Braga, Jose R. G.
Ferreira, Matheus P.
Aragao, Luiz E. O. C.
de Campos Velho, Haroldo E.
Shiguemori, Elcio H.
Wagner, Fabien H.
Número total de Autores: 9
Afiliação do(s) autor(es):
 Natl Inst Space Res INPE, Remote Sensing Div, Av Astronautas 1758, BR-12227010 Sao Jose Dos Campos - Brazil
 Mil Inst Engn IME, Cartog Engn Sect, Praca Gen Tiburcio 80, BR-22290270 Rio De Janeiro - Brazil
 Inria Sophia Antipolis, F-06902 Valbonne - France
 Luxcarta Technol, Parc Activite Argile, Lot 119b, F-06370 Mouans Sartoux - France
 Univ Exeter, Coll Life & Environm Sci, Exeter EX4 4RJ, Devon - England
 Natl Inst Space Res INPE, Associated Lab Comp & Appl Math, Av Astronautas 1758, BR-12227010 Sao Jose Dos Campos - Brazil
 Inst Adv Studies IEAv, Dept Aerosp Sci & Technol, Trevo Coronel Aviador Jose Alberto Albano Amarant, BR-12228001 Sao Jose Dos Campos - Brazil
 Fdn Sci Technol & Space Applicat FUNCATE, GeoProc Div, BR-12210131 Sao Jose Dos Campos - Brazil
Número total de Afiliações: 8
Tipo de documento:
APR 2 2020.
Citações Web of Science:
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 Recall, Precision, and F1 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. (AU)