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Mapping Population Growth from Space: Aspirations and Challenges
Abdel-Samad Mohamed ALI
Dr.
National Institute for Agro-Environmental Sciences, JAPAN,
Japan
Email: samadino@affrc.go.jp
Hiroyuki OHNO
Dr.
National Institute for Agro-Environmental Sciences, JAPAN
Email: ohno@affrc.go.jp
Multi-temporal remote sensing images are commonly used in change-detection studies, but their use in estimating population growth has not been widely reported. On the other hand, most research combining demographic data and remotely sensed imagery tends to view population as the independent, not dependent, variable. But in any complex, coupled system, it is possible for there to be two-way linkages. However, this study explores the possibility of using remotely sensed images to estimate population growth from 1986 to 1996 in Assiut Governorate, Egypt. The model based on remote sensing digital landuse change detection approach. The estimates obtained from RS model were compared with the actual population data and with the predictions from a conventional demographic model. The remote sensing based model was found to be efficient and effective in predicting population growth at both city and village levels. In addition, the remote sensing model provides direct visualization of the potential distribution of the population growth within cities and villages.
Findings of this study may have a significant importance for Middle East countries as well as for many parts of the developing world, where censuses are infrequent. The RS model approached here may provide a useful means of obtaining accurate and up-to-date intercensal population growth. However, to be sure, certain caveats should be considered. First, it is assumed that all dwelling units in the study area are occupied. Additionally, the population growth model used in this study assumed a general uniform population density per unit area. However, if these criteria and others are met, the results can be remarkably accurate. Finally, it is concluded that remotely sensed data may provide a cost-effective method to reduce, but not replace, expensive ground data collection.
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