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Tipo de ReferênciaArtigo em Evento (Conference Proceedings)
Sitemtc-m21c.sid.inpe.br
Código do Detentorisadg {BR SPINPE} ibi 8JMKD3MGPCW/3DT298S
Identificador8JMKD3MGP3W34R/3SB3SBE
Repositóriosid.inpe.br/mtc-m21c/2018/12.03.15.11
Última Atualização2020:09.28.18.52.15 simone
Metadadossid.inpe.br/mtc-m21c/2018/12.03.15.11.53
Última Atualização dos Metadados2020:09.28.18.52.16 simone
Chave SecundáriaINPE--PRE/
Chave de CitaçãoWagnerSTLFAGPA:2018:UsCoNe
TítuloUsing convolutional network to identify tree species related to forest disturbance in a neotropical forest with very high resolution multispectral images
Ano2018
Data de Acesso11 abr. 2021
Número de Arquivos1
Tamanho77 KiB
Área de contextualização
Autor1 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
Grupo1 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
Afiliação1 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 Autor1
2 alber.ipia@inpe.br
3
4 rodolfo.lotte@inpe.br
5
6
7
8
9 luiz.aragao@inpe.br
Nome do EventoAGU Fall Meeting
Localização do EventoWashington, D. C.
Data10-14 dec.
Tipo SecundárioPRE CI
Histórico2018-12-03 15:11:53 :: simone -> administrator ::
2019-01-14 17:06:39 :: administrator -> simone :: 2018
Área de conteúdo e estrutura
É a matriz ou uma cópia?é a matriz
Estágio do Conteúdoconcluido
Transferível1
Tipo do ConteudoExternal Contribution
Tipo de Versãopublisher
ResumoMapping 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.
AreaSRE
Arranjo 1urlib.net > BDMCI > Fonds > Produção > DIDSR > Using convolutional network...
Arranjo 2urlib.net > BDMCI > Fonds > Produção > SER > Using convolutional network...
Arranjo 3urlib.net > BDMCI > Fonds > Produção > COCST > Using convolutional network...
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