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Improving Flood Risk Management in the City of Lisbon: Developing a Detailed and Updated Map of Imperviousness Using Satellite Imagery

  • T. Santos
  • S. Freire
Chapter
Part of the Lecture Notes in Computational Vision and Biomechanics book series (LNCVB, volume 8)

Abstract

The spatial distribution and extent of pervious and impervious areas in the city are important variables for planning, mitigating, preparing and responding to potential urban flooding events. Remote sensing constitutes a valuable data source to derive land cover information required for flood risk assessment. The present paper describes the generation of a Land Cover Map for the city of Lisbon, Portugal. The data source is an IKONOS-2 pansharp image, from 2008, with a spatial resolution of 1 m, and a normalized Digital Surface Model (nDSM) from 2006. The methodology was based on the extraction of features of interest, namely: vegetation, soil and impervious surfaces. It is demonstrated that using a methodology based on Very-High Resolution (VHR) images, quick updating of detailed land cover information is possible and can be used to support decisions in a crisis situation where official maps are generally outdated.

Keywords

Land Cover Normalize Difference Vegetation Index Flood Risk Impervious Surface Land Cover Class 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgments

This work was conducted in the framework of project GeoSat—Methodologies to extract large scale GEOgraphical information from very high resolution SATellite images, funded by the Portuguese Foundation for Science and Technology (PTDC/GEO/64826/2006).

The authors would like to thank Logica for the opportunity of using the LiDAR data set.

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Copyright information

© Springer Science+Business Media Dordrecht 2013

Authors and Affiliations

  1. 1.Faculdade de Ciências Sociais e Humanas, FCSH, e-GEO—Research Centre for Geography and Regional PlanningUniversidade Nova de LisboaLisbonPortugal

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