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dc.contributor.author
Genchi, Sibila Andrea
dc.contributor.author
Vitale, Alejandro José
dc.contributor.author
Perillo, Gerardo Miguel E.
dc.contributor.author
Delrieux, Claudio Augusto
dc.date.available
2017-02-06T20:59:35Z
dc.date.issued
2015-02
dc.identifier.citation
Genchi, Sibila Andrea; Vitale, Alejandro José; Perillo, Gerardo Miguel E.; Delrieux, Claudio Augusto; Structure-from-Motion Approach for Characterization of Bioerosion Patterns Using UAV Imagery; Molecular Diversity Preservation International; Sensors; 15; 2-2015; 3593-3609
dc.identifier.issn
1424-8220
dc.identifier.uri
http://hdl.handle.net/11336/12566
dc.description.abstract
The aim of this work is to evaluate the applicability of the 3D model obtained through Structure-from-Motion (SFM) from unmanned aerial vehicle (UAV) imagery, in order to characterize bioerosion patterns (i.e., cavities for roosting and nesting) caused by burrowing parrots on a cliff in Bahía Blanca, Argentina. The combined use of SFM-UAV technology was successfully applied for the 3D point cloud model reconstruction. The local point density, obtained by means of a sphere of radius equal to 0.5 m, reached a mean value of 9749, allowing to build a high-resolution model (0.013 m) for resolving fine spatial details in topography. To test the model, we compared it with another point cloud dataset which was created using a low cost do-it-yourself terrestrial laser scanner; the results showed that our georeferenced model had a good accuracy. In addition, an innovative method for the detection of the bioerosion features was implemented, through the processing of data provided by SFM like color and spatial coordinates (particularly the y coordinate). From the 3D model, we also derived topographic calculations such as slope angle and surface roughness, to get associations between the surface topography and bioerosion features.
dc.format
application/pdf
dc.language.iso
eng
dc.publisher
Molecular Diversity Preservation International
dc.rights
info:eu-repo/semantics/openAccess
dc.rights.uri
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.subject
Structure-From-Motion Approach
dc.subject
Unmanned Aerial Vehicle
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Topography
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Bioerosion
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Burrowing Parrot
dc.subject.classification
Geociencias multidisciplinaria
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Ciencias de la Tierra y relacionadas con el Medio Ambiente
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CIENCIAS NATURALES Y EXACTAS
dc.title
Structure-from-Motion Approach for Characterization of Bioerosion Patterns Using UAV Imagery
dc.type
info:eu-repo/semantics/article
dc.type
info:ar-repo/semantics/artículo
dc.type
info:eu-repo/semantics/publishedVersion
dc.date.updated
2017-02-03T17:52:49Z
dc.journal.number
15
dc.journal.pagination
3593-3609
dc.journal.pais
Suiza
dc.journal.ciudad
Basel
dc.description.fil
Fil: Genchi, Sibila Andrea. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Bahía Blanca. Instituto Argentino de Oceanografía (i); Argentina. Universidad Nacional del Sur. Departamento de Geografía; Argentina
dc.description.fil
Fil: Vitale, Alejandro José. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Bahía Blanca. Instituto Argentino de Oceanografía (i); Argentina. Universidad Nacional del Sur. Departamento de Ingenieria Electrica y de Computadoras; Argentina
dc.description.fil
Fil: Perillo, Gerardo Miguel E.. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Bahía Blanca. Instituto Argentino de Oceanografía (i); Argentina. Universidad Nacional del Sur. Departamento de Geología; Argentina
dc.description.fil
Fil: Delrieux, Claudio Augusto. Universidad Nacional del Sur. Departamento de Ingenieria Electrica y de Computadoras; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
dc.journal.title
Sensors
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/url/http://www.mdpi.com/1424-8220/15/2/3593
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/doi/http://dx.doi.org/10.3390/s150203593
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/url/https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4367375/
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