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3 referências similares encontradas (inclusive a original) buscando em 22 dentre 22 Arquivos.
Data e hora local de busca: 16/05/2024 11:15.
1. Identificação
Tipo de ReferênciaArtigo em Evento (Conference Proceedings)
Sitemtc-m21c.sid.inpe.br
Código do Detentorisadg {BR SPINPE} ibi 8JMKD3MGPCW/3DT298S
Identificador8JMKD3MGP3W34R/3SG4P8E
Repositóriosid.inpe.br/mtc-m21c/2019/01.03.14.08
Última Atualização2019:01.03.14.08.33 (UTC) simone
Repositório de Metadadossid.inpe.br/mtc-m21c/2019/01.03.14.08.33
Última Atualização dos Metadados2021:03.06.05.22.35 (UTC) administrator
Chave SecundáriaINPE--PRE/
Chave de CitaçãoPereiraAESDJCCO:2018:EfSpRe
TítuloThe Effects of the Spatial Resolution of Airborne Lidar Data on Aboveground Biomass Estimation
Ano2018
Data de Acesso16 maio 2024
Tipo SecundárioPRE CI
Número de Arquivos1
Tamanho1267 KiB
2. Contextualização
Autor1 Pereira, Francisca Rocha de Souza
2 Assis, Mauro Lúcio Rodrigues de
3 Espírito Santo, Ferandno
4 Sato, Luciane
5 Dias, Emily
6 Jacon, Aline
7 Carneiro, Heitor Guerra
8 Cantinho, Roberta
9 Ometto, Jean Pierre Henry Balbaud
Grupo1 COCST-COCST-INPE-MCTIC-GOV-BR
2 COCST-COCST-INPE-MCTIC-GOV-BR
3
4 CST-CST-SESPG-INPE-MCTIC-GOV-BR
5 CST-CST-SESPG-INPE-MCTIC-GOV-BR
6 CST-CST-SESPG-INPE-MCTIC-GOV-BR
7 COCST-COCST-INPE-MCTIC-GOV-BR
8
9 COCST-COCST-INPE-MCTIC-GOV-BR
Afiliação1 Instituto Nacional de Pesquisas Espaciais (INPE)
2 Instituto Nacional de Pesquisas Espaciais (INPE)
3 Ministério da Ciência, Tecnologia, Inovação e Comunicações (MCTIC)
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 Ministério da Ciência, Tecnologia, Inovação e Comunicações (MCTIC
9 Instituto Nacional de Pesquisas Espaciais (INPE)
Endereço de e-Mail do Autor1 franrspereira@gmail.com
2 assismauro@htomail.com
3
4
5
6
7 heitorguerrac@gmail.com
8
9 jean.ometto@inpe.br
Nome do EventoIUFRO Conference
Localização do EventoPosadas, Argentina
Data01-05 oct.
Título do LivroProceedings
Tipo TerciárioPoster
Histórico (UTC)2019-01-03 14:08:33 :: simone -> administrator ::
2021-03-06 05:22:35 :: administrator -> simone :: 2018
3. Conteúdo e estrutura
É a matriz ou uma cópia?é a matriz
Estágio do Conteúdoconcluido
Transferível1
Tipo do ConteúdoExternal Contribution
Tipo de Versãopublisher
ResumoThe amount of aboveground biomass (AGB) held in vital components of vegetation play a significant role in the carbon cycle of tropical forests. Reducing uncertainty of terrestrial carbon cycle depend strongly on the accurate estimate of AGB. Lidar remote sensing provides the most precise methodology to quantify AGB at large scales, but the effects of the spatial resolution of airborne lidar data on AGB estimation is unknown. Here we examine the impact of the minimum spatial resolution threshold of lidar data to reduce the uncertainty of AGB estimations in tropical forest. For that we used a sizeable airborne lidar data from Tapajos National Forest (TNF) and ten permanent field plots. We compared two approaches: (1) we used general lidar allometric equation of AGB estimation developed for the Amazon, testing the best spatial resolution of lidar measurements at 25, 50 and 100 meters and compared with our ground data of forest inventory from TNF; (2) we developed and tested a new local lidar allometric equation to quantify AGB in TNF. Although the use of lidar cloud cover at 50 m provides unbiased estimates of AGB, our results demonstrated that local forest structure plays a significant role in this general allometric equations. Our results underscored three conclusions. First, the effects of the spatial resolution of airborne lidar data on AGB estimation were significant. We found that a minimum size-area of 50 meters of lidar is necessary to produce an unbiased estimate of AGB in a local tropical forest of Central Amazon. Second, our adjusted allometric equation for TNF, which was based in mean canopy height model, reduced the uncertainty of AGB from RMSE%: 36.8% to RMSE%: 26.2% (local model). Finally, this study highlights the need of lidar allometric equations based on local forest structure to reduce the uncertainty of AGB estimations.
ÁreaCST
Arranjo 1urlib.net > BDMCI > Fonds > Produção anterior à 2021 > COCST > The Effects of...
Arranjo 2urlib.net > BDMCI > Fonds > Produção pgr ATUAIS > CST > The Effects of...
Conteúdo da Pasta docacessar
Conteúdo da Pasta sourcenão têm arquivos
Conteúdo da Pasta agreement
agreement.html 03/01/2019 12:08 1.0 KiB 
4. Condições de acesso e uso
URL dos dadoshttp://urlib.net/ibi/8JMKD3MGP3W34R/3SG4P8E
URL dos dados zipadoshttp://urlib.net/zip/8JMKD3MGP3W34R/3SG4P8E
Idiomaen
Arquivo Alvopereira_effects.pdf
Grupo de Usuáriossimone
Visibilidadeshown
Permissão de Atualizaçãonão transferida
5. Fontes relacionadas
Unidades Imediatamente Superiores8JMKD3MGPCW/3F3T29H
8JMKD3MGPCW/449U4PL
Lista de Itens Citandosid.inpe.br/bibdigital/2021/03.06.05.18 2
Acervo Hospedeirourlib.net/www/2017/11.22.19.04
6. Notas
Campos Vaziosarchivingpolicy archivist callnumber copyholder copyright creatorhistory descriptionlevel dissemination doi e-mailaddress edition editor format isbn issn keywords label lineage mark mirrorrepository nextedition notes numberofvolumes orcid organization pages parameterlist parentrepositories previousedition previouslowerunit progress project publisher publisheraddress readergroup readpermission resumeid rightsholder schedulinginformation secondarydate secondarymark serieseditor session shorttitle sponsor subject tertiarymark type url volume
7. Controle da descrição
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1. Identificação
Tipo de ReferênciaArtigo em Revista Científica (Journal Article)
Siteplutao.sid.inpe.br
Código do Detentorisadg {BR SPINPE} ibi 8JMKD3MGPCW/3DT298S
Identificador8JMKD3MGP3W/43NH6TT
Repositóriosid.inpe.br/plutao/2020/12.07.14.52.46   (acesso restrito)
Última Atualização2020:12.08.21.32.09 (UTC) lattes
Repositório de Metadadossid.inpe.br/plutao/2020/12.07.14.52.47
Última Atualização dos Metadados2022:01.04.01.31.24 (UTC) administrator
DOI10.1016/j.jag.2020.102215
ISSN0303-2434
Rótulolattes: 9511166263268121 5 MartinsKalGelNagMac:2020:DeNeNe
Chave de CitaçãoMartinsKalGelNagMac:2020:DeNeNe
TítuloDeep neural network for complex open-water wetland mapping using high-resolution WorldView-3 and airborne LiDAR data
Ano2020
MêsDec.
Data de Acesso16 maio 2024
Tipo de Trabalhojournal article
Tipo SecundárioPRE PI
Número de Arquivos1
Tamanho11634 KiB
2. Contextualização
Autor1 Martins, Vitor S.
2 Kaleita, Amy L.
3 Gelder, Brian K.
4 Nagel, Gustavo Willy
5 Maciel, Daniel Andrade
Grupo1
2
3
4 DIDPI-CGOBT-INPE-MCTIC-GOV-BR
5 SER-SRE-SESPG-INPE-MCTIC-GOV-BR
Afiliação1 Iowa State University (ISU)
2 Iowa State University (ISU)
3 Iowa State University (ISU)
4 Instituto Nacional de Pesquisas Espaciais (INPE)
5 Instituto Nacional de Pesquisas Espaciais (INPE)
Endereço de e-Mail do Autor1 vitors@iastate.edu
2 kaleita@iastate.edu
3
4 gustavo.nagel@inpe.br
5 daniel.maciel@inpe.br
RevistaInternational Journal of Applied Earth Observation and Geoinformation
Volume93
Páginase102215
Nota SecundáriaB1_GEOCIÊNCIAS
Histórico (UTC)2020-12-07 14:52:47 :: lattes -> administrator ::
2020-12-08 21:28:59 :: administrator -> lattes :: 2020
2020-12-08 21:32:10 :: lattes -> administrator :: 2020
2022-01-04 01:31:24 :: administrator -> simone :: 2020
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-ChaveDeep learning
Small wetlands
Machine learning
Optical and LiDAR data
PCA
ResumoWetland inventory maps are essential information for the conservation and management of natural wetland areas. The classification framework is crucial for successful mapping of complex wetlands, including the model selection, input variables and training procedures. In this context, deep neural network (DNN) is a powerful technique for remote sensing image classification, but this model application for wetland mapping has not been discussed in the previous literature, especially using commercial WorldView-3 data. This study developed a new framework for wetland mapping using DNN algorithm and WorldView-3 image in the Millrace Flats Wildlife Management Area, Iowa, USA. The study area has several wetlands with a variety of shapes and sizes, and the minimum mapping unit was defined as 20 m2 (0.002 ha). A set of potential variables was derived from WorldView-3 and auxiliary LiDAR data, and a feature selection procedure using principal components analysis (PCA) was used to identify the most important variables for wetland classification. Furthermore, traditional machine learning methods (support vector machine, random forest and k-nearest neighbor) were also implemented for the comparison of results. In general, the results show that DNN achieved satisfactory results in the study area (overall accuracy = 93.33 %), and we observed a high spatial overlap between reference and classified wetland polygons (Jaccard index ∼0.8). Our results confirm that PCA-based feature selection was effective in the optimization of DNN performance, and vegetation and textural indices were the most informative variables. In addition, the comparison of results indicated that DNN classification achieved relatively similar accuracies to other methods. The total classification errors vary from 0.104 to 0.111 among the methods, and the overlapped areas between reference and classified polygons range between 87.93 and 93.33 %. Finally, the findings of this study have three main implications. First, the integration of DNN model and WorldView-3 image is useful for wetland mapping at 1.2-m, but DNN results did not outperform other methods in this study area. Second, the feature selection was important for model performance, and the combination of most relevant input parameters contributes to the success of all tested models. Third, the spatial resolution of WorldView-3 is appropriate to preserve the shape and extent of small wetlands, while the application of medium resolution image (30-m) has a negative impact on the accurate delineation of these areas. Since commercial satellite data are becoming more affordable for remote sensing users, this study provides a framework that can be utilized to integrate very high-resolution imagery and deep learning in the classification of complex wetland areas.
ÁreaSRE
Arranjo 1urlib.net > BDMCI > Fonds > Produção anterior à 2021 > DIDPI > Deep neural network...
Arranjo 2urlib.net > Fonds > Produção pgr ATUAIS > SER > Deep neural network...
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Conteúdo da Pasta docacessar
Conteúdo da Pasta sourcenão têm arquivos
Conteúdo da Pasta agreementnão têm arquivos
4. Condições de acesso e uso
Idiomaen
Arquivo Alvomartins_deep.pdf
Grupo de Usuárioslattes
Grupo de Leitoresadministrator
lattes
Visibilidadeshown
Política de Arquivamentodenypublisher denyfinaldraft24
Permissão de Leituradeny from all and allow from 150.163
Permissão de Atualizaçãonão transferida
5. Fontes relacionadas
Unidades Imediatamente Superiores8JMKD3MGPCW/3EQCCU5
8JMKD3MGPCW/3F3NU5S
8JMKD3MGPCW/439EAFB
Lista de Itens Citandosid.inpe.br/bibdigital/2013/10.18.22.34 2
sid.inpe.br/bibdigital/2020/09.18.00.06 2
DivulgaçãoWEBSCI; PORTALCAPES; SCOPUS.
Acervo Hospedeirodpi.inpe.br/plutao@80/2008/08.19.15.01
6. Notas
Campos Vaziosalternatejournal archivist callnumber copyholder copyright creatorhistory descriptionlevel e-mailaddress format isbn lineage mark mirrorrepository nextedition notes number orcid parameterlist parentrepositories previousedition previouslowerunit progress project resumeid rightsholder schedulinginformation secondarydate secondarykey session shorttitle sponsor subject tertiarymark tertiarytype url
7. Controle da descrição
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1. Identificação
Tipo de ReferênciaArtigo em Evento (Conference Proceedings)
Sitemtc-m21b.sid.inpe.br
Código do Detentorisadg {BR SPINPE} ibi 8JMKD3MGPCW/3DT298S
Identificador8JMKD3MGP3W34P/3K3ASCP
Repositóriosid.inpe.br/mtc-m21b/2015/08.11.11.50
Repositório de Metadadossid.inpe.br/mtc-m21b/2015/08.11.11.50.39
Última Atualização dos Metadados2021:01.02.03.56.41 (UTC) administrator
Chave SecundáriaINPE--PRE/
Chave de CitaçãoOozeerChOoZaSaCh:2015:CaStJu
TítuloEvaluation of WRF-chem model with high-spectral resolution lidar data of Singapore: a case study of the June 2013 biomass-burning haze event
Ano2015
Data de Acesso16 maio 2024
Tipo SecundárioPRE CI
2. Contextualização
Autor1 Oozeer, Muhammad Yaasiin
2 Chan, Andy
3 Ooi, Maggie Chel Gee
4 Zarzur, Antonio Maurício
5 Salinas, Santo
6 Chew, Boon Ning
Grupo1
2
3
4 DMD-CPT-INPE-MCTI-GOV-BR
Afiliação1 University of Nottingham
2 University of Nottingham
3 University of Nottingham
4 Instituto Nacional de Pesquisas Espaciais (INPE)
5 National University of Singapore
6 National University of Singapore
Nome do EventoAnnual Meetings Asia Oceania Geosciences Society (AOGS)
Localização do EventoSingapore
Data2-7 Aug.
Histórico (UTC)2015-08-11 11:50:39 :: simone -> administrator ::
2021-01-02 03:56:41 :: administrator -> simone :: 2015
3. Conteúdo e estrutura
É a matriz ou uma cópia?é a matriz
Estágio do Conteúdoconcluido
Transferível1
Tipo do ConteúdoExternal Contribution
ResumoThis study aims to identify the vertical transport mechanisms that uplifted the forest fire emissions from Sumatra to the troposphere during the June 2013 haze crisis. WRF-Chem was used to simulate the formation and transport of biomass-burning haze during the study period of 16th to 26th June 2013. 27-km ,9-km and 3-km spatial grids were used to observe the South-Southeast Asian synoptic weather patterns and their effects on the transport of biomass-burning emissions from Sumatra to Peninsular Malaysia. Results show that PM10 emissions were lifted above the planetary boundary layer over Peninsular Malaysia on 24th June. The model was evaluated by comparing the results with high-spectral resolution LIDAR data of Singapore as well as meteorological data for Malaysia during the haze episode. The 3-km grid was used to resolve convection explicitly to identify the mechanisms that were responsible for the vertical transport of the biomass-burning emissions. These mechanisms were able to uplift the biomass-burning emissions within the troposphere and this could have significant long-range transport and global climatic effects.
ÁreaMET
Arranjourlib.net > BDMCI > Fonds > Produção anterior à 2021 > DIDMD > Evaluation of WRF-chem...
Conteúdo da Pasta docnão têm arquivos
Conteúdo da Pasta sourcenão têm arquivos
Conteúdo da Pasta agreement
agreement.html 11/08/2015 08:50 1.0 KiB 
4. Condições de acesso e uso
Grupo de Usuáriossimone
Visibilidadeshown
Permissão de Leituraallow from all
Permissão de Atualizaçãonão transferida
5. Fontes relacionadas
Repositório Espelhourlib.net/www/2011/03.29.20.55
Unidades Imediatamente Superiores8JMKD3MGPCW/43SKC35
Lista de Itens Citando
Acervo Hospedeirosid.inpe.br/mtc-m21b/2013/09.26.14.25.20
6. Notas
Campos Vaziosarchivingpolicy archivist booktitle callnumber copyholder copyright creatorhistory descriptionlevel dissemination doi e-mailaddress edition editor electronicmailaddress format isbn issn keywords label language lineage mark nextedition notes numberoffiles numberofvolumes orcid organization pages parameterlist parentrepositories previousedition previouslowerunit progress project publisher publisheraddress readergroup resumeid 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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