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Master's Dissertation
DOI
10.11606/D.11.2016.tde-09082016-165442
Document
Author
Full name
Danitiele Cristina França Laranja
E-mail
Institute/School/College
Knowledge Area
Date of Defense
Published
Piracicaba, 2016
Supervisor
Committee
Rodriguez, Luiz Carlos Estraviz (President)
Ferraz, Sílvio Frosini de Barros
Görgens, Eric Bastos
Title in Portuguese
Estudo de parâmetros populacionais de plantios clonais de Eucalyptus spp. a partir de nuvens de pontos obtidos com escaneamento a laser aerotransportado
Keywords in Portuguese
Detecção de árvores individuais
Dupla amostragem
Estrutura do dossel
LiDAR
Abstract in Portuguese
As florestas plantadas contribuem para o desenvolvimento econômico, social e ambiental brasileiro, e o setor florestal busca plantios cada vez mais produtivos e melhor aproveitados. A tecnologia a laser aerotransportada (ALS - Airborne Laser Scanning), capaz de gerar informações tridimensionais precisas em extensas áreas e em pequeno intervalo de tempo, tem se destacado nas aplicações florestais, sendo utilizada na quantificação e caracterização de florestas. Neste estudo foram utilizados dados ALS obtidos em dois sobrevoos (2013 e 2014), cobrindo uma área de plantio do gênero Eucalyptus com diferentes clones e idades, localizada no estado de São Paulo. Esta dissertação é dividida em três partes. A primeira buscou avaliar a combinação de métricas ALS e de dupla amostragem nas estimativas de volume e na redução da intensidade amostral, comparando seus resultados aos de métodos amostrais tradicionais. Os resultados mostraram que a dupla amostragem obteve o menor erro de amostragem dentre os delineamentos, permitindo a redução do esforço amostral. A segunda parte visou caracterizar as diferenças estruturais existentes entre clones de eucalipto, com diferentes idades, a partir de métricas ALS e perfis de altura do dossel (CHP). O estudo mostrou que é possível distinguir a estrutura do dossel dos clones, o que pode contribuir com melhorias nas estimativas e no gerenciamento dos plantios comerciais. Na última parte, foram avaliados dois algoritmos de detecção de árvores individuais quanto à quantificação dos indivíduos e os efeitos da composição (tamanho de célula e filtragem) do modelo digital de alturas (CHM) no desempenho desses algoritmos. Verificou-se que as características do CHM influenciam na detecção. A estratégia com melhor desempenho resultou em um erro médio relativo de 11% no número de indivíduos.
Title in English
Study of population parameters from clonal Eucalyptus spp. plantation using point clouds obtained with airborne laser scanning
Keywords in English
Biometric model
Eucalyptus clone identification
Fast-growing forest plantations
Forest inventory
LiDAR
Sampling method
Tree count
Abstract in English
Planted forests contribute to the economic, social and environmental development of Brazil, and the forest industry aims to increase productivity and the efficient use of these plantations. Airborne Laser Scanning (ALS) technology, capable of generating accurate three-dimensional information, covering large areas in a short time, has highlighted potential in forestry applications and has been applied in forests quantification and characterization. In this study, ALS data were obtained from two flights (2013 and 2014), covering a Eucalyptus plantation area with different clones and ages, in the state of São Paulo. This thesis comprises three parts. The first study aimed to evaluate the combination of ALS metrics and double sampling design for volume estimates and reducing sampling intensity, comparing the results to traditional sampling methods. Results showed that the double sampling had the lowest error from the designs, allowing the reduction of the sampling effort. The second part was proposed to characterize the structural differences between Eucalyptus clones with different ages, based on metrics ALS and canopy height profiles (CHP). The study showed that it is possible to distinguish the canopy structure of the clones, which can contribute to improvements in estimates and management of commercial plantations. In the last study, two individual tree detection algorithms were evaluated for quantification of individuals, and the effect of the canopy height model (CHM) composition at the performance of these algorithms was analyzed. It was found that the CHM characteristics influence the tree detection. The strategy with better performance resulted in an average relative error of 11% in number of trees.
 
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Publishing Date
2016-08-17
 
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