Urbanization increases the amount of impervious surfaces, making accurate information on spatial and temporal expansion trends essential; the challenge is to develop a cost- and labor-effective technique that is compatible with the assessment of multiple geographical locations in developing countries. Several studies have identified the potential of remote sensing and multiple source information in impervious surface quantification. Therefore, this study aims to fuse datasets from the Sentinel 1 and 2 Satellites to map the impervious surfaces of nine Pakistani cities and estimate their growth rates from 2016 to 2020 utilizing the random forest algorithm. All bands in the optical and radar images were resampled to 10 m resolution, projected to same coordinate system and geometrically aligned to stack into a single product. The models were then trained, and classifications were validated with land cover samples from Google Earth’s high-resolution images. Overall accuracies of classified maps ranged from 85% to 98% with the resultant quantities showing a strong linear relationship (R-squared value of 0.998) with the Copernicus Global Land Services data. There was up to 9% increase in accuracy and up to 12 % increase in kappa coefficient from the fused data with respect to optical alone. A McNemar test confirmed the superiority of fused data. Finally, the cities had growth rates ranging from 0.5% to 2.5%, with an average of 1.8%. The information obtained can alert urban planners and environmentalists to assess impervious surface impacts in the cities.

Land cover change alters hydrological response of a watershed and usually results in flooding. This paper studies the effectiveness of porous pavements in reducing the impact of built-up area on surface runoff and peak flow in the Walnut Gulch Experimental Watershed. The built-up area is expanded into high intensity developed surface with 80%–100% imperviousness. The precipitation events between July and October of 2018 (more than 60% of annual rainfall) are used to calibrate the baseline response. Watershed is modeled in Hydrologic Engineering Center-Hydrologic Modeling System (HEC-HMS), whereas flow routing is performed in Hydrologic Engineering Center-River Analysis System (HEC-RAS). The model is calibrated and validated by using observed discharge hydrograph data at the watershed outlet (location: 31o 43' N, 110o 41' W). The precipitation and runoff observations were obtained from the United States Department of Agriculture (USDA), agricultural research service website. Land cover data was obtained from the United States Geological Survey (USGS) website. Two scenarios were modeled. In the first scenario, built-up area is increased by changing the value of imperviousness to more than 80% in HEC-HMS. In the second scenario, low impact development (LID) is integrated in the model in the form of porous concrete used in the built-up area. Model simulation of the first scenario shows an increased surface runoff volume. Moreover, the lag time between storm peak and discharge peak reduces due to decrease in the infiltration rate. In the second scenario, the surface runoff is significantly reduced due to increased infiltration resulting from LID. The results of this research provide useful insight about potential land cover change impact on the watershed response and can help in land use planning.