Abstract

Urban growth copes with problems in sustainable development. In developing countries, particularly, sustainable development of urban growth copes with severe challenges with respect to sluggish economic and social growth, population boom, environmental deterioration, unemployment, slums and so on. Time series of remote sensing data provide critical support on sustainability assessment. However, the urban spatial extend cannot be accurately extracted from land cover data. Targeting the urban growth and its sustainability in Islamabad, the capital of Pakistan, this study extracts urban area from four periods of Landsat images between 1990 and 2018 using an innovative object-based backdating change detection method and two criteria for extracting urban land from impervious surface. We prove that impervious surface cover and urban area increased 273.10% and 426.21%, respectively, over the last 3 decades. We identify five factors playing important role in urban growth: population, transportation systems, master planning, industrial and real estate development, and neighbor urban effect. In this study, we assess the socio-economic sustainability associated with slum growth and census data, and the environmental sustainability in relation to the variations of normalized difference vegetation index (NDVI) in forest areas. We found that slums increased with the corresponding growth of urban area and population, reflecting sluggish economic increase in Islamabad. We found that the area of woodland increased 9.29%, but its NDVI decreased from 0.668 to 0.551, implying a deteriorative trend of environmental condition.

Highlights

  • Urban growth is an important trend due to the needs of immigrants who move from other parts of the country

  • We conducted a comparison among object-based post-classification change detection, backdating change detection associated with change vector analysis, and object-based backdating change detection (OBBCD)

  • We compare the results from integrating change vector analysis and object-based change detection with the fuzzy c-means-based result adopted in OBBCD

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Summary

Introduction

Urban growth is an important trend due to the needs of immigrants who move from other parts of the country. Until 2018, more than 55% of the world population lived in cities. This proportion is expected to reach 68% by 2050 (United_Nations 2018). The study of urban growth sustainability is of great significance for decision making about urban planning in relation to construction programs, poverty reduction, disaster monitoring and prevention, and improvement of the environmental quality (Bhatta 2010). Current urban sustainability research is mostly based on field socio-economic surveys and demographic statistics, which is time consuming and labor intensive, and makes it difficult to grasp spatial heterogeneity within cities

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