Spectral mixture analysis of aster image to geobotanical investigation between ultramafic and mafic rocks from Niquelandia, Brazil

Abstract

The present work aims to evaluate the advantages of the ASTER sensor to detect Brazilian Savanna vegetation types over ultramafic or mafic rocks from spectral mixture analysis. The study area, the Niquelandia Complex, lies in the central portion of the State of Goias in Brazil. The Niquelandia Complex is a well-exposed heavily layered intrusion in central Brazil that comprises an area of about 1,800 square km and is approximately 15 km thick. Geology and petrology studies show many similarities between the Niquelandia Complex and well-known Precambrian layered intrusions such as Bushveld and Stillwater. The distribution and physiognomy of vegetation in the Niquelandia Massif show strong geological control. In this site, extensive areas of ultramafic rocks are covered by herbaceous vegetation dominated by grasses. The fires that occur during the driest months (June-August), might influence the physiognomy of the vegetation. However, not only fires can explain the abrupt limit among the gabroic rocks (mafic) and the ultramafic substrate. Different types of forests can be found along the streams and valleys. Their existence demonstrates the ability of many species to tolerate soils originating from ultramafic rock if soil depth and humidity are appropriate and some fire protection exists. The methodology used can be divided into three stages: (a) pre-processing, (b) endmember identification and (c) spectral mixture analysis (SMA). The images were acquired with atmosphere correction relative to the AST07 product. The Level 2 surface reflectance data set (AST07) contains surface reflectance for each of the nine VNIR and SWIR bands at 15-m and 30-m resolutions, respectively. Duplicating the pixels size from the SWIR image the spatial dimensions between VNIR and SWIR images. Endmembers were detected in three steps: (1) spectral reduction by the minimum noise fraction (MNF) transformation, (2) spatial reduction by the Pixel Purity Index (PPI) and (3) manual identification of the endmembers using the N-dimensional visualizer. The spectral classification was made using the Spectral mixture analysis (SMA). The amount of nonphotosynthetic vegetation (NPV) and photosynthetic vegetation (PV) is preponderant in the distinction between the vegetation types. These procedures allowed the identification of the main scenarios in the study area. For the Niquelandia area it was possible to separate for the SMA components vegetation patterns in soils from ultramafic or mafic rocks