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2 referências similares encontradas (inclusive a original) buscando em 22 dentre 22 Arquivos.
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1. Identificação
Tipo de ReferênciaArtigo em Evento (Conference Proceedings)
Sitemtc-m21c.sid.inpe.br
Código do Detentorisadg {BR SPINPE} ibi 8JMKD3MGPCW/3DT298S
Identificador8JMKD3MGP3W34R/3U5UNPP
Repositóriosid.inpe.br/mtc-m21c/2019/09.30.13.02
Repositório de Metadadossid.inpe.br/mtc-m21c/2019/09.30.13.02.03
Última Atualização dos Metadados2020:01.06.11.42.22 (UTC) administrator
Chave SecundáriaINPE--PRE/
Chave de CitaçãoAlmeidaGaArOmJaPeSa:2019:CoReTe
TítuloComparison of regression techniques for LiDAR-derived aboveground biomass estimation in the Amazon
Ano2019
Data de Acesso16 maio 2024
Tipo SecundárioPRE CI
2. Contextualização
Autor1 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 Curriculo1
2 8JMKD3MGP5W/3C9JHLF
Grupo1 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
Afiliação1 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 Autor1 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 EventoCongresso Mundial da IUFRO
Localização do EventoCuritiba, PR
Data29 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
3. Conteúdo e estrutura
É a matriz ou uma cópia?é a matriz
Estágio do Conteúdoconcluido
Transferível1
Tipo do ConteúdoExternal Contribution
ResumoLight 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.
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Arranjo 1urlib.net > BDMCI > Fonds > Produção anterior à 2021 > DIDSR > Comparison of regression...
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4. Condições de acesso e uso
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Grupo de Usuáriossimone
Grupo de Leitoresadministrator
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Visibilidadeshown
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5. Fontes relacionadas
Unidades Imediatamente Superiores8JMKD3MGPCW/3ER446E
8JMKD3MGPCW/3F3T29H
Lista de Itens Citando
Acervo Hospedeirourlib.net/www/2017/11.22.19.04
6. Notas
Campos Vaziosarchivingpolicy 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
7. Controle da descrição
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1. Identificação
Tipo de ReferênciaArtigo em Revista Científica (Journal Article)
Sitemtc-m21c.sid.inpe.br
Código do Detentorisadg {BR SPINPE} ibi 8JMKD3MGPCW/3DT298S
Identificador8JMKD3MGP3W34R/3TSB2EP
Repositóriosid.inpe.br/mtc-m21c/2019/08.20.14.14   (acesso restrito)
Última Atualização2019:08.20.14.14.41 (UTC) simone
Repositório de Metadadossid.inpe.br/mtc-m21c/2019/08.20.14.14.41
Última Atualização dos Metadados2024:01.23.15.53.48 (UTC) simone
DOI10.1016/j.rse.2019.111323
ISSN0034-4257
Chave de CitaçãoAlmeidaGAOJPSLGSFL:2019:CoLiHy
TítuloCombining LiDAR and hyperspectral data for aboveground biomass modeling in the Brazilian Amazon using different regression algorithms
Ano2019
MêsOct.
Data de Acesso16 maio 2024
Tipo de Trabalhojournal article
Tipo SecundárioPRE PI
Número de Arquivos1
Tamanho3707 KiB
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
RevistaRemote Sensing of Environment
Volume232
Páginase111323
Nota SecundáriaA1_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
3. Conteúdo e estrutura
É a matriz ou uma cópia?é a matriz
Estágio do Conteúdoconcluido
Transferível1
Tipo do ConteúdoExternal Contribution
Tipo de Versãopublisher
Palavras-ChaveHyperspectral remote sensing
Laser scanning
Data integration
Tropical forest
Carbon stock
ResumoAccurate 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.
ÁreaSRE
Arranjo 1urlib.net > BDMCI > Fonds > Produção anterior à 2021 > DIDSR > Combining LiDAR and...
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4. Condições de acesso e uso
Idiomaen
Arquivo Alvoalmeida_combining.pdf
Grupo de Usuáriossimone
Grupo de Leitoresadministrator
simone
Visibilidadeshown
Política de Arquivamentodenypublisher allowfinaldraft24
Permissão de Leituradeny from all and allow from 150.163
Permissão de Atualizaçãonão transferida
5. Fontes relacionadas
Unidades Imediatamente Superiores8JMKD3MGPCW/3ER446E
8JMKD3MGPCW/3F3NU5S
8JMKD3MGPCW/3F3T29H
Lista de Itens Citandosid.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
DivulgaçãoWEBSCI; PORTALCAPES; MGA; COMPENDEX; SCOPUS.
Acervo Hospedeirourlib.net/www/2017/11.22.19.04
6. Notas
NotasPrêmio CAPES Elsevier 2023 - ODS 15: Vida terrestre
Campos Vaziosalternatejournal 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
7. Controle da descrição
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