Mapping Land Cover Classes in the Southern State of Rio Grande Do Sul, Brazil, Using Multiple Endmember Spectral Mixture Analysis (MESMA) Model Applied to Hyperion/EO-1 Hyperspectral Data

Abstract

In this paper we evaluated the potential use of Multiple Endmember Spectral Mixture Analysis (MESMA) applied to EO-1 Hyperion hyperspectral data to separate land covers (soil = dunes and dry fields; green vegetation = pinus, eucalyptus and grasslands; water = without sediments, with sediments, and with chlorophyll; and shade), in the southern state of Rio Grande do Sul, Brazil. The approach involved (a) preprocessing and atmospheric correction of Hyperion image; (b) sequential use of Minimum Noise Fraction (MNF), Pixel Purity Index (PPI) and n-Dimensional Visualizer techniques in the visible to shortwave range for the initial selection of a group of endmember, and another group of pixels for model validation; (c) use of the software Visualization and Image Processing for Environmental Research (VIPER) Tools to perform the final selection of endmembers based on the spectral library, and to obtain MESMA models; and (d) evaluation of resulting fraction images and RMSE values to determine the optimal number of endmembers of the MESMA model. Results showed that a four-endmember MESMA model described the diversity of the scene components, including that of materials within the same class (e.g., pinus and eucalyptus) and produced the largest fractions and the lowest RMSE values on a per-pixel basis. Results also showed the performance of MESMA applied to Hyperion data to discriminate properly land covers in the coastal plains, even considering the low signal-to-noise ratio of the instrument.