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Title: A comparison of remote sensing approaches to distinguish unplanned and planned urbanization in Abuja, Nigeria
Author: Gumel, Ibrahim Adamu
ISNI:       0000 0004 7973 2844
Awarding Body: Edge Hill University
Current Institution: Edge Hill University
Date of Award: 2019
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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.
Supervisor: Aplin, Paul Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available