This paper is concerned with the relationships between urbanisation and residential water consumption, taking as a case study the Metropolitan Region of Barcelona. More precisely, it investigates the influence of certain demographic, behavioural and housing factors on this consumption using descriptive statistics and a regression analysis. The data are derived from a sample of 532 households in 22 municipalities of the study area. Results show that income, housing type, members per household, the presence of outdoor uses (garden and swimming pool), the kind of species planted in the garden and consumer behaviour towards conservation practices play a significant role in explaining variations in water consumption. It is concluded that, along with prices and incomes, further research is needed on other demographic and housing variables in order to obtain a more comprehensive understanding of the determinants of domestic water consumption in areas periodically affected by water stress.

Change detection from remote sensing data is often done by simple overlay of classified maps. However, such analyses can contain a significant proportion of boundary errors, especially when combining data from different sensors. This paper presents a protocol that allows reliable post-classification comparisons by taking into account classification accuracies, landscape fragmentation, planimetric accuracies, pixel sizes and grid origins. The proposed protocol has been applied, with little extra effort, in a fragmented agricultural Mediterranean zone using MSS (1970s) and TM (1990s) images. Applying the protocol, change detection had an accuracy of 85.1%, while for a direct overlay it was only 43.9% accurate. The drawback of this method is that it reduces the useful area of comparison. As the accuracy of individual classifications is critical, the paper also describes and tests a hybrid classifier that combines an unsupervised classification approach with training areas. This approach has proved more successful than maximum likelihood classifiers.