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 km2 in
1992 to 708 ± 64 km2 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.
