Multitemporal impervious surface estimation via an optimized stable/change pixel detection approach

Abstract

Remote sensing techniques have proved its efficacy for the impervious surface mapping, which is a significant indicator of urbanization process and environmental status. However, systematic and random errors in the existing methods still impact the reliability of subpixel impervious surface estimation, generating compounded errors when conducting multitemporal monitoring. The compounded errors of the conventional methods often significantly impact the temporal consistency of the results. In this study, a novel method based on a straightforward pixel change detection approach was put forward to improve the estimation of multitemporal impervious surface area. Two experimental areas located in Rome in Italy and Shenzhen in China were chosen to testify the generality of the proposed method to estimate different types of impervious surfaces worldwide. By reducing the compounded errors, the proposed method demonstrated its efficiency in achieving higher accuracy in both study areas without involving extensive data sources and intensive manual tasks. Compared with the conventional classification and regression tree algorithm, the overall mean average error and root mean square error of this study declined by more than 15.55% and 8.63%, respectively, and R2 increased from approximately 0.93 to 0.96. The proposed method also drastically reduced the standard deviation of the multitemporal percent ISA of the stable pixels. The accurate change estimation of percent ISA has been a fundamental but challenging issue associated with monitoring and understanding the urban environment. Therefore, our proposed method, with its improved ability to estimate impervious surface change both spatially and temporally, can provide accurate information required for urban environment research.

Keywords

• impervious surface
• change detection
• classification and regression tree
• remote sensing