Search

As a dynamic belt between land and oceans, coastline provides rich information on land-ocean interactions. Sensitive to climate and anthropogenic influences, the changing coastline affects intertidal mudflat resources and the coastal environment. In this study, the greenness and wetness components of the tasseled cap transformation (TCT) were used to extract coastline information. Due to the high total suspended sediment content that leads to the failure of traditional method, sea-waterbody information extraction was initially carried out by TCT. After considering the characteristics of coastline in remote sensing images and coastline morphology in the natural world, the coastline with shorter length was eliminated and the intermittent coastline was connected based on the coordinate geometry description (such as length, distance, and direction). Finally, the results of the coastline information extraction were superimposed on the original images to evaluate accuracy. The experimental results indicated that the proposed method was more effective in clearly delineating the land-ocean boundary. The producer's accuracy and user's accuracy were 0.95 and 0.91, respectively, and the length extraction error was −2.16%. Therefore, the proposed method was more successful for coastline information extraction in the area with high sediment concentration.

A bridge over water is typically an artificial object that can be damaged by changes in landform, geology, hydrology, and other surrounding conditions. Natural disaster scenarios must be studied using high-spatial-resolution remote-sensing images to detect and assess damage to bridges over water. Such studies are important for emergency rescue operations, disaster relief, and rapid disaster assessment. This article describes a method of damage detection and assessment for bridges over water using high-spatial-resolution remote-sensing images based on research regarding the selection and expression of characteristic knowledge.

Sea surface temperature (SST) is an important parameter used to describe the air-sea interaction and the state of marine structures. Atmospheric water vapor has a significant attenuation effect on thermal infrared information, which will reduce the accuracy of SST inversion. However, the effect of atmospheric water vapor on the inversion accuracy is not considered carefully in the existing SST inversion algorithm. In this study, a new method is proposed for retrieving SST from Landsat 8 Thermal Infrared Remote Sensing (TIRS) data based on the variation of atmospheric water vapor content (wvc). First, simulating atmospheric conditions by using moderate resolution atmospheric transmission (MODTRAN) based on atmospheric profiles data (air temperature and pressure). Second, calculating the bright temperature based on the radiation transfer equation and Planck's law. Then, constructing the SST retrieval model of thermal infrared remote sensing based on wvc. Finally, evaluating the accuracy of the proposed model by using simulation data. The root mean square errors (RMSEs) are within 0.5 K, indicating that the accuracy of the model is good in theory. In addition, taking the Zhoushan sea area as the research area, SST is retrieved by Landsat 8 TIRS data. The accuracy of inversion results is evaluated by advanced very high-resolution radiometer (AVHRR) SST products. The bias and RMSE based on the AVHRR SST products are within 1 K and 2 K, respectively. The results show that the accurate SST with high spatial resolution was successfully obtained by using the method. The study is of great significance to the acquisition of marine structural parameters, the exploitation of marine resources, and the monitoring of marine disasters.

A bridge over water is a typical artificial object that can be damaged by changes in landform, geology, hydrology and other conditions around the bridge. The technical solution is designed in this paper. Firstly, features of bridge over water are analyzed. Secondly, water body information is extracted according to spatial relationship and spectral feature of water body. Then, post-event bridge is recognized according the shape parameters. Finally, damage detection of bridge is performed. The experiment in Wenchuan, China showed that the method is valid for damage detection of fallen and collapsed bridges over water from high-spatial resolution remote sensing images.

The study of the variation and development trend of regional oceanographic parameters is of great significance to the prevention and mitigation of disasters, the development and utilization of oceans, the guidance of human activities at sea, and the monitoring of the El Nino phenomenon. As an important fishery in China, the changes in oceanographic parameters have severely affected the development of the Zhoushan Islands. The spatial–temporal pattern evolution of oceanographic parameters and the response of oceanographic parameters and El Nino in the Zhoushan sea area are analyzed in this study. The remote sensing datasets of sea surface salinity (SSS), sea surface temperature (SST), sea surface height (SSH), and chlorophyll concentration (Chl) in the Zhoushan sea area were collected and averaged spatially, temporally, and yearly, in order to analyze the variability monthly and yearly, and during El Nino. The results are as follows: (1) SSS is smallest near the shore and increases with distance offshore; SST has obvious seasonal variation; the maximum variation of SSH occurred in the coastal area; and a high-value zone of Chl arises during spring and summer. (2) In the recent twenty years, SSS increased at a rate of 0.1241 PSU per year. SST, SSH, and Chl decrease at 0.0294 °C per year, 0.14 cm per year, and 0.0853 mg/m3 per year, respectively. (3) Different types of El Ninos have different effects on oceanographic parameters.