Estimação da biomassa em um manguezal restaurado passivamente com uso de geotecnologias

• Ano de defesa: 2025
• Autor(a) principal: Alves, Allana Karla Costa
• Orientador(a): Ribeiro, Adauto de Souza
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Programa de Pós-Graduação: Pós-Graduação em Desenvolvimento e Meio Ambiente
• Palavras-chave em Português: Biomassa acima do solo; Mangues; Aeronaves remotamente pilotadas; LiDAR; Créditos de carbono
• Palavras-chave em Inglês: Above-ground biomass; Mangroves; Remotely piloted aircraft; Carbon credits
• Área do conhecimento CNPq: OUTROS
• Link de acesso: https://ri.ufs.br/jspui/handle/riufs/22758

The evaluation and quantification of biomass in mangroves are fundamental for valuing carbon credits and meeting global climate change mitigation goals, as outlined in the Sustainable Development Goals (SDGs) 13 (Climate Action), 14 (Life Below Water), and 15 (Life on Land). This study aimed to estimate above-ground biomass (AGB) in a passive restoration mangrove area located in the municipality of Nossa Senhora do Socorro, Sergipe, previously degraded by shrimp farming activities. To achieve this, a point cloud was generated using two remote sensing technologies: Digital Aerial Photogrammetry with the use of Remotely Piloted Aircraft (DAP-RPA) and Portable Terrestrial LiDAR (LiDAR-PLS). A forest inventory was conducted in 22 plots of 0.025 hectares for data validation, and biomass estimation was performed using the corrected equation by Chave et al. (2014), with values ranging from 2 to 246 Mg.ha⁻¹. Variables extracted from the point clouds were used as predictors in multiple linear regression (MLR) models, and the results were compared to the traditional field method (forest inventory with direct measurements of DBH and height). The model based on DAP-RPA presented a coefficient of determination (R²) of 0.86 and a root mean squared error (RMSE) of 24.03 Mg.ha⁻¹ (33.60%), while the model based on LiDAR-PLS obtained an R² of 0.62, with an RMSE of 40.42 Mg.ha⁻¹ (56.54%). The results highlight the potential of remote sensing technologies for accurately estimating biomass in mangrove ecosystems, particularly DAP-RPA, which showed greater statistical performance. These tools offer greater precision in carbon stock quantification and can significantly contribute to conservation initiatives, environmental restoration, and carbon credit generation, aligning with global climate commitments.