Mestrado em Ciências Florestais

URI Permanente para esta coleção

http://repositorio.ufes.br/handle/10/17467

Nível: Mestrado Acadêmico
Ano de início: 2008
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=1424

Navegar

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

Agora exibindo 1 - 4 de 4
  • Nenhuma Miniatura disponível
    Item
    Avaliação de diâmetro e altura total de plantio misto com laser scanner portátil
    (Universidade Federal do Espírito Santo, 2022-08-29) Oliveira, Klisman; Silva, Gilson Fernandes da; https://orcid.org/0000000178536284; http://lattes.cnpq.br/8643263800313625; https://orcid.org/0000000180519387; http://lattes.cnpq.br/1741069350944065; Torres, Carlos Moreira Miquelino Eleto; https://orcid.org/0000-0003-0255-2637; http://lattes.cnpq.br/2087212860072636; Almeida, André Quintão de; https://orcid.org/0000-0002-5063-1762; http://lattes.cnpq.br/5929672339693607
    The use of remote sensing equipment has been growing in the forestry scenario, associated with time savings and reduction of field work that can bring to projects, one of which is Light Detection and Ranging (LiDAR). The objective of the present work was to estimate the total height and diameter at breast height of different species in a mixed forest stand with Light Detection and Ranging data. A forest inventory was carried out to collect diameter at breast height and total height, from twelve different species of the Atlantic Forest, being collected ten class centers by species, totaling 120 measured arboreal individuals. The collections took place by means of a Portable Laser Scanner (PLS), the walking was carried out with the equipment in the eight areas studied, ranging from two to ten years of age. Through the walking, it was possible to generate the 3D point cloud of the plantations, from where the segmentation and cleaning of the studied trees was carried out, thus generating a point cloud for each isolated tree. Through the TreeLS and LiDR packages of the R software, it was possible to extract the metrics of diameter at breast height and total height of the point clouds of each tree. At the end of the segmentation, 92 trees of eleven different species were isolated, among the problems found for the isolation of the trees, the competition of the younger plantations with weeds and invasive trees. Another problem found was the competition between crowns, taking into account the intertwining between crowns and obstruction of the lasers by obstacles, which generated occlusions and noise, which made it impossible to isolate the trees. For the diameter metric, species in general the RMSE reached a value of 38.17%, the species Schizolobium parahyba obtained the best statistics reaching an RMSE of 13.14% and Bias 40.65%. The best statistics that Schizolobium parahyba arrived at can be associated with the similarity of growth with commercial species, from which the metrics in the used packages are usually processed. For the total height data obtained with PLS, the general RMSE (%) was 15.14 %, while its bias (%) was 17.73 %, in which four studied species obtained RMSE (%) below 10%, being Ceiba speciosa, Inga edulis, Tibouchina granulosa and Cedrela fissilis, being C. fissilis the only species with RMSE (%) below 5%. The species in general showed overestimated results of diameters at breast height and difficulty in segmenting the trees, whereas the total height metrics were obtained with greater precision, and similar to the statistics found in the literature.
  • Nenhuma Miniatura disponível
    Item
    Detecção de estradas florestais usando dados LiDAR
    (Universidade Federal do Espírito Santo, 2025-07-30) Brisson, Estefany Vaz; Almeida, André Quintão de; https://orcid.org/0000-0002-5063-1762; http://lattes.cnpq.br/5929672339693607; Fiedler, Nilton César ; https://orcid.org/0000-0002-3895-661X; http://lattes.cnpq.br/8699171075880935; https://orcid.org/0009-0002-2570-7435; http://lattes.cnpq.br/4251068388633494; Lucas, Fernanda Moura Fonseca ; https://orcid.org/0000-0002-3181-2568; http://lattes.cnpq.br/7275203342714463; Ramalho, Antônio Henrique Cordeiro ; https://orcid.org/0000-0002-0037-5422; http://lattes.cnpq.br/7064955262943008; Sampietro, Jean Alberto ; https://orcid.org/0000-0001-6555-7166; http://lattes.cnpq.br/2015018876517184
    Knowledge of forest road networks is essential for sustainable forest management, including silvicultural operations, harvesting, transportation, and firefighting. Mapping roads in dense forest areas using costly field methods or low-spatial-resolution sensing can be inaccurate, especially on narrow roads or under canopy cover. Light Detection and Ranging (LiDAR) technology has a high penetration capacity in difficult-to-access forest environments with dense vegetation, thus presenting itself as a promising technology to support forest road detection. Therefore, the objective of this study was to detect different types of forest roads in commercial pine plantations using data from airborne LiDAR sensors. The research was structured in two sections: in the first section, an accurate and representative Digital Terrain Model for dense forests was developed based on LiDAR sensor data; in the second section, a classification of covered and uncovered roads was conducted using the automatic image classification method Random Forest in the areas of interest. The area comprises approximately 800 ha of forest aged 7 to 16 years and approximately 4,143 km of roads located in northern Spain. Image segmentation techniques and the Random Forest automatic classifier were used to map the covered and uncovered forest roads. The input variables for the classification were based on height and intensity values generated by the LiDAR point cloud. The classification process achieved an overall accuracy of 97%, resulting in LiDAR-identified forest roads for 86% of the reference roads (field survey). The height variable stood out in the identification of Exposed Roads (ER), and the combination of height and intensity variables stood out in the identification of Covered Roads (CR). The quality metrics composed of completeness, correctness, and quality individually obtained values for ER (74%, 76%, and 60%) and for CR (86%, 66%, and 60%). Although the highest integrity (completeness) was observed for covered roads, the lowest errors were observed in identifying exposed roads. However, due to the road's length being 1,903 km longer than that of the covered road, the error distribution was disproportionate. The proposed method can provide accurate road mapping to support forest management, although improvements are needed in identifying roads below the canopy, which can be improved by adding more detailed site features
  • Nenhuma Miniatura disponível
    Item
    Estimação de características dendométricas para floresta estacional semidecidual submontana com o uso de dados OLI e SRTM
    (Universidade Federal do Espírito Santo, 2018-02-20) Gonçalves, Anny Francielly Ataide; Almeida, André Quintão de; Silva, Gilson Fernandes da; Binoti, Daniel Henrique Breda; Mendonça, Adriano Ribeiro de
    Brazil's forestry policy predict that all states of the federation should update the forest inventory. Linked to this, it is necessary to use techniques, such as remote sensing, that make it possible to obtain accurate information and reduce costs in the development of this activity. The objective of this study was to evaluate the use of Landsat 8 OLI sensor data and SRTM data in equations for estimation of variables basal area and volume of wood for a fragment of Submontane Semidecidual Seasonal Forest belonging to the Private Reserve of the Natural Heritage (RPPN) Cafundó, located in the municipality of Cachoeiro do Itapemirim, ES. The forest inventory was realized out in 25 plots of 1,000 m² (20 m x 50 m) and the estimates of basal area and volume of wood with bark were obtained by means of allometric equations. Subsequently, these estimates were related to the variables derived from the remote sensing, through the regression analysis. In the regression analysis, the dependent variables were the basal area and volume of bark wood, and the independent variables were the OLI sensor spectral bands, the ratio between bands, vegetation indices and relief characteristics extracted from the SRTM, tested for different spectral windows. The technique of selection of explanatory variables used was the exhaustive search and the statistical evaluation of the regression made use of the 2 R , RMSE (%), residue dispersion and Leave-one-out ( 2 CV R and RMSEcv) cross-validation. For the studied variables, it was observed that the 3 x 3 pixel spectral window was the most related to the data of basal area and volume of wood, and relief variables extracted from the SRTM presented good performance when combined with the spectral variables of the sensor OLI. For the basal area, the equation that best fit the data presented 2 R of 0,6554, 2 CV R of 0,6244, RMSE (%) of 14,53% and RMSEcv (%) of 18,15%. In relation to volume, the equation presented 2 R of 0,6039, 2 CV R of 0,5380, RMSE (%) of 23,03% and RMSEcv (%) of 30,30%. The estimation of the basal area and volume of wood for the Submontane Semidecidual Seasonal Forest fragment using spectral data presented satisfactory results, emphasizing the importance of topography in the prediction of these variables in the studied area.
  • Nenhuma Miniatura disponível
    Item
    Uso de dados de LiDAR aerotransportado e terrestre móvel no inventário quantitativo de árvores urbanas
    (Universidade Federal do Espírito Santo, 2025-07-30) Souza, Emerson Eduardo Oliveira de; Mendonça, Adriano Ribeiro de; https://orcid.org/0000-0003-3307-8579; http://lattes.cnpq.br/9110967421921927; https://orcid.org/0000-0003-1639-5353; http://lattes.cnpq.br/8624018008470565; Moura, Cristiane Coelho de; https://orcid.org/0000-0001-6743-8638; http://lattes.cnpq.br/8485099797100386; Silva, Gilson Fernandes da; https://orcid.org/0000-0001-7853-6284; http://lattes.cnpq.br/8643263800313625; Almeida, André Quintão de; https://orcid.org/0000-0002-5063-1762; http://lattes.cnpq.br/5929672339693607
    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.