Impact of Precipitation and Agricultural Productivity on Groundwater Storage in Rahim Yar Khan District, Pakistan

Mistry, Gaurang; Stephen, Haroon; Ahmad, Sajjad

doi:10.1061/9780784482322.011

Cited by 3 publications

(1 citation statement)

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“…Impervious surface knowledge is an important indicator of urbanization degree [8] as well as environmental quality [9]. The consequences of the surge in impervious cover include increases in the volume, duration, and intensity of surface runoff [10][11][12]; decreases of groundwater recharge [13][14][15][16][17][18][19][20]; and the degradation of receiving water sources, as they directly impact the flow of nonpoint source pollutants [21], which also affect the hydrologic cycle. Timely and accurate information on the changing land cover of developing cities will aid in decision making processes.…”

Section: Introductionmentioning

confidence: 99%

Impervious Surfaces Mapping at City Scale by Fusion of Radar and Optical Data through a Random Forest Classifier

2021

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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.

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