Mauritia flexuosa palm trees airborne mapping with deep convolutional neural network

Abstract Accurately mapping individual tree species in densely forested environments is crucial to forest inventory. When considering only RGB images, this is a challenging task for many automatic photogrammetry processes. The main reason for that is the spectral similarity between species in RGB sc...

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Autores principales:	Luciene Sales Dagher Arce, Lucas Prado Osco, Mauro dos Santos de Arruda, Danielle Elis Garcia Furuya, Ana Paula Marques Ramos, Camila Aoki, Arnildo Pott, Sarah Fatholahi, Jonathan Li, Fábio Fernando de Araújo, Wesley Nunes Gonçalves, José Marcato Junior
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/8dec4f8d90b64f1498485e3188e1019e
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id	oai:doaj.org-article:8dec4f8d90b64f1498485e3188e1019e
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spelling	oai:doaj.org-article:8dec4f8d90b64f1498485e3188e1019e2021-12-02T19:16:18ZMauritia flexuosa palm trees airborne mapping with deep convolutional neural network10.1038/s41598-021-98522-72045-2322https://doaj.org/article/8dec4f8d90b64f1498485e3188e1019e2021-10-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-98522-7https://doaj.org/toc/2045-2322Abstract Accurately mapping individual tree species in densely forested environments is crucial to forest inventory. When considering only RGB images, this is a challenging task for many automatic photogrammetry processes. The main reason for that is the spectral similarity between species in RGB scenes, which can be a hindrance for most automatic methods. This paper presents a deep learning-based approach to detect an important multi-use species of palm trees (Mauritia flexuosa; i.e., Buriti) on aerial RGB imagery. In South-America, this palm tree is essential for many indigenous and local communities because of its characteristics. The species is also a valuable indicator of water resources, which comes as a benefit for mapping its location. The method is based on a Convolutional Neural Network (CNN) to identify and geolocate singular tree species in a high-complexity forest environment. The results returned a mean absolute error (MAE) of 0.75 trees and an F1-measure of 86.9%. These results are better than Faster R-CNN and RetinaNet methods considering equal experiment conditions. In conclusion, the method presented is efficient to deal with a high-density forest scenario and can accurately map the location of single species like the M. flexuosa palm tree and may be useful for future frameworks.Luciene Sales Dagher ArceLucas Prado OscoMauro dos Santos de ArrudaDanielle Elis Garcia FuruyaAna Paula Marques RamosCamila AokiArnildo PottSarah FatholahiJonathan LiFábio Fernando de AraújoWesley Nunes GonçalvesJosé Marcato JuniorNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-13 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Luciene Sales Dagher Arce Lucas Prado Osco Mauro dos Santos de Arruda Danielle Elis Garcia Furuya Ana Paula Marques Ramos Camila Aoki Arnildo Pott Sarah Fatholahi Jonathan Li Fábio Fernando de Araújo Wesley Nunes Gonçalves José Marcato Junior Mauritia flexuosa palm trees airborne mapping with deep convolutional neural network
description	Abstract Accurately mapping individual tree species in densely forested environments is crucial to forest inventory. When considering only RGB images, this is a challenging task for many automatic photogrammetry processes. The main reason for that is the spectral similarity between species in RGB scenes, which can be a hindrance for most automatic methods. This paper presents a deep learning-based approach to detect an important multi-use species of palm trees (Mauritia flexuosa; i.e., Buriti) on aerial RGB imagery. In South-America, this palm tree is essential for many indigenous and local communities because of its characteristics. The species is also a valuable indicator of water resources, which comes as a benefit for mapping its location. The method is based on a Convolutional Neural Network (CNN) to identify and geolocate singular tree species in a high-complexity forest environment. The results returned a mean absolute error (MAE) of 0.75 trees and an F1-measure of 86.9%. These results are better than Faster R-CNN and RetinaNet methods considering equal experiment conditions. In conclusion, the method presented is efficient to deal with a high-density forest scenario and can accurately map the location of single species like the M. flexuosa palm tree and may be useful for future frameworks.
format	article
author	Luciene Sales Dagher Arce Lucas Prado Osco Mauro dos Santos de Arruda Danielle Elis Garcia Furuya Ana Paula Marques Ramos Camila Aoki Arnildo Pott Sarah Fatholahi Jonathan Li Fábio Fernando de Araújo Wesley Nunes Gonçalves José Marcato Junior
author_facet	Luciene Sales Dagher Arce Lucas Prado Osco Mauro dos Santos de Arruda Danielle Elis Garcia Furuya Ana Paula Marques Ramos Camila Aoki Arnildo Pott Sarah Fatholahi Jonathan Li Fábio Fernando de Araújo Wesley Nunes Gonçalves José Marcato Junior
author_sort	Luciene Sales Dagher Arce
title	Mauritia flexuosa palm trees airborne mapping with deep convolutional neural network
title_short	Mauritia flexuosa palm trees airborne mapping with deep convolutional neural network
title_full	Mauritia flexuosa palm trees airborne mapping with deep convolutional neural network
title_fullStr	Mauritia flexuosa palm trees airborne mapping with deep convolutional neural network
title_full_unstemmed	Mauritia flexuosa palm trees airborne mapping with deep convolutional neural network
title_sort	mauritia flexuosa palm trees airborne mapping with deep convolutional neural network
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/8dec4f8d90b64f1498485e3188e1019e
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Mauritia flexuosa palm trees airborne mapping with deep convolutional neural network

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