Rapid Mapping With Remote Sensing Data
During Flooding 2005 In Switzerland By
Object-based Methods: A Case Study
Author(s)
Y. A. Buehler, T. W. Kellenberger, D. Small & K. I. Itten
Abstract
Rapid mapping and monitoring with remote sensing techniques is an important
source of information for decision-makers faced with large scaled disasters. In
August 2005 several regions in central Switzerland were affected by severe
flooding. The \“National Emergency Operations Centre” (NEOC) of Switzerland
invoked the International Charter on \“Space and Major Disasters”, requesting
support by remote-sensing data for disaster-management. In this paper, a SPOT-5
and a RADARSAT-1 satellite-scene acquired a few days after the flood-peak are
described and processed to map the affected area with object-based methods. The
main difficulties with optical images are the small extent of the Swiss landscape,
the large height-differences in the relief, shadows, clouds and snow covered
areas. Comparing different approaches, it was found that beside the NIR and
SWIR band the application of a DEM and a VISNIR water index showed the
most promising results for a fast discrimination of the affected areas. The objectbased
classification is mainly dependent on threshold-values and Boolean
operators. As investigations show, the spatial resolution of the acquired radar
dataset in this case is not sufficient to map the affected areas. The results of the
classification based on SPOT-5 scenes are up-to-date maps of flooded areas. We
show that maps for decision-makers can be produced using auxiliary
topographical and land use data. Aerial infrared images acquired from
SWISSTOPO after the flooding were used to test the accuracy of the result.
Procedures for rapid mapping applications in future disaster cases are discussed.
Keywords: rapid mapping, flooding, disaster response, international charter on
space and major disasters, natural hazards, object-based classification.
Keywords
rapid mapping, flooding, disaster response, international charter on
space and major disasters, natural hazards, object-based classification.
Related Book
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