| 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/3SB3SBE | |||
| Repositório | sid.inpe.br/mtc-m21c/2018/12.03.15.11 | |||
| Última Atualização | 2020:09.28.18.52.15 (UTC) simone | |||
| Repositório de Metadados | sid.inpe.br/mtc-m21c/2018/12.03.15.11.53 | |||
| Última Atualização dos Metadados | 2020:09.28.18.52.16 (UTC) simone | |||
| Chave Secundária | INPE--PRE/ | |||
| Chave de Citação | WagnerSTLFAGPA:2018:UsCoNe | |||
| Título | Using convolutional network to identify tree species related to forest disturbance in a neotropical forest with very high resolution multispectral images  | |||
| Ano | 2018 | |||
| Data de Acesso | 11 maio 2024 | |||
| Tipo Secundário | PRE CI | |||
| Número de Arquivos | 1 | |||
| Tamanho | 77 KiB | |||
| 2. Contextualização | ||||
| Autor | 1 Wagner, Fabien Hubert 2 Sanchez Ipia, Alber Hamersson 3 Tarabakla, Yuliya 4 Lotte, Rodolfo Georjute 5 Ferreira, Matheus Pinheiro 6 Aidar, Marcos P. M. 7 Gloor, Manuel 8 Phillips, Oliver L. 9 Aragão, Luiz Eduardo Oliveira e Cruz de | |||
| Grupo | 1 DIDSR-CGOBT-INPE-MCTIC-GOV-BR 2 COCST-COCST-INPE-MCTIC-GOV-BR 3 4 SER-SRE-SESPG-INPE-MCTIC-GOV-BR 5 SER-SRE-SESPG-INPE-MCTIC-GOV-BR 6 7 8 9 DIDSR-CGOBT-INPE-MCTIC-GOV-BR | |||
| Afiliação | 1 Instituto Nacional de Pesquisas Espaciais (INPE) 2 Instituto Nacional de Pesquisas Espaciais (INPE) 3 INRIA 4 Instituto Nacional de Pesquisas Espaciais (INPE) 5 Instituto Nacional de Pesquisas Espaciais (INPE) 6 7 University of Leeds 8 University of Leeds 9 Instituto Nacional de Pesquisas Espaciais (INPE) | |||
| Endereço de e-Mail do Autor | 1 2 alber.ipia@inpe.br 3 4 rodolfo.lotte@inpe.br 5 6 7 8 9 luiz.aragao@inpe.br | |||
| Nome do Evento | AGU Fall Meeting | |||
| Localização do Evento | Washington, D. C. | |||
| Data | 10-14 dec. | |||
| Histórico (UTC) | 2018-12-03 15:11:53 :: simone -> administrator :: 2019-01-14 17:06:39 :: administrator -> simone :: 2018 | |||
| 3. Conteúdo e estrutura | ||||
| É a matriz ou uma cópia? | é a matriz | |||
| Estágio do Conteúdo | concluido | |||
| Transferível | 1 | |||
| Tipo do Conteúdo | External Contribution | |||
| Tipo de Versão | publisher | |||
| Resumo | Mapping tree species at landscape scale to provide information for ecologists and forest managers is a new challenge for the remote sensing community. Here, we tested the potential of a recent deep learning algorithm to identify and segment tree species associated with forest disturbance in very high-resolution multispectral images (0.3 m) from WorldView-3 satellite. The study was conducted in a region of the critically endangered Brazilian Atlantic Rainforest, which is a global priority for biodiversity conservation due to its abundance of species of flora and fauna occurring across an extremely fragmented and degraded landscape. The convolutional network generated in this study for identifying trees from different species was trained with about 1500 high-resolution true colour synthetic optical images and their labelled masks for each species. Additionally, we created a new framework for measuring disturbance levels within forest fragments based on the spatial distribution of individual disturbance-related trees. Our deep learning network segmented tree species with overall accuracies of above 95% and Dice coefficients of above 0.85. Then, the segmentation of tree species was produced over a region >1000 km² using WorldView-3 Red, Green and Blue bands pan-sharpened at 0.3 m. We found that the crowns of disturbance-related species covered between 1 and 5 % of the natural forest canopies. Our results based on the trees distribution shown that disturbance tends to increase with fragment size and revealed information that were not accessible from classical landscape fragmentation analysis, which is mainly based on size and connection of the forest fragments. We are still far from recognizing all the species, however, species that are indicator of disturbance and early successional stage of forests can be accurately mapped. Our work shows how deep learning algorithm can support applications such as mapping tree species and forest disturbance at the landscape scale from space. | |||
| Área | SRE | |||
| Arranjo 1 | urlib.net > BDMCI > Fonds > Produção anterior à 2021 > DIDSR > Using convolutional network... | |||
| Arranjo 2 | urlib.net > BDMCI > Fonds > Produção pgr ATUAIS > SER > Using convolutional network... | |||
| Arranjo 3 | urlib.net > BDMCI > Fonds > Produção anterior à 2021 > COCST > Using convolutional network... | |||
| 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/3SB3SBE | |||
| URL dos dados zipados | http://urlib.net/zip/8JMKD3MGP3W34R/3SB3SBE | |||
| Idioma | en | |||
| Arquivo Alvo | wagner_using.pdf | |||
| Grupo de Usuários | simone | |||
| Grupo de Leitores | administrator simone | |||
| Visibilidade | shown | |||
| Permissão de Atualização | não transferida | |||
| 5. Fontes relacionadas | ||||
| Unidades Imediatamente Superiores | 8JMKD3MGPCW/3ER446E 8JMKD3MGPCW/3F3NU5S 8JMKD3MGPCW/3F3T29H | |||
| 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 mirrorrepository 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 volume | |||
| 7. Controle da descrição | ||||
| e-Mail (login) | simone | |||
| atualizar | ||||