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Authordc.contributor.authorMaack, Joachim 
Authordc.contributor.authorKattenborn, Teja 
Authordc.contributor.authorEwald Fassnacht, Fabian 
Authordc.contributor.authorEnssle, Fabian 
Authordc.contributor.authorHernández Palma, Héctor 
Authordc.contributor.authorCorvalán Vera, Patricio 
Authordc.contributor.authorKoch, Barbara 
Admission datedc.date.accessioned2015-09-14T16:02:44Z
Available datedc.date.available2015-09-14T16:02:44Z
Publication datedc.date.issued2015
Cita de ítemdc.identifier.citationEuropean Journal of Remote Sensing - 2015, 48: 245-261en_US
Identifierdc.identifier.otherDOI: 10.5721/EuJRS20154814
Identifierdc.identifier.urihttps://repositorio.uchile.cl/handle/2250/133623
General notedc.descriptionArtículo de publicación ISIen_US
Abstractdc.description.abstractWe used spectral, textural and photogrammetric information from very-high resolution (VHR) stereo satellite data (Pleiades and WorldView-2) to estimate forest biomass across two test sites located in Chile and Germany. We compared Random Forest model performances of different predictor sets (spectral, textural, and photogrammetric), forest inventory designs and filter sizes (texture information). Best model performances were obtained with photogrammetric combined with either textural or spectral information and smaller, but more field plots. Stereo-VHR images showed a great potential for canopy height model (CHM) generation and could be an adequate alternative to LiDAR and InSAR techniques.en_US
Patrocinadordc.description.sponsorshipBIOCOMSA project (SA) 208-7321en_US
Lenguagedc.language.isoenen_US
Publisherdc.publisherItalian Society of Remote Sensingen_US
Type of licensedc.rightsAtribución-NoComercial-SinDerivadas 3.0 Chile*
Link to Licensedc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/cl/*
Keywordsdc.subjectBiomass modellingen_US
Keywordsdc.subjectWordView-2en_US
Keywordsdc.subjectPléiadesen_US
Keywordsdc.subjectrandom foresten_US
Keywordsdc.subjectphotogrammetryen_US
Keywordsdc.subjectcanopy height modelsen_US
Títulodc.titleModeling forest biomass using Very-High-Resolution data - Combining textural, spectral and photogrammetric predictors derived from spaceborne stereo imagesen_US
Document typedc.typeArtículo de revistaen_US


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Atribución-NoComercial-SinDerivadas 3.0 Chile
Except where otherwise noted, this item's license is described as Atribución-NoComercial-SinDerivadas 3.0 Chile