Object oriented methodology for change detection technique: the case of Scopello-Sicily

Abstract

This paper describes a change detection approach based on an object-based classification of remote sensing data for the change detection. The history of urban growth and urbanisation reveals that urban areas belong to the most dynamic land cover types on earth. The trend of urban growth is usually towards the urban-rural fringe where there are less built areas, irrigation and other water management systems. Regardless of the regional economic importance, urban growth, particularly the expansion of residential and commercial land use towards the periphery of urban areas, has an impact on the ecosystem. It is evident that such trend of urban growth has an impact on natural resources and on land cover dynamics at large. In Sicily, land cover in general and urban land cover in particular has experienced a remarkable change in the last two decades. Hence, updated urban land cover mapping and change analysis are particularly useful for land use and environmental management in the country. Information from satellite remote sensing plays a useful role in understanding the nature of changes in land cover/land use and projecting possible future changes. Such information is essential for future urban development plans. In spite of these facts, there are no extensive studies carried out in the country to analyses urban land cover changes using multi - temporal and multi - resolution remote sensing data. This paper presents an object-oriented image classification methodology to detect and analyse multi-temporal satellite data. Furthermore, urban growth patterns were analysed using selected class metrics. Finally, the paper also addresses driving forces for urban land cover change with reference to demographic and related factors.