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 | Acesso ao texto completo restrito à biblioteca da Embrapa Semiárido. Para informações adicionais entre em contato com cpatsa.biblioteca@embrapa.br. |
Registro Completo
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Biblioteca(s): |
Embrapa Semiárido. |
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Data corrente: |
03/01/2023 |
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Data da última atualização: |
03/01/2023 |
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Tipo da produção científica: |
Capítulo em Livro Técnico-Científico |
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Autoria: |
BARMPOUTIS, P.; STATHAKI, T.; LLOYD, J.; MOURA, M. S. B. de. |
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Afiliação: |
PANAGIOTIS BARMPOUTIS, Imperial College London, UK; TANIA STATHAKI, Imperial College London, UK; JONATHAN LLOYD, Imperial College London, UK; MAGNA SOELMA BESERRA DE MOURA, CPATSA. |
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Título: |
Tropical tree species 3D modelling and classification based on LiDAR technology. |
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Ano de publicação: |
2022 |
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Fonte/Imprenta: |
In: INFORMATION RESOURCES MANAGEMENT ASSOCIATION. Research anthology on ecosystem conservation and preserving biodiversity. Pensilvânia: IGI Global: Engineering Science Reference, 2022. |
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Páginas: |
cap. 17, p. 326-346. |
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DOI: |
10.4018/978-1-6684-5678-1.ch017 |
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Idioma: |
Inglês |
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Conteúdo: |
Over the last decade or so, laser scanning technology has become an increasingly popular and important tool for forestry inventory, enabling accurate capture of 3D information in a fast and environmentally friendly manner. To this end, the authors propose here a system for tropical tree species classification based on 3D scans of LiDAR sensing technology. In order to exploit the interrelated patterns of trees, skeleton representations of tree point clouds are extracted, and their structures are divided into overlap- ping equal-sized 3D segments. Subsequently, they represent them as third-order sparse structure tensors setting the value of skeleton coordinates equal to one. Based on the higher-order tensor decomposition of each sparse segment, they 1) estimate the mode-n singular values extracting intra-correlations of tree branches and 2) model tropical trees as linear dynamical systems extracting appearance information and dynamics. The proposed methodology was evaluated in tropical tree species and specifically in a dataset consisting of 26 point clouds of common Caatinga dry-forest trees. |
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Palavras-Chave: |
Árvores tropicais; Tecnologia de detecção LiDAR; Uso Sustentável. |
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Thesagro: |
Biodiversidade; Caatinga; Desenvolvimento Sustentável; Ecologia; Floresta; Inventário Florestal. |
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Thesaurus NAL: |
Forestry; Natural resources; Natural resources conservation; Sustainable development. |
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Categoria do assunto: |
P Recursos Naturais, Ciências Ambientais e da Terra |
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Marc: |
LEADER 02252naa a2200337 a 4500
001 2150588
005 2023-01-03
008 2022 bl uuuu u00u1 u #d
024 7 $a10.4018/978-1-6684-5678-1.ch017$2DOI
100 1 $aBARMPOUTIS, P.
245 $aTropical tree species 3D modelling and classification based on LiDAR technology.$h[electronic resource]
260 $c2022
300 $acap. 17, p. 326-346.
520 $aOver the last decade or so, laser scanning technology has become an increasingly popular and important tool for forestry inventory, enabling accurate capture of 3D information in a fast and environmentally friendly manner. To this end, the authors propose here a system for tropical tree species classification based on 3D scans of LiDAR sensing technology. In order to exploit the interrelated patterns of trees, skeleton representations of tree point clouds are extracted, and their structures are divided into overlap- ping equal-sized 3D segments. Subsequently, they represent them as third-order sparse structure tensors setting the value of skeleton coordinates equal to one. Based on the higher-order tensor decomposition of each sparse segment, they 1) estimate the mode-n singular values extracting intra-correlations of tree branches and 2) model tropical trees as linear dynamical systems extracting appearance information and dynamics. The proposed methodology was evaluated in tropical tree species and specifically in a dataset consisting of 26 point clouds of common Caatinga dry-forest trees.
650 $aForestry
650 $aNatural resources
650 $aNatural resources conservation
650 $aSustainable development
650 $aBiodiversidade
650 $aCaatinga
650 $aDesenvolvimento Sustentável
650 $aEcologia
650 $aFloresta
650 $aInventário Florestal
653 $aÁrvores tropicais
653 $aTecnologia de detecção LiDAR
653 $aUso Sustentável
700 1 $aSTATHAKI, T.
700 1 $aLLOYD, J.
700 1 $aMOURA, M. S. B. de
773 $tIn: INFORMATION RESOURCES MANAGEMENT ASSOCIATION. Research anthology on ecosystem conservation and preserving biodiversity. Pensilvânia: IGI Global: Engineering Science Reference, 2022.
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