No abstract

Landuse/cover in Nairobi City is changing rapidly because of the increased interactions of human activities with the environment as population increases. We used multi-temporal Landsat images (1976, 1988 and 2000) together with physical and socio-economic data in a post-classification analysis with GIS to map landuse/cover distribution and to analyse factors influencing the landuse/cover changes for Nairobi City. An unsupervised classification approach, which uses a minimum spectral distance to assign pixels to clusters, was used with the overall accuracy ranging from 87 per cent to 90 per cent. Landuse/cover statistics revealed that substantial landuse/cover changes have taken place and that the built-up areas have expanded by about 47 km 2 over the study period (1976-2000). Forests have decreased substantially while agricultural lands have been on the increase. Rapid economic developments together with the increasing population were noted to be the major factors influencing rapid landuse/cover changes.Urban expansion has replaced agricultural farmlands and other natural vegetation, thereby affecting habitat quality and leading to serious environmental degradation. The random, unplanned growths of environmentally degraded squatter settlements were noted to be emerging in the rural fringes. Successful planning of Nairobi's development will require reliable information about landuse/cover changes and factors influencing such changes.

Africa's urban population growth has been especially rapid, averaging about 5% per year over the past two decades. As a result, many urban areas have experienced dramatic growth that is seriously outstripping the capacity of most cities to provide adequate services for their residents. Although population growth and urbanization rates in Africa have slowed recently due to a number of factors including HIV/AIDS, urban growth is still expected to double by 2030, leading to dramatic sprawl with serious environmental and social consequences. Using Nairobi as an example of a rapidly urbanizing African city, we studied the dynamics of land use and land cover change using satellite data and addressed the need for models and urban management tools that can guide sustainable urban planning policies. Cellular Automata, which integrate biophysical factors with dynamic spatial modeling, are used in this study. The model was calibrated and tested using time series of urbanized areas derived from land use/cover maps, produced from remotely sensed imagery, with future urban growth projected to 2030. Model assessment results showed high levels of accuracy, indicating that simulation findings were realistic, thereby confirming the effectiveness of the model. Results further showed that the model is a useful and effective tool to foresee the spatial consequences of planning policies in the context of many African cities. The forecast for Nairobi showed unsustainable sprawl. [

Changes in wildlife conservation areas have serious implications for ecological systems and the distribution of wildlife species. Using the Masai Mara ecosystem as an example, we analyzed long-term land use/cover changes and wildlife population dynamics. Multitemporal satellite images, together with physical and social economic data were employed in a post classification analysis with GIS to analyze outcomes of different land use practices and policies. The results show rapid land use/cover conversions and a drastic decline for a wide range of wildlife species. Integration of land use/cover monitoring data and wildlife resources data can allow for the analysis of changes, and can be used to project trends to provide knowledge about potential land use/cover change scenarios and ecological impacts.

Urban population is increasing in Africa's major cities at a much faster rate than in the rest of the world, leading to dramatic sprawl with associated undesirable environmental and social consequences. Using Nairobi as an example of a major African city , we studied urban growth and addressed the need for urban management tools that can provide perspective scenarios of urban growth. This paper describes land use/cover changes and urban growth modeling for predicting the urban growth of Nairobi city using Cellular Automata (CA) , which integrates biophysical factors with dynamic spatial modeling. The model was calibrated and tested using time series of urbanized areas derived from remote sensing imageries , and future growth projected out to 2030. The results show that Nairobi city is experiencing fast spatial expansion with simulated urban land taking up most of the available land within the city and the immediate surroundings. The predicated rapid growth of urban areas has led to an unsustainable sprawled urban growth that has caused major changes to the landscape and loss of vital resource lands. The results show the capability of urban growth modeling in addressing regional planning issues.