In this paper, we developed a novel method of combining remote sensing tools at the sub-pixel level for accurate identification of impervious surface time series changes. We examined the use of the red-green-blue impervious surface model (RGB-IS) in detecting time series internal modification of urban regions by integrating Landsat data collected over four different periods between 1987 and 2009 (i.e., 1987, 2000, 2002, and 2009). The performance of this approach was compared with two conventional methods, namely standard RGB-normalized difference vegetation index (NDVI) and post-classification technique. In contrast to conventional techniques, RGB-IS could monitor between-class changes, within-class changes, and location of these modifications. The proposed method was independent of seasonal changes and was also able to serve as a useful alternative for quick mapping growth hotspots and updating transportation corridor map. The results also showed that Cixi County, Zhejiang Province, China experienced tremendous impervious surface changes, especially along the corridors of newly constructed highways and around urban areas over the past 22 years.
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