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The purpose of this study is to analyze the geomorphological environment changes of tidal flat in the Cheonsu Bay. Especially, it centers on the changes in the sedimentary environment using remote sensing data. Multi-temporal Landsat data and topographic maps were used in this study. The results are summarized as follows: the tidal flat of Cheonsu Bay changes in many ways depending on the direction of the tidal current. In the neighborhood of Ganwoldo, the scale of the tidal flat has continuously been expanded due to the superiority of sedimentation after a tide embankment was built. When we analyzed the grain size of sediments and implemented in-situ field survey, it was found that the innermost part of the bay consists of a mud flat, with the midway part mixed flat, and the nearest part to the sea sand flat. On the other hand, in the neighborhood of Seomot isle and its beach, sedimentation is superior in the eastern part whereas erosion is superior in the western part. In other words, the western coast of the beach is contacted with the open seas and under much influence of ocean wave. The eastern coast is placed at the entrance of the bay and has sand bar and tidal flat developed due to submarine deposits that are accumulated on the sea floor by the tidal current. In conclusions, remote sensing methods can be effectively applied for quantitative analysis of geomorphological changes in tidal flat, and it is expected that the proposed schemes can be applied to another geomorphological environments such as beach, sand dune, and sand wave.
In this paper, we consider the maximum likelihood and Bayes estimation of the scale parameter of the half-logistic distribution based on a multiply type II censored sample. However, the maximum likelihood estimator(MLE) and Bayes estimator do not exist in an explicit form for the scale parameter. We consider a simple method of deriving an explicit estimator by approximating the likelihood function and discuss the asymptotic variances of MLE and approximate MLE. Also, an approximation based on the Laplace approximation (Tierney & Kadane, 1986) is used to obtain the Bayes estimator. In order to compare the MLE, approximate MLE and Bayes estimates of the scale parameter, Monte Carlo simulation is used.