title: A Fusion of Remotely Sensed Data to Map the Impervious Surfaces of Growing Cities of Punjab, Pakistan
Creators: name: Shrestha, Binita
Publisher: name: University of Nevada, Las Vegas
Year: 2020

description: Urban population is expected to exceed 70% of the world’s total by the middle of the 21st century. Thus, growth in number as well as the sizes of the cities are certain in the near future. The urbanization rates will be much higher in the developing countries than the developed. Such phenomena are accompanied by conversion of land cover from its natural use to built up environment to accommodation growing population. Built up surfaces include road networks, buildings, parking lots and pathways. They are permanently impervious and hydrologically active surfaces. Large volume and discharges of runoff characterize impervious surfaces with frequently occurring flash floods in cities. Besides, proliferating impervious surface is responsible for increasing surface temperature due to Urban Heat Island effect and are the major Nonpoint Source pollutants in the receiving water bodies. At the face of climate change, the consequence of urbanization and increasing impervious surface is exacerbating. Therefore, for sustainable development, spatial and temporal expansion information of impervious surfaces is essential to the planners. Thus, the overall objective of this thesis is to map the impervious surfaces and estimate the expansion rates in the growing cities of Punjab, Pakistan in the last four years.

In this thesis, combined and individual datasets from Sentinel-1 and Sentinel-2 satellites were used to extract the amounts of impervious surfaces at city scale and to estimate the expansion rates of various cities of Punjab, Pakistan. The study period for the change analysis is from 2015-2021 based on the availability of satellite imagery. The satellite imageries were obtained from the Copernicus Services Data Hub. Information on different land covers in the form of reflectance, backscattering signal, and texture from a wide range of electromagnetic spectrum of light derived from Sentinels were used to map impervious surfaces. The following land covers were defined: barren soil, vegetation, water, and built-up surface. Four classification models were created from Random Forest algorithms and trained with land covers samples from Google Earth high resolution imagery. The 10 cities considered in this study were among the 50 cities extensively studied by the Urban Unit Pakistan covering the dynamics of Punjab in terms of urban extent, population distribution, area, and expansion. They make up the 21st largest cities in the province as well as represent spatial distribution from north to south. They include various climatic conditions ranging from arid in Multan to humid subtropical in Rawalpindi. They also represent different topographies of the cities such as plain and hilly. Validation samples for each land cover were also obtained from high resolution images to assess the classified land cover maps. Apart from validation of classified maps, quantitative comparison of resultant impervious surfaces was also conducted. For the purpose, the study used published datasets from Atlas of Urban Expansion and the Copernicus Land Service. If available, administrative boundaries of the cities were also used to define the urban extent. For other cities, coordinates were manually defined.

The combined Sentinel datasets were able to improve the overall accuracy and kappa coefficient of the classified maps by up to 11% and 7% respectively. McNemar test revealed that the models trained with fused data performed better than the models trained with optical alone data for land cover classification. The cities were expanding at rates ranging from 0.5% to 2.5% annually. The highest rate was encountered in Rawalpindi-Islamabad which is also the capital city of Pakistan. At least for one of the study years (2015/6 or 2020/21) the area was being overestimated by the single optical data. For instance, the optical data overestimated the impervious area of Lahore by a factor of 1.12 times while that of Bahawalpur by a factor of 1.2 times. The incorrect original results attributed to misclassification of barren soil as built up. This conclusion emphasized that additional information on backscattering signal and texture derived from radar image aided to reduce the misclassified bare soil pixels into built up. Spectrum plots also showed that sigma db and variance bands from radar image added a distinct feature to the classifier to distinguish built-up surfaces from other non built-up surfaces. The built-up surface had the highest value in backscatter signals and variance texture bands.

This study emphasized the usefulness of combining freely available remote sensing datasets for updating the city scale impervious surfaces cover information in developing countries. The contribution includes the assessment of suitability of combined Sentinel datasets to map the impervious surface at city scale. It also evaluates the rate of expansion of the cities. In conclusion, the combined radar and optical data can enhance the accuracy of classified maps for impervious cover mapping with benefits in complex topographies to update impervious surface information in developing countries. The results from this study could be used as inputs in hydrological and runoff models for urban studies. Other useful applications could be service allocation, drainage improvement, location determination for low impact development (LID) structures, stormwater utility fee determinations, flood control, and pollutants removal from runoff.