Qi Zhang, a post-doctoral associate at the Frederick S. Pardee Center for the Study of the Longer-Range Future, recently published a paper exploring an approach to detect expanding impervious surfaces, such as rooftops, roads, and sidewalks, in Nanchang, China. The paper, titled “An efficient approach to capture continuous impervious surface dynamics using spatial-temporal rules and dense Landsat time series stacks,” was published in the journal Remote Sensing of Environment.
Economic development and population growth is leading to rapid urbanization around the world. By 2050, the global urban population is expected to exceed 60 percent, resulting in a continued expansion of impervious surface cover. Increasing the extent of impervious surfaces can lead to a host of processes that affect environmental change and human well-being, such as increasing surface runoffs, transporting pollution, and increasing surface temperature as a result of urban heat island effect.
However, monitoring impervious surface change is difficult because the expansion often follows a nonlinear trend with high spatial and temporal heterogeneity. In the paper, Zhang and his co-authors propose a novel approach to accurately and efficiently detect impervious surface change, and assess the approach using the case of Nanchang, China.