Urbanization creates new development in open spaces and agricultural fields, synonymous with increasing impervious surfaces. Such surfaces restrain the natural infiltration of water, and directly affect the non-point source pollution. Thus, consequential events like flooding and surface water degradation require spatial and quantitative information on impervious surfaces. Remote sensing technologies are widely used in impervious surface mapping of various geographical locations for environmental monitoring. In this study, the datasets from recently launched European Space Agency satellites (Sentinel-1 and Sentinel-2) and random forest classifier are used. The impervious surface growth of the study area, Lahore city, in 2015 and 2021, and growth trends are assessed. Results are validated with classification accuracy and comparison with similar datasets. The objective is to develop a reliable impervious surface mapping method with land cover quantification technique from multisource datasets. With a chi-square value of greater than 3.84 obtained from the McNemar test, the performance of fused data was superior to that of optical alone data in the classification. Over a 5-year period, Lahore grew at an annual rate of 2.14% comparable to the findings of Copernicus Land Services and the Atlas of Urban Expansion with an underestimation of 1% and 8.75%, respectively. Improvements in overall accuracy (2.7%) and kappa coefficient (5%) were seen in classified maps from fused datasets. Fusion of Sentinel datasets provide a reliable means of impervious surface mapping at city scale as an indicator of environmental quality which is valuable for the sustainable management of the city.
Keywords: Data fusion; Environmental monitoring; Land cover classification; Random forest; Sentinel images; Urban monitoring.
© 2021. The Author(s), under exclusive licence to Springer Nature Switzerland AG.