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Identifying and quantifying the abundance of economically important palms in tropical moist forest using UAV imagery

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TagleCasapia_2019_RS_Palms_CC.pdf (7.922Mb)
Date
01/2020
Author
Tagle Casapia, Ximena
Falen, Lourdes
Bartholomeus, Harm
Cardenas, Rodolfo
Flores, Gerardo
Herold, Martin
Honorio Coronado, Euridice N.
Baker, Timothy R.
Keywords
Object-based image analysis
Unmanned aerial vehicles imagery
Crown delineation
Textural parameters
Palm tree identification
G Geography (General)
3rd-DAS
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Abstract
Sustainable 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.
Citation
Tagle 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 . https://doi.org/10.3390/rs12010009
Publication
Remote Sensing
Status
Peer reviewed
DOI
https://doi.org/10.3390/rs12010009
ISSN
2072-4292
Type
Journal article
Rights
Copyright © 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 (http://creativecommons.org/licenses/by/4.0/).
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  • University of St Andrews Research
URI
http://hdl.handle.net/10023/24744

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