Cite this paper:
YANG Fengshuo, YANG Xiaomei, WANG Zhihua, LU Chen, LI Zhi, LIU Yueming. Object-based classification of cloudy coastal areas using medium-resolution optical and SAR images for vulnerability[J]. HaiyangYuHuZhao, 2019, 37(6): 1955-1970

Object-based classification of cloudy coastal areas using medium-resolution optical and SAR images for vulnerability

YANG Fengshuo1,2, YANG Xiaomei1,2,4, WANG Zhihua1, LU Chen1,2, LI Zhi2,3, LIU Yueming1,2
1 State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China;
2 University of Chinese Academy of Sciences, Beijing 100049, China;
3 State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China;
4 Jiangsu Centre for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China
Efficient and accurate access to coastal land cover information is of great significance for marine disaster prevention and mitigation. Although the popular and common sensors of land resource satellites provide free and valuable images to map the land cover, coastal areas often encounter significant cloud cover, especially in tropical areas, which makes the classification in those areas non-ideal. To solve this problem, we proposed a framework of combining medium-resolution optical images and synthetic aperture radar (SAR) data with the recently popular object-based image analysis (OBIA) method and used the Landsat Operational Land Imager (OLI) and Phased Array type L-band Synthetic Aperture Radar (PALSAR) images acquired in Singapore in 2017 as a case study. We designed experiments to confirm two critical factors of this framework:one is the segmentation scale that determines the average object size, and the other is the classification feature. Accuracy assessments of the land cover indicated that the optimal segmentation scale was between 40 and 80, and the features of the combination of OLI and SAR resulted in higher accuracy than any individual features, especially in areas with cloud cover. Based on the land cover generated by this framework, we assessed the vulnerability of the marine disasters of Singapore in 2008 and 2017 and found that the high-vulnerability areas mainly located in the southeast and increased by 118.97 km2 over the past decade. To clarify the disaster response plan for different geographical environments, we classified risk based on altitude and distance from shore. The newly increased high-vulnerability regions within 4 km offshore and below 30 m above sea level are at high risk; these regions may need to focus on strengthening disaster prevention construction. This study serves as a typical example of using remote sensing techniques for the vulnerability assessment of marine disasters, especially those in cloudy coastal areas.
Key words:    coastal area|marine disaster|vulnerability assessment|remote sensing|land use/cover|objectbased image analysis (OBIA)   
Received: 2018-09-26   Revised: 2019-01-06
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