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: 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, Catherine Torres de"

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    Avaliação 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/8643263800313625
    The 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