Urbanization continues to be one of the leading drivers of Land Cover Use Change (LCLUC) globally, although African countries appear to be at the forefront of these current urban expansion trends. In fact, predictions indicate that there will be more people living in cities in Africa in the next 30 years, than there are people living in cities right now around the world; with this increase in population growth it is also expected that we will see a 12-fold increase in urban land area. These predictions indicate that a majority of LCLUC globally will be transforming both natural and managed landscapes to residential neighborhoods, informal settlements, and intensely modified cityscapes.
To date, the study of urbanization and its consequences and impacts has occurred at two very different scales and resolutions: (1) Large scale (regional, national, or global), lower resolution (≥30m pixels) urbanization patterns, and; (2) small scale (city, neighborhood, parcel), high resolution (<1m pixels) studies of urban heterogeneity. One approach is good for understanding urban growth, while the other method’s strength enables scientists to analyze landscape quality, human-environmental interactions, and tradeoffs in ecosystem services. This project aims to merge these divergent characterizations of urbanization so that we can capture large scale urbanization processes, while still quantifying the heterogeneity and quality of urban land uses across three African case study countries: Ethiopia, Nigeria, and South Africa. Furthermore, with this approach we can capture diverse multifunctional land uses within and around cities that provide a number of important ecosystem services to people near and far.
To accomplish this goal, we will develop a framework for multi-resolution data fusions for a two tiered LCLUC mapping approach which will dissect the urban agglomeration into more refined density and infrastructure driven urban classes with fine resolution identification of features and resources within classes. Spatial products will support the assessment of United Nations defined Sustainable Development Goal (SDG) indicators, identify hotspots of urbanization-driven LCLUC, and aid in sustainable urban planning for equitable access to services. Through the use of widely-available data sources and the testing of frameworks for flexibility in adapting to sensor turnover, our approach will allow for the transferability of developed methods to additional countries and continents which are also experiencing rapid urban development and expansions with continuity into the future. Key project objectives will include:
1) Incorporate multiple moderate resolution data sources for the development of enhanced LCLUC classifications;
2) Identify “LCLUC hotspots” using the SDG indicator 11.3.1 and assess tradeoffs and synergies of social and ecosystem services impacted by LCLUC within and surrounding “hotspot” urban growth centers;
3) Develop a moderate and VHR data fusion framework for fine resolution land cover/land use (LCLU) and change detection products within focal urban hotspots;
4) Assess the equitable distribution of social and ecological services within focal urban hotspots to support policy and sustainable planning goals.