A comparison of remote sensing approaches to distinguish unplanned and planned urbanization in Abuja, Nigeria.


    Student thesis: Doctoral Thesis


    The process of urbanization experienced world-wide has increased rapidly in recent
    decades, with this trend set to continue. Urbanization is more pronounced in cities in the
    Global South, and this brings with it significant social and environmental problems such as
    uncontrolled urban sprawl and uneven resource distribution. While much urbanization in
    the Global South is unplanned, there have been some rare attempts at strategic, large-scale
    urban planning. One such example is Abuja, the capital of Nigeria, which is a new planned
    city with its origins in a Master Plan devised in the 1970’s.
    This research uses multi-temporal remote sensing to investigate urbanization in Abuja over
    the last 40 years to critique the original Abuja Master Plan, showing the extent to which
    urban development has kept with, or diverged from, the original Master Plan. The study
    also investigated the potential of using remote sensing methods to distinguish unplanned
    and planned urban settlements in Abuja, Nigeria.
    First a time-series of multispectral Landsat images was acquired; cloud-free images from
    1975, 1986, 1990, 1999, 2002, 2008 and 2014 were used, with some years specifically
    selected to correspond with important dates in Nigeria’s socio-political development, and
    to match major milestone targets as prescribed by the Master Plan.
    The research also combined Landsat Thematic Mapper (TM)/Enhanced Thematic Mapper
    plus (ETM+) image classifications of urban built-up land cover with Defence Meteorological
    Satellite Program-Operational Linescan System (DMSP-OLS) stable nighttime lights imagery
    to investigate, distinguish and map unplanned and planned urban areas. DMSP-OLS stable
    nighttime lights imagery from 1999, 2002 and 2008 were selected. Thresholding techniques
    with ancillary information were successfully applied to distinguish areas of unplanned and
    planned developments.
    Finally, the research focused on developing and applying deep learning and random forest
    classification techniques on Very High Resolution (VHR) imagery to characterise and map
    unplanned and planned built-up land at a finer spatial scale. This approach was able to
    address some of the obvious limitations resulting from using coarse (DSMP-OLS) and
    medium (Landsat) resolution imagery encountered in the earlier part of the research in
    attempting to distinguish unplanned and planned built-up settlements. The results of the
    study have shown deep learning can be successfully adapted to map unplanned and planned settlements in a city of the Global South, while random forest performed poorly
    in distinguishing planned and unplanned settlements.
    Date of Award24 Oct 2019
    Original languageEnglish
    Awarding Institution
    • Edge Hill University
    SupervisorPAUL APLIN (Director of Studies)

    Cite this

    A comparison of remote sensing approaches to distinguish unplanned and planned urbanization in Abuja, Nigeria.
    GUMEL, I. A. (Author). 24 Oct 2019

    Student thesis: Doctoral Thesis