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1. Identificação
Tipo de ReferênciaArtigo em Revista Científica (Journal Article)
Sitemtc-m21d.sid.inpe.br
Código do Detentorisadg {BR SPINPE} ibi 8JMKD3MGPCW/3DT298S
Identificador8JMKD3MGP3W34T/45STJ2L
Repositóriosid.inpe.br/mtc-m21d/2021/12.01.17.54   (acesso restrito)
Última Atualização2021:12.01.17.54.19 (UTC) simone
Repositório de Metadadossid.inpe.br/mtc-m21d/2021/12.01.17.54.19
Última Atualização dos Metadados2022:04.03.22.27.45 (UTC) administrator
DOI10.1080/15481603.2021.1969630
ISSN1548-1603
Chave de CitaçãoJaconGalvSilvSant:2021:ExHy
TítuloAboveground biomass estimates over Brazilian savannas using hyperspectral metrics and machine learning models: experiences with Hyperion/EO-1
Ano2021
MêsOct.
Data de Acesso29 abr. 2024
Tipo de Trabalhojournal article
Tipo SecundárioPRE PI
Número de Arquivos1
Tamanho1772 KiB
2. Contextualização
Autor1 Jacon, Aline Daniele
2 Galvão, Lênio Soares
3 Silva, Ricardo Dal'Agnol da
4 Santos, João Roberto dos
Identificador de Curriculo1
2 8JMKD3MGP5W/3C9JHLF
3
4 8JMKD3MGP5W/3C9JHF4
ORCID1 0000-0003-2585-5198
2 0000-0002-8313-0497
3 0000-0002-7151-8697
4 0000-0002-1139-9577
Grupo1 SER-SRE-DIPGR-INPE-MCTI-GOV-BR
2 DIOTG-CGCT-INPE-MCTI-GOV-BR
3
4 DIOTG-CGCT-INPE-MCTI-GOV-BR
Afiliação1 Instituto Nacional de Pesquisas Espaciais (INPE)
2 Instituto Nacional de Pesquisas Espaciais (INPE)
3 University of Manchester
4 Instituto Nacional de Pesquisas Espaciais (INPE)
Endereço de e-Mail do Autor1
2 lenio.galvao@hotmail.com
3 ricds@hotmail.com
RevistaGiscience and Remote Sensing
Volume58
Número7
Páginas1112-1129
Nota SecundáriaB1_GEOCIÊNCIAS B1_CIÊNCIAS_AGRÁRIAS_I B2_INTERDISCIPLINAR B3_CIÊNCIAS_AMBIENTAIS
Histórico (UTC)2021-12-01 17:55:23 :: simone -> administrator :: 2021
2021-12-16 19:23:20 :: administrator -> simone :: 2021
2021-12-16 19:23:28 :: simone -> administrator :: 2021
2022-04-03 22:27:45 :: administrator -> simone :: 2021
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
aboveground biomass (AGB)
savannas
Cerrado
machine learning
Hyperion/EO-1
ResumoWe investigated the potential of hyperspectral remote sensing to estimate aboveground biomass (AGB) over the Brazilian savannas (Cerrado), the second-largest source of carbon emissions in Brazil. For this purpose, a Hyperion/Earth Observing-1 (EO-1) image was collected in the dry season at the Ecological Station of Águas Emendadas (ESAE). In order to estimate the AGB, we evaluated the performance of five machine learning models (Classification and Regression Trees CART; Cubist CB, Partial Least Squares Regression PLS; Random Forest RF; and Support Vector Machine SVM) and four sets of metrics (reflectance, narrowband vegetation indices VIs; absorption band parameters; and the combination of these attributes). The lowest root mean square error (RMSE) was obtained for RF using VIs (29%) and a combination of metrics (28%). For VIs, RF differed from CUB, PLS and SVM at 5% significance level. From cross-validation results, the RMSE was 26.36% for grasslands, 35.04% for open savannas, and 24.85% for dense savannas. The RF model with VIs had the most stable predictive performance across the models, as indicated by small variations in RMSE from CART to SVM. The five most important ranked VIs in the RF model were the Normalized Difference Vegetation Index (NDVI), Pigment Specific Simple Ratio (PSSR), Enhanced Vegetation Index (EVI), Red Edge Normalized Difference Vegetation Index (RENDVI) and Structure Insensitive Pigment Index (SIPI). Most of their relationships with AGB were non-linear. The resultant AGB estimates showed consistent results with a vegetation cover map of the ESAE. Areas of the ESAE with AGB lower than 10 Mg.ha−1 were coincident with the occurrence of grassland physiognomies (savanna grasslands and shrub savannas), while areas with AGB higher than 25 Mg.ha−1 matched the occurrence of dense savanna physiognomies (woodland savanna and dense woodland savanna). Grassland areas showed larger values of coefficient of variation (CV) than areas of dense savannas. These first-hand results set a baseline of models and metrics for AGB modeling of savannas during the future transition from current sampling-type hyperspectral missions (< 10 km of swath) to large-coverage hyperspectral satellites (> 100 km of swath).
ÁreaSRE
Arranjo 1urlib.net > BDMCI > Fonds > Produção pgr ATUAIS > SER > Aboveground biomass estimates...
Arranjo 2urlib.net > BDMCI > Fonds > Produção a partir de 2021 > CGCT > Aboveground biomass estimates...
Conteúdo da Pasta docacessar
Conteúdo da Pasta sourcenão têm arquivos
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4. Condições de acesso e uso
Idiomaen
Arquivo Alvojacon_2021_aboveground.pdf
Grupo de Usuáriossimone
Grupo de Leitoresadministrator
simone
Visibilidadeshown
Política de Arquivamentodenypublisher denyfinaldraft
Permissão de Leituradeny from all and allow from 150.163
Permissão de Atualizaçãonão transferida
5. Fontes relacionadas
Unidades Imediatamente Superiores8JMKD3MGPCW/3F3NU5S
8JMKD3MGPCW/46KUATE
Lista de Itens Citandosid.inpe.br/bibdigital/2013/10.18.22.34 5
sid.inpe.br/bibdigital/2022/04.03.22.23 1
sid.inpe.br/mtc-m21/2012/07.13.14.53.28 1
DivulgaçãoWEBSCI; PORTALCAPES; SCOPUS.
Acervo Hospedeirourlib.net/www/2021/06.04.03.40
6. Notas
Campos Vaziosalternatejournal archivist callnumber copyholder copyright creatorhistory descriptionlevel e-mailaddress format isbn label lineage mark mirrorrepository nextedition notes 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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