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Spatial resolution imagery requirements for identifying structure damage in a hurricane disaster: A cognitive approach / S. Battersby in Photogrammetric Engineering & Remote Sensing, PERS, vol 78 n° 6 (June 2012)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 78 n° 6 (June 2012) . - pp 625 - 635 Titre : Spatial resolution imagery requirements for identifying structure damage in a hurricane disaster: A cognitive approach Type de document : Article/Communication Auteurs : S. Battersby, Auteur ; M. Hodgson, Auteur ; Jing Wang, Auteur Année de publication : 2012 Article en page(s) : pp 625 - 635 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse visuelle [Termes IGN] classification à base de connaissances [Termes IGN] cognition [Termes IGN] dommage matériel [Termes IGN] pouvoir de résolution géométrique [Termes IGN] risque naturel [Termes IGN] seuillage d'image [Termes IGN] tempête [Termes IGN] zone sinistrée Résumé : (Auteur) In disaster response, timely collection and exploitation of remotely sensed imagery is of increasing importance. Image exploitation approaches during the immediate (first few days) aftermath of a disaster are predominantly through visual analysis rather than automated classification methods. While the temporal needs for obtaining the imagery are fairly clear (within a one- to three-day window), there have only been educated guesses about the spatial resolution requirements necessary for the imagery for visual analysis. In this paper, we report results from an empirical study to identify the coarsest spatial resolution that is adequate for tasks required immediately following a major disaster. The study was conducted using cognitive science experimental methods and evaluated the performance of individuals with varying image interpretation skills in the task of mapping hurricane-related residential structural damage. Through this study, we found 1.5 m as a threshold for the coarsest spatial resolution imagery that can successfully be used for this task. The results of the study are discussed in terms of the likelihood of collection of this type of imagery within the temporal window required for emergency management operations. Numéro de notice : A2012-250 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.78.6.625 En ligne : https://doi.org/10.14358/PERS.78.6.625 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=31696 [article]

A process oriented areal interpolation technique: a coastal county example / B. Kar in Cartography and Geographic Information Science, vol 39 n° 1 (January 2012)  

Public [article]  inCartography and Geographic Information Science > vol 39 n° 1 (January 2012) . - pp 3 - 16 Titre : A process oriented areal interpolation technique: a coastal county example Type de document : Article/Communication Auteurs : B. Kar, Auteur ; M. Hodgson, Auteur Année de publication : 2012 Article en page(s) : pp 3 - 16 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Rédaction cartographique [Termes IGN] figuration de la densité [Termes IGN] interpolation par pondération de zones [Termes IGN] Miami Résumé : (Auteur) The Modifiable Areal Unit Problem (MAUP) is the classic term for describing different totals observed from spatially different aggregation units. In a typical analytical problem (e.g. estimating total population within a watershed from census unit totals) the spatial distribution of populations within the census units arc modeled. To minimize MAUP errors, areal interpolation techniques arc used to model such sub-unit population distributions. Areal interpolation techniques are highly dependent on ancillary data (e.g. land use/cover data) and typically do not include "intelligent" relations about where people choose to live, other than a weighted association between nominal land cover/use and population density. The purpose of this research was to design and implement an "intelligent" areal interpolation method for housing data in coastal environments, validate the accuracy, and compare to other techniques. This study was conducted for Miami-Dade County in Florida at census scales from county to block. Parcel boundary data was used as a reference layer to validate each technique. Not surprisingly, all techniques perform best at finer spatial resolutions (e.g. block level) with error increasing at coarser resolutions. The accuracy of the dasymetric technique is directly related to the accuracy of ancillary data. The new intelligent technique, (referred to as the process-oriented technique from here onwards) models the relationship between housing unit density distribution and proximity to the coast. This process-oriented technique performed better than the arcal weighting and the dasymetric mapping technique. Combining the 'process-oriented' technique with a dasymetric technique provided the least amount of error. Numéro de notice : A2012-293 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1559/152304063913 En ligne : https://doi.org/10.1559/152304063913 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=31739 [article]

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Code-barres	Cote	Support	Localisation	Section	Disponibilité
032-2012011	RAB	Revue	Centre de documentation	En réserve L003	Disponible