Mauritia flexuosa palm trees airborne mapping with deep convolutional neural network

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, whi...

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Bibliographic Details
Published in:Scientific reports Vol. 11; no. 1; pp. 19619 - 13
Main Authors: Arce, Luciene Sales Dagher, Osco, Lucas Prado, Arruda, Mauro dos Santos de, Furuya, Danielle Elis Garcia, Ramos, Ana Paula Marques, Aoki, Camila, Pott, Arnildo, Fatholahi, Sarah, Li, Jonathan, Araújo, Fábio Fernando de, Gonçalves, Wesley Nunes, Marcato Junior, José
Format: Journal Article
Language:English
Published: London Nature Publishing Group UK 04.10.2021
Nature Publishing Group
Nature Portfolio
Subjects:
631/114
631/158
Deep learning
Forests
Humanities and Social Sciences
Local communities
Mapping
Mauritia flexuosa
multidisciplinary
Neural networks
Photogrammetry
Plant species
Science
Science (multidisciplinary)
Species
Trees
Water resources
ISSN:2045-2322, 2045-2322
Online Access:Get full text
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Summary: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.
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ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-021-98522-7