dc.contributor | Universidade Estadual Paulista (Unesp) | |
dc.date.accessioned | 2014-05-27T11:27:17Z | |
dc.date.available | 2014-05-27T11:27:17Z | |
dc.date.created | 2014-05-27T11:27:17Z | |
dc.date.issued | 2012-12-01 | |
dc.identifier | International Geoscience and Remote Sensing Symposium (IGARSS), p. 808-811. | |
dc.identifier | 2153-6996 | |
dc.identifier | http://hdl.handle.net/11449/73817 | |
dc.identifier | 10.1109/IGARSS.2012.6351439 | |
dc.identifier | WOS:000313189401008 | |
dc.identifier | 2-s2.0-84873163079 | |
dc.identifier | 2985771102505330 | |
dc.identifier | 0000-0003-0516-0567 | |
dc.description.abstract | Traditional methods of submerged aquatic vegetation (SAV) survey last long and then, they are high cost. Optical remote sensing is an alternative, but it has some limitations in the aquatic environment. The use of echosounder techniques is efficient to detect submerged targets. Therefore, the aim of this study is to evaluate different kinds of interpolation approach applied on SAV sample data collected by echosounder. This study case was performed in a region of Uberaba River - Brazil. The interpolation methods evaluated in this work follow: Nearest Neighbor, Weighted Average, Triangular Irregular Network (TIN) and ordinary kriging. Better results were carried out with kriging interpolation. Thus, it is recommend the use of geostatistics for spatial inference of SAV from sample data surveyed with echosounder techniques. © 2012 IEEE. | |
dc.language | eng | |
dc.relation | International Geoscience and Remote Sensing Symposium (IGARSS) | |
dc.rights | Acesso aberto | |
dc.source | Scopus | |
dc.subject | Geographic Information Systems | |
dc.subject | Interpolation | |
dc.subject | Rivers | |
dc.subject | Submerged aquatic vegetation | |
dc.subject | Underwater acoustics | |
dc.subject | Aquatic environments | |
dc.subject | Data sample | |
dc.subject | Echo sounders | |
dc.subject | Geo-statistics | |
dc.subject | Height estimation | |
dc.subject | High costs | |
dc.subject | Interpolation method | |
dc.subject | Kriging interpolation | |
dc.subject | Nearest neighbors | |
dc.subject | Optical remote sensing | |
dc.subject | Ordinary kriging | |
dc.subject | Sample data | |
dc.subject | Study case | |
dc.subject | Submerged aquatic vegetations | |
dc.subject | Submerged macrophytes | |
dc.subject | Submerged targets | |
dc.subject | Triangular Irregular Networks | |
dc.subject | Weighted averages | |
dc.subject | Geographic information systems | |
dc.subject | Geology | |
dc.subject | Remote sensing | |
dc.subject | Surveys | |
dc.subject | Vegetation | |
dc.title | Submerged macrophytes height estimation by echosounder data sample | |
dc.type | Actas de congresos | |