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dc.contributor.authorTagle Casapia, Ximena
dc.contributor.authorFalen, Lourdes
dc.contributor.authorBartholomeus, Harm
dc.contributor.authorCardenas, Rodolfo
dc.contributor.authorFlores, Gerardo
dc.contributor.authorHerold, Martin
dc.contributor.authorHonorio Coronado, Euridice N.
dc.contributor.authorBaker, Timothy R.
dc.identifier.citationTagle Casapia , X , Falen , L , Bartholomeus , H , Cardenas , R , Flores , G , Herold , M , Honorio Coronado , E N & Baker , T R 2020 , ' Identifying and quantifying the abundance of economically important palms in tropical moist forest using UAV imagery ' , Remote Sensing , vol. 12 , no. 1 , 9 .
dc.identifier.otherPURE: 276941020
dc.identifier.otherPURE UUID: 4a0ae115-7880-4c14-bc6b-6afb45458c05
dc.identifier.otherWOS: 000515391700009
dc.identifier.otherScopus: 85079676519
dc.identifier.otherORCID: /0000-0003-2314-590X/work/104252817
dc.description.abstractSustainable management of non-timber forest products such as palm fruits is crucial for the long-term conservation of intact forest. A major limitation to expanding sustainable management of palms has been the need for precise information about the resources at scales of tens to hundreds of hectares, while typical ground-based surveys only sample small areas. In recent years, small unmanned aerial vehicles (UAVs) have become an important tool for mapping forest areas as they are cheap and easy to transport, and they provide high spatial resolution imagery of remote areas. We developed an object-based classification workflow for RGB UAV imagery which aims to identify and delineate palm tree crowns in the tropical rainforest by combining image processing and GIS functionalities using color and textural information in an integrative way to show one of the potential uses of UAVs in tropical forests. Ten permanent forest plots with 1170 reference palm trees were assessed from October to December 2017. The results indicate that palm tree crowns could be clearly identified and, in some cases, quantified following the workflow. The best results were obtained using the random forest classifier with an 85% overall accuracy and 0.82 kappa index.
dc.relation.ispartofRemote Sensingen
dc.rightsCopyright © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (
dc.subjectObject-based image analysisen
dc.subjectUnmanned aerial vehicles imageryen
dc.subjectCrown delineationen
dc.subjectTextural parametersen
dc.subjectPalm tree identificationen
dc.subjectG Geography (General)en
dc.titleIdentifying and quantifying the abundance of economically important palms in tropical moist forest using UAV imageryen
dc.typeJournal articleen
dc.description.versionPublisher PDFen
dc.contributor.institutionUniversity of St Andrews. School of Geography & Sustainable Developmenten
dc.description.statusPeer revieweden

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