| 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/3UHG65H | |||
| Repositório | sid.inpe.br/mtc-m21c/2019/12.10.11.31 | |||
| Última Atualização | 2020:09.28.19.11.27 (UTC) simone | |||
| Repositório de Metadados | sid.inpe.br/mtc-m21c/2019/12.10.11.31.30 | |||
| Última Atualização dos Metadados | 2020:09.28.19.11.27 (UTC) simone | |||
| Chave Secundária | INPE--PRE/ | |||
| Chave de Citação | WagnerDaStPhGlAr:2019:AnReDi | |||
| Título | Analysing the Regional Distribution of a Key Canopy Palm Species Using a Convolutional Network in an Amazon Forest  | |||
| Ano | 2019 | |||
| Data de Acesso | 16 maio 2024 | |||
| Tipo Secundário | PRE CI | |||
| Número de Arquivos | 1 | |||
| Tamanho | 76 KiB | |||
| 2. Contextualização | ||||
| Autor | 1 Wagner, Fabien Hubert 2 Dalagnol, Ricardo 3 Streher, Annia Susin 4 Phillips, Oliver L. 5 Gloor, Emanuel Ulrich 6 Aragão, Luiz Eduardo Oliveira e Cruz de | |||
| Grupo | 1 DIDSR-CGOBT-INPE-MCTIC-GOV-BR 2 DIDSR-CGOBT-INPE-MCTIC-GOV-BR 3 4 5 6 DIDSR-CGOBT-INPE-MCTIC-GOV-BR | |||
| Afiliação | 1 Instituto Nacional de Pesquisas Espaciais (INPE) 2 Instituto Nacional de Pesquisas Espaciais (INPE) 3 Universidade Estadual Paulista (UNESP) 4 University of Leeds 5 University of Leeds 6 Instituto Nacional de Pesquisas Espaciais (INPE) | |||
| Endereço de e-Mail do Autor | 1 fabien.wagner@inpe.br 2 ricardo.silva@inpe.br 3 4 5 6 luiz.aragao@inpe.br | |||
| Nome do Evento | AGU Fall Meeting | |||
| Localização do Evento | San Francisco, CA | |||
| Data | 09-13 dec. | |||
| Histórico (UTC) | 2019-12-10 11:31:30 :: simone -> administrator :: 2019-12-12 12:16:44 :: administrator -> simone :: 2019 | |||
| 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 | Mapping plant species at landscape scale to provide information for ecologists and forest managers is a new challenge for the remote sensing community. Here, we use a deep learning algorithm associated with very high-resolution multispectral images (0.5 m) from GeoEye satellite to identify and segment a palm tree species, Attalea speciosa, in the canopy of an Amazon forest. This study was conducted in a region of the critically endangered Brazilian Amazon Rainforest, between two deforestation fronts, which is a global conservation priority due to its abundance of species of flora and fauna and its carbon stock. The convolutional network generated in this study for identifying palm trees was trained with about 1024 high-resolution true colour optical images and their labelled masks. Additionally, we analysed the spatial distribution of the palm trees at the regional scale based on patches locations and edaphic conditions. Our deep learning network segmented palm trees patches with overall accuracies of 95.5 % and Dice coefficients of 0.67. Then, the segmentation of tree species was produced over a region >2500 km² using GeoEye Red, Green and Blue bands pan-sharpened at 0.5 m. We found that the palm trees covered 5 % of the natural forest canopies and were distributed in more than one million patches. Our results based on the palm trees distribution shown that their abundance tends to vary primarily with local soil water content over the landscape. Overall, their distribution over the region seems to indicate a relatively pristine landscape. However, we observed that they are sparsely distributed in secondary forests and could likely be used as an indicator of large past perturbation. Our work shows how deep learning algorithm can support applications such as mapping plant species to understand plant distributions and landscape features. | |||
| Área | SRE | |||
| Arranjo | urlib.net > BDMCI > Fonds > Produção anterior à 2021 > DIDSR > Analysing the Regional... | |||
| Conteúdo da Pasta doc | acessar | |||
| Conteúdo da Pasta source | não têm arquivos | |||
| Conteúdo da Pasta agreement |
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| 4. Condições de acesso e uso | ||||
| URL dos dados | http://urlib.net/ibi/8JMKD3MGP3W34R/3UHG65H | |||
| URL dos dados zipados | http://urlib.net/zip/8JMKD3MGP3W34R/3UHG65H | |||
| Idioma | en | |||
| Arquivo Alvo | wagner_analysis.pdf | |||
| Grupo de Usuários | simone | |||
| Grupo de Leitores | administrator simone | |||
| Visibilidade | shown | |||
| Permissão de Atualização | não transferida | |||
| 5. Fontes relacionadas | ||||
| Repositório Espelho | urlib.net/www/2017/11.22.19.04.03 | |||
| Unidades Imediatamente Superiores | 8JMKD3MGPCW/3ER446E | |||
| Acervo Hospedeiro | urlib.net/www/2017/11.22.19.04 | |||
| 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 nextedition notes numberofvolumes orcid organization pages parameterlist parentrepositories previousedition previouslowerunit progress project publisher publisheraddress readpermission resumeid rightsholder schedulinginformation secondarydate secondarymark serieseditor session shorttitle sponsor subject tertiarymark tertiarytype type url versiontype volume | |||
| 7. Controle da descrição | ||||
| e-Mail (login) | simone | |||
| atualizar | ||||