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Monitoring and Interpretation of Urban Land Subsidence Using Radar Interferometric Time Series and Multi-Source GIS Database

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Remote Sensing and GIS Technologies for Monitoring and Prediction of Disasters

Part of the book series: Environmental Science and Engineering ((ENVSCIENCE))

Abstract

Subtle and gradual land subsidence in the Netherlands is a well-known phenomenon and it potentially involves a high risk of subsidence disaster in the densely populated areas. Based on the conventional geodetic techniques such as leveling, the slow deformations in urban areas are difficult to monitor due to low temporal and spatial sampling frequencies. We pre-sent the potential of Persistent Scatterer Interferometry (PSI) as an efficient satellite based technique of deformation monitoring. Time series of radar multi-epoch images (up-to 15 years) are analyzed to extract the tar-gets with a stable phase behavior over time. PSI is particularly useful in application to urban environment as the buildings and solid structures are favorable radar point targets.

For the interpretation of the PS data, we propose an approach of multi-source geo-information combination. Geographical Information Sys-tem is used to combine different geo-information sources such as land-use, cadaster databases, aerial photos and digital ortho-map. This combination leads to the possibility of interpretation of PS data with reference to physical ground features, and hence, provides an idea of the reasons of the subsidence. Moreover, a Web-GIS interactive visualization interface is de-signed to share the PS derived land subsidence data to a variety of users. Test cases of PSI analysis for the cities of Delft and Rotterdam in the Netherlands are presented and discussed.

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© 2008 Springer-Verlag Berlin Heidelberg

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Gehlot, S., Hanssen, R.F. (2008). Monitoring and Interpretation of Urban Land Subsidence Using Radar Interferometric Time Series and Multi-Source GIS Database. In: Nayak, S., Zlatanova, S. (eds) Remote Sensing and GIS Technologies for Monitoring and Prediction of Disasters. Environmental Science and Engineering. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-79259-8_8

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