Discriminação de fitofisionomias de floresta de várzea a partir do algoritmo Iterated Conditional Modes aplicado aos dados SAR/R99 (QUAD-POL/Banda L)

Abstract

This study seeks to evaluate the capability of data generated by the synthetic aperture radar SA R R99 sensor to map phytophysiognomies found in the Amanã and Mamirauá Sustainable Development Reserves (RDSA and RDSM). By means of L-band (1.28 GHz), full polarimetric (HH, VV, VH, HV), amplitude data acquired with the SAR R99 sensor, distinctions among flooded forest phytophysiognomies in the RDSA and RDSM and around were achieved. The Iterated Conditional Modes (ICM) algorithm was employed to perform the local/contextual polarimetric classification of the data. Results showed that the use of multivariate distributions in amplitude with a band of texture produced classifications of superior quality in relation to those obtained with the uni/bivariate polarimetric data. This approach allowed to obtain a Kappa index of 0,8963 and the distinction of three vegetation classes of interest, demonstrating the potential of SAR R99 and the ICM algorithm to map flooded vegetation of the Amazon.