1. Identificação | |
Tipo de Referência | Artigo em Evento (Conference Proceedings) |
Site | mtc-m21c.sid.inpe.br |
Código do Detentor | isadg {BR SPINPE} ibi 8JMKD3MGPCW/3DT298S |
Identificador | 8JMKD3MGP3W34R/3U5UNPP |
Repositório | sid.inpe.br/mtc-m21c/2019/09.30.13.02 |
Repositório de Metadados | sid.inpe.br/mtc-m21c/2019/09.30.13.02.03 |
Última Atualização dos Metadados | 2020:01.06.11.42.22 (UTC) administrator |
Chave Secundária | INPE--PRE/ |
Chave de Citação | AlmeidaGaArOmJaPeSa:2019:CoReTe |
Título | Comparison of regression techniques for LiDAR-derived aboveground biomass estimation in the Amazon |
Ano | 2019 |
Data de Acesso | 21 maio 2024 |
Tipo Secundário | PRE CI |
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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 |
Identificador de Curriculo | 1 2 8JMKD3MGP5W/3C9JHLF |
Grupo | 1 DIDSR-CGOBT-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 6 DIDSR-CGOBT-INPE-MCTIC-GOV-BR 7 COCST-COCST-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) |
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 6 francisca.pereira@inpe.br 7 luciane.sato@inpe.br |
Nome do Evento | Congresso Mundial da IUFRO |
Localização do Evento | Curitiba, PR |
Data | 29 set. - 05 out. |
Histórico (UTC) | 2019-09-30 13:02:03 :: simone -> administrator :: 2019-10-01 16:31:11 :: administrator -> simone :: 2019 2019-12-06 19:28:34 :: simone -> administrator :: 2019 2020-01-06 11:42:22 :: 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 |
Resumo | Light Detection And Ranging (LiDAR) is an active remote sensor that has been successfully applied for characterizing canopy structure, especially to estimate aboveground biomass (AGB). Parametric models, mainly the linear regression with stepwise feature selection (LMstep), are the most common approaches used for estimating AGB. However, non-parametric machine learning techniques, such as Support Vector Regression (SVR), Stochastic Gradient Boosting (SGB), and Random Forest (RF), can better address complex relationships between biomass and remote sensing variables. Therefore, it is desirable to assess the performance of different regression strategies. This study aims to compare eight regression techniques for LiDAR-based AGB estimation: LMstep, Linear Models with Regularization (LMR), Partial Least Squares (PLS), K-Nearest Neighbor (KNN), SVR, RF, SGB, and Cubist. For this purpose, 34 LiDAR metrics were regressed against AGB from 147 inventory plots across the Brazilian Amazon Biome. Models performance were evaluated by the average Root Mean Squared Error (RMSE) and R2 from a 5-fold cross-validation strategy with 10 repetitions. The Kruskal-Wallis test was used to evaluate statistical differences among models. Results showed that LMstep presented the highest RMSE (68.85 Mg.ha-1) and lowest R2 (0.66), while SVR had the lowest RMSE (65.23 Mg.ha-1) and highest R2 (0.69). However, the differences in performance of the models were not statistically significant. Thus, we confirmed the results of previous studies that showed that simple approaches, such as linear regression models, performed just as well as advanced machine learning methods for estimating AGB based on LiDAR data. |
Área | SRE |
Arranjo 1 | urlib.net > BDMCI > Fonds > Produção anterior à 2021 > DIDSR > Comparison of regression... |
Arranjo 2 | urlib.net > BDMCI > Fonds > Produção anterior à 2021 > COCST > Comparison of regression... |
Conteúdo da Pasta doc | não têm arquivos |
Conteúdo da Pasta source | não têm arquivos |
Conteúdo da Pasta agreement | |
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4. Condições de acesso e uso | |
Idioma | en |
Grupo de Usuários | simone |
Grupo de Leitores | administrator simone |
Visibilidade | shown |
Permissão de Atualização | não transferida |
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5. Fontes relacionadas | |
Unidades Imediatamente Superiores | 8JMKD3MGPCW/3ER446E 8JMKD3MGPCW/3F3T29H |
Lista de Itens Citando | |
Acervo Hospedeiro | urlib.net/www/2017/11.22.19.04 |
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6. Notas | |
Campos Vazios | archivingpolicy archivist booktitle callnumber copyholder copyright creatorhistory descriptionlevel dissemination doi e-mailaddress edition editor format isbn issn keywords label lineage mark mirrorrepository nextedition notes numberoffiles numberofvolumes orcid organization pages parameterlist parentrepositories previousedition previouslowerunit progress project publisher publisheraddress readpermission rightsholder schedulinginformation secondarydate secondarymark serieseditor session shorttitle size sponsor subject targetfile tertiarymark tertiarytype type url versiontype volume |
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7. Controle da descrição | |
e-Mail (login) | simone |
atualizar | |
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