Monitoring urban sprawl using simulated PROBA-V data

Abstract

Urbanization proceeds currently at a rapid pace and the impact on natural ecosystems cannot be neglected. Consequently, it is important to be able to monitor the expansion of urban areas. Yet the process of extracting them from satellite imagery is not trivial. Urban is a non-uniform class with spectral proximity to barren land. In this article, a method for extracting urban areas from medium-resolution Earth observation data is presented. The information source is simulated data of the PROBA-V sensor. Visual and near-infrared bands are classified by the adaptive neuro-fuzzy inference system (ANFIS) neuro-fuzzy classifier into urban and non-urban classes. The method can overcome the main difficulty in similar efforts, i.e. the extensive commission errors of barren to the class urban. The main novelty relies on exploiting annual spectral variability of each land-use class at the pixel level. The basic assumption is that urban and barren areas may have similar spectral values but they have different phenological cycles. The overall accuracy obtained by the classification is 91.57% with a Cohen's kappa coefficient (khat) of 0.84. Sufficient correlation at the city level is also achieved. Change detection is also possible in terms of hot-spot identification, however marginally suitable for medium-sized cities.