Earth observation data based rapid flood-extent modelling for tsunami-devastated coastal areas |
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| Institution: | 1. School of Geoscience and Technology, Southwest Petroleum University, Chengdu 610500, China;2. State Key Laboratory of oil and gas reservoir geology and exploitation, Southwest Petroleum University, Chengdu 610500, China;3. Petrochina Tarim Oilfield Company, Korla 841000, China;4. CNPC USA Corparation;1. Agro-Meteorology Division, National Institute for Agro-Environmental Sciences, 3-1-3 Kannondai, Tsukuba, Ibaraki 304-8604, Japan;2. Technische Universität Dresden, Institute of Hydrology and Meteorology, Chair of Meteorology, Dresden D-01062, Germany;3. The National Laboratory for Agriculture and the Environment (USDA-ARS-NLAE), 2110 University Blvd, Ames, IA 50011, USA;4. European Commission, Joint Research Centre, Institute for Environment and Sustainability, Ispra, Italy;5. Institute of Geography, University of Cologne, Cologne 50923, Germany;6. Agrosphere Institute (IBG-3), Institute of Bio- and Geosciences, Jülich 52425, Germany;7. Fundación Centro de Estudios Ambientales del Mediterráneo (CEAM), Charles Robert Darwin, 14, Parque Tecnológico, Paterna 46980, Spain;8. INRA, UMR INRA-AgroParisTech ECOSYS, Thiverval-Grignon 78850, France;9. CESBIO (CNES/CNRS/UPS/IRD), 18, Avenue Edouard Belin, Toulouse Cedex 9 31401, France |
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| Abstract: | Earth observation (EO)-based mapping and analysis of natural hazards plays a critical role in various aspects of post-disaster aid management. Spatial very high-resolution Earth observation data provide important information for managing post-tsunami activities on devastated land and monitoring re-cultivation and reconstruction. The automatic and fast use of high-resolution EO data for rapid mapping is, however, complicated by high spectral variability in densely populated urban areas and unpredictable textural and spectral land-surface changes. The present paper presents the results of the SENDAI project, which developed an automatic post-tsunami flood-extent modelling concept using RapidEye multispectral satellite data and ASTER Global Digital Elevation Model Version 2 (GDEM V2) data of the eastern coast of Japan (captured after the Tohoku earthquake). In this paper, the authors developed both a bathtub-modelling approach and a cost-distance approach, and integrated the roughness parameters of different land-use types to increase the accuracy of flood-extent modelling. Overall, the accuracy of the developed models reached 87–92%, depending on the analysed test site. The flood-modelling approach was explained and results were compared with published approaches. We came to the conclusion that the cost-factor-based approach reaches accuracy comparable to published results from hydrological modelling. However the proposed cost-factor approach is based on a much simpler dataset, which is available globally. |
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| Keywords: | Flood mapping Coastal areas Cost function Rapid mapping Tsunami RapidEye Disaster Coast Floods Multispectral Data Fusion |
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