{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,19]],"date-time":"2026-02-19T15:19:03Z","timestamp":1771514343802,"version":"3.50.1"},"reference-count":35,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2019,8,12]],"date-time":"2019-08-12T00:00:00Z","timestamp":1565568000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Bijzonder Onderzoeksfonds","award":["BOF.STA.2017.0033.01"],"award-info":[{"award-number":["BOF.STA.2017.0033.01"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The amount of freely available satellite data is growing rapidly as a result of Earth observation programmes, such as Copernicus, an initiative of the European Space Agency. Analysing these huge amounts of geospatial data and extracting useful information is an ongoing pursuit. This paper presents an alternative method for flood detection based on the description of spatio-temporal dynamics in satellite image time series (SITS). Since synthetic aperture radar (SAR) satellite data has the capability of capturing images day and night, irrespective of weather conditions, it is the preferred tool for flood mapping from space. An object-based approach can limit the necessary computer power and computation time, while a graph-based approach allows for a comprehensible interpretation of dynamics. This method proves to be a useful tool to gain insight in a flood event. Graph representation helps to identify and locate entities within the study site and describe their evolution throughout the time series.<\/jats:p>","DOI":"10.3390\/rs11161883","type":"journal-article","created":{"date-parts":[[2019,8,13]],"date-time":"2019-08-13T04:31:21Z","timestamp":1565670681000},"page":"1883","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["Object-Based Flood Analysis Using a Graph-Based Representation"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1857-2966","authenticated-orcid":false,"given":"Bos","family":"Debusscher","sequence":"first","affiliation":[{"name":"Laboratory of Forest Management and Spatial Information Techniques (FORSIT), Department of Environment, Ghent University, 9000 Ghent, Belgium"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3161-2144","authenticated-orcid":false,"given":"Frieke","family":"Van Coillie","sequence":"additional","affiliation":[{"name":"Laboratory of Forest Management and Spatial Information Techniques (FORSIT), Department of Environment, Ghent University, 9000 Ghent, Belgium"}]}],"member":"1968","published-online":{"date-parts":[[2019,8,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"180","DOI":"10.1016\/j.isprsjprs.2013.09.014","article-title":"Geographic object-based image analysis\u2014Towards a new paradigm","volume":"87","author":"Blaschke","year":"2014","journal-title":"ISPRS J. 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