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Bridges over water are important artificial objects that can be damaged by natural disasters. Accurate identification and damage detection of such bridges through the use of high-spatial-resolution optical remotely sensed imagery are important in emergency rescue and lifeline safety assessment. In this study, we detail a knowledge-based method of identification and damage detection of bridges spanning water using high-spatial-resolution optical remotely sensed imagery. Data on the body of water are extracted to define spatial extent and improve the timeliness of identification and damage detection, the threshold values of the rectangle degree and area are set to remove false bridge targets, and the damaged parts are detected according to the bridge’s rectangular characteristics and the relationship with the body of water. First, the characteristics, such as spectral, geometric, and textural, and spatial relationships of the bridge over water, are analyzed. Second, to limit the spatial extent of bridge identification and improve computational efficiency, data on the body of water are extracted. Third, the post-event bridge is identified from the viewpoint of bridge integrity based on shape and area parameters. Damage detection is then performed according to the bridge’s integrity. Finally, the results are evaluated for both non-positional and positional accuracy. Results of experiments carried out in Huiyang and Wenchuan, China, show that the proposed method, using high-spatial-resolution optical remotely sensed imagery, is effective for identification and damage detection of fallen and collapsed bridges spanning water. Therefore, the method is useful in updating the geographic database of bridges and assessing damage to them caused by natural disasters.

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.

Land use/land cover change (LUCC) is the most direct manifestation of the interaction between human activities and the natural environment, which is of great significance to regional development, environmental change, and resource bearing capacity. As the first prefecture-level city in China that established by islands, the spatial-temporal variation of LUCC in Zhoushan City was analyzed by classification of Landsat images from 1985 to 2015. The results showed that: (1) Construction land expanded rapidly along the coastal plains, the area increased about 5 times mainly by developing cropland and reclaiming land from sea and tidal flat; (2) By reclaiming land from sea, there are different degrees of expansion along the coastline of various islands, 37.99 km2 new land was obtained and nearly half of the land has been developed into construction land. (3) The area of cropland decreased significantly due to the occupation of construction land while the forest is well protected which has increased slightly in area.

As an important fishery in China, the changes of marine hydrological elements have seriously affected the development of Zhoushan. The spatio-temporal pattern evolution of marine hydrological elements and the response of marine hydrological elements and El-Nino in Zhoushan sea area are analyzed in this paper. The results are as follows: (1) Marine hydrological elements have obviously seasonal variation and spatial distribution; (2) In 1995-2016, sea surface salinity (SSS) and sea surface temperature (SST) have a yearly upward trend, chlorophyll concentration (Chl) has a yearly downward trend; (3) The response of marine hydrological elements at different El-Nino stages shows a difference.

Since the launch of landsat8, high-quality surface observation data have been acquired. But the corresponding sea surface temperature (SST) products have not been seen. Moreover, the existing SST inversion algorithm do not carefully consider the effect of water vapor on the accuracy. In this study, a new method is proposed for the retrieve of SST from Landsat 8 Thermal Infrared Remote Sensing (TIRS) data based on the variation of atmospheric water vapor content. The steps are briefly described as follows: 1) constructing the SST retrieval model of thermal infrared remote sensing by using the radiation transfer equation with atmospheric profile data (air temperature and pressure); 2) retrieving SST of Zhoushan sea area, China from Landsat 8 TIRS Data; 3) evaluating the accuracy of proposed model by simulation data and AVHRR SST product. The bias and RMSE based on the simulation data are within 0.4 K. The bias and RMSE based on AVHRR SST product are 1.6063 K and 1.8507 K, respectively. The results show that the accurate SST with high spatial resolution could be obtained by using the method.

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.