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Integrating high resolution drone imagery and forest inventory to distinguish canopy and understory trees and quantify their contributions to forest structure and dynamics

Tree growth and survival differ strongly between canopy trees (those directly exposed to overhead light), and understory trees. However, the structural complexity of many tropical forests makes it difficult to determine canopy positions. The integration of remote sensing and ground-based data enable...

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Autores principales: Araujo, Raquel Fernandes, Chambers, Jeffrey Q., Celes, Carlos Henrique Souza, Muller-Landau, Helene C., dos Santos, Ana Paula Ferreira, Emmert, Fabiano, Ribeiro, Gabriel H. P. M., Gimenez, Bruno Oliva, Lima, Adriano J. N., Campos, Moacir A. A., Higuchi, Niro
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7728260/
https://www.ncbi.nlm.nih.gov/pubmed/33301487
http://dx.doi.org/10.1371/journal.pone.0243079
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author Araujo, Raquel Fernandes
Chambers, Jeffrey Q.
Celes, Carlos Henrique Souza
Muller-Landau, Helene C.
dos Santos, Ana Paula Ferreira
Emmert, Fabiano
Ribeiro, Gabriel H. P. M.
Gimenez, Bruno Oliva
Lima, Adriano J. N.
Campos, Moacir A. A.
Higuchi, Niro
author_facet Araujo, Raquel Fernandes
Chambers, Jeffrey Q.
Celes, Carlos Henrique Souza
Muller-Landau, Helene C.
dos Santos, Ana Paula Ferreira
Emmert, Fabiano
Ribeiro, Gabriel H. P. M.
Gimenez, Bruno Oliva
Lima, Adriano J. N.
Campos, Moacir A. A.
Higuchi, Niro
author_sort Araujo, Raquel Fernandes
collection PubMed
description Tree growth and survival differ strongly between canopy trees (those directly exposed to overhead light), and understory trees. However, the structural complexity of many tropical forests makes it difficult to determine canopy positions. The integration of remote sensing and ground-based data enables this determination and measurements of how canopy and understory trees differ in structure and dynamics. Here we analyzed 2 cm resolution RGB imagery collected by a Remotely Piloted Aircraft System (RPAS), also known as drone, together with two decades of bi-annual tree censuses for 2 ha of old growth forest in the Central Amazon. We delineated all crowns visible in the imagery and linked each crown to a tagged stem through field work. Canopy trees constituted 40% of the 1244 inventoried trees with diameter at breast height (DBH) > 10 cm, and accounted for ~70% of aboveground carbon stocks and wood productivity. The probability of being in the canopy increased logistically with tree diameter, passing through 50% at 23.5 cm DBH. Diameter growth was on average twice as large in canopy trees as in understory trees. Growth rates were unrelated to diameter in canopy trees and positively related to diameter in understory trees, consistent with the idea that light availability increases with diameter in the understory but not the canopy. The whole stand size distribution was best fit by a Weibull distribution, whereas the separate size distributions of understory trees or canopy trees > 25 cm DBH were equally well fit by exponential and Weibull distributions, consistent with mechanistic forest models. The identification and field mapping of crowns seen in a high resolution orthomosaic revealed new patterns in the structure and dynamics of trees of canopy vs. understory at this site, demonstrating the value of traditional tree censuses with drone remote sensing.
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spelling pubmed-77282602020-12-17 Integrating high resolution drone imagery and forest inventory to distinguish canopy and understory trees and quantify their contributions to forest structure and dynamics Araujo, Raquel Fernandes Chambers, Jeffrey Q. Celes, Carlos Henrique Souza Muller-Landau, Helene C. dos Santos, Ana Paula Ferreira Emmert, Fabiano Ribeiro, Gabriel H. P. M. Gimenez, Bruno Oliva Lima, Adriano J. N. Campos, Moacir A. A. Higuchi, Niro PLoS One Research Article Tree growth and survival differ strongly between canopy trees (those directly exposed to overhead light), and understory trees. However, the structural complexity of many tropical forests makes it difficult to determine canopy positions. The integration of remote sensing and ground-based data enables this determination and measurements of how canopy and understory trees differ in structure and dynamics. Here we analyzed 2 cm resolution RGB imagery collected by a Remotely Piloted Aircraft System (RPAS), also known as drone, together with two decades of bi-annual tree censuses for 2 ha of old growth forest in the Central Amazon. We delineated all crowns visible in the imagery and linked each crown to a tagged stem through field work. Canopy trees constituted 40% of the 1244 inventoried trees with diameter at breast height (DBH) > 10 cm, and accounted for ~70% of aboveground carbon stocks and wood productivity. The probability of being in the canopy increased logistically with tree diameter, passing through 50% at 23.5 cm DBH. Diameter growth was on average twice as large in canopy trees as in understory trees. Growth rates were unrelated to diameter in canopy trees and positively related to diameter in understory trees, consistent with the idea that light availability increases with diameter in the understory but not the canopy. The whole stand size distribution was best fit by a Weibull distribution, whereas the separate size distributions of understory trees or canopy trees > 25 cm DBH were equally well fit by exponential and Weibull distributions, consistent with mechanistic forest models. The identification and field mapping of crowns seen in a high resolution orthomosaic revealed new patterns in the structure and dynamics of trees of canopy vs. understory at this site, demonstrating the value of traditional tree censuses with drone remote sensing. Public Library of Science 2020-12-10 /pmc/articles/PMC7728260/ /pubmed/33301487 http://dx.doi.org/10.1371/journal.pone.0243079 Text en © 2020 Araujo et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Araujo, Raquel Fernandes
Chambers, Jeffrey Q.
Celes, Carlos Henrique Souza
Muller-Landau, Helene C.
dos Santos, Ana Paula Ferreira
Emmert, Fabiano
Ribeiro, Gabriel H. P. M.
Gimenez, Bruno Oliva
Lima, Adriano J. N.
Campos, Moacir A. A.
Higuchi, Niro
Integrating high resolution drone imagery and forest inventory to distinguish canopy and understory trees and quantify their contributions to forest structure and dynamics
title Integrating high resolution drone imagery and forest inventory to distinguish canopy and understory trees and quantify their contributions to forest structure and dynamics
title_full Integrating high resolution drone imagery and forest inventory to distinguish canopy and understory trees and quantify their contributions to forest structure and dynamics
title_fullStr Integrating high resolution drone imagery and forest inventory to distinguish canopy and understory trees and quantify their contributions to forest structure and dynamics
title_full_unstemmed Integrating high resolution drone imagery and forest inventory to distinguish canopy and understory trees and quantify their contributions to forest structure and dynamics
title_short Integrating high resolution drone imagery and forest inventory to distinguish canopy and understory trees and quantify their contributions to forest structure and dynamics
title_sort integrating high resolution drone imagery and forest inventory to distinguish canopy and understory trees and quantify their contributions to forest structure and dynamics
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7728260/
https://www.ncbi.nlm.nih.gov/pubmed/33301487
http://dx.doi.org/10.1371/journal.pone.0243079
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