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Using Remote Sensing to Monitor Urban Sprawl in the Nairobi City Metropolitan Area with a Special Focus on Kiambu County, Kenya

MAINGI, ALEX
http://rave.ohiolink.edu/etdc/view?acc_num=bgsu1731061812912017

Abstract Details

2024, Master of Science (MS), Bowling Green State University, Applied Geospatial Science.
Cities around the globe are undergoing significant transformations due to rapid urbanization, fueled by factors such as population growth, economic development, and the migration of people from rural to urban areas. By 2050, an estimated two-thirds of the global population will reside in urban areas, posing significant challenges for sustainable development. Remote sensing data, combined with machine learning modeling approaches play a crucial role in monitoring and analyzing urban sprawl. This study investigates the potential of a machine learning (ML) classification algorithm coupled with data fusion remote sensing techniques to improve land-use and land-cover (LULC) change detection in Kiambu County, Kenya, for the period 2000-2022. It utilizes Landsat data from 2000 to 2022, augmented by Harmonized Landsat Sentinel-2 (HLS) and Sentinel-1 SAR data from 2013 to 2022, for urban land use/land cover (LULC) change detection. Google Earth Engine (GEE) facilitated preprocessing and analysis, refining Synthetic Aperture Radar (SAR) imagery and employing Random Forest (RF) for classification. Integrating Landsat 8/HLS and SAR data enhanced classification accuracy, supported by feature selection, hyperparameter tuning, and spectral band ratios to mitigate data errors. Key indices like NDBI, NBR2, BSI, NDWI, NDVI, and SAVI were crucial for classifying land cover types. From 2000 to 2022, Landsat-based analysis shows significant urbanization. Urban areas grew from 17.8% in 2000 to 22.4% by 2005, 25.7% in 2010, 29.6% in 2015, and 31.9% by 2022. Specifically, for 2015, using Landsat 8 alone, urban areas covered 23.4% (594.0 km²), while fusing Landsat 8 with SAR data raised this to 28.7% (729.4 km²) with improved testing accuracy of 91.7% and validation accuracy of 87.5%. Integrating optical data (HLS and Landsat 8) with SAR and applying ML techniques on GEE, the classification accuracy improved by 5.7% compared to optical data alone. Overall, urbanization in Kiambu County has accelerated in the study period necessitating balanced promotion of sustainable development and food security through the adoption of integrated land-use planning, promotion of sustainable agriculture, preservation of green infrastructure, promotion of public participation in decision-making, and the establishment of a GIS-based management system that can facilitate rapid decision-making.
Anita Simic (Committee Co-Chair)
Kefa Otiso (Committee Co-Chair)
117 p.
Geographic Information Science; Remote Sensing
Urban sprawl; Remote sensing, Land Use Land Cover (LULC) Change; Machine learning; Data fusion; Harmonized Landsat Sentinel-2 (HLS); Synthetic Aperture Radar (SAR); Google Earth Engine (GEE); Kiambu County, Kenya; Nairobi Metropolitan Area;

Recommended Citations

Citations

  • MAINGI, A. (2024). Using Remote Sensing to Monitor Urban Sprawl in the Nairobi City Metropolitan Area with a Special Focus on Kiambu County, Kenya [Master's thesis, Bowling Green State University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=bgsu1731061812912017

    APA Style (7th edition)

  • MAINGI, ALEX. Using Remote Sensing to Monitor Urban Sprawl in the Nairobi City Metropolitan Area with a Special Focus on Kiambu County, Kenya. 2024. Bowling Green State University, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=bgsu1731061812912017.

    MLA Style (8th edition)

  • MAINGI, ALEX. "Using Remote Sensing to Monitor Urban Sprawl in the Nairobi City Metropolitan Area with a Special Focus on Kiambu County, Kenya." Master's thesis, Bowling Green State University, 2024. http://rave.ohiolink.edu/etdc/view?acc_num=bgsu1731061812912017

    Chicago Manual of Style (17th edition)

bgsu1731061812912017
© 2024, some rights reserved.Creative Commons License Using Remote Sensing to Monitor Urban Sprawl in the Nairobi City Metropolitan Area with a Special Focus on Kiambu County, Kenya by ALEX MAINGI is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported License. Based on a work at etd.ohiolink.edu.
This open access ETD is published by Bowling Green State University and OhioLINK.
Release 3.2.12