• Proceedings of the KSRS Conference
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• 1998.09a
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• pp.49-57
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• 1998

CO2 transfer velocity is one of the key parameters for CO2 flux estimation at air - sea interface. However, current studies show that significant inconsistency still exists in its estimation when using different models and remotely sensed data sets, which acts as one of the main uncertainties involved in the computation of CO2 exchange coefficient between air - sea interface. In this study, wind data collected from SSM/I and scatterometer onboard ERS-1, in conjunction with operational NOAA/AVHRR, are applied to different models for calculating CO2 exchange coefficient in the world ocean. Their interrelationship and discrepancies inherent with different models and satellite data are analyzed. Finally, the seasonal and inter-annual variation of CO2 exchanges coefficient for different ocean basins are presented and discussed.

• Journal of KIBIM
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• v.7 no.2
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• pp.36-43
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• 2017

Population growth and rapid urbanization has been converting large amounts of rural vegetation into urbanized areas. This human induced change has increased temperature in urban areas in comparison to adjacent rural regions. Various studies regarding to urban heat island have been conducted in different disciplines in order to analyze the environmental issue. Especially, different types of thermal infrared remote sensing data are applied to urban heat island research. This article reviews research focusing on thermal infrared remote sensing for urban heat island and urban planning studies. Seven studies of analyses for the relationships between urban heat island and other dependent indicators in urban planning discipline are reviewed. Despite of different types of thermal infrared remote sensing data, units of analysis, land use and land cover, and other dependent variable, each study results in meaningful outputs which can be implemented in urban planning strategies. As the application of thermal infrared remote sensing data is critical to measure urban heat island, it is important to understand its advantages and disadvantages for better analyses of urban heat island based on this review. Despite of its limitations - spatial resolution, overpass time, and revisiting cycle, it is meaningful to conduct future research on urban heat island with thermal infrared remote sensing data as well as its application to urban planning disciplines. Based on the results from this review, future research with remotely sensed data of urban heat island and urban planning could be modified and better results and mitigation strategies could be developed.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.292-294
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• 2003

Evapotranspiration estimations are essential for monitoring drought, wild land fire risk etc. In this study, a surface energy balance method, which combines meteorological observations with spectral data derived from remote sensing measurements, was used to estimate the regional evapotranspiration in the Mongolia, a large arid and semi-arid region with heterogeneous surface conditions. The Surface Energy Balance method has been applied to Landsat+ETM and NOAA-AVHRR sensors for the estimation of evapotranspiration in the grassland of Mongolia. As a result, a daily evapotranspiration map of Mongolia was produced.

• Korean Journal of Remote Sensing
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• v.11 no.1
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• pp.1-17
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• 1995

Using remotely sensed data(MOS-1) and other spatial data such as aerial photos and topographic maps, 10 kind of thematic layers were prepared with Arc/Info system for watershed management of Nam Khane River, northern part of Laos. The characteristics of landuse distribution of some criteria which like village, sub-basin, elevation and slope were clarified by overlaying each layer. Therefore, statistic data including shifting cultivation area were produced from database layer. Through the manipulation of some data of each layer, suitable area for permanent paddy cropping converted from the fallow and shifting cultivation area was extracted.

• Korean Journal of Remote Sensing
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• v.17 no.4
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• pp.345-357
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• 2001

This review provides a summary of data assimilation applied to the seas around Korea. Currently the worldwide efforts are devoted to applying advanced assimilation to realistic cases, thanks to improvements in mathematical foundations of assimilation methods and the computing capabilities, and also to the availability of extensive observational data such as from satellites. Over the seas around Korea, however, the latest developments in the advanced assimilation methods have yet to be applied. Thus it would be timely to review the progress in data assimilation over the seas. Firstly, the definition and necessity of data assimilation are described, continued by a brief summary of major assimilation methods. Then a review of past research on the ocean data assimilation in the regional seas around Korea is given and future trends are considered. Special consideration is given to the assimilation of remotely-sensed data.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.1326-1328
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• 2003

An elliptical basis function (EBF) network is proposed in this study for the classification of remotely sensed images. Though similar in structure, the EBF network differs from the well-known radial basis function (RBF) network by incorporating full covariance matrices and uses the expectation-maximization (EM) algorithm to estimate the basis functions. Since remotely sensed data often take on mixture -density distributions in the feature space, the proposed network not only possesses the advantage of the RBF mechanism but also utilizes the EM algorithm to compute the maximum likelihood estimates of the mean vectors and covariance matrices of a Gaussian mixture distribution in the training phase. Experimental results show that the EM-based EBF network is faster in training, more accurate, and simpler in structure.

• Korean Journal of Remote Sensing
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• v.10 no.2
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• pp.63-82
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• 1994

Multitemporal analysis with remotely sensed data is complicated by numerous intervening factors, including atmospheric attenuation and occurrence of clouds that obscure the relationship between ground and satellite observed spectral measurements. A reconstruction system was developed to increase the discrimination capability for imagery that has been modified by residual dffects resulting from imperfect sensing of the target and by atmospheric attenuation of the signal. Utilizing temporal information based on an adaptive timporal filter, it recovers missing measurements resulting from cloud cover and sensor noise and enhances the imagery. The temporal filter effectively tracks a systematic trend in remote sensing data by using a polynomial model. The reconstruction system were applied to the AVHRR data collected over Korean Peninsula. The results show that missing measurements are typically recovered successfully and the temporal trend in vegetation change is exposed clearly in the reconstructed series.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.594-596
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• 2003

Due to the fact that the composition and variability of aerosols is considered rather complex, it is difficult to employ a simple and straightforward physical model in calculating the aerosol size distribution in the absence of actual data. This complicates the already difficult retrieval of various atmospheric parameters from remotely sensed data. Thus, the main purpose of this study is trying to find an effective aerosol size coefficient that is stable, and can depict the particle size distribution. This paper also attempts to construct an 'effective aerosol size coefficient' database for each respective season, where it can quickly and effectively supply pertinent information of the atmosphere's opacity.

• Korean Journal of Remote Sensing
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• v.35 no.4
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• pp.573-587
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• 2019

A multi-temporal approach using remotely sensed time series data obtained over multiple years is a very useful method for monitoring land covers and land-cover changes. While spectral-based methods at any particular time limits the application utility due to instability of the quality of data obtained at that time, the approach based on the temporal profile can produce more accurate results since data is analyzed from a long-term perspective rather than on one point in time. In this study, a multi-temporal approach applying a multi-periodic harmonic model is proposed for classification of remotely sensed data. A harmonic model characterizes the seasonal variation of a time series by four parameters: average level, frequency, phase, and amplitude. The availability of high-quality data is very important for multi-temporal analysis.An satellite image usually have many unobserved data and bad-quality data due to the influence of observation environment and sensing system, which impede the analysis and might possibly produce inaccurate results. Harmonic analysis is also very useful for real-time data reconstruction. Multi-periodic harmonic model is applied to the reconstructed data to classify land covers and monitor land-cover change by tracking the temporal profiles. The proposed method is tested with the MODIS and GOCI NDVI time series over the Korean Peninsula for 5 years from 2012 to 2016. The results show that the multi-periodic harmonic model has a great potential for classification of land-cover types and monitoring of land-cover changes through characterizing annual temporal dynamics.

• Journal of Navigation and Port Research
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• v.27 no.6
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• pp.645-652
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• 2003

A virtual vessel traffic control system is introduced to contribute to prevent a marine accident such as collision and stranding from happening. Existing VTS has its limit. The virtual vessel traffic control system consists of both data acquisition by satellite remote sensing and a simulation of traffic environment stress based on the satellite data, remotely sensed data And it could be used to provide timely and detailed information about the marine safety, including the location, speed and direction of ships, and help us operate vessels safely and efficiently. If environmental stress values are simulated for the ship information derived from satellite data, proper actions can be taken to prevent accidents. Since optical sensor has a high spatial resolution, JERS satellite data are used to track ships and extract their information. We present an algorithm of automatic identification of ship size and velocity. It lastly is shown that based on ship information extracted from JERS data, a qualitative evaluation method of environmental stress is introduced.