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...     

Rainfall thresholds for the triggering of landslides considering previous soil moisture conditions (Asturias,NW Spain)

Given its geological and climatic conditions and its rugged orography, Asturias is one of the most landslide prone areas in the North of Spain. Most of the landslides occur during intense rainfall episodes. Thus, precipitation is considered the main triggering factor in the study area, reaching average annual values of 960 mm. Two main precipitation patterns are frequent: (i) long-lasting periods of moderate rainfall during autumn and winter and (ii) heavy short rainfall episodes during spring and early summer. In the present work, soil moisture conditions in the locations of 84 landslides are analysed during two rainfall episodes, which represent the most common precipitation patterns: October–November 2008 and June 2010. Empirical data allowed the definition of available water capacity percentages of 99–100% as critical soil moisture conditions for the landslide triggering. Intensity-duration rainfall thresholds were calculated for each episode, considering the periods with sustained high soil moisture levels before the occurrence of each analysed landslide event. For this purpose, data from daily water balance models and weather stations were used. An inverse relationship between the duration of the precipitation and its intensity, consistent with published intensity-duration thresholds, was observed, showing relevant seasonal differences.     

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.     

Image-aided platform orientation determination with a GNSS/low-cost IMU system using robust-adaptive Kalman filter

The availability of miniaturized sensors with enhanced capabilities, new methods for image processing, and easy access to small and low-weight airborne platforms for data acquisition, including unmanned vehicles, opens new possibilities for geodetic navigation applications and developing new developments in sensor fusion. In this context, the development of efficient methods, based on low-cost sensors, to extract precise georeferenced information from digital cameras is of utmost interest. We present a method to improve the performance of the integration of GNSS/low-cost IMU by exploiting the orientation changes retrieved from digital images. In this work, a robust-adaptive Kalman filter is also introduced to further improve the performance of the method deployed. The adaptive factor and the robust factor accomplished are determined by innovation information and the threshold value of orientation changes between consecutive images. Results from airborne tests used to assess the performance of the method are presented. The results show that using a non-metric camera, the Euler angle estimation accuracy of the GNSS/low-cost IMU integration can be improved to be close to 0.5 degree and an additional improvement, which can reach 59%, can be achieved after using the robust-adaptive Kalman filter.     

3D-Monitoring Big Geo Data on a seaport infrastructure based on FIWARE

Many organizations of all kinds are using new technologies to assist the acquisition and analysis of data. Seaports are a good example of this trend. Seaports generate data regarding the management of marine traffic and other elements, as well as environmental conditions given by meteorological sensors and buoys. However, this enormous amount of data, also known as Big Data, is useless without a proper system to organize, analyze and visualize it. SmartPort is an online platform for the visualization and management of a seaport data that has been built as a GIS application. This work offers a Rich Internet Application that allows the user to visualize and manage the different sources of information produced in a port environment. The Big Data management is based on the FIWARE platform, as well as “The Internet of Things” solutions for the data acquisition. At the same time, Glob3 Mobile (G3M) framework has been used for the development of map requirements. In this way, SmartPort supports 3D visualization of the ports scenery and its data sources.