MyJournals Home  

RSS FeedsRemote Sensing, Vol. 12, Pages 296: Insar Maps of Land Subsidence and Sea Level Scenarios to Quantify the Flood Inundation Risk in Coastal Cities: The Case of Singapore (Remote Sensing)

 
 

16 january 2020 23:00:16

 
Remote Sensing, Vol. 12, Pages 296: Insar Maps of Land Subsidence and Sea Level Scenarios to Quantify the Flood Inundation Risk in Coastal Cities: The Case of Singapore (Remote Sensing)
 


Global mean sea level rise associated with global warming has a major impact on coastal areas and represents one of the significant natural hazards. The Asia-Pacific region, which has the highest concentration of human population in the world, represents one of the larger areas on Earth being threatened by the rise of sea level. Recent studies indicate a global sea level of 3.2 mm/yr as measured from 20 years of satellite altimetry. The combined effect of sea level rise and local land subsidence, can be overwhelming for coastal areas. The Synthetic Aperture Radar (SAR) interferometry technique is used to process a time series of TerraSAR-X images and estimate the land subsidence in the urban area of Singapore. Interferometric SAR (InSAR) measurements are merged to the Representative Concentration Pathway (RCP) 4.5 and RCP 8.5 sea-level rise scenarios to identify projected inundated areas and provide a map of flood vulnerability. Subsiding rates larger than 5 mm/year are found near the shore on the low flat land, associated to areas recently reclaimed or built. The projected flooded map of Singapore are provided for different sea-level rise scenarios. In this study, we show that local land subsidence can increase the flood vulnerability caused by sea level rise by 2100 projections. This can represent an increase of 25% in the flood area in the central area of Singapore for the RCP4.5 scenario.


 
232 viewsCategory: Geology, Physics
 
Remote Sensing, Vol. 12, Pages 297: A GA-Based Multi-View, Multi-Learner Active Learning Framework for Hyperspectral Image Classification (Remote Sensing)
Remote Sensing, Vol. 12, Pages 295: Comparison of Different Machine Learning Methods for Debris Flow Susceptibility Mapping: A Case Study in the Sichuan Province, China (Remote Sensing)
 
 
blog comments powered by Disqus


MyJournals.org
The latest issues of all your favorite science journals on one page

Username:
Password:

Register | Retrieve

Search:

Physics


Copyright © 2008 - 2024 Indigonet Services B.V.. Contact: Tim Hulsen. Read here our privacy notice.
Other websites of Indigonet Services B.V.: Nieuws Vacatures News Tweets Nachrichten