Canopy gaps and associated losses of biomass – combining UAV imagery and field data in a central Amazon forest

Simonetti, Adriana; Araujo, Raquel Fernandes; Celes, Carlos Henrique Souza; da Silva e Silva, Flávia Ranara; dos Santos, Joaquim; Higuchi, Niro; Trumbore, Susan; Magnabosco Marra, Daniel

doi:https://doi.org/10.5194/bg-20-3651-2023

Articles | Volume 20, issue 17

https://doi.org/10.5194/bg-20-3651-2023

© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/bg-20-3651-2023

© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 20, issue 17

Research article

|

13 Sep 2023

Research article |

| 13 Sep 2023

Canopy gaps and associated losses of biomass – combining UAV imagery and field data in a central Amazon forest

Adriana Simonetti, Raquel Fernandes Araujo, Carlos Henrique Souza Celes, Flávia Ranara da Silva e Silva, Joaquim dos Santos, Niro Higuchi, Susan Trumbore, and Daniel Magnabosco Marra

Supplement

https://doi.org/10.5194/bg-20-3651-2023-supplement

Data sets

L1A - Discrete airborne LiDAR transects collected by EBA in the Brazilian Amazon (Mato Grosso, Amazonas e Pará) J. Ometto, E. B. Gorgens, F. R. d. S. Pereira, L. Sato, M. L. R. Assis, R. Cantinho, M. Longo, A. D. Jacon, and M. Keller https://doi.org/10.5281/zenodo.7636454

Model code and software

Canopy gaps and associated losses of biomass - combining UAV imagery and field data in a Central Amazon forest A. Simonetti, R. Fernandes Araujo, C. Henrique Souza Celes, F. Ranara da Silva e Silva, J. dos Santos, N. Higuchi, S. Trumbore, and D. Magnabosco Marra https://doi.org/10.5281/zenodo.8298693

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Short summary

We combined 2 years of monthly drone-acquired RGB (red–green–blue) imagery with field surveys in a central Amazon forest. Our results indicate that small gaps associated with branch fall were the most frequent. Biomass losses were partially controlled by gap area, with branch fall and snapping contributing the least and greatest relative values, respectively. Our study highlights the potential of drone images for monitoring canopy dynamics in dense tropical forests.