Human population distribution modelling at regional level using very high resolution satellite imagery

• OBIA of QuickBird image useful for regional human population distribution modelling.
• Distinct frequency distribution of houses for 5 factors driving settlement patterns.
• In addition, removal of artefacts at admin boundaries with pycnophylactic approach.
• Results show strong impact of river proximity and slope on population distribution.

Modelling the distribution of human population based on satellite-derived information has become an important field of research, providing valuable input e.g. for human impact assessments related to the management of threatened ecosystems. However, few regional-scale studies have been conducted in developing countries, where detailed land cover data is usually absent, and the potential of very high resolution (VHR) satellite imagery in this context has not been explored yet. This study uses results obtained through object-based image analysis (OBIA) of QuickBird imagery for a subset of a highly populated rural area in western Kenya. Functions are established that approximate frequency distributions of QuickBird-derived locations of houses in relation to five factors. These factors are known to impact settlement patterns and data is available for the entire study area. Based on an overall probability coefficient (weight) calculated from the single functions, human population is redistributed at the smallest administrative level available (version A). In addition, the problem of artefacts remaining at administrative boundaries is addressed by combining the approach with the pycnophylactic smoothing algorithm (Tobler, 1979) (version B). The results show distinct patterns of population distribution, with particular influence of rivers/streams and slope, while version B in addition is free of boundary artefacts. Despite some limitations compared to models based on detailed land cover data (e.g. the ability of capturing abrupt changes in population density), a visual and numerical evaluation of the results shows that using houses as classified from VHR imagery for a study area subset works well for redistributing human population at the regional level. This approach might be suitable to be applied also in other regions of e.g. sub-Saharan Africa.