Integration of SPOT-5 and ETM+ images to detect land cover change in urban environment

Abstract

Urbanization, stimulated by dramatic economic development, has been proceeding on an unprecedented scale and rate in many cities in the Yangtze River Delta, which is the biggest economic region of eastern China. A lot of problems have been identified, including agricultural land and wetland loss, water pollution and soil erosion. There is a great need to detect and monitor the land cover change in rapid urban expansion using remote sensing, accurately and timely, for planning and management. However, change detection capabilities are intrinsically limited by the spatial resolution of the digital imagery in urban. The application of multi-sensor data provides the potential to more accurately detect land-cover changes through integration of different features of sensor data. This paper integrated SPOT-5 XS data (10 m resolution with shortwave infrared band of 20 m resolution) and ETM+ Pan data (15 m resolution) and applied principal component analysis (PCA) of multi-sensor data to detect changes. Then supervised classification was adopted to quantify the changes of “from-to”. The study demonstrates that this method provides a very useful way in monitoring rapid land cover change in urban