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Uncertainty modeling and analysis of surface area calculation based on a regular grid digital elevation model (DEM) / Chang Li in International journal of geographical information science IJGIS, vol 32 n° 9-10 (September - October 2018)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 32 n° 9-10 (September - October 2018) . - pp 1837 - 1859 Titre : Uncertainty modeling and analysis of surface area calculation based on a regular grid digital elevation model (DEM) Type de document : Article/Communication Auteurs : Chang Li, Auteur ; Sisi Zhao, Auteur ; Qing Wang, Auteur ; Wenzhong Shi, Auteur Année de publication : 2018 Article en page(s) : pp 1837 - 1859 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique [Termes IGN] autocorrélation spatiale [Termes IGN] incertitude des données [Termes IGN] méthode de Monte-Carlo [Termes IGN] modèle numérique de surface [Termes IGN] propagation d'erreur Résumé : (Auteur) In the field of digital terrain analysis (DTA), the principle and method of uncertainty in surface area calculation (SAC) have not been deeply developed and need to be further studied. This paper considers the uncertainty of data sources from the digital elevation model (DEM) and SAC in DTA to perform the following investigations: (a) truncation error (TE) modeling and analysis, (b) modeling and analysis of SAC propagation error (PE) by using Monte-Carlo simulation techniques and spatial autocorrelation error to simulate DEM uncertainty. The simulation experiments show that (a) without the introduction of the DEM error, higher DEM resolution and lower terrain complexity lead to smaller TE and absolute error (AE); (b) with the introduction of the DEM error, the DEM resolution and terrain complexity influence the AE and standard deviation (SD) of the SAC, but the trends by which the two values change may be not consistent; and (c) the spatial distribution of the introduced random error determines the size and degree of the deviation between the calculated result and the true value of the surface area. This study provides insights regarding the principle and method of uncertainty in SACs in geographic information science (GIScience) and provides guidance to quantify SAC uncertainty. Numéro de notice : A2018-305 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2018.1469136 Date de publication en ligne : 04/05/2018 En ligne : https://doi.org/10.1080/13658816.2018.1469136 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=90448 [article]

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