Spectral mixture analysis of ASTER image in Brazilian Savanna

Abstract

The present work aims to evaluate the advantages of the ASTER sensor for the distinction of Brazilian Savanna vegetation types from spectral mixture analysis. The study area is the Military Instruction Field which has an extensive area with approximately 115.014 ha of native Brazilian Savanna. The methodology used can be divided in three stages: a) preprocessing, b) endmembers identification and c) Spectral Mixture Analysis (SMA). The images were acquired with atmosphere correction. The union of the spatial dimensions between VNIR and SWIR images was done by duplicating the pixels size from the SWIR image. Since the study area is located in two adjacent ASTER images a mosaic was done in order to combine both. Endmembers were detected in three steps: a) spectral reduction by the Minimum Noise Fraction (MNF) transformation, b) spatial reduction by the Pixel Purity Index (PPI) and c) manual identification of the endmembers using the N-dimensional visualizer. The spectral classification was done using the Spectral Mixture Analysis (SMA). The classification was done relative to the different vegetation types and bodies of water with vegetation occurrence. The amount of nonphotosynthetic vegetation (NPV) and photosynthetic vegetation (PV) is preponderant in the distinction between the vegetation types. These procedures allowed identifying the main scenarios in the study area.