Doutorado em Ciências Florestais
URI Permanente para esta coleção
Nível: Doutorado
Ano de início: 2013
Conceito atual na CAPES: 5
Ato normativo: Portaria nº 398 de 29 de maio de 2025, publicado no DOU de 02/06/2025. Homologação do Parecer CNE/CES nº 176/2025
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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- ItemAvaliação de estágios sucessionais de florestas estacionais semideciduais com uso de dados hiperespectrais e LiDAR obtidos a partir de aeronave remotamente pilotada(Universidade Federal do Espírito Santo, 2025-06-03) Pinon, Tobias Baruc Moreira; Almeida, André Quintão de ; https://orcid.org/0000-0002-5063-1762; http://lattes.cnpq.br/5929672339693607; Effgen, Emanuel Maretto ; https://orcid.org/0000-0002-9031-6337; http://lattes.cnpq.br/0205196565849611; Mendonça, Adriano Ribeiro de ; https://orcid.org/0000-0003-3307-8579; http://lattes.cnpq.br/9110967421921927; https://orcid.org/0000-0001-9200-1024; http://lattes.cnpq.br/8571909054406808; Almeida, Catherine Torres de ; https://orcid.org/0000-0002-8140-2903; http://lattes.cnpq.br/5534145837431294; Fernandes, Milton Marques ; https://orcid.org/0000-0002-9394-0020; http://lattes.cnpq.br/2151263512584100; Martins Neto, Rorai Pereira; https://orcid.org/0000-0001-5318-2627; http://lattes.cnpq.br/4925375972651580; Silva, Gilson Fernandes da ; https://orcid.org/0000-0001-7853-6284; http://lattes.cnpq.br/8643263800313625The Atlantic Forest in the state of Espírito Santo has undergone intense degradation, highlighting the urgent need for rapid and accurate methods for its monitoring and conservation. Brazilian Resolution Conama No. 29/1994 establishes criteria for classifying secondary vegetation into successional stages, which determine the potential for forest use. However, this classification, when carried out in the field, is heavily reliant on the expertise of the technical team, due to factors such as training, subjective criteria, and the lack of adequate instruments—potentially compromising the reliability of the results. In this context, the objective of this study was to classify successional stages of vegetation using data acquired by hyperspectral and LiDAR sensors mounted on a Remotely Piloted Aircraft (RPA). The research was conducted in regenerating pasturelands and forest fragments located in southern Espírito Santo, where dendrometric variables such as diameter at breast height (DBH) and total tree height were collected within 30 × 30 m plots. These field measurements were related to hyperspectral (with and without shadow) and LiDAR-derived metrics to estimate dendrometric parameters—mean diameter (D), mean height (H), and basal area (G)—using regression models. Model accuracy was evaluated using the root mean square error (RMSE), adjusted coefficient of determination (adjusted R²), and histograms of percentage error. Successional stage classification was performed using a rule-based method under two scenarios: one with three stages (initial, intermediate, and advanced), and another including the regenerating pasture class. In addition, an unsupervised classification was conducted using hierarchical clustering based on the estimated dendrometric variables and structural and spectral metrics, resulting in five groups: three successional stages and two pasture categories (open and dense shrublands). A principal component analysis (PCA) was also applied. The variables D and H were estimated with higher accuracy using combined data (adjusted R² = 88% and 90%, respectively), while G performed best with LiDAR data alone (adjusted R² = 92%). Shadow pixel removal slightly improved model performance, although its impact on predictive quality was limited. The rule-based classification with three categories achieved an overall accuracy of 88% (Kappa = 0.81), decreasing to 68% (Kappa = 0.59) with the inclusion of the regenerating pasture class. The unsupervised classification using the estimated variables for five classes (open and dense shrublands, and successional stages) reached an accuracy of 64% (Kappa = 0.55). Conversely, the classification based solely on hyperspectral metrics showed high agreement with field-defined stages (92%), whereas LiDAR metrics presented lower correspondence. Multivariate analysis revealed that spectral and structural metrics adequately represent the successional gradient. The integration of hyperspectral and LiDAR data proved effective for the automated mapping of large and inaccessible areas, providing a promising tool to complement forest inventories and reduce subjectivity in the application of legal criteria
- ItemEstimação 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 deThe 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.
- ItemEstimação de áreas seccionais de troncos de árvores individuais por meio de dados coletados remotamente(Universidade Federal do Espírito Santo, 2025-04-09) Lavagnoli, Gabriel Lessa da Silva; Silva, Gilson Fernandes da; https://orcid.org/0000-0001-7853-6284; http://lattes.cnpq.br/8643263800313625; https://orcid.org/0000-0002-9007-1990; http://lattes.cnpq.br/9310315398167707; Almeida, André Quintão de; https://orcid.org/0000-0002-5063-1762; http://lattes.cnpq.br/5929672339693607; Soares, Carlos Pedro Boechat; https://orcid.org/0000-0001-6475-3376; http://lattes.cnpq.br/0959425632265455; Cosenza, Diogo Nepomuceno; https://orcid.org/0000-0001-8495-8002; http://lattes.cnpq.br/0496006405127895; Mendonça, Adriano Ribeiro de; https://orcid.org/0000-0003-3307-8579; http://lattes.cnpq.br/9110967421921927This thesis investigates the accuracy in the measurement of tree trunk cross-sectional areas, highlighting the practical importance of this variable for forest inventories and its implications for volume and biomass estimates. The work is structured into two complementary studies. The first study evaluates the impacts of convexity and isoperimetric deficits on traditional measurement methods, such as calipers and diameter tapes, comparing them to a photographic method developed by the author, which calculates areas and estimates contours through pixel counting. The results showed that traditional methods exhibit significant systematic errors, arising from the incorrect assumption of perfect circularity of cross-sections, whereas the photographic method demonstrated high precision, with mean relative errors below 0.1%. The second study proposes a computational methodology for estimating cross-sectional areas from point clouds obtained using a GeoSLAM LIDAR sensor, comparing the measurements with those obtained from a high-precision infrared scanner (EinScan). The research involved the analysis of 56 eucalyptus trees, comprising more than 1,000 cross-sections. Additional simulations of traditional methods were also conducted for direct comparison. It was observed that traditional techniques, once again, tended to overestimate the areas (with a mean bias of approximately 2.8%), while the LiDAR-based method showed the opposite trend, with a mean bias of -8.12%. However, after applying a specific mathematical correction, the LiDAR estimates achieved excellent accuracy, with a relative root mean square error (RMSE) of 2.4%, a mean relative bias close to zero, and a mean absolute relative error (MAE) of 1.65%, demonstrating great potential for practical applications after appropriate adjustments.
- ItemModelagem 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 daThis 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.
- ItemNovo algoritmo para detecção automática e mensuração de alturas de árvores individuais em plantios florestais usando scanner a laser móvel(Universidade Federal do Espírito Santo, 2025-03-31) Silva, Valeria Alves da; Silva, Gilson Fernandes da; https://orcid.org/0000-0001-7853-6284; http://lattes.cnpq.br/8643263800313625; https://orcid.org/; http://lattes.cnpq.br/; Mendonça, Adriano Ribeiro de; https://orcid.org/0000-0003-3307-8579; http://lattes.cnpq.br/9110967421921927; Almeida, André Quintão de; https://orcid.org/0000-0002-5063-1762; http://lattes.cnpq.br/5929672339693607; Soares, Carlos Pedro Boechat; http://lattes.cnpq.br/0959425632265455; Cosenza, Diogo Nepomuceno; https://orcid.org/0000-0001-8495-8002; http://lattes.cnpq.br/0496006405127895Measuring the total height (H) of trees in forest plantations is crucial for several reasons. Tree height is a key variable in calculating tree volume and biomass. Accurate height measurements, combined with diameter at breast height (D) data, allow for accurate estimates of individual tree volume and, by extension, total volume and biomass of the plantation. This is essential for assessing timber yield, carbon sequestration potential, and overall forest productivity. The height distribution within a plantation reflects stand density and structure. Accurate measurements of tree height are vital for the economic assessment of the plantation. Knowing volume and biomass allows for more accurate estimates of timber value and potential plantation revenue. In the context of climate change, accurate estimates of forest biomass (influenced by height) are necessary for carbon accounting and monitoring carbon sequestration efforts. In summary, accurate measurement of tree height (H) is not merely a component of forest inventory; it is an integral part of the overall forestry inventory. is a fundamental parameter that underpins many crucial aspects of forest management, from economic assessment to ecological assessments and sustainable resource planning. This work presents an algorithm for measuring the total height (H) of trees in forest plantations efficiently and accurately using Mobile Laser Scanner (MLS) data. In the first part of the paper, the algorithm focuses on detecting individual tree trunks in plantations. It employs DBSCAN and RANSAC methods for accurate detection, achieving 100% accuracy under ideal conditions and approximately 96% in challenging scenarios. The efficiency of the algorithm was evaluated on various computer configurations. In the second part of the paper, the algorithm measures the total height (H) of trees found in the plantation by its trunk identification method. The algorithm achieved significant accuracy, particularly in the challenge of accurately measuring shorter measurements obtained using a tape measure and a total station, demonstrating superior accuracy compared to the algorithms used in the experiments (TreeLS and 3DFin), especially for shorter trees. The study also analyzes the error distribution for each method, and the proposed algorithm stands out by presenting a more normal and less skewed error distribution than the other algorithms. Although slightly slower than 3DFin, its improved accuracy makes it a valuable tool for forest inventory. Areas for future improvements include processing speed and handling for processing low-density point clouds.
- ItemPredição e projeção do crescimento e da produção de plantios de eucalipto por meio de imagens multiespectrais de média resolução espacial(Universidade Federal do Espírito Santo, 2020-02-11) Santos, Jeangelis Silva; Mendonça, Adriano Ribeiro de; https://orcid.org/0000000333078579; http://lattes.cnpq.br/9110967421921927; https://orcid.org/0000000347857573; http://lattes.cnpq.br/8339532503141256 ; Gonçalves, Fabio Guimarães; http://lattes.cnpq.br/1116245566543036 ; Carvalho, Samuel de Padua Chaves e; https://orcid.org/0000-0002-5590-9049; http://lattes.cnpq.br/6176482316661283; Almeida, André Quintão de; Silva, Gilson Fernandes da; https://orcid.org/0000000178536284; http://lattes.cnpq.br/8643263800313625The efficient management and planning of forest areas depends directly on the acquisition of accurate information about the stands. Information about the development of forests can be previously obtained by growth and yield models. However, the adjustment of these models requires data from continuous forest inventories, which are complex and costly activities. One of the alternatives that can reduce the costs of the forest inventory is the use of remote sensing tools. Therefore, the objective of this work was to propose a methodology for using medium spatial resolution multiespectral data for the prediction and projection of growth and yield and to determine the technical age of harvesting of eucalyptus forests, aiming at reducing the number of plots measured in the forest inventory. For this purpose, two databases were used: one containing information on age and volume per hectare of 40 permanent plots measured between 2006 and 2011, with ages varying from two to seven years, and other containing time series of Tasseled Cap (TC) metrics extracted from ETM+/Landsat 7 imagery, smoothed by the Savitzky-Golay filter. To assess the possibility of reducing the number of plots measured in the continuous forest inventory when using remote sensing data, three scenarios were proposed, with different sampling intensities: 1) one plot every 28 ha; 2) one plot every 42 ha, and; 3) one plot every 83 ha. The estimation was performed by artificial neural networks and, in the prediction, the input variables were the age of the stand and the metrics of the Tasseled Cap transformation (brightness, greenness and wetness). For the projection, the variables were the current and future age and the current volume, obtained by the prediction for the first year of the continuous forest inventory. The prediction and projection were applied wall-to-wall, and the projection maps were used to calculate the mean and current annual increment and to determine the technical age of harvest. In the wall-to-wall prediction, the RMSE values ranged from 7.92% in scenario 1 to 10.67% in scenario 3. As for the projection, the RMSE varied from 9.68% in scenario 2 to 11.75% in scenario 3. In general, there was no major discrepancy between the accuracy measures in the three scenarios. In addition, all the scenarios analyzed for prediction and projection presented estimated values within the confidence interval of the forest inventory. The mean and current monthly increment values projected by the different scenarios analyzed did not differ from that obtained by the continuous forest inventory, with the growth curve inflection and forest maturity points being very close. Therefore, it can be concluded that the use of remotely sensed data allowed to accurately estimate the prediction and projection of growth and production of eucalyptus forests. In addition, by applying the methodology presented here, it is possible to significantly reduce the sampling intensity by up to one plot every 83 ha, with accuracy compatible with the methodology traditionally used in the continuous forest inventory.