Mapping the density of giant trees in the Amazon

Authors: Róbson Borges de Lima, Diego Armando Silva da Silva, Matheus Henrique Nunes,  Paulo R. de Lima Bittencourt, Peter Groenendijk, Cinthia Pereira de Oliveira, Daniela Granato‐Souza, Rinaldo Luíz Caraciolo Ferreira,  José A. Aleixo da Silva, Jesús Aguirre‐Gutiérrez, Toby D. Jackson, João Ramos Matos Filho
+9 more Perseu da Silva Aparício,  Joselane P. Gomes da Silva, José Júlio de Toledo, Marcelino Carneiro Guedes, Danilo R. A. Almeida, Níro Higuchi, Fabien Hubert Wagner, Jean Pierre Ometto, Eric Bastos Görgens
Cited by: 0

Abstract

Tall trees (height ≥ 60 m) are keystone elements of tropical forests, strongly influencing biodiversity, carbon storage, and ecosystem resilience. Yet, their density and spatial distribution remain poorly quantified, especially in remote Amazonian regions, limiting our understanding of their ecological roles and contribution to forestclimate interactions. We combined airborne LiDAR data from 900 transects across the Brazilian Amazon with environmental predictors to model tall-tree density. Spatial extrapolations allowed us to generate regional distribution estimates and assess associations with climate, topography, and disturbance regimes. Our model predicts that tall trees are unevenly distributed, with c. 14% of the estimated density concentrated in c. 1% of the Amazon and c. 50% within c. 11%. The highest densities occur in Roraima and the Guiana Shield provinces, where water availability is high and lightning or storm incidence is low. Modeled density strongly correlates with aboveground biomass, highlighting the disproportionate contribution of tall trees to carbon stocks. We estimate c. 55.5 million tall trees across the Brazilian Amazon. These findings demonstrate that tall-tree distribution is a crucial but underused predictor for biomass models. Understanding their ecological and spatial dynamics is vital for forest conservation and climate-resilience strategies under increasing anthropogenic pressures.

Fields of Study & Subjects

Remote Sensing and LiDAR Applications Forest ecology and management Forest Management and Policy

Publication details

Journal / source
Biblioteca Digital da Memória Científica do INPE (National Institute for Space Research)
Publication date
1 January 2026
Language
EN