Tree Crown Delineation Algorithm Based on a Convolutional Neural Network

Autor(es)

Braga, Jose R. G. ^[1] ; Peripato, Vinicius ^[1] ; Dalagnol, Ricardo ^[1] ; Ferreira, Matheus P. ^[2] ; Tarabalka, Yuliya ^{[3, 4]} ; Aragao, Luiz E. O. C. ^{[5, 1]} ; de Campos Velho, Haroldo E. ^[6] ; Shiguemori, Elcio H. ^[7] ; Wagner, Fabien H. ^{[1, 8]}

Afiliação do(s) autor(es)

^[1] Natl Inst Space Res INPE, Remote Sensing Div, Av Astronautas 1758, BR-12227010 Sao Jose Dos Campos - Brazil

^[2] Mil Inst Engn IME, Cartog Engn Sect, Praca Gen Tiburcio 80, BR-22290270 Rio De Janeiro - Brazil

^[3] Inria Sophia Antipolis, F-06902 Valbonne - France

^[4] Luxcarta Technol, Parc Activite Argile, Lot 119b, F-06370 Mouans Sartoux - France

^[5] Univ Exeter, Coll Life & Environm Sci, Exeter EX4 4RJ, Devon - England

^[6] Natl Inst Space Res INPE, Associated Lab Comp & Appl Math, Av Astronautas 1758, BR-12227010 Sao Jose Dos Campos - Brazil

^[7] Inst Adv Studies IEAv, Dept Aerosp Sci & Technol, Trevo Coronel Aviador Jose Alberto Albano Amarant, BR-12228001 Sao Jose Dos Campos - Brazil

^[8] Fdn Sci Technol & Space Applicat FUNCATE, GeoProc Div, BR-12210131 Sao Jose Dos Campos - Brazil

• Tipo de documento: Artigo Científico
• Fonte: REMOTE SENSING; v. 12, n. 8 APR 2 2020.
• Citações Web of Science: 1

Resumo

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)

• Processo FAPESP: 15/50484-0 - Diversidade funcional dos biomas Amazônia, Mata Atlântica e Cerrado nos ambientes intactos e em regeneração por meio de imagens hiperspectrais
• Beneficiário: Fabien Hubert Wagner
• Linha de fomento: Auxílio à Pesquisa - Apoio a Jovens Pesquisadores
• Processo FAPESP: 18/15001-6 - ARBOLES: um entendimento da biodiversidade e resiliência das florestas LATAM baseado em características funcionais
• Beneficiário: Luiz Eduardo Oliveira e Cruz de Aragão
• Linha de fomento: Auxílio à Pesquisa - Regular
• Processo FAPESP: 15/22987-7 - Avaliação do impacto de mudanças climáticas sobre a dinâmica de biomassa e carbono na Amazônia
• Beneficiário: Ricardo Dal'Agnol da Silva
• Linha de fomento: Bolsas no Brasil - Doutorado
• Processo FAPESP: 18/06072-7 - Registro automático de imagens de alta resolução de florestas tropicais, capturada por VANT, com imagens de satélite
• Beneficiário: Jose Renato Garcia Braga
• Linha de fomento: Bolsas no Brasil - Programa Capacitação - Treinamento Técnico
• Processo FAPESP: 16/17652-9 - Diversidade funcional dos biomas Amazônia, Mata Atlântica e Cerrado nos ambientes intactos e em regeneração por meio de imagens hiperspectrais
• Beneficiário: Fabien Hubert Wagner
• Linha de fomento: Bolsas no Brasil - Apoio a Jovens Pesquisadores