Abstract
The safety level of a shoreline is essential for flood control projects and policy formulation or modification from both economic and environmental perspectives. With the development of remote sensing (RS) techniques, high spatial-spectral resolution and quick-revolution satellite images are now available and widely used in environment monitoring and management. It is therefore possible to more efficiently and conveniently identify the components of, and extract information for, shoreline environments. However, the problem is that the shoreline is always a long curve with a relatively narrow width, which limits the application of RS technology. This paper presents a method of recognizing different types of shoreline and of conveniently extracting the geographical coordinates of potential shoreline defense by analyzing and processing ecological information from an optical satellite RS data interpretation of land cover on both side of the shoreline. An application of this model in a low-resolution image case proved that the model can be used in the primary survey of a shoreline monitoring service platform as the basic tile level. The classification model is designed such that the requirements of image resolution for efficiently extracting information from the shoreline are low and the limitations imposed by a narrow shoreline width are avoided.
Highlights
Along with the development of the economy and convenience of transportation, there is an increasing trend of people to migrate to shoreline areas, including coastal plains, deltas, and coastal areas [1], which are generally protected by flood defense structures, such as manmade dikes or gate-dams, natural embankments, dunes, and cliffs
This paper proposes a model that locates the shoreline first and provides potential information on the type of shoreline structure and the weak points of detection by analyzing the land cover located near the shoreline in the satellite images
By the process of majority analysis, isolated pixels were seen in Figure 8a) was applied to the fused image to create a basic land cover image shown in Figure transformed into the majority class surrounding them, and a smooth classification image was created
Summary
Along with the development of the economy and convenience of transportation, there is an increasing trend of people to migrate to shoreline areas, including coastal plains, deltas, and coastal areas [1], which are generally protected by flood defense structures, such as manmade dikes or gate-dams, natural embankments, dunes, and cliffs. Economic activities are increasingly concentrated in shoreline areas [2]. Living with flooding has been proposed as an alternative to hazard control [3], the trend toward increased human activities in shoreline areas has not changed [4]. These activities are under pressure from streams constrained in riverbanks and sea water held back by sea dikes. Considering the possibility of global climate change and extreme nature events, the shoreline areas will face real risks to society and the economy [3]
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