Spatiotemporal analysis of remotely sensed Landsat time series data for monitoring 32 years of urbanization

The world is witnessing a dramatic shift of settlement pattern from rural to urban population, particularly in developing countries. The rapid Addis Ababa urbanization reflects this global phenomenon and the subsequent socio-economic and environmental impacts, are causing massive public uproar and political instability. The objective of this study was to use remotely sensed Landsat data to identify and quantify the land use and land cover types, as well as changes over time. Maximum likelihood algorithm of the supervised image classification was used to map land use land cover types, which consisted of Vegetation areas, built-up areas, agricultural lands, Bare lands, and Scrublands, for 1985, 2003, and 2017 images. Built-up areas (69 %) are the dominant land cover type in the study area, followed by Agricultural lands (22%) and Vegetation areas (7%), though the compositions have changed since 1985. Rapid urban growth is evidenced by the expansion of built-up areas by 370% The growth is at the expense of agricultural and vegetation areas, exposing farmers to loss of massive farmland and woodlands. Additionally, urbanization eroding percent green and open spaces, while also degrading the diversity of the city’s land use land cover types. With one of the world s highest fertility rates and massive rural-to-urban migration, unsustainable Addis Ababa urbanization is likely to continue for the foreseeable future. It is, therefore, critical to adapt sustainable urban planning, which involves consideration of Compact City, Secondary Cities, and Edge city designs to mitigate the adverse impacts of the rapid Addis Ababa urbanization.

Keywords:

Change detection , Spatiotemporal analysis , Time Series , urbanization