| Texto completo | |
| Autor(es): Mostrar menos - |
Bendini, Hugo N.
;
Fonseca, Leila M. G.
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Soares, Anderson R.
;
Rufin, Philippe
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Schwieder, Marcel
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Rodrigues, Marcos A.
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Maretto, Raian, V
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Korting, Thales S.
;
Leitao, Pedro J.
;
Sanches, Ieda D. A.
;
Hostert, Patrick
;
IEEE
Número total de Autores: 12
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| Tipo de documento: | Artigo Científico |
| Fonte: | IGARSS 2020 - 2020 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM; v. N/A, p. 4-pg., 2020-01-01. |
| Resumo | |
Mapping agriculture with high accuracy is important to generate reliable information about crop production. Pixel-based methods still present problems with noise and usually require post-processing approaches to reach satisfactory results. Object-based Image Analysis (OBIA) enable the detection of homogeneous objects in remote sensing images based on spectral similarity. However, traditional OBIA does not consider the multi-temporal characteristics of land cover or land use, such as agriculture. The objective of this study is to evaluate a phenological object-based approach with dense Landsat image time series for mapping agriculture in different level of detail in the Brazilian Cerrado. We derived pixel-wise EVI fitted time series with 8-day temporal resolution and applied multi-resolution segmentation using all image bands to incorporate the influence of space and time. Then we generated phenological metrics and applied OBIA of agricultural lands in Brazil using a hierarchical classification scheme. The overall accuracies for each hierarchical level were around 90%, and the spatial consistency of the generated maps is promising. (AU) | |
| Processo FAPESP: | 17/24086-2 - Gerenciamento de metadados de grandes volumes de dados de sensoriamento remoto |
| Beneficiário: | Thales Sehn Körting |
| Modalidade de apoio: | Auxílio à Pesquisa - Regular |