The use of earth observation techniques to improve catchment-scale pollution predictions

Remote sensing can potentially provide information useful in improving pollution transport modelling in agricultural catchments. Realisation of this potential will depend on the availability of the raw data, development of information extraction techniques, and the impact of the assimilation of the derived information into models. High spatial resolution hyperspectral imagery of a farm near Hereford, UK is analysed. A technique is described to automatically identify the soil and vegetation endmembers within a field, enabling vegetation fractional cover estimation. The aerially-acquired laser altimetry is used to produce digital elevation models of the site. At the subfield scale the hypothesis that higher resolution topography will make a substantial difference to contaminant transport is tested using the AGricultural Non-Point Source (AGNPS) model. Slope aspect and direction information are extracted from the topography at different resolutions to study the effects on soil erosion, deposition, runoff and nutrient losses. Field-scale models are often used to model drainage water, nitrate and runoff/sediment loss, but the demanding input data requirements make scaling up to catchment level difficult. By determining the input range of spatial variables gathered from EO data, and comparing the response of models to the range of variation measured, the critical model inputs can be identified. Response surfaces to variation in these inputs constrain uncertainty in model predictions and are presented. Although optical earth observation analysis can provide fractional vegetation cover, cloud cover and semi-random weather patterns can hinder data acquisition in Northern Europe. A Spring and Autumn cloud cover analysis is carried out over seven UK sites close to agricultural districts, using historic satellite image metadata, climate modelling and historic ground weather observations. Results are assessed in terms of probability of acquisition probability and implications for future earth observation missions. (C) 2003 Elsevier Ltd. All rights reserved.

A knowledge-based evolution strategy for the multi-objective minimum spanning tree problem

A fast Knowledge-based Evolution Strategy, KES, for the multi-objective minimum spanning tree, is presented. The proposed algorithm is validated, for the bi-objective case, with an exhaustive search for small problems (4-10 nodes), and compared with a deterministic algorithm, EPDA and NSGA-II for larger problems (up to 100 nodes) using benchmark hard instances. Experimental results show that KES finds the true Pareto fronts for small instances of the problem and calculates good approximation Pareto sets for larger instances tested. It is shown that the fronts calculated by YES are superior to NSGA-II fronts and almost as good as those established by EPDA. KES is designed to be scalable to multi-objective problems and fast due to its small complexity.

Climatic and oceanic associations with daily rainfall extremes over southern Africa

Changes in climate variability and, in particular, changes in extreme climate events are likely to be of far more significance for environmentally vulnerable regions than changes in the mean state. It is generally accepted that sea-surface temperatures (SSTs) play an important role in modulating rainfall variability. Consequently, SSTs can be prescribed in global and regional climate modelling in order to study the physical mechanisms behind rainfall and its extremes. Using a satellite-based daily rainfall historical data set, this paper describes the main patterns of rainfall variability over southern Africa, identifies the dates when extreme rainfall occurs within these patterns, and shows the effect of resolution in trying to identify the location and intensity of SST anomalies associated with these extremes in the Atlantic and southwest Indian Ocean. Derived from a Principal Component Analysis (PCA), the results also suggest that, for the spatial pattern accounting for the highest amount of variability, extremes extracted at a higher spatial resolution do give a clearer indication regarding the location and intensity of anomalous SST regions. As the amount of variability explained by each spatial pattern defined by the PCA decreases, it would appear that extremes extracted at a lower resolution give a clearer indication of anomalous SST regions.

Shopping motives as antecedents of e-satisfaction and e-loyalty

Customer loyalty is fundamental to the profitability and survival of e-tailers. Yet research on antecedents of e-loyalty is relatively limited. This study contributes to the literature by investigating the effect of motives for online shopping on e-satisfaction and e-loyalty. A structural equations model is developed and tested through data from an online survey involving 797 customers of two UK-based e-tailers focussing on hedonic products. The results suggest that convenience, variety seeking, and social interaction help predict e-satisfaction, and that social interaction is the only shopping motive examined with a direct relationship to e-loyalty. Data also show that e-satisfaction is a strong determinant of e-loyalty. These findings are discussed in the light of previous research and avenues of future research are proposed.

Conceptualising and measuring the equity of online brands

Although brand equity is an important source of competitive advantage online, previous conceptualisations and measures overlook the unique characteristics of the internet that render consumers co-creators of brand value. In view of this, a threephased research programme was undertaken to identify the facets of online retail/service (ORS) brand equity and then develop and validate a scale for its measurement. ORS brand equity was found to be a second order construct with five correlated yet distinct dimensions: emotional connection, online experience, responsive service nature, trust, and fulfilment. A series of tests showed that the ensuing 12-item scale has strong psychometric properties. The implications of this research for marketing researchers and practitioners are discussed.

Antecedents of attitude and intention towards counterfeit symbolic and experiential products

This paper investigates the impact of price consciousness, perceived risk, and ethical obligation on attitude and intention towards counterfeit products. Data were collected from a sample of 200 respondents via an online questionnaire. A conceptual model was derived and tested via structural equation modelling in the contexts of symbolic and experiential counterfeit products. Findings show differences in the factors (and weight thereof) impacting attitude and purchase intention in the two product contexts. Specifically, ethical obligation and perceived risk are found to be significant predictors of attitude towards both symbolic and counterfeit products, while price consciousness is found to predict only attitude towards experiential products, but not purchase intention in either counterfeit product context.