Estrutura vertical e hotspots de carbono: os Manguezais amazônicos entre os mais altos do mundo

Abstract

The mangroves of the Brazilian Amazon coast play a key role in carbon sequestration, coastal protection, and in improving our understanding of processes associated with climate change. However, the vertical structure and aboveground biomass (AGB) of mangroves in the northeastern sector of the state of Pará—particularly in the Marapanim River Estuary (ERM)—remain poorly documented. Considering that allometric parameters such as diameter at breast height (DBH) and canopy height (H) are directly related to AGB and, consequently, to carbon stocks, this study aimed to estimate AGB and to characterize canopy height gradients in the ERM. Field measurements conducted in two zones (North and South), combined with unmanned aerial vehicle (UAV) surveys, enabled the identification of individual Rhizophora and Avicennia trees using QuickBird imagery. UAV-derived canopy heights, together with in situ DBH measurements, allowed the development of an empirical equation based on the allometric models. Additionally, datasets from the Shuttle Radar Topography Mission (SRTM), TanDEM-X, and UAVs were integrated to compare biomass estimates across different spatial scales and sensor resolutions. The results indicate that mangroves in the ERM include individuals exceeding 49 m in height, placing them among the tallest mangroves ever recorded globally. Maximum biomass estimated from UAV data reached approximately 800 Mg/ha, whereas SRTM and TanDEM-X produced lower values (93 Mg/ha and 136 Mg/ha, respectively), reflecting differences associated with sensor resolution, forest density, canopy heterogeneity, and each model’s ability to capture canopy-top elevations. Overall, these findings highlight the Brazilian Amazon mangroves as among the tallest and most carbon-rich on the planet, underscoring their importance for blue carbon, climate mitigation, and advanced remote sensing applications, including the calibration of satellite-based biomass estimation algorithms.