Estimativa de biomassa e carbono em povoamento de Cordia trichotoma (Vell.) Arrab. ex Steud utilizando imagens multiespectrais

• Ano de defesa: 2026
• Autor(a) principal: Sperandei, Álisson Pacheco
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal de Santa Maria; Brasil; UFSM; Programa de Pós-Graduação em Engenharia Florestal; Centro de Ciências Rurais
• Palavras-chave em Português: Sensoriamento remoto; Mitigação climática; Aeronave remotamente pilotada (ARP); Silvicultura de precisão; Remote sensing; Climate mitigation; Remotely piloted aircraft (RPA); Precision forestry; CNPQ::CIENCIAS AGRARIAS::RECURSOS FLORESTAIS E ENGENHARIA FLORESTAL
• Link de acesso: http://repositorio.ufsm.br/handle/1/38371

Forests play a fundamental role by removing carbon dioxide from the atmosphere and storing carbon in their roots, stems, leaves, and soil. Traditional methods for quantifying biomass and carbon stock are often costly and difficult to apply over large areas. In this context, the use of remote sensing technologies has emerged as an efficient and accurate alternative for estimating aboveground biomass and carbon stock. The present study aimed to estimate the aboveground biomass and carbon stock of a plantation of Cordia trichotoma (Vell.) Arrab. ex Steud. using high spatial resolution multispectral imagery acquired by an Unmanned Aerial Vehicle (UAV). Six allometric equations (Trorey, Stofells, Stoate, Chave2, Husch, and Kopezky) fitted from forest inventory data were used, along with 17 vegetation indices derived from UAV-based aerial imagery. Automated crown segmentation was also performed based on Object-Based Image Analysis (OBIA) and the Watershed classifier, enabling the integration of field and remote sensing data. The estimates obtained were compared across different approaches: forest inventory, allometric models, and machine learning algorithms, namely Random Forest (RF) and Support Vector Machine (SVM), in order to evaluate the accuracy and potential of remote sensing techniques for quantifying aboveground biomass and carbon stock. The results indicated that the Stofells allometric model and the SVM algorithm showed the best performance in estimating AGB and carbon stock in the C. trichotoma plantation.