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(Referência obtida automaticamente do Web of Science, por meio da informação sobre o financiamento pela FAPESP e o número do processo correspondente, incluída na publicação pelos autores.)

Tree species classification in tropical forests using visible to shortwave infrared WorldView-3 images and texture analysis

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Autor(es):
Ferreira, Matheus Pinheiro [1, 2] ; Wagner, Fabien Hubert [1] ; Aragao, Luiz E. O. C. [3, 1] ; Shimabukuro, Yosio Edemir [1] ; de Souza Filho, Carlos Roberto [4]
Número total de Autores: 5
Afiliação do(s) autor(es):
[1] Natl Inst Space Res INPE, Remote Sensing Div, Av Astronautas 1758, BR-12227010 Sao Jose Dos Campos, SP - Brazil
[2] Mil Inst Engn IME, Cartog Engn Sect, Praca Gen Tiburcio 80, BR-22290270 Rio De Janeiro, RJ - Brazil
[3] Univ Exeter, Coll Life & Environm Sci, Exeter EX4 4RJ, Devon - England
[4] Univ Campinas UNICAMP, Geosci Inst, R Joao Pandia Calogeras 51, BR-13083870 Campinas, SP - Brazil
Número total de Afiliações: 4
Tipo de documento: Artigo Científico
Fonte: ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING; v. 149, p. 119-131, MAR 2019.
Citações Web of Science: 7
Resumo

Tropical 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, 400-1040 nm) and shortwave-infrared (SWIR, 1210-2365 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 pan sharpened 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 producer's 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 producer's 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. (AU)

Processo FAPESP: 16/24977-1 - Mapeamento e extração de atributos químicos de espécies arbóreas da Mata Atlântica por meio de imagens hiperespectrais e modelagem de transferência radiativa
Beneficiário:Matheus Pinheiro Ferreira
Linha de fomento: Bolsas no Brasil - Pós-Doutorado
Processo FAPESP: 15/50484-0 - Diversidade funcional dos biomas Amazônia, Mata Atlântica e Cerrado nos ambientes intactos e em regeneração por meio de imagens hiperspectrais
Beneficiário:Fabien Hubert Wagner
Linha de fomento: Auxílio à Pesquisa - Apoio a Jovens Pesquisadores