Mapping key indicators of forest restoration in the amazon using a low-cost drone and artificial intelligence

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dc.creatorAlbuquerque, Rafael Walter de
dc.creatorVieira, Daniel Luis Mascia
dc.creatorFerreira, Manuel Eduardo
dc.creatorSoares, Lucas Pedrosa
dc.creatorOlsen, Søren Ingvor
dc.creatorAraujo, Luciana Spinelli
dc.creatorVicente, Luiz Eduardo
dc.creatorTymus, Julio Ricardo Caetano
dc.creatorBalieiro, Cintia Pedrina Palheta
dc.creatorMatsumoto, Marcelo Hiromiti
dc.creatorCarvalho, Carlos Henrique Grohmann de
dc.date.accessioned2024-09-04T18:14:47Z
dc.date.available2024-09-04T18:14:47Z
dc.date.issued2022
dc.description.abstractMonitoring the vegetation structure and species composition of forest restoration (FR) in the Brazilian Amazon is critical to ensuring its long-term benefits. Since remotely piloted aircrafts (RPAs) associated with deep learning (DL) are becoming powerful tools for vegetation monitoring, this study aims to use DL to automatically map individual crowns of Vismia (low resilience recovery indicator), Cecropia (fast recovery indicator), and trees in general (this study refers to individual crowns of all trees regardless of species as All Trees). Since All Trees can be accurately mapped, this study also aims to propose a tree crown heterogeneity index (TCHI), which estimates species diversity based on: the heterogeneity attributes/parameters of the RPA image inside the All Trees results; and the Shannon index measured by traditional fieldwork. Regarding the DL methods, this work evaluated the accuracy of the detection of individual objects, the quality of the delineation outlines and the area distribution. Except for Vismia delineation (IoU = 0.2), DL results presented accurate values in general, as F1 and IoU were always greater than 0.7 and 0.55, respectively, while Cecropia presented the most accurate results: F1 = 0.85 and IoU = 0.77. Since All Trees results were accurate, the TCHI was obtained through regression analysis between the canopy height model (CHM) heterogeneity attributes and the field plot data. Although TCHI presented robust parameters, such as p-value < 0.05, its results are considered preliminary because more data are needed to include different FR situations. Thus, the results of this work show that low-cost RPA has great potential for monitoring FR quality in the Amazon, because Vismia, Cecropia, and All Trees can be automatically mapped. Moreover, the TCHI preliminary results showed high potential in estimating species diversity. Future studies must assess domain adaptation methods for the DL results and different FR situations to improve the TCHI range of action.
dc.identifier.citationALBUQUERQUE, Rafael Walter et al. Mapping key indicators of forest restoration in the amazon using a low-cost drone and artificial intelligence. Remote Sens, Basileia, v. 14, n. 4, e830, 2022. DOI: 10.3390/rs14040830. Disponível em: https://www.mdpi.com/2072-4292/14/4/830. Acesso em: 2 ago. 2024.
dc.identifier.doi10.3390/rs14040830
dc.identifier.issne- 2072-4292
dc.identifier.urihttp://repositorio.bc.ufg.br//handle/ri/25428
dc.language.isoeng
dc.publisher.countrySuica
dc.publisher.departmentInstituto de Estudos Socioambientais - IESA (RMG)
dc.rightsAcesso Aberto
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectCecropia
dc.subjectDeep learning
dc.subjectDrones
dc.subjectPhotogrammetry
dc.subjectRemotely piloted aircraft
dc.subjectRGB
dc.subjectSpecies diversity
dc.subjectTree crown heterogeneity index
dc.subjectTree species
dc.subjectVismia
dc.titleMapping key indicators of forest restoration in the amazon using a low-cost drone and artificial intelligence
dc.typeArtigo

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