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Article

Tree Crown Delineation Algorithm Based on a Convolutional Neural Network

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Remote Sensing Division, National Institute for Space Research—INPE, Av. dos Astronautas 1758, São José dos Campos 12227-010, Brazil
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Cartographic Engineering Section, Military Institute of Engineering—IME, Praça Gen.Tibúrcio 80, Rio de Janeiro 22290-270, Brazil
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Inria Sophia Antipolis, Cedex Sophia Antipolis, 06902 Valbonne, France
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Luxcarta Technology, Parc d’Activité l’Argile, Lot 119b, 06370 Mouans Sartoux, France
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College of Life and Environmental Sciences, University of Exeter, Exeter EX4 4RJ, UK
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Associated Laboratory for Computing and Applied Mathematics, National Institute for Space Research—INPE, Av. dos Astronautas 1758, São José dos Campos 12227-010, Brazil
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Department of Aerospace Science and Technology, Institute for Advanced Studies—IEAv, Trevo Coronel Aviador José Alberto Albano do Amarante 01, São José dos Campos 12228-001, Brazil
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GeoProcessing Division, Foundation for Science, Technology and Space Applications—FUNCATE, São José dos Campos 12210-131, Brazil
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Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(8), 1288; https://doi.org/10.3390/rs12081288
Received: 19 February 2020 / Revised: 8 April 2020 / Accepted: 14 April 2020 / Published: 18 April 2020
(This article belongs to the Special Issue Machine Learning Methods for Environmental Monitoring)
Tropical forests concentrate the largest diversity of species on the planet and play a key role in maintaining environmental processes. Due to the importance of those forests, there is growing interest in mapping their components and getting information at an individual tree level to conduct reliable satellite-based forest inventory for biomass and species distribution qualification. Individual tree crown information could be manually gathered from high resolution satellite images; however, to achieve this task at large-scale, an algorithm to identify and delineate each tree crown individually, with high accuracy, is a prerequisite. In this study, we propose the application of a convolutional neural network—Mask R-CNN algorithm—to perform the tree crown detection and delineation. The algorithm uses very high-resolution satellite images from tropical forests. The results obtained are promising—the R e c a l l , P r e c i s i o n , and F 1 score values obtained were were 0.81 , 0.91 , and 0.86 , respectively. In the study site, the total of tree crowns delineated was 59,062 . These results suggest that this algorithm can be used to assist the planning and conduction of forest inventories. As the algorithm is based on a Deep Learning approach, it can be systematically trained and used for other regions. View Full-Text
Keywords: tree crown delineation; tropical forests; optical satellite images; deep learning tree crown delineation; tropical forests; optical satellite images; deep learning
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MDPI and ACS Style

G. Braga, J.R.; Peripato, V.; Dalagnol, R.; P. Ferreira, M.; Tarabalka, Y.; O. C. Aragão, L.E.; F. de Campos Velho, H.; Shiguemori, E.H.; Wagner, F.H. Tree Crown Delineation Algorithm Based on a Convolutional Neural Network. Remote Sens. 2020, 12, 1288. https://doi.org/10.3390/rs12081288

AMA Style

G. Braga JR, Peripato V, Dalagnol R, P. Ferreira M, Tarabalka Y, O. C. Aragão LE, F. de Campos Velho H, Shiguemori EH, Wagner FH. Tree Crown Delineation Algorithm Based on a Convolutional Neural Network. Remote Sensing. 2020; 12(8):1288. https://doi.org/10.3390/rs12081288

Chicago/Turabian Style

G. Braga, José R., Vinícius Peripato, Ricardo Dalagnol, Matheus P. Ferreira, Yuliya Tarabalka, Luiz E. O. C. Aragão, Haroldo F. de Campos Velho, Elcio H. Shiguemori, and Fabien H. Wagner 2020. "Tree Crown Delineation Algorithm Based on a Convolutional Neural Network" Remote Sensing 12, no. 8: 1288. https://doi.org/10.3390/rs12081288

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