6th Annual 2018 International Conference on Geo-Spatial Knowledge and Intelligence | |
Coastal Erosion Monitoring and Hazard Degree Assessment at Penglai Sandy Coast Based on Remote Sensing | |
Wen, Shiyong^1 ; Zhang, Fengshou^1 ; Wang, Zizhu^1 ; Li, Fei^1 ; Jing, Xindi^1 ; Zhao, Jianhua^1 | |
National Marine Environmental Monitoring Centre, Dalian | |
116023, China^1 | |
关键词: Comprehensive managements; Emergency management; Geological disaster; Hazard degree assessment; Industrial layouts; Rational planning; Shoreline position; Technical support; | |
Others : https://iopscience.iop.org/article/10.1088/1755-1315/234/1/012014/pdf DOI : 10.1088/1755-1315/234/1/012014 |
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来源: IOP | |
【 摘 要 】
Coastal erosion disaster is one of the main marine geological disasters. Hazard degree of coastal erosion means that coastal erosion range may be occurred in a future period according to the occurrence mechanism of coastal erosion and its damage characteristic. To effectively cope with the coastal erosion disasters prevention and emergency management needs, the spatial distribution of coastal erosion were obtained, and a shoreline positions prediction model were established by using DSAS, GIS platform, and validated the prediction results by using fields' measure shoreline data at Penglai sandy coast. The results show that (1) there were varying degrees of erosion in the monitoring shore between 2006 and 2015. (2) There are a better coincident between the prediction model results and the fields' measurement results. (3) The spatial shoreline positions of the monitoring shore by the prediction model in 11/13/2016 and 11/13/2020 were obtained. The beach width of the monitoring shore is gradually decreasing, which affects the coastal tourism's use of the beach. These results would be provided a technical support for carrying out the coastal erosion disasters comprehensive management and rational planning of industrial layout along the coast.
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Coastal Erosion Monitoring and Hazard Degree Assessment at Penglai Sandy Coast Based on Remote Sensing | 946KB | download |