• Korean Journal of Remote Sensing
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• v.25 no.2
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• pp.113-126
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• 2009

With high temporal resolution, four times receiving during a day, MODIS images from Terra and Aqua satellites provide several advantages for monitoring spacious land. Especially, diverse MODIS products related to land, atmosphere, and ocean have been provided with radiance MODIS images. The products such as surface reflectance, NDVI, cloud mask, aerosol etc. are based on theoretical algorithms developed in academic areas. Comparing with other change detection studies mainly using the vegetation index, this study investigated temporal surface reflectance of landcovers for five years from 2004 to 2008. The near infrared (NIR) reflectance in urbanized and burned areas showed considerable difference before and after events. The specific characteristics of surface reflectance temporal profiles are possibly useful for the detection of landcover changes and classification.

• Journal of Climate Change Research
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• v.7 no.1
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• pp.31-39
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• 2016

In this study, the time series of the number of days with $25^{\circ}C$ or higher temperatures in the Jeju region were analyzed and they showed a strong trend of increase until recently. To determine the existence of a climate regime shift in this time series, the statistical change-point analysis was applied and it was found that the number of days with $25^{\circ}C$ or higher temperatures in the Jeju region increased sharply since 1993. Therefore, in order to examine the cause of the sharp increase of the days with $25^{\circ}C$ or higher temperatures in the Jeju region, the differences between the averages of 1994~2013 and the averages of 1974~1993 were analyzed for the large-scale environment. In the Korean Peninsula including the Jeju region, precipitable water and total cloud cover decreased recently due to the intensification of strong anomalous anticyclones near the Korean Peninsula in the top, middle and bottom layers of the troposphere. As a result of this, the number of days with $25^{\circ}C$ or higher temperatures in the Jeju region could increase sharply in recent years. Furthermore, in the analysis of sensible heat net flux and daily maximum temperatures at 2 m, which is the height that can be felt by people, the Korean Peninsula was included in the positive anomaly region. In addition, the frequency of typhoons affecting the Korean Peninsula decreased recently, which reduced the opportunities for air temperature drops in the Jeju region.

• Atmosphere
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• v.29 no.3
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• pp.269-282
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• 2019

While it is important to obtain the accurate information on snowfall data due to the increase in damage caused by the heavy snowfall in the winter season, it is not easy to observe the snowfall quantitatively. Recently, snow measurements using a weighing precipitation gauge have been carried out, but there is a problem that high snowfall intensity results in low accuracy. Also, the observed snowfall data are sensitive depending on wind speed, temperature, and humidity. In this study, a new process of quality control for snow water equivalent (SWE) data of the weighing precipitation gauge were proposed to cover the low accuracy of snow data and maximize the data utilization. Snowfall data (SWE) observed by Pluvio, Parsivel, snow-depth meter using laser or ultrasonic, and rainfall gauge in Cloud Physics Observation Site (CPOS) were compared and analyzed. Applying the QC algorithm including the use of number of hydrometeor particles as reference, the increased SWE per the unit time was determined and the data noise was removed and marked by flag. The SWE data converted by the number concentration of hydrometeor particles are tested as a method to restore the QC-removed data, and show good agreement with those of the weighing precipitation gauge, though requiring more case studies. The three events data for heavy snowfall disaster in Pyeongchang area was analyzed. The SWE data with improved quality was showed a good correlation with the eye-measured data ($R^2$ > 0.73).

• Journal of Korean Society on Water Environment
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• v.35 no.1
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• pp.1-8
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• 2019

The purpose of this study is to evaluate the nutritional status of Lake Jinyang using Landsat 8 satellite image band correlated with chlorophyll-a, which is also related to algae proliferation. We selected 20 Landsat 8 images dating from 2013 to 2017, taken close to water quality measurement date when the cloud cover was less than 20 %. Based on the results of the previous studies, analyzing the correlation between chlorophyll-a, and Landsat 8 satellite image band, we selected near infrared wavelength, band 5 which is closely related to the population of algae. The nutritional status was classified using the Aizaki trophic state index (TSIm). The results of the regression equation between band 5 and the observed chlorophyll-a data was used to calculate chlorophyll-a for the image data from 2013 to 2017. The concentration of chlorophyll-a ranged from 3 to $16.1mg/m^3$. To illustrate the spatial distribution of chlorophyll-a within the lake, the chlorophyll-a concentration was divided into five grades. The images on October 14, 2014 and April 10, 2016 showed relatively high value of chlorophyll-a, while January 18, 2015 and December 6, 2016 chlorophyll-a value were below 5. The images on October 14, 2014 and April 10, 2016 were rated as eutrophic status in most areas. The results of simulating water quality for the day when the water quality was not measured resulted to an approximate value for the Panmun station while the Naedong station needed some corrections.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.34-34
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• 2023

The Chah-Nimeh reservoirs, which are a sort of natural lakes located in the border of Iran and Afghanistan, are the main drinking and agricultural water resources of Sistan arid region. Considering the occurrence of intense seasonal wind, locally known as levar wind, this study aims to explore the possibility to provide a TSM (Total Suspended Matter) monitoring model of Chah-Nimeh reservoirs using multi-temporal satellite images and in-situ wind speed data. The results show that a strong correlation between TSM concentration and wind speed are present. The developed empirical model indicated high performance in retrieving spatiotemporal distribution of the TSM concentration with R²=0.98 and RMSE=0.92g/m³. Following this observation, we also consider a machine learning-based model to predicts the average TSM using only wind speed. We connect our in-situ wind speed data to the TSM data generated from the inversion of multi-temporal satellite imagery to train a neural network based mode l(Wind2TSM-Net). Examining Wind2TSM-Net model indicates this model can retrieve the TSM accurately utilizing only wind speed (R²=0.88 and RMSE=1.97g/m³). Moreover, this results of this study show tha the TSM concentration can be estimated using only in situ wind speed data independent of the satellite images. Specifically, such model can supply a temporally persistent means of monitoring TSM that is not limited by the temporal resolution of imagery or the cloud cover problem in the optical remote sensing.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.1D
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• pp.203-209
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• 2006

SAR (Synthetic Aperture Radar) technology, which is not influenced by cloud cover because of using electromagnetic wave of long wavelength, has an advantage in mapping the earth. NASA, recognizing these strong points of SAR, launched SRTM (Shuttle Radar Topography Mission), and acquired the topographic information of the earth. SRTM and NED (National Elevation Data) of USGS were used for the research and vegetation height map was produced through differentiating the two data. Correlation between SRTM-NED and planting year was analyzed to see the relationship. Strong correlation was detected and it shows the feasibility of estimating timber age and eventually creating timber age map from SRTM-NED. Additional analyses were conducted to check if the linearity is influenced by regional characteristics and forest uniformity. As results, the correlation between SRTM-NED and timber age is influenced by roughness of the terrain. Overall, this paper shows that timber age estimation using SRTM and NED can be sufficiently practical.

• International Journal of Computer Science & Network Security
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• v.24 no.9
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• pp.63-76
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• 2024

Weather forecasting has become a very popular topic nowadays among researchers because of its various effects on global lives. It is a technique to predict the future, what is going to happen in the atmosphere by analyzing various available datasets such as rain, snow, cloud cover, temperature, moisture in the air, and wind speed with the help of our gained scientific knowledge i.e., several approaches and set of rules or we can say them as algorithms that are being used to analyze and predict the weather. Weather analysis and prediction are required to prevent nature from natural losses before it happens by using a Deep Learning Approach. This analysis and prediction are the most challenging task because of having multidimensional and nonlinear data. Several Deep Learning Approaches are available: Numerical Weather Prediction (NWP), needs a highly calculative mathematical equation to gain the present condition of the weather. Quantitative precipitation nowcasting (QPN), is also used for weather prediction. In this article, we have implemented and analyzed the various distinct techniques that are being used in data mining for weather prediction.

• Journal of KIISE
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• v.43 no.6
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• pp.710-717
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• 2016

Recently, several research studies on things abstraction framework have been proposed in order to implement the multi-service Internet of Things (IoT) system, where various IoT services share the thing devices. Distributed things abstraction has an IoT service duplication problem, which aggravates power consumption of mobile devices and network traffic. On the other hand, cloud server-centric things abstraction cannot cover real-time interactions due to long network delay. Fog server-centric things abstraction has limits in insufficient IoT interfaces. In this paper, we propose Smart Fog which is a fog server-centric things abstraction framework to resolve the problems of the existing things abstraction frameworks. Smart Fog consists of software modules to operate the Smart Gateway and three interfaces. Smart Fog is implemented based on IoTivity framework and OIC standard. We construct a smart home prototype on an embedded board Odroid-XU3 using Smart Fog. We evaluate the network performance and energy efficiency of Smart Fog. The experimental results indicate that the Smart Fog shows short network latency, which can perform real-time interaction. The results also show that the proposed framework has reduction in the network traffic of 74% and power consumption of 21% in mobile device, compared to distributed things abstraction.

• Proceedings of the Korea Water Resources Association Conference
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• 2011.05a
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• pp.18-18
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• 2011

Climate change is impacting and will increasingly impact both the quantity and quality of the world's water resources in a variety of ways. In some areas warming climate results in increased rainfall, surface runoff, and groundwater recharge while in others there may be declines in all of these. Water quality is described by a number of variables. Some are directly impacted by climate change. Temperature is an obvious example. Notably, increased atmospheric concentrations of $CO_2$ triggering climate change increase the $CO_2$ dissolving into water. This has manifold consequences including decreased pH and increased alkalinity, with resultant increases in dissolved concentrations of the minerals in geologic materials contacted by such water. Climate change is also expected to increase the number and intensity of extreme climate events, with related hydrologic changes. A simple framework has been developed in New Zealand for assessing and predicting climate change impacts on water resources. Assessment is largely based on trend analysis of historic data using the non-parametric Mann-Kendall method. Trend analysis requires long-term, regular monitoring data for both climate and hydrologic variables. Data quality is of primary importance and data gaps must be avoided. Quantitative prediction of climate change impacts on the quantity of water resources can be accomplished by computer modelling. This requires the serial coupling of various models. For example, regional downscaling of results from a world-wide general circulation model (GCM) can be used to forecast temperatures and precipitation for various emissions scenarios in specific catchments. Mechanistic or artificial intelligence modelling can then be used with these inputs to simulate climate change impacts over time, such as changes in streamflow, groundwater-surface water interactions, and changes in groundwater levels. The Waimea Plains catchment in New Zealand was selected for a test application of these assessment and prediction methods. This catchment is predicted to undergo relatively minor impacts due to climate change. All available climate and hydrologic databases were obtained and analyzed. These included climate (temperature, precipitation, solar radiation and sunshine hours, evapotranspiration, humidity, and cloud cover) and hydrologic (streamflow and quality and groundwater levels and quality) records. Results varied but there were indications of atmospheric temperature increasing, rainfall decreasing, streamflow decreasing, and groundwater level decreasing trends. Artificial intelligence modelling was applied to predict water usage, rainfall recharge of groundwater, and upstream flow for two regionally downscaled climate change scenarios (A1B and A2). The AI methods used were multi-layer perceptron (MLP) with extended Kalman filtering (EKF), genetic programming (GP), and a dynamic neuro-fuzzy local modelling system (DNFLMS), respectively. These were then used as inputs to a mechanistic groundwater flow-surface water interaction model (MODFLOW). A DNFLMS was also used to simulate downstream flow and groundwater levels for comparison with MODFLOW outputs. MODFLOW and DNFLMS outputs were consistent. They indicated declines in streamflow on the order of 21 to 23% for MODFLOW and DNFLMS (A1B scenario), respectively, and 27% in both cases for the A2 scenario under severe drought conditions by 2058-2059, with little if any change in groundwater levels.

• Journal of Astronomy and Space Sciences
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• v.14 no.2
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• pp.330-346
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• 1997

Korea Multi-Purpose Satellite I (KOMPSAT-I, the first multi-purpose Korean satellite) will be launched in the third quarter of 1999, which is operated on the sun-synchronous orbit for cartography, ocean color monitoring, and space environment monitoring. The main mission of Electro-Optical Camera(EOC) which is one of KOMPSAT-I sensors is to provide images for the production of scale maps of Korea. EOC collects panchromatic imagery with the ground sample distance of 6.6m at nadir through visible spectral band of 510~730nm. For determining KOMPSAT-I crossing time over Korea, this study examines the diurnal variation of solar and atmospheric variables that can exert a great influence on the EOC imagery. The results are as follows: 1) After 10:30 a.m. at the winter solstice, solar zenith angle is less than $70^{\circ}$ and expected flux of EOC spectral band over land for clear sky is greater than about $2.4mW/cm^2$. 2) For daytime the distribution of cloud cover (clear sky) shows minimum (maximum) at about 11:00 a.m. Although the occurrence frequency of poor visibility by fog decreases from early morning toward noon, its effect on the distribution of clear sky is negligible. From the above examination it is concluded that determining KOMPSAT-I crossing time over Korea between 10:30 and 11:30 a.m. is adequate.