• Korean Journal of Remote Sensing
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• v.37 no.1
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• pp.23-40
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• 2021

This article has been retracted as a result of the review (on May 14, 2024) by the Research Ethics Committee of the Korean Society of Remote Sensing, which confirmed research misconduct (plagiarism).

• Korean Journal of Remote Sensing
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• v.16 no.3
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• pp.243-260
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• 2000

Ocean remote sensing reflectance of just above water level was modeled using inherent optical properties of seawater contents, total absorption (a) and backscattering(bb) coefficients ($R_{rs}$=0.046 $b_b$/(a+$b_b$). This modeling was based on the specific absorption and backscattering coefficients of 5 optically active seawater components； phytoplankton pigments, non-chlorophyllous suspended particles, dissolved organic matters, heterotrophic microorganisms, and the other unknown particle components. Simulated remote sensing reflectance($R_{rs}$) and water leaving radiance(Lw) spectra were well agreed with in-situ measurements obtained using a bi-directional fields remote spectrometer in coastal waters and open ocean. $R_{rs}$ values in SeaWiFS bands from the model were analyzed to develop 2-band ratio ocean color chlorophyll with those observed insitu. Also, chlorophyll algorithm based on remote reflectance developed in this study fell in those obtained by a SeaBAM working group. The model algorithms were examined and compared with those observed insitu. Also, chlorophyll algorithm based on remote reflectance developed in this study fell in those obtained by a SeaBAM working group. The remote reflectance model will be very helpful to understand the variation of water leaving radiances caused by the various components in the seawater, and to develop new ocean color algorithm for CASE-II water using neural network method or other analytical method, and in the model of fine atmospheric signal correction.

• The Proceeding of the Korean Institute of Electromagnetic Engineering and Science
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• v.3 no.2
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• pp.66-78
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• 1992

Available data for the radar clutter, and the empirical and the theoretical models for the radar clutter have been collected and analyzed. Data sets and models from the remote sensing field have been studied extensively. Although the grazing angles used in remote sensing is larger than the angles normally encountered in radar clutter application, remote sensing field has the merit of abundance of data in much more detailed target classes. The remote sensing model is also superior to the normally used clutter models in the sense that each target class has its own model, rather than being generally characterized by assumed roughness parameters.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.38 no.2
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• pp.87-95
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• 2020

South korea has been developing and managing the complete dimensions, around the rivers to rapid economic growth. In Korea, where water resources are scarce, administration and work are complicated and diversified in the computerization of related facilities and hydrologic data due to the indiscriminate development of river facilities. In general, dividing the water system based on object in remote sensing is relatively accurate in the image with the same spectral characteristics. However, the distinction between the reservoir and the river must be made manually due to the characteristics of remote sensing. Therefore, this study performed three classifications using GIS (Geographic Information System) to classify reservoirs and rivers. For the purpose of accuracy analysis, the land cover map provided by EGIS (Environmental Geographic Information Service) was used to evaluate the accuracy, and the average of 85.63% was found to be 75.40% of rivers, 89.50% of reservoirs, and 92.00% of others.

• 한국신재생에너지학회:학술대회논문집
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• 2008.05a
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• pp.319-320
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• 2008

This paper introduces remote-sensing data which can be practically applied for offshore wind resource assessment. Development of offshore wind energy is inevitable for Korea to achieve the national dissemination target of renewable energy, i.e., 5% uptil 2010. However, the only available offshore in-situ measurement, marine buoy data would not represent areal wind characteristics. Consequently, remote-sensing technology has been started to apply to offshore wind resource assessment and is actively developing. Among them, NCAR/NCEP reanalysis dataset, QuikSCAT blended dataset, and offshore wind retrieval from SAR imagery are briefly summarized in this paper.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.107-109
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• 2003

Optical sensor-based land cover categories produce spectral signature confusion along with degraded classification accuracy. In the classification tasks, the goal of fusing data from different sensors is to reduce the classification error rate obtained by single source classification. This paper describes the result of land cover/land use classification derived from solely of Landsat TM (TM) and multisensor image fusion between JERS 1 SAR (JERS) and TM data. The best radar data manipulation is fused with TM through various techniques. Classification results are relatively good. The highest Kappa Coefficient is derived from classification using principal component analysis-high pass filtering (PCA+HPF) technique with the Overall Accuracy significantly high.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.15-17
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• 2003

One of the most serious problems that the world is facing is the loss of biodiversity and habitats as a result of environmental degradation. There are several strategies to protect the habitats and biodiversity within a certain region such as establishing protected areas; monitoring the remaining forests and managing the landscape within limits have been employed. In this study, Predicted Habitat Distribution Model (simple spatial modeling) was developed using vegetation types, land use and land cover, DEM, slope, drainage, roads, human settlement areas and minimum habitat requirements of each species. Then, based on the checklist of presence and absence of each species, the final habitat maps for selected endangered species are generated. Integration of Remote Sensing (RS) and Geographical Information System (GIS) has proven a very effective tool to generate wildlife habitat maps at various levels. An effecting mapping could be performed based on satellite remote sensing and modeling biodiversity indicators in GIS.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.399-401
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• 2003

This paper presents a geostatistical contextual classifier for the classification of remote sensing data. To obtain accurate spatial/contextual information, a simple indicator kriging algorithm with local means that allows one to estimate the probability of occurrence of certain classes on the basis of surrounding pixel information is applied. To illustrate the proposed scheme, supervised classification of multi-sensor remote sensing data is carried out. Analysis of the results indicates that the proposed method improved the classification accuracy, compared to the method based on the spectral information only.

• Proceedings of the KSRS Conference
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• 2002.10a
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• pp.411-416
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• 2002

The emergence of viral diseases transmitted is nowadays a central problem in the world. Problem, which is becoming very critical in developing countries, where the health systems are not yet enough developed to face the bursting of such diseases. Emerging viral diseases constitute one of the major threats to human being that are arising in the modern world. Besides bio-chemical and medical researches, new orientations are developed to understand the environmental dimensions of such emergence. Questions concerning the inter-plays between the environmental and disease dynamics are building up new investigations, both in remote sensing and GIS, for the elaboration of levels of organization of space and environment in relation to incidences, to gain understandings in these issues. Environmental attributes attached to land cover types: area, spatial heterogeneity and physical state, are derived from remote sensing and applied to uncover related dimensions of the Dengue disease.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.1-3
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• 2003

In this study, spectroradiometer data were coupled with fluorometer data to find out the best suited bands ratio to monitor the chlorophyll a concentration for inland water. Remote sensing reflectance measurements were used to evaluate the performance of several default ocean color chlorophyll algorithms for SeaWiFS data. This study shows that the chlorophyll a concentration from fluorometer and reflectance from spectroradiometer lies in exploiting the signal provided by the chlorophyll a red absorption peak near 670nm. Two-band ratio based on a ratio of reflectance 670 and 700nm provided a good correlation for a linear model, compare with blue-green two band ratio.