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The Urban Transition in Ghana and Its Relation to Land Cover and Land Use Change Through Analysis of Multi-scale and Multi-temporal Satellite Image Data


Most countries in sub-Saharan Africa remain in the midst of a major transformation from being predominantly rural to having a majority of people living in cities. This has enormous consequences for urban and rural land cover and land use (LCLU). As urbanization proceeds, food security concerns grow not simply in tandem, but at a higher rate because urban development implies a higher caloric intake per person meaning that agricultural output (or income to buy another country's output) must grow at a higher rate than the population (Weeks 2011). Urban growth is likely to lead to sprawl, which will convert good agricultural land to urban purposes. Land beyond the former urban periphery may then transition to agriculture. This land cover and land use change (LCLUC) transition sequence of natural vegetation to agriculture to low density built environment to high density is likely to perpetuate sprawl for the major urban centers. The need to increase agricultural productivity demands that traditional subsistence agriculture be modernized to increase per hectare yield. This typically leads, somewhat paradoxically, to a lower demand for rural labor because higher yield requires mechanization rather than human labor.

Map of regional study area
Figure 1. Map of regional study area and study cities in Ghana.

In this research project, we expand the range and scope of the project team’s current work in Accra, Ghana, leveraging our existing resources to explicitly incorporate an analysis of LCLUC in the southern and most urban portion of Ghana, as shown in Figure 1. Based on the reasoning described above, the UN projects that Ghana’s rural population will decline over the next several decades. All of the population growth in rural areas is likely to be absorbed by cities, as the redundant rural population seeks jobs in urban places. This rapid growth of cities, which are already characterized by a high degree of informality and a low level of infrastructure, means that LCLU in urban areas is constantly evolving, and that this transformation of the built environment will be associated with inequalities in the social, economic, and health conditions.

We hypothesize, then, that demographic and health outcomes in Ghana are strongly impacted by LCLUC. First, we hypothesize that the rapid increase in land consolidation, driven by international investments and measured remotely by a signature pattern shift from mottled mixed land use small farms to large monocropped industrial plantations is a key independent driver of urbanization vis a vis rural farm labor displacement. We also hypothesize that the quality of life in major Ghanaian cities, measured in terms of socio-economic and health variables, is driven largely by intra-urban LCLUC. The nominal study period is 1986 to 2010, with an emphasis on 2000 to 2010 for which demographic and census data are more complete. The realities of remote sensing and demographic data collection and data availability determine the actual period of analysis, which will vary slightly across the study area. The primary geographic unit of analysis will be the enumeration area (EA); equivalent to a census tract in the US, EAs range from less than a hectare in urban areas to several hundred square kilometers in rural areas.


  1. To identify, map, and quantify LCLUC within an extensive study area of Ghana over 25 years (1986 through 2010)
  2. to understand the relationship between rural-to-urban migration as an outcome of LCLUC and concomitant drivers for the 2000 through 2010 period, and
  3. to assess LCULC and its effect on demographic and quality of life factors for four major Ghanaian urban centers during this time period


Virtually all of the major LCLUC in both rural and urban areas of Ghana are directly or indirectly driving patterns of inter-regional population change


Our interdisciplinary team consists of remote sensing, human-environment, landscape, ecological, and social scientists from:

San Diego State University (lead)

Douglas Stow (PI), John Weeks (Co-PI), Pete Coulter (Project Manager), Li An (Co-Investigator), Magdalena Benza-Fiocco (PhD student), Sory Toure (PhD Student), Ace Shih (MS student), Sean Taugher (MA student), Milo Verjaska (MS student), Nicholas Ibanez (BS student)

The George Washington University

Ryan Engstrom (PI), Avery Sandborn (MA student)

University of California Santa Barbara

David Lopez-Carr (PI)

University of Ghana

Samuel Agyei-Mensah and Foster Mensah (CERSGIS) (Collaborators)

Study Area

There are four regions of interest for which LCLUC is being identified, quantified, and mapped. These are the Greater Accra, Central, Ashanti, and Eastern regions. With these regions, LCLUC is being be mapped with the greatest detail for the cities of Accra, Kumasi, Obuasi, and Cape Coast. The cities are shown in red in Figure 1, and the four regions are shown are shown as light colored polygons, with Greater Accra at lower right, Central at lower left, Ashanti at upper left, and Eastern at upper right.

Land Cover and Land Use Classification

LandSAT image of Accra, Ghana
Figure 2. Regional Landsat data for the Greater Accra region. This Landsat 7 image is from 26 Dec. 2002, and is one of the only cloud-free Landsat 7 images available during the study period for the Greater Accra region. The outer extent of the yellow polygons represent the Greater Accra region.

Land cover and land use is being mapped for c.2000 and c.2010 periods, and changes between these LCLU maps will be utilized (in conjunction with direct image-based change detection products) to identify LCLU changes of interest. Inter-regional identification of LCLUC is based on moderate spatial resolution, multi-temporal image data primarily from Landsat TM/ETM+ and LCDM OLI optical satellite systems (Figure 2). Where/when required, data from Terra ASTER, SPOT HRV, AWiFS and DMCii., and ERS-2 synthetic aperture radar (SAR) satellite systems will be also used. In addition, a map created by the Center for Remote Sensing and Geographic Information Services (CERSGIS) representing land cover conditions for 2000 is being utilized in conjunction with imagery for identifying c.2000 land cover conditions (Figure 3). The inter-regional LCLU classification scheme below is being used to classify moderate spatial resolution imagery across the four regions.

Inter-regional LCLU classification scheme

Land cover image of Accra, Ghana
Figure 3. Center for Remote Sensing and Geographic Information Services (CERSGIS) 2000 Ghana land cover map. Cloud covered “no data” areas within the CERSGIS map were filled with MODIS land cover product (version 5.1) with classes defined by the 2000 International Geosphere Biosphere Programme (IGBP). A crosswalk was used to transform MODIS IGBP land cover classes to CERSGIS land cover classes.
  1. Forest
  2. Non-forest vegetation (open secondary forest, shrub thicket and savanna)
  3. Agriculture (mixed w/vegetation and commercial)
  4. Urban/built (city core, suburban, peri-urban, village)

High spatial resolution image data from QuickBird, IKONOS, GeoEye-1, and WorldView-2 commercial satellite systems are being utilized primarily for intra-urban mapping and analysis of LCLUC (Figure 4). The intra-urban LCLU classification scheme below is being used to classify the high spatial resolution imagery corresponding to the extents of the four cities.

Intra-urban LCLU classification scheme

  1. Undeveloped
  2. Urban agriculture
  3. Urban Residential
  4. Urban Non-residential
High resolution image of Accra, Ghana
Figure 4. Example of high spatial resolution satellite imagery for the Accra area. IKONOS and QuickBird satellite images collected in 2000 and 2002 are shown.

Analysis of Regional-scale Impacts of LCLUC on Migration, Demography, and Health

Unlike most developing countries, demographic, socioeconomic and health data for Ghana exist in higher quality and quantity than for most other developing countries, with national and nongovernment organization data collection programs starting in the early 1990s. We are utilizing quantitative spatial analysis techniques to examine relationships between LCLUC and magnitudes and changes of demographic, socioeconomic, and health variables. While EAs are the basic spatial analytical unit, analyses are also being conducted at multiple spatial scales, including: District (first level of agglomeration above EAs), Sub-regions (generated by merging EA units using socio-economic similarity and spatial autocorrelation measures), and Regions (state-level units). Relationships of LCLUC with demographic/socio-economic variables will be explored for EAs sampled in the 1993, 1998, 2003 and 2008 Demographic and Health Surveys, as well as for the 2000 and 2010 census data for all EAs within the study area.


Project Reports

Conference Papers & Presentations


Data: Demographic, Socio-economic, and Health

Data: Imagery and Geographic Information Systems (GIS) Products

Last Updated April 3, 2014

This page is supported by San Diego State University International Population Center.
For more information please contact John R. Weeks at