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: 4
Ato normativo: Ofício N. 39-12/2007/CTC/CCA/CAPES de 31/07/2007 Homologado pelo CNE (Portaria Nº 656 de 22/05/2017) Publicação DOU em 27/07/2017, Seç. 1, Pag. 36.
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

Agora exibindo 1 - 9 de 9

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/; Torres, Carlos Moreira Miquelino Eleto; https://orcid.org/; http://lattes.cnpq.br/; Almeida, André Quintão de; https://orcid.org/; http://lattes.cnpq.br/
abstract
CLASSIFICAÇÃO DO ESTÁGIO SUCESSIONAL DA VEGETAÇÃO EM ÁREAS DA MATA ATLÂNTICA COM A UTILIZAÇÃO DE NUVEM DE PONTOS 3D AÉREA E TERRESTRE
(Universidade Federal do Espírito Santo, 2022-08-22) Cabral, Ricardo Pinheiro; Silva, Gilson Fernandes da; https://orcid.org/0000000178536284; http://lattes.cnpq.br/8643263800313625; https://orcid.org/0000000246312603; http://lattes.cnpq.br/; Almeida, André Quintão de; https://orcid.org/; http://lattes.cnpq.br/; Bonilla-Bedoya, Santiago; https://orcid.org/; http://lattes.cnpq.br/
abstract
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.
INVENTÁRIO DE UMA FLORESTA DE PRODUÇÃO COM A UTILIZAÇÃO DE IMAGENS MSI/SENTINEL-2 E FOTOGRAMETRIA AÉREA DIGITAL
(Universidade Federal do Espírito Santo, 2021-04-30) Carvalho, Rachel Clemente; Mendonca, Adriano Ribeiro de; https://orcid.org/0000000333078579; http://lattes.cnpq.br/9110967421921927; https://orcid.org/0000000167085369; http://lattes.cnpq.br/; Goncalves, Fabio Guimaraes; https://orcid.org/; http://lattes.cnpq.br/; Almeida, André Quintão de; https://orcid.org/; http://lattes.cnpq.br/; Silva, Gilson Fernandes da; https://orcid.org/0000000178536284; http://lattes.cnpq.br/8643263800313625
In the forestry sector, knowledge of forest productivity is obtained through forest inventories. However, the sampling techniques traditionally applied to forest inventories have a high demand for time and high cost of execution. Therefore, it is necessar
Metodologia para detecção de colheita de eucalyptus no Espírito Santo: uma abordagem com sentinel-2
(Universidade Federal do Espírito Santo, 2023-10-31) Marques, Leon Muller; Mendonça, Adriano Ribeiro de; https://orcid.org/0000000333078579; http://lattes.cnpq.br/9110967421921927; https://orcid.org/0000000277073190; http://lattes.cnpq.br/9801928324329659; Almeida, André Quintão de; Vieira, Carlos Antonio Oliveira; Santos, Jeangelis Silva
The public sector requires tools enabling systematic monitoring of cultivated forest areas. The use of remote sensing techniques facilitates large-scale surveillance by environmental agencies. The primary aim of this study was to create a methodology for identifying Eucalyptus stands' harvesting in the state of Espírito Santo. To conduct this monitoring, the Eucalyptus stands' database provided by a company was utilized. This database was constructed from visual analysis of Sentinel-2 images from the year 2020. The accuracy of the map was validated using the Kappa coefficient. Reference data for harvesting was also obtained through visual interpretation by analyzing a fiveyear history of Sentinel-2 images. Each year, the best images from each quarter were selected, identifying changes in spectral response from forest to exposed soil, indicating harvesting.In constructing the harvest detection algorithm, land cover classes provided by the European Space Agency (ESA) between 2019 and 2020 were used, employing the Scene Classification (SCL). This product offers land cover information for each Sentinel-2 image collected every five days. Subsequently, classes of interest (cloud, soil, and vegetation) were filtered. To acquire an image with fewer cloud cover instances, three temporal categories (monthly, bimonthly, and quarterly) were analyzed, generated by aggregating weekly images. SCL classes were intersected with Eucalyptus stands, determining the temporal category with the least cloud cover in the stand database.Once the best temporal category was defined, harvest detection relied on accumulating pixels classified as soil in each new image composition, evaluating the percentage of soil in the stand. Experimentations were conducted to define the best soil percentage threshold to consider a stand as harvested. To calculate algorithm accuracy, performance evaluations were done with two distinct strategies. Strategy 1 directly compared the algorithm-identified harvest month with the reference date for each Eucalyptus stand. Strategy 2 assessed algorithm accuracy by varying one month before and after the reference date. Subsequently, the impact of slope and stand size on algorithm accuracy was analyzed, calculating errors and successes for each slope class and stand size.The Eucalyptus map obtained a Kappa of 0.851 and an overall accuracy of 94%. The quarterly temporal category proved most effective in minimizing cloud effects, as no stand exhibited over 20% cloud coverage. Strategy 2 was the most efficient, achieving an algorithm accuracy of 84.5% with a 25% soil threshold. It was observed that higher slopes corresponded to lower accuracy, while stand size showed a direct relationship: larger size led to higher accuracy.The developed algorithm represents an advancement in applying new monitoring and oversight methods for Eucalyptus plantations, and it can be adopted by the public sector.
Modelagem de variáveis biométricas em diferentes classes de vegetação por meio de dados sintéticos do satélite landsat
(Universidade Federal do Espírito Santo, 2023-09-27) Ferreira, Larissa Garcia; Mendonça, Adriano Ribeiro de; https://orcid.org/0000000333078579; http://lattes.cnpq.br/9110967421921927; https://orcid.org/0000000226340631; http://lattes.cnpq.br/6247239452984557; Almeida, André Quintão de; Silva, Gilson Fernandes da; https://orcid.org/0000000178536284; http://lattes.cnpq.br/8643263800313625
The use of passive remote sensing is an alternative to forest inventory to estimate dendrometric variables. Therefore, the main objective of this work was to evaluate the accuracy of estimates of average diameter, average total height and stem biomass at different stages of succession of the Atlantic Forest biome based on synthetic images from the Landsat satellite. Remote sensing data were obtained from synthetic images using the Continuous Change Detection and Classification (CCDC) algorithm. Spectral bands and NDVI were used as explanatory variables for modeling. Graphs of observed versus estimated variables, the adjusted coefficient of determination (𝑅̅2%) %), and the root mean square error (RMSE%) were used to evaluate the models. Model selection was performed using the F test to verify the significance of model parameters and analysis of variance was used to compare nested models. Furthermore, k-fold cross validation was performed repeated 1000 times with k equal to 10 for the selected model for each variable analyzed. The selected variables were the annual percentiles (10 to 100) of spectral bands 𝐵2, 𝐵3, 𝐵4, 𝐵5, 𝐵6e 𝐵7. The near-infrared (𝐵5) and mid-infrared 1 (𝐵5) bands were selected for all estimation equations for mean diameter, mean total height and bole biomass at different successional stages. Estimates of average diameter and average total height showed good accuracy in cross-validation. The bole biomass estimates were of low accuracy and are not recommended for estimating the biomass of the different successional stages. The use of metrics extracted from synthetic images obtained from Landsat satellite images, together with traditional forest inventory data, made it possible to estimate the biometric variables of the study area.
Modelo Espacial para Prevenção de Incêndios Florestais em Unidade de Conservação de Restinga
(Universidade Federal do Espírito Santo, 2024-02-19) Biazatti, Leonardo Duarte; Fiedler, Nilton Cesar; https://orcid.org/0000-0002-3895-661X; http://lattes.cnpq.br/8699171075880935; https://orcid.org/0000-0002-9345-8592; http://lattes.cnpq.br/9329666073648836; Simões, Danilo; https://orcid.org/0000-0001-8009-2598; http://lattes.cnpq.br/4290623857436137; Almeida, André Quintão de; https://orcid.org/0000-0002-5063-1762; http://lattes.cnpq.br/5929672339693607; Dias, Patrícia Borges; https://orcid.org/0000000252278341; http://lattes.cnpq.br/1194831380343570
The Restingas face constant threats from anthropogenic pressure, especially forest fires, which act as agents of degradation in these environments, especially for the purposes of real estate speculation. Despite advances in understanding the behavior of fires in the most varied types of vegetation, the occurrence of these events in sandbanks has been little studied. This research aimed to develop a model for predicting, preventing, detecting and helping to fight forest fires, using geotechnological tools, in the Paulo César Vinha State Park (PEPCV) and the Setiba Environmental Protection Area (APA), located in the municipality of Guarapari, which spatially and structurally represent a large part of the Atlantic Forest sandbank formations in the south of the state of Espírito Santo. Through the application of geoprocessing techniques using geotechnological tools such as Fuzzy logic, Hierarchical Process Analysis (HPA) logic, least-cost path and network analysis on the variables of land use and occupation, altitude, slope, relief orientation, proximity to roads, urban areas, and watercourses, rainfall, air temperature and land surface temperature and heat zones, the areas most at risk of forest fires were delimited, as were the priority sites for the construction of firebreaks and roads, the optimum points for installing video monitoring towers and water collection points. It was found that the area is at very high risk of fires, as 55.17% of the territory is in this risk range. The sites classified as priorities for the construction of firebreaks, because they are close to urban areas and areas of higher risk, represented 65.41% of the study area, so there was a need to install them around these sites. The road allocation model indicated 30 possible routes for opening complementary roads, always going through already anthropized sites. The model for installing video surveillance towers determined six possible allocation scenarios and, considering the 96.55% visualization rate achieved and the cost of implementation, the third scenario (2A) was the best. On the other hand, the optimization for the allocation of water collection points established sixteen possible scenarios, with the eleventh (3C) being the most promising as it showed 80% coverage of the demand points, using only eleven points, which makes it cost-effective to install. Given these results, it was possible to conclude that the high level of risk of forest fires in the PEPCV and Setiba APA highlights the urgency of implementing preventive measures aimed at reducing the risk and mitigating the possible consequences of fires in the region. In this context, this study is a source of information for designing predictive and preventive strategies for the PEPCV, as well as providing support when fighting fires. Finally, it should be noted that the methodology used is applicable, replicable and adaptable to any conservation unit.
Sistema de Detecção Remota do Desmatamento Florestal no Estado do Espírito Santo
(Universidade Federal do Espírito Santo, 2024-03-28) Leite, Igor Vieira; Almeida, André Quintão de; https://orcid.org/0000-0002-5063-1762; http://lattes.cnpq.br/5929672339693607; Mendonça, Adriano Ribeiro; https://orcid.org/0000-0003-3307-8579; http://lattes.cnpq.br/9110967421921927; https://orcid.org/0000-0003-2910-0251; http://lattes.cnpq.br/2626092800028236; Silva, Jeferson Pereira Martins; https://orcid.org/; http://lattes.cnpq.br/6748966859692740; Moreira, Taís Rizzo; https://orcid.org/0000-0001-5536-6286; http://lattes.cnpq.br/6717864186103246
Deforestation and forest degradation pose a significant threat to biodiversity and environmental balance. The state of Espírito Santo, although known for its rich biodiversity, faces challenges in monitoring these environmental impacts. Orbital remote sensing and the development of robust platforms for geospatial data processing, such as Google Earth Engine (GEE), emerge as alternatives to overcome these monitoring challenges. In this context, this study aimed to evaluate a system with high spatial and temporal resolution to monitor forest degradation and deforestation in the Atlantic Forest of Espírito Santo, using Sentinel-2 images. The native forest base map was derived from the annual cover map data of MapBiomas, combined with Sentinel-2 images in the Scene Classification Layer (SCL) band for soil, vegetation, and cloud classes. To validate the data obtained by FlorESat, a confusion matrix was calculated. A pixellevel concordance analysis between classes was performed using the alert database compiled by RAD MapBiomas, which contains 403 polygons from 2019 to 2022. FlorESat's deforestation mapping indicated that the total deforested area for the same years was 1,780.14 ha. In the validation, the proposed system showed mapping accuracy, precision, and specificity of 93.3%, 94.7%, and 93.8% respectively, for 52 randomly delineated polygons in forest areas in ES when compared to photointerpretation. Additionally, it was found that 59.85% of the pixels identified by the FlorESat tool matched directly with the alert database issued and validated in the field. Of the non-coincident pixels, 65.11% were covered by clouds and 34.89% were mapped as forests, highlighting a limitation of orbital data. However, when considering the scenario where cloud pixels are considered as deforested areas, the percentage of concordance was 85.99% between the two datasets.
Uso da fotogrametria aérea digital via imagens coletadas por drone no inventário quantitativo de uma floresta urbana
(Universidade Federal do Espírito Santo, 2023-10-30) Souza, Laís Goncalves Pires de; Mendonca, Adriano Ribeiro de; https://orcid.org/0000000333078579; http://lattes.cnpq.br/9110967421921927; https://orcid.org/0000000187394070; http://lattes.cnpq.br/5588447816273811; Callegaro, Rafael Marian; Almeida, André Quintão de; Moura, Cristiane Coelho de
In urban areas, trees play a crucial role in changing the landscape and local microclimate, in addition to promoting carbon sequestration and providing leisure and recreation spaces for the population. However, the establishment and maintenance of trees in cities pose a challenge for local administration, requiring environmental knowledge of the region, species, and deployment location. Currently, forest inventory enhanced with remote sensing data emerges as a facilitator of urban planning, expediting the tree inventory process and, consequently, decision-making. This study aimed to assess the accuracy of digital aerial photogrammetry (FAD) using images collected by a remotely piloted aircraft (RPA) in detecting trees and estimation of biometric variables in an urban forest inventory. The inventory was conducted on Governador Lindemberg Avenue, located in the municipality of Jerônimo Monteiro, Espírito Santo. High spatial resolution images were obtained by a multirotor RPA during the field inventory period. Subsequently, tree individuals were automatically identified, and their canopies were segmented using FAD-3D data. Finally, total height (H), diameter at 1,3m above ground (D), and canopy diameter (dc) values were estimated from regression models fitted with 3D point cloud height metrics. A total of 144 individuals were inventoried. For FAD validation, errors found were 0,32% for Digital Terrain Model (MDT) and 16,23% for total height. The windowed Variable detection algorithm (wV) using the point cloud as data source automatically identified 78% of individuals. For the comparison of canopy diameters, errors were 17,94%, 21,2% and 29,5% for manual measurements, FAD images, and field measurements with four rays, eight rays, and automatically obtained diameters through canopy identification and segmentation in FAD images, respectively. Regression models errors for H, D and dc were 8,97%, 36,76% and 15,68% respectively. The survey demonstrated the automatic identification of trees and extraction of traditional metrics for generating models to obtain variables of interest. The MDT obtained obtained provided satisfactory results for tree height estimation through FAD-RPA. Manual measurements with FAD images were considered satisfactory for canopy diameter, proving to be the best method for this variable. Additionally, regression models with tradicional metrics obtained were satisfactory for H and dc estimation, showing accurate RMSE and R² values. However, the trunk diameter model showed different results. In conclusion, conducting an aerial photogrammetric survey of urban areas using a remotely piloted aircraft is feasible and can provide valuable data for urban tree planning.

Navegar

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

Resultados por página

Opções de Ordenação