1. Identificação | |
Tipo de Referência | Artigo em Revista Científica (Journal Article) |
Site | mtc-m21c.sid.inpe.br |
Código do Detentor | isadg {BR SPINPE} ibi 8JMKD3MGPCW/3DT298S |
Identificador | 8JMKD3MGP3W34R/3TSB2EP |
Repositório | sid.inpe.br/mtc-m21c/2019/08.20.14.14 (acesso restrito) |
Última Atualização | 2019:08.20.14.14.41 (UTC) simone |
Repositório de Metadados | sid.inpe.br/mtc-m21c/2019/08.20.14.14.41 |
Última Atualização dos Metadados | 2024:01.23.15.53.48 (UTC) simone |
DOI | 10.1016/j.rse.2019.111323 |
ISSN | 0034-4257 |
Chave de Citação | AlmeidaGAOJPSLGSFL:2019:CoLiHy |
Título | Combining LiDAR and hyperspectral data for aboveground biomass modeling in the Brazilian Amazon using different regression algorithms |
Ano | 2019 |
Mês | Oct. |
Data de Acesso | 16 maio 2024 |
Tipo de Trabalho | journal article |
Tipo Secundário | PRE PI |
Número de Arquivos | 1 |
Tamanho | 3707 KiB |
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2. Contextualização | |
Autor | 1 Almeida, Catherine Torres de 2 Galvão, Lênio Soares 3 Aragão, Luiz Eduardo Oliveira e Cruz de 4 Ometto, Jean Pierre Henry Balbaud 5 Jacon, Aline Daniele 6 Pereira, Francisca Rocha de Souza 7 Sato, Luciane Yumie 8 Lopes, Aline Pontes 9 Graça, Paulo Maurício Lima de Alencastro 10 Silva, Camila Valéria de Jesus 11 Ferreira-Ferreira, Jefferson 12 Longo, Marcos |
Identificador de Curriculo | 1 2 8JMKD3MGP5W/3C9JHLF |
Grupo | 1 SER-SRE-SESPG-INPE-MCTIC-GOV-BR 2 DIDSR-CGOBT-INPE-MCTIC-GOV-BR 3 DIDSR-CGOBT-INPE-MCTIC-GOV-BR 4 COCST-COCST-INPE-MCTIC-GOV-BR 5 SER-SRE-SESPG-INPE-MCTIC-GOV-BR 6 DIDSR-CGOBT-INPE-MCTIC-GOV-BR 7 COCST-COCST-INPE-MCTIC-GOV-BR 8 SER-SRE-SESPG-INPE-MCTIC-GOV-BR |
Afiliação | 1 Instituto Nacional de Pesquisas Espaciais (INPE) 2 Instituto Nacional de Pesquisas Espaciais (INPE) 3 Instituto Nacional de Pesquisas Espaciais (INPE) 4 Instituto Nacional de Pesquisas Espaciais (INPE) 5 Instituto Nacional de Pesquisas Espaciais (INPE) 6 Instituto Nacional de Pesquisas Espaciais (INPE) 7 Instituto Nacional de Pesquisas Espaciais (INPE) 8 Instituto Nacional de Pesquisas Espaciais (INPE) 9 Instituto Nacional de Pesquisas da Amazônia (INPA) 10 Lancaster University 11 Instituto de Desenvolvimento Sustentável Mamirauá 12 California Institute of Technology |
Endereço de e-Mail do Autor | 1 catherine.almeida@inpe.br 2 lenio.galvao@inpe.br 3 luiz.aragao@inpe.br 4 jean.ometto@inpe.br 5 alinejacon@hotmail.com 6 francisca.pereira@inpe.br 7 luciane.sato@inpe.br 8 aline.lopes@inpe.br 9 pmlag@inpa.gov.br 10 c.silva@lancaster.ac.uk 11 jefferson.ferreira@mamiraua.org.br 12 mlongo@jpl.nasa.gov |
Revista | Remote Sensing of Environment |
Volume | 232 |
Páginas | e111323 |
Nota Secundária | A1_INTERDISCIPLINAR A1_GEOCIÊNCIAS A1_ENGENHARIAS_I A1_CIÊNCIAS_BIOLÓGICAS_I A1_CIÊNCIAS_AMBIENTAIS A1_CIÊNCIAS_AGRÁRIAS_I A1_BIODIVERSIDADE |
Histórico (UTC) | 2019-08-20 14:14:41 :: simone -> administrator :: 2019-08-20 14:35:29 :: administrator -> simone :: 2019 2019-08-20 15:01:34 :: simone -> administrator :: 2019 2020-01-06 11:42:18 :: administrator -> simone :: 2019 |
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3. Conteúdo e estrutura | |
É a matriz ou uma cópia? | é a matriz |
Estágio do Conteúdo | concluido |
Transferível | 1 |
Tipo do Conteúdo | External Contribution |
Tipo de Versão | publisher |
Palavras-Chave | Hyperspectral remote sensing Laser scanning Data integration Tropical forest Carbon stock |
Resumo | Accurate estimates of aboveground biomass (AGB) in tropical forests are critical for supporting strategies of ecosystem functioning conservation and climate change mitigation. However, such estimates at regional and local scales are still highly uncertain. Airborne Light Detection And Ranging (LiDAR) and Hyperspectral Imaging (HSI) can characterize the structural and functional diversity of forests with high accuracy at a sub-meter resolution, and potentially improve the AGB estimations. In this study, we compared the ability of different data sources (airborne LiDAR and HSI, and their combination) and regression methods (linear model - LM, linear model with ridge regularization - LMR, Support Vector Regression - SVR, Random Forest - RF, Stochastic Gradient Boosting - SGB, and Cubist - CB) to improve AGB predictions in the Brazilian Amazon. We used georeferenced inventory data from 132 sample plots to obtain a reference field AGB and calculated 333 metrics (45 from LiDAR and 288 from HSI) that could be used as predictors for statistical AGB models. We submitted the metrics to a correlation filtering followed by a feature selection procedure (recursive feature elimination) to optimize the performance of the models and to reduce their complexity. Results showed that both LiDAR and HSI data used alone provided relatively high accurate models if adequate metrics and algorithms are chosen (RMSE = 67.6 Mg.ha−1 , RMSE% = 36%, R2 = 0.58, for the best LiDAR model; RMSE = 68.1 Mg.ha−1 , RMSE % = 36%, R2 = 0.58, for the best HSI model). However, HSI-only models required more metrics (512) than LiDAR-only models (25). Models combining metrics from both datasets resulted in more accurate AGB estimates, regardless of the regression method (RMSE = 57.7 Mg.ha−1 , RMSE% = 31%, R2 = 0.70, for the best model). The most important LiDAR metrics for estimating AGB were related to the upper canopy cover and tree height percentiles, while the most important HSI metrics were associated with the near infrared and shortwave infrared spectral regions, particularly the leaf/canopy water and lignin-cellulose absorption bands. Finally, an analysis of variance (ANOVA) showed that the remote sensing data source (LiDAR, HSI, or their combination) had a greater effect size than the regression algorithms. Thus, no single algorithm outperformed the others, although the LM method was less suitable when applied to the HSI and hybrid datasets. Results show that the synergistic use of LiDAR and hyperspectral data has great potential for improving the accuracy of the biomass estimates in the Brazilian Amazon. |
Área | SRE |
Arranjo 1 | urlib.net > BDMCI > Fonds > Produção anterior à 2021 > DIDSR > Combining LiDAR and... |
Arranjo 2 | urlib.net > BDMCI > Fonds > Produção pgr ATUAIS > SER > Combining LiDAR and... |
Arranjo 3 | urlib.net > BDMCI > Fonds > Produção anterior à 2021 > COCST > Combining LiDAR and... |
Conteúdo da Pasta doc | acessar |
Conteúdo da Pasta source | não têm arquivos |
Conteúdo da Pasta agreement | |
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4. Condições de acesso e uso | |
Idioma | en |
Arquivo Alvo | almeida_combining.pdf |
Grupo de Usuários | simone |
Grupo de Leitores | administrator simone |
Visibilidade | shown |
Política de Arquivamento | denypublisher allowfinaldraft24 |
Permissão de Leitura | deny from all and allow from 150.163 |
Permissão de Atualização | não transferida |
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5. Fontes relacionadas | |
Unidades Imediatamente Superiores | 8JMKD3MGPCW/3ER446E 8JMKD3MGPCW/3F3NU5S 8JMKD3MGPCW/3F3T29H |
Lista de Itens Citando | sid.inpe.br/bibdigital/2013/10.19.20.40 4 sid.inpe.br/mtc-m21/2012/07.13.14.53.28 2 sid.inpe.br/bibdigital/2013/10.18.22.34 1 |
Divulgação | WEBSCI; PORTALCAPES; MGA; COMPENDEX; SCOPUS. |
Acervo Hospedeiro | urlib.net/www/2017/11.22.19.04 |
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6. Notas | |
Notas | Prêmio CAPES Elsevier 2023 - ODS 15: Vida terrestre |
Campos Vazios | alternatejournal archivist callnumber copyholder copyright creatorhistory descriptionlevel e-mailaddress format isbn label lineage mark mirrorrepository nextedition number orcid parameterlist parentrepositories previousedition previouslowerunit progress project rightsholder schedulinginformation secondarydate secondarykey session shorttitle sponsor subject tertiarymark tertiarytype url |
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7. Controle da descrição | |
e-Mail (login) | simone |
atualizar | |
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