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The Pearl River Delta is experiencing very fast urban growth in recent years which has caused rapid loss of the valuable agricultural land in this fertile region. There is a great need to monitor the rapid urban expansion using remote sensing for urban planning and management purposes. However, it has been well recognized that there is significant over-estimation of land use change in using multi-temporal images for change detection because of inadequate creation of classification signatures. This paper presents a principal component analysis of stacked multi-temporal images method to reduce such errors. The study demonstrates that this method can reduce errors in change detection using multitemporal images and provide a very useful way in monitoring rapid land use changes and urban expansion in the Pearl River Delta and other parts of the world.