A rule-based spectral unmixing algorithm for extracting annual time series of sub-pixel impervious surface fraction. Issue 10 (18th May 2020)

• Record Type: Journal Article
• Authors: Patidar, Nitesh; Keshari, Ashok K.
• Is Part Of: International journal of remote sensing. Volume 41:Issue 10(2020)
• Page Start: 3970
• Page End: 3992
• Publication Date: 2020-05-18
• Subjects: Remote sensing -- Periodicals; Télédétection -- Périodiques; 621.3678
• DOI: 10.1080/01431161.2019.1711243
• Languages: English
• ISSNs: 0143-1161

Abstract

ABSTRACT: The spatial distribution and the temporal dynamics of impervious surface (IS) are important for understanding urbanization and its impact on surface energy balance, ecohydrological processes, and urban heat island. Despite the development of various methods, quantification of IS on multi-temporal scale is still challenging in complex urban areas due to the mixed pixel problem and the spectral ambiguity between classes. This study aimed at developing an algorithm, named Rule-Based Spectral Unmixing Algorithm (RBSUA), to derive sub-pixel IS fraction at annual scale using a time series of satellite images. A rule-based composite scheme is developed to integrate temporal contextual information into the popular Multiple Endmember Spectral Mixture Analysis (MESMA) to improve the classification of spectrally ambiguous classes. The developed algorithm also encompasses a temporal filtering to derive consistent IS fractions. Evaluation of the algorithm in a complex urban area – the National Capital Region (NCR), India – shows an improved performance in separating spectrally ambiguous classes, particularly IS and soil, and obtaining consistent series of IS fraction. The developed algorithm yielded the accuracy of annual IS fraction between 88% and 91% which is considerably higher than those from the original MESMA (48% to 83%). The application of the RBSUA in the NCR, India, shows that the IS has increased in the study area from the initial value of 377 ± 78 km 2 in 1992 to 708 ± 64 km 2 in 2017. The results suggested that the developed algorithm was effective in deriving IS at the sub-pixel level and detecting changes at the annual frequency.