Paper Info
Title
Subsidence Monitoring with INSAR Techniques Aided by Laser Scanning Data and Topographic Map: A Case Study of Rotterdam Reclaimed Areas
Abstract
Land reclamation is a pragmatic urban solution for coastal development, serving for building harbors, airports, industrial zones. Considering the intensive human activities over the reclaimed land and natural processes such as soil compaction and coastal erosion, the reclaimed lands may show graduate or instantaneous subsidences. It is of great significance to consecutively monitor and detect such subsidences before they unveil themselves as hazards. Here we use multi-epoch InSAR (Synthetic Aperture Radar) technique to produce deformation time series of the InSAR measurement points. To detect anomalies in the deformation time series of any individual ground object, we first use laser scanning data - AHN3, to adjust the geoposition of the InSAR measurement points and classify the objects e.g. ground, buildings, vegetation and water. Next we use the topographic map - TOP10NL, to recognize every individual object, e.g. a single building. As such, the precision of the geolocation of the InSAR measurement points can be improved, and the InSAR measurement points from any individual object, defined as a cluster (a group of spatial-related points), can be categorized among the others. Then we use a cluster-wise multiple hypothesis testing method to identify the spatial and temporal anomalies. Our methods are demonstrated in the case study of the Rotterdam reclaimed areas, The Netherlands.
Year
DOI
Venue
2019
10.1109/IGARSS.2019.8897936
IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium
Keywords
Field
DocType
Multi-epoch SAR,subsidence,reclamation
Vegetation,Interferometric synthetic aperture radar,Laser scanning,Topographic map,Synthetic aperture radar,Computer science,Remote sensing,Geolocation,Subsidence,Land reclamation
Conference
ISSN
ISBN
Citations 
2153-6996
978-1-5386-9155-7
0
PageRank 
References 
Authors
0.34
0
1
Name
Order
Citations
PageRank
Ling Chang1244.58