@inbook{3f0ecd54e9134a27bba3ec5f75288f5f,
title = "Improving flood risk management in the city of Lisbon: Developing a detailed and updated map of imperviousness using satellite imagery",
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information is possible and can be used to support decisions in a crisis situation where official maps are generally outdated.",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 = "ArcGIS, Asphalt, IKONOS, Landsat, Permeability",
author = "Teresa Santos and Freire, \{S{\'e}rgio Manuel Carneiro\}",
note = "info:eu-repo/grantAgreement/FCT/5876/147304/PT\# UID/SOC/04647/2013",
year = "2013",
month = jan,
day = "1",
doi = "10.1007/978-94-007-0726-9\_16",
language = "English",
isbn = "978-94-007-0725-2",
series = "Lecture Notes in Computational Vision and Biomechanics",
publisher = "Springer",
pages = "291--305",
editor = "\{R.S. Tavares\}, \{Jo{\~a}o Manuel\} and \{M. Natal Jorge\}, Renato",
booktitle = "Topics in Medical Image Processing and Computational Vision",
address = "Netherlands",
}