Dual-season and full-polarimetric C band SAR assessment for vegetation mapping in the Amazon varzea wetlands

Author(s)

de Almeida Furtado, Luiz Felipe ^[1] ; Freire Silva, Thiago Sanna ^[2] ; Ledo de Moraes Novo, Evlyn Marcia ^[1]

Affiliation

^[1] INPE, Div Sensoriamento Remoto, BR-12227010 Sao Jose Dos Campos, SP - Brazil

^[2] UNESP Univ Estadual Paulista, Dept Geog, Ecosyst Dynam Observ, Inst Geociencias & Ciencias Exatas, BR-13506900 Rio Claro, SP - Brazil

• Document type: Journal article
• Source: REMOTE SENSING OF ENVIRONMENT; v. 174, p. 212-222, MAR 1 2016.
• Web of Science Citations: 26

Abstract

This study answered the following questions: 1) Is polarimetric C-band SAR (PoISAR) more efficient than dual polarization (dual-pol) C-band SAR for mapping varzea floodplain vegetation types, when using images of a single hydrological period? 2) Are single-season C-band PoISAR images more accurate for mapping varzea vegetation types than dual-season dual-pol C-band SAR images? 3) What are the most efficient polarimetric descriptors for mapping varzea vegetation types? We applied the Random Forests algorithm to classify dual-pol SAR images and polarimetric descriptois derived from two full-polarimetric Radarsat-2 C-band images acquired during the low and high water seasons of Lago Grande de Curuai floodplain, lower Amazon, Brazil. We used the Kappa index of agreement (kappa), Allocation Disagreement (AD) and Quantity Disagreement (QD), and Producer's and User's accuracy measurements to assess the classification results. Our results showed that single-season full-polarimetric C-band data can yield more accurate classifications than single-season dual-pol C-band SAR imagery and similar accuracies to dual-season dual-pol C-band SAR classifications. Still, dual season PoISAR achieved the highest accuracies, showing that seasonality is paramount for obtaining high accuracies in wetland land cover classification, regardless of SAR image type. On average, single-season classifications of low-water periods were less accurate than high-water classifications, likely due to plant phenology and flooding conditions. Classifications using model-based polarimetric decompositions (such as Freeman-Durden, Yamaguchi and van Zyl) produced the highest accuracies (kappa greater than 0.8; AD ranging from 7.5% to 2.5%; QD ranging from 15% to 12%), while eigenvector-based decompositions such as Touzi and Cloude-Pottier had the worst accuracies (kappa ranging from 0.5 to 0.7; AD greater than 10%; QD smaller than 10%). Vegetation types with dense canopies (Shrubs, Floodable Forests and Emergent Macrophytes), whose classification is challenging using C-band, were accurately classified using dual-season full-polarimetric SAR data, with Producer's and User's accuracies between 80% and 90%. We conclude that full polarimetric C-band imagery can yield very accurate classifications of varzea vegetation (kappa similar to 0.8, AD similar to 3% and QD similar to 10%) and can be used as an operational tool for forested wetland mapping. (C) 2015 Elsevier Inc. All rights reserved. (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 Opportunities: Scholarships in Brazil - Post-Doctoral
• 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 Opportunities: Regular Research Grants