Estimação de área basal, volume e biomassa em um fragmento de Caatinga Hiperxerófila densa no alto sertão sergipano com base em dados MSI/Sentinel-2

• Ano de defesa: 2018
• Autor(a) principal: Fernandes, Márcia Rodrigues de Moura
• Tipo de documento: Tese
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal do Espírito Santo; BR; Doutorado em Ciências Florestais; Centro de Ciências Agrárias e Engenharias; UFES; Programa de Pós-Graduação em Ciências Florestais
• Palavras-chave em Português: Semiarid; Measurement; Remote sensing; Semiárido; Mensuração; Sensoriamento Remoto; ODS; REDD++; Recursos Florestais e Engenharia Florestal; 630
• Link de acesso: http://repositorio.ufes.br/handle/10/10805

The aim of this study was to estimate the basal area, the wood of volume and the aerial biomass of the Caatinga vegetation of the semi-arid region of Sergipe based on MSI/Sentinel-2 sensor data. In order to reach this objective, the dendrometric variables were measured: the diameter at the height of 1.30 m of the soil (DBH) and the total height (H), obtained by means of systematic sampling, with fixed square plots of 30 mx 30 m (900 m2 ), totaling 40 plots. The independent variables were extracted from the spectral bands in the spectral windows 3 x 3, 5 x 5, 7 x 7 and 9 x 9 pixels, and calculated the ratio of bands, vegetation indices, image fractionvegetation and texture metrics based on co-occurrence matrix. The variables derived from Sentinel-2 were examined for their accuracy in the estimation of the variables basal area (m2 ), wood of volume (m3 ) and aerial biomass (Mg) using multiple linear (MLR) regression analysis and Artificial Neural Networks (ANN). The statistics coefficient of determination (R 2 ), root mean square error (RMSE and RMSE%) and bias (B%) were used in the evaluation of the estimates generated by the models. The results of this study demonstrated that the texture metrics, calculated in window sizes 5 x 5 and 7 x 7 pixels, were more accurate. The best statistics were in the estimation of the basal area that presented a R 2 = 0.9591, RQME = 0.63 m2 ha-1 (10.19%) and bias = -0.39% in the validation of the MLR; and R 2 = 0.9782, RQME = 0.68 m2 ha-1 (10.85%) and bias = -0.80% in ANN validation. In the end, it was concluded that the use of independent variables from the MSI sensor in the analysis MLR and ANN estimate basal area, wood of volume and aerial biomass presented as an effective and accurate method, emphasizing the importance of the texture of the image in the prediction of these variables in the studied area.