Mapping Key Indicators of Forest Restoration in the Amazon Using a Low-Cost Drone and Artificial Intelligence

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  • Rafael Walter Albuquerque
  • Daniel Luis Mascia Vieira
  • Manuel Eduardo Ferreira
  • Lucas Pedrosa Soares
  • Olsen, Søren Ingvor
  • Luciana Spinelli Araujo
  • Luiz Eduardo Vicente
  • Julio Ricardo Caetano Tymus
  • Cintia Palheta Balieiro
  • Marcelo Hiromiti Matsumoto
  • Carlos Henrique Grohmann

Monitoring 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.

TidsskriftRemote Sensing
Udgave nummer4
Sider (fra-til)1-28
StatusUdgivet - 2022

Bibliografisk note

Funding Information:
Funding: This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior – Brasil (CAPES) – Finance Code 001. This study was also financed in part by Embrapa Genetic Resources and Biotechnology and in part by Embrapa Meio Ambiente. M.E.F. (grant #315699/2020-5) and C.H.G. (grants #423481/2018-5 and #304413/2018-6) are CNPq Research Fellows.

Publisher Copyright:
© 2022 by the authors. Licensee MDPI, Basel, Switzerland.

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