Improving flood risk management in the city of Lisbon

Developing a detailed and updated map of imperviousness using satellite imagery

Research output: Chapter in Book/Report/Conference proceedingChapter

1 Citation (Scopus)

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information is possible and can be used to support decisions in a crisis situation where official maps are generally outdated.

Original languageEnglish
Title of host publicationLecture Notes in Computational Vision and Biomechanics
EditorsJoão Manuel R.S. Tavares, Renato M. Natal Jorge
PublisherSpringer Netherlands
Pages291-305
Number of pages15
ISBN (Electronic)978-94-007-0726-9
ISBN (Print)978-94-007-0725-2
DOIs
Publication statusPublished - 1 Jan 2013

Publication series

NameLecture Notes in Computational Vision and Biomechanics
Volume8
ISSN (Print)2212-9391
ISSN (Electronic)2212-9413

    Fingerprint

Keywords

  • ArcGIS
  • Asphalt
  • IKONOS
  • Landsat
  • Permeability

Cite this

Santos, T., & Freire, S. (2013). Improving flood risk management in the city of Lisbon: Developing a detailed and updated map of imperviousness using satellite imagery. In J. M. R.S. Tavares, & R. M. Natal Jorge (Eds.), Lecture Notes in Computational Vision and Biomechanics (pp. 291-305). (Lecture Notes in Computational Vision and Biomechanics; Vol. 8). Springer Netherlands. https://doi.org/10.1007/978-94-007-0726-9_16