Amazonian species evaluation using leaf-based spectroscopy data and dimensionality reduction approaches
Autor(a) principal: | |
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Data de Publicação: | 2022 |
Outros Autores: | , , , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.1016/j.rsase.2022.100742 http://hdl.handle.net/11449/223718 |
Resumo: | Sampling trees in natural environment can be used in studies ranging from floristic composition and phytogeography to management and growth modelling, and accurate inventories are based on highly labor-intensive methods. Relying on hyperspectral approach, this study aimed to differentiate spectral libraries of four Amazon tree species. We first prepared the spectroradiometer data on representative bands on foliar biochemistry, followed by reflectance inflection difference and finally, we applied vegetation indices. Next, the discriminant analysis was reasoned on multivariate approach, were successfully discriminated the spectral curves related to each of evaluated tree species. By visual analysis, some regions of the electromagnetic spectrum with higher differentiation in reflectance responses can be seen, in portions of the visible spectrum (0.5–0.65 μm), near-infrared (0.913–1.25 μm) and short-wave infrared 2 (2.1–2.5 μm). There was a higher contribution in distinguishing between species based on specific RID (Reflectance Inflection Difference) heights, such as seen on specific representative bands, where RID approach reached 99.87% of data variability related to principal component 1 (PC1) and 99.72% for leaf structure-based bands in PC1. Principal component analysis applied to the vegetation indices brought satisfactory results, with PC1 highly related to the variability of the vegetation indices results (99.37%). Adopting this approach in hyperspectral data at the leaf level and well-defined classes results in good responses. We emphasize the importance of using combined vegetation indices, with greater contributions by indices developed for quantization or absorption of electromagnetic radiation by chlorophyll, which are based in the visible region. These results can improve further research by using remote sensing techniques, as create brand-new data for Amazonian tree species policymaking, conservation and research. |
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Amazonian species evaluation using leaf-based spectroscopy data and dimensionality reduction approachesAmazonian treesForest managementHyperspectral dataMultivariate analysisVegetation indicesSampling trees in natural environment can be used in studies ranging from floristic composition and phytogeography to management and growth modelling, and accurate inventories are based on highly labor-intensive methods. Relying on hyperspectral approach, this study aimed to differentiate spectral libraries of four Amazon tree species. We first prepared the spectroradiometer data on representative bands on foliar biochemistry, followed by reflectance inflection difference and finally, we applied vegetation indices. Next, the discriminant analysis was reasoned on multivariate approach, were successfully discriminated the spectral curves related to each of evaluated tree species. By visual analysis, some regions of the electromagnetic spectrum with higher differentiation in reflectance responses can be seen, in portions of the visible spectrum (0.5–0.65 μm), near-infrared (0.913–1.25 μm) and short-wave infrared 2 (2.1–2.5 μm). There was a higher contribution in distinguishing between species based on specific RID (Reflectance Inflection Difference) heights, such as seen on specific representative bands, where RID approach reached 99.87% of data variability related to principal component 1 (PC1) and 99.72% for leaf structure-based bands in PC1. Principal component analysis applied to the vegetation indices brought satisfactory results, with PC1 highly related to the variability of the vegetation indices results (99.37%). Adopting this approach in hyperspectral data at the leaf level and well-defined classes results in good responses. We emphasize the importance of using combined vegetation indices, with greater contributions by indices developed for quantization or absorption of electromagnetic radiation by chlorophyll, which are based in the visible region. These results can improve further research by using remote sensing techniques, as create brand-new data for Amazonian tree species policymaking, conservation and research.Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Federal University of Mato Grosso (UFMT) Post-Graduate Program in Environmental Sciences (PPGCAM), Mato GrossoState University of Mato Grosso (UNEMAT), Mato GrossoState University of Mato Grosso (UNEMAT), Alta Floresta, Mato GrossoUniversity of São Paulo (USP) Institute of Biosciences Department of BotanyLouisiana State University (LSU) AgCenter School of Plant Environmental and Soil SciencesState University of São Paulo (UNESP) JaboticabalFederal University of Mato Grosso do Sul (UFMS), Chapadão do Sul, Mato Grosso do SulState University of São Paulo (UNESP) JaboticabalCAPES: 001CNPq: 303767/2020-0CNPq: 309250/2021-8Post-Graduate Program in Environmental Sciences (PPGCAM)State University of Mato Grosso (UNEMAT)Universidade de São Paulo (USP)and Soil SciencesUniversidade Estadual Paulista (UNESP)Universidade Federal de Mato Grosso do Sul (UFMS)Della-Silva, João LucasSilva Junior, Carlos Antonio daLima, MendelsonRibeiro, Ricardo da SilvaShiratsuchi, Luciano ShozoRossi, Fernando Saragosa [UNESP]Teodoro, Larissa Pereira RibeiroTeodoro, Paulo Eduardo2022-04-28T19:52:41Z2022-04-28T19:52:41Z2022-04-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlehttp://dx.doi.org/10.1016/j.rsase.2022.100742Remote Sensing Applications: Society and Environment, v. 26.2352-9385http://hdl.handle.net/11449/22371810.1016/j.rsase.2022.1007422-s2.0-85127098921Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengRemote Sensing Applications: Society and Environmentinfo:eu-repo/semantics/openAccess2022-04-28T19:52:41Zoai:repositorio.unesp.br:11449/223718Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T23:40:16.336411Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
Amazonian species evaluation using leaf-based spectroscopy data and dimensionality reduction approaches |
title |
Amazonian species evaluation using leaf-based spectroscopy data and dimensionality reduction approaches |
spellingShingle |
Amazonian species evaluation using leaf-based spectroscopy data and dimensionality reduction approaches Della-Silva, João Lucas Amazonian trees Forest management Hyperspectral data Multivariate analysis Vegetation indices |
title_short |
Amazonian species evaluation using leaf-based spectroscopy data and dimensionality reduction approaches |
title_full |
Amazonian species evaluation using leaf-based spectroscopy data and dimensionality reduction approaches |
title_fullStr |
Amazonian species evaluation using leaf-based spectroscopy data and dimensionality reduction approaches |
title_full_unstemmed |
Amazonian species evaluation using leaf-based spectroscopy data and dimensionality reduction approaches |
title_sort |
Amazonian species evaluation using leaf-based spectroscopy data and dimensionality reduction approaches |
author |
Della-Silva, João Lucas |
author_facet |
Della-Silva, João Lucas Silva Junior, Carlos Antonio da Lima, Mendelson Ribeiro, Ricardo da Silva Shiratsuchi, Luciano Shozo Rossi, Fernando Saragosa [UNESP] Teodoro, Larissa Pereira Ribeiro Teodoro, Paulo Eduardo |
author_role |
author |
author2 |
Silva Junior, Carlos Antonio da Lima, Mendelson Ribeiro, Ricardo da Silva Shiratsuchi, Luciano Shozo Rossi, Fernando Saragosa [UNESP] Teodoro, Larissa Pereira Ribeiro Teodoro, Paulo Eduardo |
author2_role |
author author author author author author author |
dc.contributor.none.fl_str_mv |
Post-Graduate Program in Environmental Sciences (PPGCAM) State University of Mato Grosso (UNEMAT) Universidade de São Paulo (USP) and Soil Sciences Universidade Estadual Paulista (UNESP) Universidade Federal de Mato Grosso do Sul (UFMS) |
dc.contributor.author.fl_str_mv |
Della-Silva, João Lucas Silva Junior, Carlos Antonio da Lima, Mendelson Ribeiro, Ricardo da Silva Shiratsuchi, Luciano Shozo Rossi, Fernando Saragosa [UNESP] Teodoro, Larissa Pereira Ribeiro Teodoro, Paulo Eduardo |
dc.subject.por.fl_str_mv |
Amazonian trees Forest management Hyperspectral data Multivariate analysis Vegetation indices |
topic |
Amazonian trees Forest management Hyperspectral data Multivariate analysis Vegetation indices |
description |
Sampling trees in natural environment can be used in studies ranging from floristic composition and phytogeography to management and growth modelling, and accurate inventories are based on highly labor-intensive methods. Relying on hyperspectral approach, this study aimed to differentiate spectral libraries of four Amazon tree species. We first prepared the spectroradiometer data on representative bands on foliar biochemistry, followed by reflectance inflection difference and finally, we applied vegetation indices. Next, the discriminant analysis was reasoned on multivariate approach, were successfully discriminated the spectral curves related to each of evaluated tree species. By visual analysis, some regions of the electromagnetic spectrum with higher differentiation in reflectance responses can be seen, in portions of the visible spectrum (0.5–0.65 μm), near-infrared (0.913–1.25 μm) and short-wave infrared 2 (2.1–2.5 μm). There was a higher contribution in distinguishing between species based on specific RID (Reflectance Inflection Difference) heights, such as seen on specific representative bands, where RID approach reached 99.87% of data variability related to principal component 1 (PC1) and 99.72% for leaf structure-based bands in PC1. Principal component analysis applied to the vegetation indices brought satisfactory results, with PC1 highly related to the variability of the vegetation indices results (99.37%). Adopting this approach in hyperspectral data at the leaf level and well-defined classes results in good responses. We emphasize the importance of using combined vegetation indices, with greater contributions by indices developed for quantization or absorption of electromagnetic radiation by chlorophyll, which are based in the visible region. These results can improve further research by using remote sensing techniques, as create brand-new data for Amazonian tree species policymaking, conservation and research. |
publishDate |
2022 |
dc.date.none.fl_str_mv |
2022-04-28T19:52:41Z 2022-04-28T19:52:41Z 2022-04-01 |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://dx.doi.org/10.1016/j.rsase.2022.100742 Remote Sensing Applications: Society and Environment, v. 26. 2352-9385 http://hdl.handle.net/11449/223718 10.1016/j.rsase.2022.100742 2-s2.0-85127098921 |
url |
http://dx.doi.org/10.1016/j.rsase.2022.100742 http://hdl.handle.net/11449/223718 |
identifier_str_mv |
Remote Sensing Applications: Society and Environment, v. 26. 2352-9385 10.1016/j.rsase.2022.100742 2-s2.0-85127098921 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Remote Sensing Applications: Society and Environment |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.source.none.fl_str_mv |
Scopus reponame:Repositório Institucional da UNESP instname:Universidade Estadual Paulista (UNESP) instacron:UNESP |
instname_str |
Universidade Estadual Paulista (UNESP) |
instacron_str |
UNESP |
institution |
UNESP |
reponame_str |
Repositório Institucional da UNESP |
collection |
Repositório Institucional da UNESP |
repository.name.fl_str_mv |
Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP) |
repository.mail.fl_str_mv |
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1808129541705564160 |