APPLYING A PHENOLOGICAL OBJECT-BASED IMAGE ANALYSIS (PHENOBIA) FOR AGRICULTURAL LAND CLASSIFICATION: A STUDY CASE IN THE BRAZILIAN CERRADO

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Bendini, Hugo N. ; Fonseca, Leila M. G. ; Soares, Anderson R. ; Rufin, Philippe ; Schwieder, Marcel ; Rodrigues, Marcos A. ; Maretto, Raian, V ; Korting, Thales S. ; Leitao, Pedro J. ; Sanches, Ieda D. A. ; Hostert, Patrick ; IEEE

• 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