Harnessing high altitude solar power

As an intermediate solution between Glaser's satellite solar power (SSP) and ground-based photovoltaic (PV) panels, this paper examines the collection of solar energy using a high-altitude aerostatic platform. A procedure to calculate the irradiance in the medium/high troposphere, based on experimental data, is described. The results show that here a PV system could collect about four to six times the energy collected by a typical U.K.-based ground installation, and between one-third and half of the total energy the same system would collect if supported by a geostationary satellite (SSP). The concept of the aerostat for solar power generation is then briefly described together with the equations that link its main engineering parameters/variables. A preliminary sizing of a facility stationed at 6 km altitude and its costing, based on realistic values of the input engineering parameters, is then presented

Introduction: neural networks in remote sensing

Abstract. Over the past decade there have been considerable increases in both the quantity of remotely sensed data available and the use of neural networks. These increases have largely taken place in parallel, and it is only recently that several researchers have begun to apply neural networks to remotely sensed data. This paper introduces this special issue which is concerned specifically with the use of neural networks in remote sensing. The feed-forward back-propagation multi-layer perceptron (MLP) is the type of neural network most commonly encountered in remote sensing and is used in many of the papers in this special issue. The basic structure of the MLP algorithm is described in some detail while some other types of neural network are mentioned. The most common applications of neural networks in remote sensing are considered, particularly those concerned with the classification of land and clouds, and recent developments in these areas are described. Finally, the application of neural networks to multi-source data and fuzzy classification are considered. <br/

The automated detection and recognition of internal waves

A new framework is presented for an automated approach to identify possible internal wave packets in synthetic aperture radar (SAR) images as a means to infer primary information about the internal waves. An operational version of this framework is expected to be useful to both oceanographers and modellers for analysing the importance of internal waves for the mixing required to warm and advect deep-sea water to the surface. The framework is based on a combination of techniques using wavelets, edge discrimination, edge linking and edge parallelism analysis. Six satellite images of the Eastern Atlantic have been used to demonstrate and test the framework. The determination of the type of signature and the wavelength of the internal wave has been demonstrated and the accuracy of the approach is assessed. <br/

Aerostat for electrical power generation: concept feasibility

The current paper examines the feasibility of using a high altitude tethered aerostat as a platform for producing a substantial quantity of electric energy and transmitting it to Earth using the mooring cable.Based on realistic values for the relevant engineering parameters that describe the technical properties of the materials and subsystems, a static analysis of the aerostat in its deployed configuration has been carried out. The results of the computations, although of a preliminary nature, demonstrate that the concept is technically feasible. There are, nevertheless, issues to be addressed to improve the performance. However none of these issues is deemed to negate the technical feasibility of this concept. A test case is investigated in terms of preliminary sizing of the aerostat, including mooring cable and solar cell coverage, and it shows the capability to deliver power to the ground in excess of 95 kW. A brief assessment of the cost has also been carried out to investigate the potential gains offered by this system to produce solar electric energy

Neural network training: using non-logarithmic or logarithmic training data for the inversion of ocean colour spectra?

A bio?optical model coupled with the radiative transfer model Hydrolight was used to create 18,000 synthetic ocean colour spectra corresponding to open ocean and coastal waters. The bio?optical model took into account the optical properties of the three oceanic constituents, chlorophyll?a, suspended non?chlorophyllous particles and coloured dissolved organic matter (CDOM) as well as of normal seawater. The resulting spectra were input into multilayer perceptron neural network algorithms with the aim of computing the original concentrations of chlorophyll?a, non?chlorophyllous particles and CDOM initially input into the bio?optical model. The process of training the neural networks is essential for the accuracy of the inversion the neural net performs on the coupled bio?optical and radiative transfer models. The objective of this paper is to investigate the performance difference of a neural network trained with untransformed as opposed to logarithmically transformed dat

A comparison of multi-layer perceptron and multilinear regression algorithms for the inversion of synthetic ocean colour spectra

Artificial radiance sets were used as inputs to Multi-layer Perceptron and multilinear regression algorithms to study their retrieval capabilities for optically active constituents in sea water. The radiative transfer model Hydrolight was used to produce 18,000 artificial reflectance spectra representing various case 1 and case 2 water conditions. The remote sensing reflectances were generated at the Medium Resolution Imaging Spectrometer (MERIS) wavebands 412, 442, 490, 510, 560, 620, 665 and 682 nm from randomly generated triplet combinations of chlorophyll a, non-chlorophyllous particles and CDOM (Coloured Dissolved Organic Matter) concentrations. These reflectances were contaminated with different noise terms, before they were used to assess the performance of multilayer perceptron and multilinear regression algorithms. The potential of both algorithms for retrieving optically active constituents was demonstrated with the neural network showing more accurate results for case 2 scenarios