Gravimetric and magnetic fabric study of the Sintra Igneous complex: laccolith-plug emplacement in the Western Iberian passive margin

International Journal of Earth Sciences - The geometry and emplacement of the ~ 96 km2, Late Cretaceous Sintra Igneous complex (SIC, ca. 80 Ma) into the West Iberian passive...     

The Juchatengo complex: an upper-level ophiolite assemblage of late Paleozoic age in Oaxaca,southern Mexico

International Journal of Earth Sciences - The Juchatengo complex (JC) suite is located between the Proterozoic Oaxacan complex to the north and the Xolapa complex to the south, and was amalgamated...     

Mapping fractional landscape soils and vegetation components from Hyperion satellite imagery using an unsupervised machine-learning workflow

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.     

The Effect of Key Parameters on the Strength of a Dispersive Soil Stabilized with Sustainable Binders

Geotechnical and Geological Engineering - Dispersive soils are susceptible to phenomena of internal and external erosion when in contact with relatively pure water due to its particle’s...     

Hydrologic models coupled with 2D hydrodynamic model for high-resolution urban flood simulation

Natural Hazards - Floods are the most frequent natural disaster and pose a very challenging threat to many cities worldwide. Understanding the flood dynamic is essential for developing strategies...     

Predicting seiche hazard for coastal harbours along the northern and western coasts of France

Natural Hazards - Long-period waves propagating inside harbours can lead to the generation of seiche that can affect and significantly disrupt port operations. This study is based on the analysis...