In this study we worked with 306 students from higher year levels at the Kusu Elementary School, located on the Pacific coast of the Ise Bay region in the City of Yokkaichi of Mie Prefecture, to investigate their day-to-day risk perception of waterfronts through a survey on play activities near the Yoshizaki coast and nearby waterways as well as their perception of such sites. Children play along the Yoshizaki coast enjoying its natural characteristics, such as the beaches and shallows as well as the creatures that live there. Since the waterways flow through the town, they are a familiar presence for the children, who commonly play by casually interacting with marine life, such as fish and crabs. The fact that they are aware of the abundance of biodiversity, such as sea turtles, along the Yoshizaki Coast became clear from the perception survey. However, due to the presence of garbage and other factors, negative perceptions outweigh positive perceptions. Furthermore, the children have hardly any recognition of danger in terms of the coast. On the other hand, they have a greater perception of danger for waterways than for the coast.

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An investigation is performed on a method for calculating eigenvalues in large-scale network systems. The proposed method uses two calculation techniques; numerical solution of a differential equation model to set up appropriate initial guesses of dependent variables for a root-finding method and numerical differentiation of a given function by a complexstep method to obtain highly-accurate numerical derivatives. For comparison, a finite-difference method is also used for numerical differentiation. The results reveal that regarding steady-state dependent variable values for differential equations models, both numerical differentiation methods provide calculated values within machine accuracy. Regarding the matrix values used for constituting characteristic equations, on the other hand, the complex-step method provides calculated values within machine accuracy, whereas the finite-difference method provides calculated values with 12^-13 significant digits of accuracy, which results in a low accuracy of eigenvalues. Although the accuracies of eigenvalues calculated by the complex-step method may be lowered slightly by matrix operation, most of them are fundamentally within machine accuracy and highly accurate. In conclusion, the proposed method provides highly reliable eigenvalues and is useful to analyze large-scale network systems.

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SAR (Synthetic Aperture Radar) images can show ground features, even when they were taken at night or in cloudy weather. Therefore, it has been said that SAR images may have a great potential use for disaster remote sensing. In this paper, we used only full polarimetric SAR data taken after the March 11, 2011 earthquake in eastern Japan to evaluate the inundated areas. First, we derived a coherency matrix for each pixel from the scattering matrix of the SAR data. The image was then classified into 4 classes (water, vegetation, field, and urban area) based on the elements of the coherency matrix. After that, the inundated areas were extracted by masking rivers and lakes included in the water area. As a comparison, AVNIR-2 (Advanced Visible and Near Infrared Radiometer-Ⅱ) image was also classified into the same 4 classes with its inundated areas extracted. Ground validation data was retrieved from Google Earth images. As a result, the overall accuracy and kappa coefficient was 74% and 0.65 respectively for the full polarimetric SAR and 95% and 0.93 respectively for the AVNIR-2. Results indicate that the full polarimetric SAR image after the earthquake was useful enough to estimate inundated areas.

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