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Monitoring recovery after earthquakes through the integration of remote sensing, GIS, and ground observations: the case of L’Aquila (Italy) / Diana Contreras in Cartography and Geographic Information Science, Vol 43 n° 2 (April - May 2016)  

Public [article]  inCartography and Geographic Information Science > Vol 43 n° 2 (April - May 2016) . - pp 115 - 133 Titre : Monitoring recovery after earthquakes through the integration of remote sensing, GIS, and ground observations: the case of L’Aquila (Italy) Type de document : Article/Communication Auteurs : Diana Contreras, Auteur ; Thomas Blaschke, Auteur ; Dirk Tiede, Auteur ; Marianne Jilge, Auteur Année de publication : 2016 Article en page(s) : pp 115 - 133 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] analyse de données [Termes IGN] détection de changement [Termes IGN] Italie [Termes IGN] outil d'aide à la décision [Termes IGN] séisme [Termes IGN] système d'information géographique Résumé : (Auteur) The usefulness of remote sensing (RS), geographical information systems, and ground observations for monitoring changes in urban areas has been demonstrated through many examples over the last two decades. Research has generally focused on the relief phase following a disaster, but we have instead investigated the subsequent phases involving early recovery, recovery, and development. Our aim was to determine to what extent integration of the available tools, techniques, and methods can be used to efficiently monitor the progress of recovery following an earthquake. Changes in buildings within the Italian city of L’Aquila following the 2009 earthquake were identified from Earth observation data and are used as indicators of progress in the recovery process. These changes were identified through (1) visual analysis, (2) automated change detection using a set of decision rules formulated within an object-based image analysis framework, and (3) validation based on a combination of visual and semiautomated interpretations. An accuracy assessment of the automated analysis showed a producer accuracy of 81% (error of omission: 19%) and a user accuracy of 55% (error of commission: 45%). The use of RS made it possible for the identification of changes to be spatially exhaustive, and also to increase the number of categories used for a recovery index. In addition, using RS allowed the area requiring extensive fieldwork (to monitor the progress of the recovery process) to be reduced. Numéro de notice : A2016-282 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1080/15230406.2015.1029520 En ligne : https://doi.org/10.1080/15230406.2015.1029520 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=80853 [article]

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