Pre-classification and post-classification change-detection techniques to monitor land-cover and land-use change using multi-temporal Landsat imagery: a case study on Pisa Province in Italy

This article explores the simultaneous use of pre-classification and post-classification change-detection techniques to map and monitor land-cover and land-use change using multi-temporal Landsat Multi-spectral Scanner and Enhanced Thematic Mapper plus data over one of the most important tourism centres of Italy (e. g. Pisa Province) for 1972, 2000 and 2006. Pre-classification approaches of principal-component analysis and band combination are potentially tailored to reduce data redundancy of the satellite imagery in order to highlight different objects of significance for change-detection analysis across time-series data. In this work, the application of pre-classification techniques could contribute to produce land-cover and land-use maps with higher quality of classification. At this point, the average value of overall classification accuracies for the three classification outputs was an estimated 90%. Then, 'from-to' change information, as well as the area and the type of landscape transformations, are provided through the post-classification technique. The findings of this study show that the province of Pisa has significantly experienced a high rate of deforestation and urban development over past decades. It is revealed that artificial structures (e. g. urban and industrial zones) in Pisa Province increased at a change rate of around 265% and forested land decreased from approximately 45% to 32% of the total area of the province between 1972 and 2006. Likewise, the perceptible growth of built-up structures from about 4% to 10.6% in Pisa City during this 34-year period has imposed a heavy pressure on the landscape of Pisa.