Uso de dados de LiDAR aerotransportado e terrestre móvel no inventário quantitativo de árvores urbanas

• Ano de defesa: 2025
• Autor(a) principal: Souza, Emerson Eduardo Oliveira de
• Orientador(a): Mendonça, Adriano Ribeiro de
• Banca de defesa: Moura, Cristiane Coelho de , Silva, Gilson Fernandes da , Almeida, André Quintão de
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal do Espírito Santo; Mestrado em Ciências Florestais
• Programa de Pós-Graduação: Programa de Pós-Graduação em Ciências Florestais
• Departamento: Centro de Ciências Agrárias e Engenharias
• País: BR
• Palavras-chave em Português: Arborização urbana; Inventário florestal aprimorado; Sensoriamento remoto
• Área do conhecimento CNPq: Recursos Florestais e Engenharia Florestal
• Link de acesso: http://repositorio.ufes.br/handle/10/20628

Urban tree planting plays an essential role in providing ecosystem services such as thermal regulation, surface runoff control, and air quality improvement. However, proper management of these trees depends on accurate, up-to-date, and efficient inventories. Given the operational limitations of conventional methods, this study evaluated the use of airborne LIDAR (ALS) and terrestrial. LIDAR (TLS) data for tree detection and the estimation of biometric variables in an urban environment. The main objective was to analyze the accuracy of these technologies in estimating variables such as total height (H), diameter at breast height (DBH), and crown diameter (de) along an urban street in the municipality of Jerónimo Monteiro, ES, Brazil. The methodology involved acquiring data with LIDAR sensors mounted on a remotely piloted aircraft (RPA) and on mobile terrestrial scanning equipment, in addition to traditional field inventory, Point clouds were pre-processed, classified, and normalized. Subsequently, digital terrain models (DTMs) were generated, individual trees were detected and segmented (ITD), structural metrics were extracted, and multiple linear regression models were fitted to estimate the variables of interest. The results showed that ALS presented higher accuracy in total height (H) estimation, with an adjusted Rª of 0.95 and RMSE of 6.69%. On the other hand, TLS performed better in the estimation of DBH (adjusted. R³ of 0.47 and RMSE of 26.21%) and cd (adjusted R³ of 0.55 and RMSE of 19.67%), providing better detail of the trees' lateral structure. The best-performing detection algorithm was the Local Maximum Filter (LMF) with a variable linear window, especially when applied directly to the TLS point cloud. Statistical modeling using point cloud-derived variables showed robust performance, particularly with metrics such as zq95, zkurt, and ikurt. It is concluded that both ALS and TLS are effective tools for urban forest inventory, with complementary potential. The combination of ALS's spatial coverage and TLS's structural detail can optimize urban planning and tree management, contributing to more efficient strategies for monitoring and managing urban green areas.