Mapping fractional landscape soils and vegetation components from Hyperion satellite imagery using an unsupervised machine-learning workflow |
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| Authors: | Michael J Friedel Massimo Buscema Luiz Eduardo Vicente Fabio Iwashita Andréa Koga-Vicente |
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| Institution: | 1. Hydrogeology Department, GNS Science, Lower Hutt, New Zealand;2. Mathematical &3. Statistical Sciences, University of Colorado, Denver, CO, USA;4. Mathematical &5. Semeion Institute, Rome, Italy;6. Satellite Monitoring Unit, Brazilian Corporation of Agricultural Research-EMBRAPA, Campinas, Brazil;7. Earth Sciences Department, University of Florence, Florence, Italy;8. Center for Meteorological and Climatological Research in Agriculture, University of Campinas, Campinas, Brazil |
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| Abstract: | An unsupervised machine-learning workflow is proposed for estimating fractional landscape soils and vegetation components from remotely sensed hyperspectral imagery. The workflow is applied to EO-1 Hyperion satellite imagery collected near Ibirací, Minas Gerais, Brazil. The proposed workflow includes subset feature selection, learning, and estimation algorithms. Network training with landscape feature class realizations provide a hypersurface from which to estimate mixtures of soil (e.g. 0.5 exceedance for pixels: 75% clay-rich Nitisols, 15% iron-rich Latosols, and 1% quartz-rich Arenosols) and vegetation (e.g. 0.5 exceedance for pixels: 4% Aspen-like trees, 7% Blackberry-like trees, 0% live grass, and 2% dead grass). The process correctly maps forests and iron-rich Latosols as being coincident with existing drainages, and correctly classifies the clay-rich Nitisols and grasses on the intervening hills. These classifications are independently corroborated visually (Google Earth) and quantitatively (random soil samples and crossplots of field spectra). Some mapping challenges are the underestimation of forest fractions and overestimation of soil fractions where steep valley shadows exist, and the under representation of classified grass in some dry areas of the Hyperion image. These preliminary results provide impetus for future hyperspectral studies involving airborne and satellite sensors with higher signal-to-noise and smaller footprints. |
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| Keywords: | Hyperspectral machine learning remote sensing soils and vegetation unsupervised workflow |
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