Role of market agents in mitigating the climate change effects on food economy

Natural Hazards - Agriculture’s ability to adapt to the impacts of climate change is critical for agricultural households as well as the general public and policymakers. Economic agents can...     

Structural control of El Sela granites and associated uranium deposits, Southern Eastern Desert, Egypt

The El Sela area is a part of the basement complex of the Eastern Desert of Egypt and the Pan-African Shield. The area comprises outcrops of dismembered ophiolites thrust over arc volcano-sedimentary sequence and intruded by different syn- to post-tectonic granitoids. Structural analysis of the area enabled the separation and definition of four structural episodes: (E1) folding–thrusting episode associated with the cratonization of the arc/inter-arc rock association and the intrusion of the syntectonic (Older) granites. (E2) Upright folding episode associated with the compression and shortening to the ENE–WSW direction which is different from the NNW–SSE shortening direction during E1; at the end of E2, late tectonic granites were intruded. (E3) Post-tectonic granitic intrusion episode: two mica granite and granitic dikes were intruded during this episode. (E4) Fracturing, faulting, and post-granitic dike extrusion episodes caused different faults that took place after cratonization until the present. There are three generations of folds during ductile deformation (E1 and E2). The F2 folds are nearly coaxial (along ENE–WSW trend) with the F1 folds. The F3 folding is displayed by folds generally trending NNW–SSE. Therefore, the ENE–WSW and NNW–SSE trends can considered as preexisting discontinuities and mechanical anisotropy of the crust in the following structure episodes. Brittle deformation (E3 and E4) reveals the importance of those trends which control the multi-injections and many alteration features in the study area. During reactivation, a simple shear parallel to the inherited ductile fabrics was responsible for the development of mineralized structures along the ENE–WSW and NNW–SSE trends. So they can be considered as paleochannel trends for deep-seated structures and can act as a good trap for uranium and/or other mineral resources. Most of the uranium anomalies are delineated along ENE–WSW and NNW–SSE shear zones where quartz-bearing veins bounded the lamprophyre dike and microgranites and dissected them in relation to the successive fracturation and brecciation corresponding to the repeated rejuvenation of the structures. Therefore, the structural controls of the uranium mineralizations in the El Sela area appear to be related to the interaction between inherited ductile fabrics and overprinting brittle structures.     

Reliable estimation of orbit errors in spaceborne SAR interferometry

An approach to improve orbital state vectors by orbit error estimates derived from residual phase patterns in synthetic aperture radar interferograms is presented. For individual interferograms, an error representation by two parameters is motivated: the baseline error in cross-range and the rate of change of the baseline error in range. For their estimation, two alternatives are proposed: a least squares approach that requires prior unwrapping and a less reliable gridsearch method handling the wrapped phase. In both cases, reliability is enhanced by mutual control of error estimates in an overdetermined network of linearly dependent interferometric combinations of images. Thus, systematic biases, e.g., due to unwrapping errors, can be detected and iteratively eliminated. Regularising the solution by a minimum-norm condition results in quasi-absolute orbit errors that refer to particular images. For the 31 images of a sample ENVISAT dataset, orbit corrections with a mutual consistency on the millimetre level have been inferred from 163 interferograms. The method itself qualifies by reliability and rigorous geometric modelling of the orbital error signal but does not consider interfering large scale deformation effects. However, a separation may be feasible in a combined processing with persistent scatterer approaches or by temporal filtering of the estimates.     

Artificial Neural Network (ANN) Based Inversion of Benthic Substrate Bottom Type and Bathymetry in Optically Shallow Waters - Initial Model Results

Ocean-colour remote sensing in optically shallow waters is influenced by contribution from the water column depth as well as by the substrate type. Therefore, it is required to include the contribution from the water column and substrate bottom type for bathymetry estimation. In this report we demonstrate the use of Artificial Neural Network (ANN) based approach to spectrally distinguish various benthic bottom types and estimate depth of substrate bottom simultaneously in optically shallow waters. We have used in-water radiative transfer simulation modeling to generate simulated top-of-the-water column reflectance the four major benthic bottom types viz. sea grass, coral sand, green algae and red algae using Hydrolight simulation model. The simulated remote sensing reflectance, for the four benthic bottom types having benthic bottom depth up to 30 m were generated for moderately clear waters. A multi-layer perceptron (MLP) type neural network was trained using the simulated data. ANN based approach was used for classification of the benthic bottom type and simultaneous inversion of bathymetry. Simulated data was inverted to yield benthic bottom type classification with an accuracy of ~98% for the four benthic substrate types and the substrate depth were estimated with an error of 0% for sea grass, 1% for coral sand and 1–3% for green and red algae up to 25 m, whereas for substrate bottom deeper than 25 m depth the classification errors increased by 2–5% for three substrate bottom types except sea grass bottom type. The initial results are promising which needs validation using the in-situ measured remote sensing reflectance spectra for implementing further on satellite data.     

Measured performance of the application of EGNOS in the road traffic sector

European Geostationary Navigation Overlay Service (EGNOS) is a satellite-based system developed to improve the performance of GPS in the European region. It not only enhances the estimated positioning accuracy but also provides an integrity solution. We assess the EGNOS performance through analysis of real measurements, performed in different road user environments. We investigate the use of the integrity concept in future Global Navigation Satellite System (GNSS)-based applications in the road environment, considering both suburban and dense urban areas. The analysis is motivated by the fact that there is an increasing number of road applications, such as billing system, transportation of dangerous goods, remote tracking of trucks and snow plows, fleet management and “pay as you go” car insurances, where the system liability has to be guaranteed. The scope of the analysis is to investigate to which extent the use of the integrity concept can be used for such purpose; the assessment is based on real measurement campaigns in representative environments, providing statistical results in terms of accuracy and integrity, and in a wider sense of the feasibility of the use of EGNOS for augmenting the reliability of GNSS positioning for road applications.     

Coherent integration time limit of a mobile receiver for indoor GNSS applications

There is an emerging requirement for processing global navigation satellite system (GNSS) signals indoor where the signal is very weak and subjected to spatial fading. Typically, longer coherent integration intervals provide the additional processing gain required for the detection and processing of such weak signals. However, the arbitrary physical motion of the handset imputed by the user limits the effectiveness of longer coherent integration intervals due to the spatial decorrelation of the multipath-faded GNSS signal. In this paper, limits of coherent integration due to spatial decorrelation are derived and corroborated with experimental verification. A general result is that the processing gain resulting from direct coherent integration saturates after the antenna has moved through a certain distance, which for typical indoor propagation, is about half a carrier wavelength. However, a refined Doppler search coupled with a prolonged coherent integration interval extends this limit, which is effectively a manifestation of selective diversity.     

Modelling and Remote Sensing of Land Surface Temperature in Turkey

This study introduces artificial neural networks (ANNs) for the estimation of land surface temperature (LST) using meteorological and geographical data in Turkey (26?C45°E and 36?C42°N). A generalized regression neural network (GRNN) was used in the network. In order to train the neural network, meteorological and geographical data for the period from January 2002 to December 2002 for 10 stations (Adana, Afyon, Ankara, Eski?ehir, ?stanbul, ?zmir, Konya, Malatya, Rize, Sivas) spread over Turkey were used as training (six stations) and testing (four stations) data. Latitude, longitude, elevation and mean air temperature are used in the input layer of the network. Land surface temperature is the output. However, land surface temperature has been estimated as monthly mean by using NOAA-AVHRR satellite data in the thermal range over 10 stations in Turkey. The RMSE between the estimated and ground values for monthly mean with ANN temperature(LST_ANN) and Becker and Li temperature(LST_B-L) method values have been found as 0.077?K and 0.091?K (training stations), 0.045?K and 0.003?K (testing stations), respectively.     

An Artificial Intelligent design for GPS Surveying Networks

Metaheuristic techniques, which are based on ideas of Artificial Intelligence, are among the best methods for solving computationally the GPS surveying network problem. In this paper, the ant colony optimization metaheuristic, which is inspired by the behavior of real ant colonies, is developed to efficiently provide a general framework for optimizing GPS surveying networks. In this framework, a set of ants co-operate together using an indirect communication procedure to find good GPS observation schedules. A GPS surveying network can be defined as a set of stations, co-ordinated by a series of sessions formed by placing receivers on the stations. The problem is to search for the best order in which to observe these sessions to give the best schedule at minimum cost. Computational results obtained by applying the proposed technique on several networks, with known and unknown optimal schedules, prove the effectiveness of the proposed metaheuristic technique to solve the GPS surveying network problem.