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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/3TCFC6B
Repositóriosid.inpe.br/mtc-m21c/2019/05.27.11.21   (acesso restrito)
Última Atualização2019:05.27.11.21.11 (UTC) simone
Repositório de Metadadossid.inpe.br/mtc-m21c/2019/05.27.11.21.11
Última Atualização dos Metadados2020:01.06.11.42.14 (UTC) administrator
DOI10.1016/j.foreco.2019.05.016
ISSN0378-1127
Chave de CitaçãoSilveiraEWACMMSTCS:2019:PrMoPl
TítuloPre-stratified modelling plus residuals kriging reduces the uncertainty of aboveground biomass estimation and spatial distribution in heterogeneous savannas and forest environments
Ano2019
MêsAug.
Data de Acesso13 maio 2024
Tipo de Trabalhojournal article
Tipo SecundárioPRE PI
Número de Arquivos1
Tamanho9208 KiB
2. Contextualização
Autor 1 Silveira, Eduarda M. O.
 2 Espírito Santos, Fernando D.
 3 Wulder, Michael A.
 4 Acerbi Júnior, Fausto W.
 5 Carvalho, Mônica C.
 6 Mello, Carlos R.
 7 Mello, José Márcio
 8 Shimabukuro, Yosio Edemir
 9 Terra, Marcela Castro Nunes Santos
10 Carvalho, Luis Marcelo T.
11 Scolforo, José R. S.
Identificador de Curriculo 1
 2
 3
 4
 5
 6
 7
 8 8JMKD3MGP5W/3C9JJCQ
Grupo 1
 2
 3
 4
 5
 6
 7
 8 DIDSR-CGOBT-INPE-MCTIC-GOV-BR
Afiliação 1 Universidade Federal de Lavras (UFLA)
 2 University of Leicester
 3 Canadian Forest Service (Pacific Forestry Centre)
 4 Universidade Federal de Lavras (UFLA)
 5 Universidade Federal de Lavras (UFLA)
 6 Universidade Federal de Lavras (UFLA)
 7 Universidade Federal de Lavras (UFLA)
 8 Instituto Nacional de Pesquisas Espaciais (INPE)
 9 Universidade Federal de Lavras (UFLA)
10 Universidade Federal de Lavras (UFLA)
11 Universidade Federal de Lavras (UFLA)
Endereço de e-Mail do Autor 1 dudalavras@hotmail.com
 2
 3 mike.wulder@canada.ca
 4 fausto@dcf.ufla.br
 5 monicacanaam@gmail.com
 6 crmello@deg.ufla.br
 7 josemarcio@dcf.ufla.br
 8 yosio.shimabukuro@inpe.br
 9 marcelacsn@gmail.com
10 passarinho@ufla.br
11 jscolforo@dcf.ufla.br
RevistaForest Ecology and Management
Volume445
Páginas96-109
Nota SecundáriaA1_ZOOTECNIA_/_RECURSOS_PESQUEIROS A1_INTERDISCIPLINAR A1_GEOGRAFIA A1_ENGENHARIAS_III A1_CIÊNCIAS_AMBIENTAIS A1_BIODIVERSIDADE A2_GEOCIÊNCIAS A2_ENGENHARIAS_I A2_CIÊNCIAS_AGRÁRIAS_I B1_MATERIAIS B1_ENGENHARIAS_II B1_CIÊNCIAS_BIOLÓGICAS_I B1_BIOTECNOLOGIA
Histórico (UTC)2019-05-27 11:21:11 :: simone -> administrator ::
2019-05-27 11:21:12 :: administrator -> simone :: 2019
2019-05-27 11:22:28 :: simone -> administrator :: 2019
2020-01-06 11:42:14 :: 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-ChaveAGB
Random forests
Brazilian biomes
Climate Seasonality
ResumoMapping aboveground biomass (AGB)is a challenge in heterogeneous environments, such as the Brazilian savannas and tropical forests located in Minas Gerais state (MG), Brazil. The factors linked to AGB stocks vary in climate, soil characteristics, and stand-level structural attributes over short distances, making generalization of AGB difficult over regional-scales. We offer the hypothesis that stratification into vegetation types at the plot level plus a regression kriging technique, can reduce the variability of factors controlling AGB, helping to select the appropriate predictor variables and result in an ability to produce reliable models and maps. To do so, we incorporate remotely sensed data (Landsat and MODerate resolution Imaging Spectroradiometer-MODIS), spatio-environmental variables, and forest inventory data to develop spatial-explicit maps of AGB across three important Brazilian biomes (savanna, Atlantic forest, and semi-arid woodland). We modelled and predicted the spatial distribution of AGB of six individual vegetation types of savanna-forest biomes (shrub savanna, woodland savanna, densely wooded savanna, deciduous forest, semi-deciduous forest and rain forest), utilizing a random forests (RF)algorithm plus residual kriging, selecting the lowest number of variables that offer the best predictive performance. The stratified models notably improved the AGB prediction by reducing the mean absolute error MAE (%)and the root-mean-square error RMSE (Mg/ha)for all vegetation types, mainly for shrub savanna (MAE reduced from 82.69 to 54.73%). The AGB spatial distribution is governed mainly by precipitation and seasonality. The south and east of MG presented high values of AGB due to the predominance of semi-deciduous trees and rain forest conditions within Atlantic forest biome (total of 491,456,607 Mg), with a higher amount rain over the year, lower temperatures, and lower precipitation seasonality. Rain forests have the largest mean AGB per area (157.71 Mg/ha)while semi-deciduous forests hold the largest AGB stocks in the state (583,176,472 Mg). Shrub savannas, located in the central, northwest and north regions of MG (lower amount of rain, higher temperatures and strong seasonality), accounted the lowest amount of AGB in both total AGB (27,906,281 Mg)and AGB per area (18.80 Mg/ha). Our study demonstrates that stratification can reduce variability and improve estimates by developing individual models and selecting optimal predictor variables dependent on the characteristics of specific vegetation types. The methods demonstrated and the resultant maps and estimates improve the quality of regional biomass estimates needed to understand and mitigate climate change, enabling researchers to refine estimates of greenhouse gas emissions.
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Arquivo AlvoSilveira1-s2.0-S0378112719301185-main.pdf
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5. Fontes relacionadas
Unidades Imediatamente Superiores8JMKD3MGPCW/3ER446E
DivulgaçãoWEBSCI; PORTALCAPES; COMPENDEX; SCOPUS.
Acervo Hospedeirourlib.net/www/2017/11.22.19.04
6. Notas
Campos Vaziosalternatejournal archivist callnumber copyholder copyright creatorhistory descriptionlevel e-mailaddress format isbn label lineage mark mirrorrepository nextedition notes 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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