Semi-automatic derivation of channel network from a high-resolution DTM: the example of an Italian alpine region (Articolo in rivista)

Type
Label
  • Semi-automatic derivation of channel network from a high-resolution DTM: the example of an Italian alpine region (Articolo in rivista) (literal)
Anno
  • 2013-01-01T00:00:00+01:00 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#doi
  • 10.5721/EuJRS20134609 (literal)
Alternative label
  • Cavalli M, Trevisani S, Goldin B, Mion E, Crema S, Valentinotti R (2013)
    Semi-automatic derivation of channel network from a high-resolution DTM: the example of an Italian alpine region
    in European Journal of Remote Sensing
    (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori
  • Cavalli M, Trevisani S, Goldin B, Mion E, Crema S, Valentinotti R (literal)
Pagina inizio
  • 152 (literal)
Pagina fine
  • 174 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#url
  • http://www.aitjournal.com/articleView.aspx?ID=597 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#numeroVolume
  • 46 (literal)
Rivista
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#pagineTotali
  • 23 (literal)
Note
  • ISI Web of Science (WOS) (literal)
  • Scopu (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni
  • Cavalli M., Goldin B, Mion E, Crema S: CNR IRPI - Padova, Italy Trevisani S: University IUAV of Venice, Faculty of Architecture e associato IRPI Valentinotti R: Autonomous Province of Trento, Italy (literal)
Titolo
  • Semi-automatic derivation of channel network from a high-resolution DTM: the example of an Italian alpine region (literal)
Abstract
  • High-resolution digital terrain models (HR-DTMs) of regional coverage open interesting scenarios for the analysis of landscape, including derivation and analysis of channel network. In this study, we present the derivation of the channel network from a HR-DTM for the Autonomous Province of Trento. A preliminary automatic extraction of the raw channel network was conducted using a curvature-based algorithm applied to a 4 m resolution DTM derived from an airborne LiDAR survey carried out in 2006. The raw channel network automatically extracted from the HR-DTM underwent a supervised control to check the spatial pattern of the hydrographic network. The supervised control was carried out by means of different informative layers (i.e. geomorphometric indexes, orthophoto imagery and technical cartography) resulting in an accurate and fine-scale channel network. (literal)
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