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Individualization of Pinus radiata Canopy from 3D UAV Dense Point Clouds Using Color Vegetation Indices

The location of trees and the individualization of their canopies are important parameters to estimate diameter, height, and biomass, among other variables. The very high spatial resolution of UAV imagery supports these processes. A dense 3D point cloud is generated from RGB UAV images, which is use...

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Autores principales: Cabrera-Ariza, Antonio M., Lara-Gómez, Miguel A., Santelices-Moya, Rómulo E., Meroño de Larriva, Jose-Emilio, Mesas-Carrascosa, Francisco-Javier
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8963004/
https://www.ncbi.nlm.nih.gov/pubmed/35214232
http://dx.doi.org/10.3390/s22041331
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author Cabrera-Ariza, Antonio M.
Lara-Gómez, Miguel A.
Santelices-Moya, Rómulo E.
Meroño de Larriva, Jose-Emilio
Mesas-Carrascosa, Francisco-Javier
author_facet Cabrera-Ariza, Antonio M.
Lara-Gómez, Miguel A.
Santelices-Moya, Rómulo E.
Meroño de Larriva, Jose-Emilio
Mesas-Carrascosa, Francisco-Javier
author_sort Cabrera-Ariza, Antonio M.
collection PubMed
description The location of trees and the individualization of their canopies are important parameters to estimate diameter, height, and biomass, among other variables. The very high spatial resolution of UAV imagery supports these processes. A dense 3D point cloud is generated from RGB UAV images, which is used to obtain a digital elevation model (DEM). From this DEM, a canopy height model (CHM) is derived for individual tree identification. Although the results are satisfactory, the quality of this detection is reduced if the working area has a high density of vegetation. The objective of this study was to evaluate the use of color vegetation indices (CVI) in canopy individualization processes of Pinus radiata. UAV flights were carried out, and a 3D dense point cloud and an orthomosaic were obtained. Then, a CVI was applied to 3D point cloud to differentiate between vegetation and nonvegetation classes to obtain a DEM and a CHM. Subsequently, an automatic crown identification procedure was applied to the CHM. The results were evaluated by contrasting them with results of manual individual tree identification on the UAV orthomosaic and those obtained by applying a progressive triangulated irregular network to the 3D point cloud. The results obtained indicate that the color information of 3D point clouds is an alternative to support individualizing trees under conditions of high-density vegetation.
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spelling pubmed-89630042022-03-30 Individualization of Pinus radiata Canopy from 3D UAV Dense Point Clouds Using Color Vegetation Indices Cabrera-Ariza, Antonio M. Lara-Gómez, Miguel A. Santelices-Moya, Rómulo E. Meroño de Larriva, Jose-Emilio Mesas-Carrascosa, Francisco-Javier Sensors (Basel) Article The location of trees and the individualization of their canopies are important parameters to estimate diameter, height, and biomass, among other variables. The very high spatial resolution of UAV imagery supports these processes. A dense 3D point cloud is generated from RGB UAV images, which is used to obtain a digital elevation model (DEM). From this DEM, a canopy height model (CHM) is derived for individual tree identification. Although the results are satisfactory, the quality of this detection is reduced if the working area has a high density of vegetation. The objective of this study was to evaluate the use of color vegetation indices (CVI) in canopy individualization processes of Pinus radiata. UAV flights were carried out, and a 3D dense point cloud and an orthomosaic were obtained. Then, a CVI was applied to 3D point cloud to differentiate between vegetation and nonvegetation classes to obtain a DEM and a CHM. Subsequently, an automatic crown identification procedure was applied to the CHM. The results were evaluated by contrasting them with results of manual individual tree identification on the UAV orthomosaic and those obtained by applying a progressive triangulated irregular network to the 3D point cloud. The results obtained indicate that the color information of 3D point clouds is an alternative to support individualizing trees under conditions of high-density vegetation. MDPI 2022-02-09 /pmc/articles/PMC8963004/ /pubmed/35214232 http://dx.doi.org/10.3390/s22041331 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/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 (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Cabrera-Ariza, Antonio M.
Lara-Gómez, Miguel A.
Santelices-Moya, Rómulo E.
Meroño de Larriva, Jose-Emilio
Mesas-Carrascosa, Francisco-Javier
Individualization of Pinus radiata Canopy from 3D UAV Dense Point Clouds Using Color Vegetation Indices
title Individualization of Pinus radiata Canopy from 3D UAV Dense Point Clouds Using Color Vegetation Indices
title_full Individualization of Pinus radiata Canopy from 3D UAV Dense Point Clouds Using Color Vegetation Indices
title_fullStr Individualization of Pinus radiata Canopy from 3D UAV Dense Point Clouds Using Color Vegetation Indices
title_full_unstemmed Individualization of Pinus radiata Canopy from 3D UAV Dense Point Clouds Using Color Vegetation Indices
title_short Individualization of Pinus radiata Canopy from 3D UAV Dense Point Clouds Using Color Vegetation Indices
title_sort individualization of pinus radiata canopy from 3d uav dense point clouds using color vegetation indices
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8963004/
https://www.ncbi.nlm.nih.gov/pubmed/35214232
http://dx.doi.org/10.3390/s22041331
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