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Open AccessArticle

Identifying and Quantifying the Abundance of Economically Important Palms in Tropical Moist Forest Using UAV Imagery

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Dirección de Investigación en Manejo Integral del Bosque y Servicios Eco sistémicos - PROBOSQUES, Instituto de Investigaciones de la Amazonía Peruana (IIAP), Av. A. Quiñones Km. 2.5. Iquitos 16007, Peru
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Laboratory of Geo-Information Science and Remote Sensing, Wageningen University & Research, Droevendaalsesteeg 3, 06708 PB Wageningen, The Netherlands
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Dirección de Gestión del Conocimiento e Investigación en Información de Diversidad Amazónica - GESCON, Instituto de Investigaciones de la Amazonía Peruana (IIAP), Av. A Quiñones Km. 2.5. Iquitos 16007, Peru
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School of Geography, University of Leeds, Leeds LS2 9JT, UK
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Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(1), 9; https://doi.org/10.3390/rs12010009
Received: 20 November 2019 / Revised: 11 December 2019 / Accepted: 16 December 2019 / Published: 18 December 2019
(This article belongs to the Section Forest Remote Sensing)
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. View Full-Text
Keywords: object-based image analysis; unmanned aerial vehicles imagery; crown delineation; textural parameters; palm tree identification object-based image analysis; unmanned aerial vehicles imagery; crown delineation; textural parameters; palm tree identification
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MDPI and ACS Style

Tagle Casapia, X.; Falen, L.; Bartholomeus, H.; Cárdenas, R.; Flores, G.; Herold, M.; Honorio Coronado, E.N.; Baker, T.R. Identifying and Quantifying the Abundance of Economically Important Palms in Tropical Moist Forest Using UAV Imagery. Remote Sens. 2020, 12, 9.

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