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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

Land cover classification of Lago Grande de Curuai floodplain (Amazon, Brazil) using multi-sensor and image fusion techniques

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Author(s):
de Almeida Furtado, Luiz Felipe [1] ; Freire Silva, Thiago Sanna [2] ; Farias Fernandes, Pedro Jose [3] ; Ledo de Moraes Novo, Evlyn Marcia [1]
Total Authors: 4
Affiliation:
[1] Inst Nacl Pesquisas Espaciais, Div Sensoriamento Remoto, BR-12227010 Sao Jose Dos Campos, SP - Brazil
[2] Univ Estadual Paulista, Inst Geociencias & Ciencias Exatas, Dept Geog, BR-13506900 Rio Claro, SP - Brazil
[3] Univ Fed Fluminense, Inst Geociencias, Dept Geog, Niteroi, RJ - Brazil
Total Affiliations: 3
Document type: Journal article
Source: Acta Amazonica; v. 45, n. 2, p. 195-202, JUN 2015.
Web of Science Citations: 10
Abstract

Given the limitations of different types of remote sensing images, automated land-cover classifications of the Amazon varzea may yield poor accuracy indexes. One way to improve accuracy is through the combination of images from different sensors, by either image fusion or multi-sensor classifications. Therefore, the objective of this study was to determine which classification method is more efficient in improving land cover classification accuracies for the Amazon varzea and similar wetland environments - (a) synthetically fused optical and SAR images or (b) multi-sensor classification of paired SAR and optical images. Land cover classifications based on images from a single sensor (Landsat TM or Radarsat-2) are compared with multi-sensor and image fusion classifications. Object-based image analyses (OBIA) and the J.48 data-mining algorithm were used for automated classification, and classification accuracies were assessed using the kappa index of agreement and the recently proposed allocation and quantity disagreement measures. Overall, optical-based classifications had better accuracy than SAR-based classifications. Once both datasets were combined using the multi-sensor approach, there was a 2% decrease in allocation disagreement, as the method was able to overcome part of the limitations present in both images. Accuracy decreased when image fusion methods were used, however. We therefore concluded that the multi-sensor classification method is more appropriate for classifying land cover in the Amazon varzea. (AU)

FAPESP's process: 10/11269-2 - Modeling of the spatial dynamics of macrophyte communities in the Amazon floodplain
Grantee:Thiago Sanna Freire Silva
Support type: Scholarships in Brazil - Post-Doctorate
FAPESP's process: 11/23594-8 - Remote sensing applications for modeling human impacts on the ecological properties of wetland and aquatic environments in the Solimões/Amazon floodplain
Grantee:Evlyn Márcia Leão de Moraes Novo
Support type: Regular Research Grants