Doutorado em Ciências Florestais

URI Permanente para esta coleção

http://repositorio.ufes.br/handle/10/17466

Nível: Doutorado
Ano de início: 2013
Conceito atual na CAPES: 4
Ato normativo:Ofício N. 39-12/2007/CTC/CCA/CAPES de 31/07/2007
Periodicidade de seleção: Semestral
Área(s) de concentração:Ciências Florestais
Url do curso: https://cienciasflorestais.ufes.br/pt-br/pos-graduacao/PPGCFL/detalhes-do-curso?id=1425

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Navegando Doutorado em Ciências Florestais por Autor "Almeida, André Quintão de"

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    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
    (Universidade Federal do Espírito Santo, 2018-10-26) Fernandes, Márcia Rodrigues de Moura; Almeida, André Quintão de; Silva, Gilson Fernandes da; Gonçalves, Fabio Guimarães; Binoti, Daniel Henrique Breda; Mendonça, Adriano Ribeiro de
    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.
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    Modelagem de riscos de incêndios florestais e otimização da alocação das estruturas de combate por meio de técnicas de inteligência artificial
    (Universidade Federal do Espírito Santo, 2023-08-31) Silva, Jeferson Pereira Martins; Silva, Gilson Fernandes da; https://orcid.org/0000000178536284; http://lattes.cnpq.br/8643263800313625; https://orcid.org/0000000315521127; http://lattes.cnpq.br/6748966859692740; Barros Junior, Antonio Almeida de; https://orcid.org/0000-0002-2449-7221; http://lattes.cnpq.br/5104467305835940; Almeida, André Quintão de; https://orcid.org/0000-0002-5063-1762; http://lattes.cnpq.br/5929672339693607; Pezzopane, Jose Eduardo Macedo; https://orcid.org/0000000300244016; http://lattes.cnpq.br/3640768649683482; Silva, Evandro Ferreira da
    This study presents an approach to wildfire management integrating a WebGIS system and artificial intelligence. To train the deep learning model, data related to vegetation, topography, anthropogenic factors, and historical fire records for the year 2008 in Andalusia, Spain were collected. The dataset was duly normalized and split into 70% for training, 10% for validation, and 20% for testing. Various algorithms and activation functions were evaluated, with the combination of Adam and Relu standing out, recording an accuracy of 0.86 during training. Based on this model, a risk map was generated. By applying the K-means method to this map, high-risk areas were identified, and central points for the installation of firefighting infrastructures were suggested. To validate the model's efficacy, the suggested positions were compared with the actual locations of firefighting aircraft in Andalusia, Spain. With 31 clusters and a risk threshold of 0.75, the proximity between the proposed coordinates and the actual ones was notable, reinforcing the practical potential of the approach proposed in this study.