• Economic and Environmental Geology
• /
• v.55 no.5
• /
• pp.541-550
• /
• 2022

A pre-existing landform is created by weathering and erosion along the bedrock fault and the weak zone. A neotectonic landform is formed by neotectonic movements such as earthquakes, volcanoes, and Quaternary faults. It is difficult to clearly distinguish the landform in the actual field because the influence of the tectonic activity in the Korean Peninsula is relatively small, and the magnitude of surface processes (e.g., erosion and weathering) is intense. Thus, to better understand the impact of tectonic activity and distinguish between pre-existing landforms and neotectonic landforms, it is necessary to understand the development process of pre-existing landforms depending on the bedrock characteristics. This study used a two-dimensional numerical landscape evolution model (LEM) to study the spatio-temporal development of landscape according to the different erodibility under the same factors of climate and the uplift rate. We used hill-slope indices (i.e., relief, mean elevation, and slope) and channels (i.e., longitudinal profile, normalized channel steepness index, and stream order) to distinguish the difference according to different bedrocks. As a result of the analysis, the terrain with high erosion potential shows low mean elevation, gentle slope, low stream order, and channel steepness index. However, the value of the landscape with low erosion potential differs from that with high erodibility. In addition, a knickpoint came out at the boundary of the bedrock. When researching the actual topography, the location around the border of difference in bedrock has only been considered a pre-existing factor. This study suggested that differences in bedrock and various topographic indices should be comprehensively considered to classify pre-existing and active tectonic topography.

• KSCE Journal of Civil and Environmental Engineering Research
• /
• v.29 no.1B
• /
• pp.23-33
• /
• 2009

Climate change, abnormal weather, and unprecedented extreme weather events have appeared globally. Interest in their size, frequency, and changes in spatial distribution has been heightened. However, the events do not display regional or regular patterns or cycles. Therefore, it is difficult to carry out quantified evaluation of their frequency and tendency. For more objective evaluation of extreme weather events, this study proposed a rainfall extreme weather index (STARDEX, 2005). To compare the present and future spatio-temporal distribution of extreme weather events, each index was calculated from the past data collected from 66 observation points nationwide operated by Korea Meteorological Administration (KMA). Tendencies up to now have been analyzed. Then, using SRES B2 scenario and 2045s (2031-2050) data from YONU CGCM simulation were used to compute differences among each of future extreme weather event indices and their tendencies were spatially expressed.The results shows increased rainfall tendency in the East-West inland direction during the summer. In autumn, rainfall tendency increased in some parts of Gangwon-do and the south coast. In the meanwhile, the analysis of the duration of prolonged dry period, which can be contrasted with the occurrence of rainfall or its concentration, showed that the dryness tendency was more pronounced in autumn rather than summer. Geographically, the tendency was more remarkable in Jeju-do and areas near coastal areas.

• The Journal of the Korea institute of electronic communication sciences
• /
• v.19 no.1
• /
• pp.137-142
• /
• 2024

Recently, deep learning technology has become those methods as de facto standards in the area of medical data representation. But, deep learning inherently requires a large amount of training data, which poses a challenge for its direct application in the medical field where acquiring large-scale data is not straightforward. Additionally, brain signal modalities also suffer from these problems owing to the high variability. Research has focused on designing deep neural network structures capable of effectively extracting spectro-spatio-temporal characteristics of brain signals, or employing self-supervised learning methods to pre-learn the neurophysiological features of brain signals. This paper analyzes methodologies used to handle small-scale data in emerging fields such as brain-computer interfaces and brain signal-based state prediction, presenting future directions for these technologies. At first, this paper examines deep neural network structures for representing brain signals, then analyzes self-supervised learning methodologies aimed at efficiently learning the characteristics of brain signals. Finally, the paper discusses key insights and future directions for deep learning-based brain signal analysis.

• Journal of the Korean Association of Geographic Information Studies
• /
• v.8 no.2
• /
• pp.81-94
• /
• 2005

The purpose of this study is to test the applicability of landscape metrics for quantifying and monitoring the landscape structure in the Geumho River watershed, which has undergone heavy environmental disturbances. Landscape metrics were computed from land cover maps(1985, 1999) for the forest patches. The number of variables were reduced from 12 metrics to 3 factors through factor analysis. These factors accounted for above 91% of the variation in the original metrics. We also determined the relative effects of land development on the changes of forest landscape structure using multiple linear regression analysis. At the forest patches, the conversion of forest to urban areas and agriculture resulted in increased fragmentation. Patch area and patch size decreased. and patch density increased as a result of the conversion of forest to agriculture($R^2=0.696$, p<0.01). The heterogeneity of patch size and complexity of patch shape mainly decreased as a result of the conversion of forest to urban areas($R^2=0.405$, p<0.01). The density of core area and edge showed the tendency increase, but there was no relationship with the conversion of forest to urban area and agriculture The future research will be needed to analyze correlations between landscape structures and specific environmental and socioeconomic landscape functions.

• Korean Journal of Remote Sensing
• /
• v.32 no.2
• /
• pp.141-153
• /
• 2016

Reliable assessment of Land Use and Land Cover (LULC) changes greatly improves many practical issues in hydrography, socio-geographical research such as the observation of erosion and accretion, coastal monitoring, ecological effects evaluation. Remote sensing imageries can offer the outstanding capability to monitor nature and extent of land and associated changes over time. Nowadays accurate analysis using remote sensing imageries with high spatio-temporal resolution is required for environmental monitoring. This study develops a methodology of mapping and change detection in LULC by using classified Korea Multi-Purpose Satellite-2 (KOMPSAT-2) multispectral imageries at Jeonbuk and Jeonnam provinces including protected tidal flats located in the west coasts of Korean peninsula from 2008 to 2015. The LULC maps generated from unsupervised classification were analyzed and evaluated by post-classification change detection methods. The LULC assessment in Jeonbuk and Jeonnam areas had not showed significant changes over time although developed area was gradually increased only by 1.97% and 4.34% at both areas respectively. Overall, the results of this study quantify the land cover change patterns through pixel based analysis which demonstrate the potential of multispectral KOMPSAT-2 images to provide effective and economical LULC maps in the coastal zone over time. This LULC information would be of great interest to the environmental and policy mangers for the better coastal management and political decisions.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.21 no.8
• /
• pp.579-592
• /
• 2020

This study was designed to assess the water quality and phytoplankton community including chlorophyll a in blood cockle (Tegillarca granosa) farms in May, August and November of 2017 in the northwestern Yeoja Bay, Korea. As a result, the seasonal characteristics of water types by water temperature and salinity were clear. Nutrients were abundant in silicate throughout the season, but phosphate was scarce in spring and summer, and nitrogen sources were scarce in autumn. The species composition of phytoplankton community was a very simple distribution, and the standing crop was also very low. The annual dominant species is dominated by the diatoms, with Skeletonema costatum-ls, Nitzschia longissima in spring, Pleurrosigma normanii, Coscinodiscus gigas in summer, and N. longissima, Pseudonitschia pungens, Chaetoceros curvisetus, Eucampia zodiacus in autumn. In summer the results were different from other coastal waters of Korea. The principal component analysis(PCA) and correlation analysis showed that the characteristics of water quality and biological environments differed according to the season. Furthermore, it was determined by the supply of materials through fresh water on land, seawater congestion caused by the refueling of surface sediments with lower depth, and the balance of biological production and mineralization of organic matters in blood cockle farms.

• Journal of the Korean Association of Geographic Information Studies
• /
• v.10 no.4
• /
• pp.60-74
• /
• 2007

For quantitative analysis of spatio-temporal distribution pattern on marine environment, spatial autocorrelation statistics on the both global and local aspects was applied to the observed data obtained from Gwangyang Bay in South Sea of Korea. Global indexes such as Moran's I and General G were used for understanding environmental distribution pattern in the whole study area. LISAs (local indicators of spatial association) such as Moran's I ($I_i$) and $G_i{^*}$ were considered to find similarity between a target feature and its neighborhood features and to detect hot spot and/or cold spot. Additionally, the significance test on clustered patterns by Z-scores was carried out. Statistical results showed variations of spatial patterns quantitatively in the whole year. Then all of general water quality, nutrients, chlorophyll-a and phytoplankton had strong clustered pattern in summer. When global indexes showed strong clustered pattern, the front region with a negative $I_i$ which means a strong spatial variation was observed. Also, when global indexes showed random pattern, hot spot and/or cold spot were/was found in the small local region with a local index $G_i{^*}$. Therefore, global indexes were useful for observing the strength and time series variations of clustered patterns in the whole study area, and local indexes were useful for tracing the location of hot spot and/or cold spot. Quantification of both spatial distribution pattern and clustering characteristics may play an important role to understand marine environment in depth and to find the reasons for spatial pattern.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
• /
• v.19 no.4
• /
• pp.256-264
• /
• 2014

This study was carried out to estimate the benthic environments and polychaetous community in Garolim Bay, through five field surveys from April 2006 to April 2007. Garilim Bay is a semi-enclosed bay and composed of a biramous tidal channel and nearby wide tidal flats. Surface sediment in the inner bay was composed of fine grained particles whereas that in the mouth area of bay was of coarse grained ones. Benthic polychaete worms were the most dominant taxa occupying 65.1% of total benthic macrofauna. Species number was higher in the inner bay than mouth and outer area of bay, and in the bay higher on the tidal flat than channel area. Density was higher on the tidal flat than channel area. Dominant polychaetous species were Prionospio sp., Heteromastus filiformis, Lumbrineris longifolia and so on, which is known as opportunistic species. Prionospio sp. and H. filiformis inhabited mainly on the tidal flats in inner bay, while L. longifolia in the channel area and mouth of the bay. Cluster analysis and nMDS showed the typical inner-to-outward distribution of station groups, which indicated the sequential difference of the species composition of each station group. To assess the benthic healthiness of Garolim Bay by AMBI and BPI analysis, the benthic condition was analyzed from slightly polluted in the outer and mouth of the bay to moderately polluted in the inner bay. Assumed from dominant species composition and benthic healthiness condition, benthic environments of Garolim Bay was slightly unstable and disturbed and organic enrichment was currently underway by massed fisheries farms.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
• /
• v.40 no.4
• /
• pp.275-292
• /
• 2022

In order to detect climate changes using satellite imagery, the GCOS (Global Climate Observing System) defines requirements such as spatio-temporal resolution, stability by the time change, and uncertainty. Due to limitation of GK-2A sensor performance, the level-2 products can not satisfy the requirement, especially for spatial resolution. In this paper, we found the optimal pan-sharpening algorithm for GK-2A products. The six pan-sharpening methods included in CS (Component Substitution), MRA (Multi-Resolution Analysis), VO (Variational Optimization), and DL (Deep Learning) were used. In the case of DL, the synthesis property based method was used to generate training dataset. The process of synthesis property is that pan-sharpening model is applied with Pan (Panchromatic) and MS (Multispectral) images with reduced spatial resolution, and fused image is compared with the original MS image. In the synthesis property based method, fused image with desire level for user can be produced only when the geometric characteristics between the PAN with reduced spatial resolution and MS image are similar. However, since the dissimilarity exists, RD (Random Down-sampling) was additionally used as a way to minimize it. Among the pan-sharpening methods, PSGAN was applied with RD (PSGAN_RD). The fused images are qualitatively and quantitatively validated with consistency property and the synthesis property. As validation result, the GSA algorithm performs well in the evaluation index representing spatial characteristics. In the case of spectral characteristics, the PSGAN_RD has the best accuracy with the original MS image. Therefore, in consideration of spatial and spectral characteristics of fused image, we found that PSGAN_RD is suitable for GK-2A products.

• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.23 no.3
• /
• pp.145-151
• /
• 2023

3D-CNN is one of the deep learning techniques for learning time series data. Such three-dimensional learning can generate many parameters, so that high-performance machine learning is required or can have a large impact on the learning rate. When learning dynamic hand-gestures in spatiotemporal domain, it is necessary for the improvement of the efficiency of dynamic hand-gesture learning with 3D-CNN to find the optimal conditions of input video data by analyzing the learning accuracy according to the spatiotemporal change of input video data without structural change of the 3D-CNN model. First, the time ratio between dynamic hand-gesture actions is adjusted by setting the learning interval of image frames in the dynamic hand-gesture video data. Second, through 2D cross-correlation analysis between classes, similarity between image frames of input video data is measured and normalized to obtain an average value between frames and analyze learning accuracy. Based on this analysis, this work proposed two methods to effectively select input video data for 3D-CNN deep learning of dynamic hand-gestures. Experimental results showed that the learning interval of image data frames and the similarity of image frames between classes can affect the accuracy of the learning model.