highly and least-disturbed forests was significantly different (P < 0.05). This disturbance level equates to a decrease in AGB CS of 36.8% in the highly disturbed compared with the least-disturbed forest. In all forests, dominant species sequestrated more than 58% of carbon. The AGB CS in response to elevation and disturbance index and SOC stocks in response to soil pH attained unimodal pattern. The stand structures, such as canopy cover and basal area, had significant positive relation with AGB CS. Conclusions: Study results confirmed that carbon stocks of studied forests were comparable to carbon stocks of protected forests. The biotic, edaphic, topographic, and disturbance factors played a significant variation in carbon stocks of forests. Further study should be conducted to quantify carbon stocks of herbaceous, litter, and soil microbes to account the role of the whole forest ecosystem.

• Korean Journal of Remote Sensing
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• v.35 no.2
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• pp.337-342
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• 2019

This letter describes the development of WRI (Wildfire Risk Index) using GDAPS (Global Data Assimilation and Prediction System) and satellite data, and its application to the Goseong-Sokcho and Gangneung-Donghae wildfires in April 4, 2019. We made sure that the proposed WRI represented the change of wildfire risk of around March 19 and April 4 very well. Our approach can be a viable option for wildfire risk monitoring, and future works will be necessary for the utilization of GK-2A products and the coupling with the wildfire prediction model of the Korea Forest Service.