GI_Forum 2017, Volume 5, Issue 1 Journal for Geographic Information Science
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Verlag der Österreichischen Akademie der Wissenschaften Austrian Academy of Sciences Press
A-1011 Wien, Dr. Ignaz Seipel-Platz 2
Tel. +43-1-515 81/DW 3420, Fax +43-1-515 81/DW 3400 https://verlag.oeaw.ac.at, e-mail: verlag@oeaw.ac.at |
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DATUM, UNTERSCHRIFT / DATE, SIGNATURE
BANK AUSTRIA CREDITANSTALT, WIEN (IBAN AT04 1100 0006 2280 0100, BIC BKAUATWW), DEUTSCHE BANK MÜNCHEN (IBAN DE16 7007 0024 0238 8270 00, BIC DEUTDEDBMUC)
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GI_Forum 2017, Volume 5, Issue 1 Journal for Geographic Information Science
ISSN 2308-1708 Online Edition ISBN 978-3-7001-8158-3 Online Edition
Martin Sudmanns,
Dirk Tiede,
Lorenz Wendt,
Andrea Baraldi
S. 217 - 227 doi:10.1553/giscience2017_01_s217 Verlag der Österreichischen Akademie der Wissenschaften doi:10.1553/giscience2017_01_s217
Abstract: Our study uses a dense temporal stack of 78 Landsat 8 images for surface water extraction using automatic Earth Observation (EO) image pre-processing, coupled with analyses over time for flood detection. The analysis is conducted with our IQ (ImageQuerying) system developed in-house, which allows ad-hoc executing of spatio-temporal queries against semantically enriched EO images. To facilitate high performance analyses, the data are stored as a spatio-temporal data cube in an array database. The analyses are automatically-translated database queries, which increase reproducibility, readability and comprehensibility for a human operator and can be conducted within just a few minutes. The specific analysis for this contribution is based on flood-extent mapping over different user-definable time spans. The results indicate areas that have been flooded at least once in the selected time span and are therefore prone to being flooded in future events. Additional spatial queries (e.g., for the indication of cloud cover) support the quality assessment of the flood analyses. We compared our result with a flood mask derived from a SAR (synthetic aperture radar) image of a single event in Somalia (Hiran province). Larger flooded areas overlap in both analyses, despite the non-synchronous acquisition times of the images. The results can be used as input for improved risk assessment and management of floods. Keywords: remote sensing, time series, flood mapping, big Earth data, data cube Published Online: 2017/06/30 09:28:15 Object Identifier: 0xc1aa5576 0x00369d97 Rights:https://creativecommons.org/licenses/by-nd/4.0/
GI_Forum publishes high quality original research across the transdisciplinary field of Geographic Information Science (GIScience). The journal provides a platform for dialogue among GI-Scientists and educators, technologists and critical thinkers in an ongoing effort to advance the field and ultimately contribute to the creation of an informed GISociety. Submissions concentrate on innovation in education, science, methodology and technologies in the spatial domain and their role towards a more just, ethical and sustainable science and society. GI_Forum implements the policy of open access publication after a double-blind peer review process through a highly international team of seasoned scientists for quality assurance. Special emphasis is put on actively supporting young scientists through formative reviews of their submissions. Only English language contributions are published.
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Verlag der Österreichischen Akademie der Wissenschaften Austrian Academy of Sciences Press
A-1011 Wien, Dr. Ignaz Seipel-Platz 2
Tel. +43-1-515 81/DW 3420, Fax +43-1-515 81/DW 3400 https://verlag.oeaw.ac.at, e-mail: verlag@oeaw.ac.at |