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Tipo de ReferênciaJournal Article
Sitemtc-m21c.sid.inpe.br
Código do Detentorisadg {BR SPINPE} ibi 8JMKD3MGPCW/3DT298S
Identificador8JMKD3MGP3W34R/3SMPQ6H
Repositóriosid.inpe.br/mtc-m21c/2019/02.07.16.19   (acesso restrito)
Última Atualização2019:02.07.16.19.03 administrator
Metadadossid.inpe.br/mtc-m21c/2019/02.07.16.19.03
Última Atualização dos Metadados2020:01.06.11.42.09 administrator
DOI10.1016/j.isprsjprs.2019.01.019
ISSN0924-2716
Chave de CitaçãoFerreiraWagAraShiSou:2019:TrSpCl
TítuloTree species classification in tropical forests using visible to shortwave infrared WorldView-3 images and texture analysis
Ano2019
MêsMar.
Data de Acesso17 abr. 2021
Tipo de Trabalhojournal article
Número de Arquivos1
Tamanho4339 KiB
Área de contextualização
Autor1 Ferreira, Matheus Pinheiro
2 Wagner, Fabien Hubert
3 Aragão, Luiz Eduardo Oliveira e Cruz de
4 Shimabukuro, Yosio Edemir
5 Souza Filho, Carlos Roberto de
Identificador de Curriculo1
2
3
4 8JMKD3MGP5W/3C9JJCQ
Grupo1
2 DIDSR-CGOBT-INPE-MCTIC-GOV-BR
3 DIDSR-CGOBT-INPE-MCTIC-GOV-BR
4 DIDSR-CGOBT-INPE-MCTIC-GOV-BR
Afiliação1 Instituto Militar de Engenharia (IME)
2 Instituto Nacional de Pesquisas Espaciais (INPE)
3 Instituto Nacional de Pesquisas Espaciais (INPE)
4 Instituto Nacional de Pesquisas Espaciais (INPE)
5 Universidade Estadual de Campinas (UNICAMP)
Endereço de e-Mail do Autor1 matheus@ime.eb.br
2
3 luiz.aragao@inpe.br
4 yosio.shimabukuro@inpe.br
RevistaISPRS Journal of Photogrammetry and Remote Sensing
Volume149
Páginas119-131
Tipo SecundárioPRE PI
Nota SecundáriaA1_GEOCIÊNCIAS A2_INTERDISCIPLINAR A2_CIÊNCIAS_AMBIENTAIS B1_ENGENHARIAS_IV B1_BIODIVERSIDADE C_CIÊNCIAS_AGRÁRIAS_I
Histórico2019-02-07 16:19:03 :: simone -> administrator ::
2019-02-07 16:19:03 :: administrator -> simone :: 2019
2019-02-07 16:19:51 :: simone -> administrator :: 2019
2020-01-06 11:42:09 :: administrator -> simone :: 2019
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É a matriz ou uma cópia?é a matriz
Estágio do Conteúdoconcluido
Transferível1
Tipo do ConteudoExternal Contribution
Tipo de Versãopublisher
Palavras-ChaveTropical forests, Biodiversity, Tree species discrimination, Very-high resolution, Canopy structure, GLCM.
ResumoTropical forest conservation and management can significantly benefit from information about the spatial distribution of tree species. Very-high resolution (VHR) spaceborne platforms have been hailed as a promising technology for mapping tree species over broad spatial extents. WorldView-3, the most advanced VHR sensor, provides spectral data in 16 bands covering the visible to near-infrared (VNIR, 4001040 nm) and shortwaveinfrared (SWIR, 12102365 nm) wavelength ranges. It also collects images at unprecedented levels of details using a panchromatic band with 0.3-m of spatial resolution. However, the potential of WorldView-3 at its full spectral and spatial resolution for tropical tree species classification remains unknown. In this study, we performed a comprehensive assessment of WorldView-3 images acquired in the dry and wet seasons for tree species discrimination in tropical semi-deciduous forests. Classification experiments were performed using VNIR individually and combined with SWIR channels. To take advantage of the sub-metric resolution of the panchromatic band for classification, we applied an individual tree crown (ITC)-based approach that employed pansharpened VNIR bands and gray level co-occurrence matrix texture features. We determined whether the combination of images from the two annual seasons improves the classification accuracy. Finally, we investigated which plant traits influenced species detection. The new SWIR sensing capabilities of WorldView-3 increased the average producers accuracy up to 7.8%, by enabling the detection of non-photosynthetic vegetation within ITCs. The combination of VNIR bands from the two annual seasons did not improve the classification results when compared to the results obtained using images from each season individually. The use of VNIR bands at their original 1.2-m spatial resolution yielded average producers accuracies of 43.1 ± 3.1% and 38.8 ± 3% in the wet and dry seasons, respectively. The ITC-based approach improved the accuracy to 70 ± 8% in the wet and 68.4 ± 7.4% in the dry season. Texture analysis of the panchromatic band enabled the detection of species-specific differences in crown structure, which improved species detection. The use of texture analysis, pan-sharpening, and ITC delineation is a potential approach to perform tree species classification in tropical forests with WorldView-3 satellite images.
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