Remote sensing applied in vegetation analysis of Amana and Mamiraua sustainable-use protected areas

The knowledge of the Amazon biodiversity, especially that related to its vegetation cover, has been the subject of several studies involving the investigation of its ecological-evolutional processes and its dynamics as an integrated and complex set of biological units. The development of Remote Sensing technologies and methodologies is becoming increasingly essential in the analysis and monitoring of vast areas dominated by the Amazon rainforest. Thus, this study seeks to evaluate the capability of data generated by the Moderate Resolution Imaging Spectroradiometer (MODIS)/Terra, the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER)/Terra and the Synthetic Aperture Radar (SAR)/R99-B sensors, in discriminating phytophysiognomies found in the Amanã and Mamirauá Sustainable Development Reserves (RDSA and RDSM). Product MOD13, derived from MODIS data processing, comprising the Vegetation Indices EVI (Enhanced vegetation Index) and NDVI (Normalized Difference Vegetation Index), was used to evaluate the seasonal/temporal behavior of four existing phytophysiognomies in RDSA and RDSM between 2004 and 2005. Results showed that (i) the vegetation indices were sensitive to the structural characteristics of the approached ecosystem and phytophysiognomies; (ii) the EVI index best discerned among the phytophysiognomies; (iii) temporal endmembers were distinguished for different classes of forests and may serve as important references for future work involving the dynamics of the landscape. ASTER data, including visible (0.52-0.69 µm), nearinfrared (0.78-0.86 µm) and shortwave infrared (1.60-2.43 µm) bands were processed through advanced spectral classification techniques, such as the Spectral Angle Mapper (SAM) e Mixture Tuned Matched Filtering (MTMF), besides NDVI. The SAM method allowed the recognition of six dominant phytophysiognomies in the RDSA. The MTMF, which involves a more robust spectral unmixing method, provided equivalent results. Using ASTER data, it was also possible to demonstrate the close relation between the spectral patterns and the NDVI values for the vegetation cover. By means of L band (1.28 GHz), full polarimetric (HH, VV, VH, HV), SAR-amplitude data acquired with the SAR R99-B sensor, distinctions among flooded forest phytophysiognomies in the RDSA and RDSM was pursued. 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 the correct discrimination of three vegetation classes of interest, proving the potential of the SAR data and the algorithm ICM in forest mapping in the Amazon (AU)