ABSTRACT
The Iron Quadrangle (IQ) region in Minas Gerais is remarkably geobiodiverse, despite a long history of anthropogenic pressures such as mining and urbanization, but still lacks detailed studies on the distribution of its remaining native vegetation in different substrates. In this study, we utilized Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) images, besides Gamma-spectrometry (Gamma) survey data associated with existing geological mapping (GM) and extensive fieldwork, to discriminate and quantify remnants of vegetation on ferruginous substrates in the IQ. The Maximum Likelihood (ML) algorithm was used to classify the vegetation types, thus named: open Rupestrian Field, shrubby Rupestrian Field, Capão Forest, Cerrado stricto sensu, Cerrado Field, Seasonal Forests, Pastures and Reforestation (the latter three regardless of substrate type) associated with the predominant substrates (ferruginous ironstone, phyllites, and quartzite). The use of ASTER images alone did not allow a reliable separation of ferruginous and non-ferruginous substrates, but the integration of all different data (ASTER-ML + Gamma + GM) allowed the provisional mapping of the vegetation associated with ferruginous substrates, potentially ferruginous and non-ferruginous substrates. The resulting map shows that the vegetation on ferruginous and potentially ferruginous substrates cover 8.7% and 6.9% of the IQ, respectively. The detailed analysis of the distribution and fragmentation of phytophysiognomies on ferruginous substrates is of great importance for developing strategies to conserve the geobiodiversity of the IQ, and need to be further refined by checking and field mapping by novel approaches.
Keywords:
Supervised classification; Remote sensing; Aerogeophysical data
