Seasonal effects of impervious surface estimation in subtropical monsoon regions

Abstract

Accurate impervious surface estimation (ISE) is challenging due to the diversity of land covers and the vegetation phenology and climate. This study investigates the variation of impervious surfaces estimated from different seasons of satellite images and the seasonal sensitivity of different methods. Four Landsat ETM+images of four different seasons and two popular methods (i.e. artificial neural network (ANN) and support vector machine (SVM)) are employed to estimate the impervious surface on the pixel level. Results indicate that winter (dry season) is the best season to estimate impervious surface even though plants are not in their growing season. Less cloud and less variable source areas (VSA) (seasonal water body) become the major advantages of winter for the ISE, as cloud is easily confused with bright impervious surfaces, and water in VSA is confused with dark impervious surfaces due to their similar spectral reflectance. For the seasonal sensitivity of methods, ANN appears more stable as its accuracy varied less than that obtained with SVM. However, both the methods showed a general consistency of the seasonal changes of the accuracy, indicating that winter time is the best season for impervious surfaces estimation with optical satellite images in subtropical monsoon regions.