• Korean Journal of Remote Sensing
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• v.15 no.3
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• pp.217-225
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• 1999

Through the ISODATA method of unsupervised classification, the micro-landform of Julpo-Bay tidal flat was classified into mudflat, mixedflat, and sandflat using Landsat TM image. Each showed an apparent differences in its topographical characteristics and grain size composition. Mudflat occupied innermost part of the tidal flat, sandflat located closest to the entrance of the bay and mixed flat in the center is. For example, mudlflats are formed with flat faces and tidal channel. Topographically, mudflat consist of tidal channels and flat intermediate surface. Its average relief of them is about 2 meter. Meanwhile, sandflat comprised very flat landform with well-developed ripple marks of less than 10cm average relief. And Mixed flat stood in between. In addition, Out of 7 bands of Landsat TM images, band 5 and 7 provided the highest power level for discrimination between micro-landforms of the tidal flat. Band 4 showed a clear boundary between the land and tidal flat, and band 3 did its share by showing well a boundary between the sea surface and the tidal flat.

• Journal of Environmental Impact Assessment
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• v.20 no.3
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• pp.349-356
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• 2011

This research is aim to analyze of changing landscape and according to phenological cycle from image information of coastal environment obtained by multi-media were analyzed by camera and satellite image. The digital camera and satellite image were used for tidal flat vegetation monitoring during the construction of Sihwa lake. The vegetation type and phenological cycle of Sihwa tidal flat have been changed with the Sihwa lake ecosystem. The environment changes of Sihwa tidal flat area and ecological change were analyzed by field work digital camera images and satellite images. The airborne, UAV and satellite images were classified with the changed elements of coastal ecological environment and tidal flat vegetation monitoring carried out the changed area and shape of vegetation distribution with time series images.

• Korean Journal of Remote Sensing
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• v.21 no.5
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• pp.405-415
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• 2005

It is difficult to classify tidal flat surface that is composed of a mixture of mud, sand, water and microphytobenthos. We used a Linear Spectral Unmixing (LSU) method for effectively classifying the tidal flat surface characteristics within a pixel. This study aims at 1) detecting algal mat using LSU in the Saemangeum tidal flats, 2) determining a suitable end-member selection method in tidal flats, and 3) find out a habitual characteristics of algal mat. Two types of end-member were built; one is a reference end-member derived from field spectrometer measurements and the other image end-member. A field spectrometer was used to measure spectral reflectance, and a spectral library was accomplished by shape difference of spectra, r.m.s. difference of spectra, continuum removal and Mann-Whitney U-test. Reference end-members were extracted from the spectral library. Image end-members were obtained by applying Principle Component Analysis (PCA) to an image. The LSU method was effective to detect microphytobenthos, and successfully classified the intertidal zone into algal mat, sediment, and water body components. The reference end-member was slightly more effective than the image end-member for the classification. Fine grained upper tidal flat is generally considered as a rich habitat for algal mat. We also identified unusual microphytobenthos that inhabited coarse grained lower tidal flats.

• Journal of Environmental Impact Assessment
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• v.23 no.2
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• pp.150-156
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• 2014

The aim of this study is to detection of changed vegetation area of Saemangeum tidal flat with comparison of topography and surface sediments during the dyke construction. Sedimentary facies of four seasons of 2001 from inside Saemangeum tidal flat revealed homogeneous layers in the upper part, however near sea side tidal flat were detecting with carried out rapid sediment deposition during the dyke construction using satellite image spatial analysis. The sedimentation types inside Saemangeum tidal flat were classified with vegetation types, which were well matched with the sedimentation pattern revealed by change in vegetation patterns.

• Spatial Information Research
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• v.11 no.1
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• pp.13-22
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• 2003

The study of geomorphology of tidal flat using remote sensing image has been considered useful because of it's ability to acquire data periodically. Especially, the Near Infrared band of satellite image has been used to divide between land and sea area. This study extracted a borderline of the tidal flat using Landsat-5 images and generated DEM(Digital elevation model) using tide level data as elevation value. DEM is a useful tool for three-dimensional survey of geomorphology and can be used for survey of tidal flat. This study divided 8 images of 1990's into two parts - before 1994 and after 1994 - and generated DEM respectively. In this work, the areas of tidal flats are calculated and it was revealed the area of tidal flat was decreased after 1994.

• Journal of Advanced Information Technology and Convergence
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• v.9 no.2
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• pp.29-39
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• 2019

Tidal Flats are very important natural resource and various efforts have been made to protect it from environmental pollutions. The projects to monitor the environmental changes by periodically observing the creatures in tidal flats are underway. However, they are being done inefficiently by people directly observing. In this paper, we propose an object segmentation method that can be applied to the applications which automatically monitor the living creatures in tidal flats. In the proposed method, a foreground map representing the location of objects is obtained by using a temporal difference method, and then a superpixel method is applied to detect the detailed boundary of an object. The region of a crab is extracted finally by combining the foreground map and the superpixel information. Experimental results show that the proposed method separates crab regions from a tidal flat image easily and accurately.

• Proceedings of the KSRS Conference
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• 2002.10a
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• pp.664-669
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• 2002

Intertidal zone (tidal flat) is a place which is sometimes dry and sometimes wet depending on the tidal rhythm. Direct measurement of topography in the intertidal zone is very difficult to be achieved. The interface between wet and dry parts in the tidal flat, which can be identified from near infrared band of satellite image, is a 'depth contour' which corresponds to the sea level at the time of satellite pass. Aquisition of topography data in tidal flat is possible by combining various techniques such as (1) identification of the interface between wet and dry parts, (2) GCP correction of satellite image, and (3) realtime prediction of sea level elevation at the time of satellite pass. The algorithm was successfully applied in obtaining topography (bathymetry) data in the intertidal zone of Asan Bay in the west coast of Korea from 26 satellite images. The method is expected to be very efficient in making bathymetry data base in the western and southern parts of Korea where tidal flats are well developed in wide regions.

• Korean Journal of Remote Sensing
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• v.8 no.2
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• pp.131-145
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• 1992

A low cost personal computer and image processing S/W were empolyed to derive Digtal Elevation Model(DEM) of tidal flat from multitemporal LANDSAT TM images, and to create three-dimensional(3D) perspective views of the tidel flat on Komso bay in west coasts of Korea. The method for generation of Digital Elevation Model(DEM) in tidal flat was considered by overlapping techniques of multitemporal LANDSAT TM images and interpolations. The boundary maps of tidal flat extracted from multitemporal images with different water high were digitally combined in x, y, z space with tide in formation and used as an inputcontour data to obtain an elevation model by interpolation using spline function. Elevation errors of less than $\pm$0.1m were achived using overlapping techniques and a spline interpolation approach, respectively. The derived DEM allows for the generation of a perspective grid and drape on the satellite image values to create a realistic terrain visualization model so that the tidal flat may be viewed from and desired direction. As the result of this study, we obtained elevation model of tidal flats which contribute to characterize of topography and monitoring of morphological evolution of tidal flats. Moreover, the modal generated here can be used for simulation of innudation according to tide and support other studies as a supplementary data set.

• Proceedings of the KSRS Conference
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• 1998.09a
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• pp.110-115
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• 1998

Through the ISODATA method, the micro-landform of Julpo-Bay tidal flat was classified into mudflat, mixedflat, and sandflat using Landsat TM image. Each showed an apparent differences in its topographical characteristics and grain size composition. For example, mudflats are formed with flat faces and tidal channel of dissected gully. Its characteristics of grain size analysis that the grains have less than mean grain size 4 phi. Its sorting is bad (higher than 1 S.D.), and it showed strongly positive skewness. But sandflat is topographically flat without tidal channel. It has developed with ripple marks. According to the grain size analysis of deposits, the soil is coarse size with 90% of sand and its sorting is well(lower than 1 S.D.) Also, it showed strongly negative skewness. Mixed flat is in between mudflat and sandflat in its characteristics.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.24 no.6
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• pp.89-96
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• 2021

In this study, a deep-learning image analysis model was established and validated for AI-based monitoring of the tidal flat ecosystem for marine protected creatures Ocypode stimpsoni and their habitat. The data in the study was constructed using an unmanned aerial vehicle, and the U-net model was applied for the deep learning model. The accuracy of deep learning model learning results was about 0.76 and about 0.8 each for the Ocypode stimpsoni and their burrow whose accuracy was higher. Analyzing the distribution of crabs and burrows by putting orthomosaic images of the entire study area to the learned deep learning model, it was confirmed that 1,943 Ocypode stimpsoni and 2,807 burrow were distributed in the study area. Through this study, the possibility of using the deep learning image analysis technology for monitoring the tidal ecosystem was confirmed. And it is expected that it can be used in the tidal ecosystem monitoring field by expanding the monitoring sites and target species in the future.