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.
Highlights
The uncontrolled expansion of urban centers and intensive cultivation of rice, pinus and eucalyptus have greatly contributed to the contamination of soil and lagoon bodies of the coastal plain of Rio Grande do Sul, southern Brazil
In this paper we evaluate the performance of hyperspectral data (Hyperion orbital sensor) and a Multiple Endmember Spectral Mixture Analysis (MESMA) model to discriminate land cover classes in the coastal plains of Rio Grande do Sul
The Pixel Purity Index (PPI) process was applied to the first nine Minimum Noise Fraction (MNF) bands, which represented a coherent part of the image, to detect endmember candidate pixels
Summary
The uncontrolled expansion of urban centers and intensive cultivation of rice, pinus and eucalyptus have greatly contributed to the contamination of soil and lagoon bodies of the coastal plain of Rio Grande do Sul, southern Brazil. In order to support the planning and monitoring of these areas, traditional techniques of digital classification (e.g. MaxVer) have been widely used in images obtained by orbital sensors. Because of the similarity spectrum between some types of targets (e.g. pinus vs eucalyptus) and low spectral resolution sensors used for these studies, for example, ETM+ (Landsat 7) with eight spectral bands, different targets are not properly broken down, producing qualification and quantification errors. The instrument is capable of acquiring orbital images with 242 bands (10 nm of bandwidth) in the visible (VIS), near infrared (NIR) and shortwave infrared (SWIR) (400 – 2500 nm range) with a spatial resolution of 30 m and a swath width of 7.7 km [1]. The sensor allows the extraction of an almost continuous reflectance spectrum for each scene element [2, 3]
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