Human population distribution modelling at regional level using very high resolution satellite imagery
Introduction
Reliable information on the spatial distribution of population has been identified as key information for a better understanding of human impacts on land and water resources (Tian, Yue, Zhu, & Clinton, 2005). Traditionally, information on population numbers and demographic characteristics stem from census data made available per administrative unit (Liu, Clarke, & Herold, 2006). However, population is unlikely to be distributed uniformly within these conceptual census boundaries (Hay, Noor, Nelson, & Tatem, 2005; Langford, Higgs, Radcliffe, & White, 2008). Thus, the spatial pattern as provided by choropleth mapping does not reflect the actual population distribution (Mennis, 2003). To address this problem, areal interpolation methods have been developed that transform population data into a regular, continuous grid (raster) in which each grid cell (pixel) is assigned a representative estimate of the total population for that particular cell (Yue et al., 2005). As human population distribution is closely linked to various exogenous factors (Briggs, Gulliver, Fecht, & Vienneau, 2007), several sophisticated techniques, called intelligent or smart interpolation, have been introduced (Liu, Kyriakidis, & Goodchild, 2008). These GIS-based population distribution models disaggregate population of census units based on spatially explicit ancillary information of which land cover data is often used as main explanatory variable (e.g. Gallego, 2010; Tian et al., 2005).
To derive the ancillary information necessary for estimating population quantities, the use of satellite imagery has become commonplace. While some studies simply use land cover information derived from satellite imagery (e.g. Linard, Alegana, Noor, Snow, & Tatem, 2010; Viel & Tran, 2009), Li and Weng (2005) employ a stepwise regression analysis to sequentially test potential candidate variables from Landsat imagery such as vegetation indices or specific bands. Other techniques have been developed that make use of night-time light imagery (Briggs et al., 2007; Dobson, Bright, Coleman, Durfee, & Worley, 2000; Sutton, Roberts, Elvidge, & Baugh, 2001; Zhuo et al., 2009) or image texture indices (Chen, 2002). However, although ascribed high potential in the context of population distribution modelling (Hay et al., 2005), the opportunities of very high resolution (VHR) satellite imagery have not yet been fully explored (Liu et al., 2008).
A common technique of incorporating ancillary information into population distribution modelling is dasymetric mapping (e.g. Eicher & Brewer, 2001). By introducing empirical sampling, Mennis (2003) improved the determination of percentage assignment values for different land cover classes. Other techniques, sometimes referred to as intelligent or smart interpolation, use multiple other factors instead of or additional to land cover data, such as terrain or road network, and assign coefficients according to their influence on population. From the single coefficients an overall weight is calculated for each grid cell and then used for population redistribution (e.g. Dobson et al., 2000; Tian et al., 2005). However, the assignment of coefficients is mostly based on heuristic rules and assumptions without a solid evidence-base for such rules (Hay et al., 2005). Recently, some studies have attempted to improve this limitation, e.g. Briggs et al. (2007) by using satellite-derived light emissions data, or Liao, Wang, Meng, and Li (2010) by integrating generic programming and generic algorithm techniques. Still, VHR satellite imagery has been rarely employed for deriving coefficients that describe the relationship between population and various ancillary information sources.
The vast majority of studies on population distribution modelling published in the past 20 years were conducted in Europe (e.g. Gallego, Batista, Rocha, & Mubareka, 2011) and China (e.g. Tian et al., 2005; Zhuo et al., 2009) or have a global coverage such as LandScan (Dobson et al., 2000) or Gridded Population of the World (GPW) (CIESIN, 2000). The outcomes of these global studies are increasingly applied also in the developing world, e.g. for assessing conflict risk under climate change in East Africa (O'Loughlin et al., 2012), or modelling malnutrition in Mali (Jankowska, Lopez-Carr, Funk, Husak, & Chafe, 2012). However, regional scale models from the developing world, particularly from rural areas that take into account specific drivers of settlement patterns, such as rivers for water supply, are scarce. The recent initiative AfriPop addresses this need by improving existing global products for Africa (Linard et al., 2010; Tatem & Linard, 2011), but their country-scale modelling might still not be sufficient for regional-scale planning and research purposes.
This paper explores the value of classification results obtained through object-based image analysis (OBIA) of VHR satellite imagery for implementing GIS-based regional population distribution models. The first objective is to use the locations of houses, as classified from QuickBird imagery for a subset of a larger study area in rural western Kenya, to establish functions that approximate the frequency distribution of the houses in relation to selected population impacting factors. The second objective is to redistribute population for the entire study area, preserving population figures at the level of the smallest available administrative unit, based on an overall weight calculated from the single frequency distribution functions. Finally, in order to address the problem of artefacts at administrative boundaries that has not been solved by existing approaches using proximity factors (e.g. Yue et al., 2005), the third objective is to combine our approach with a mass-preserving pycnophylactic smoothing algorithm (Tobler, 1979) and to evaluate the resulting patterns.
Section snippets
Study area
The study area is located in western Kenya between 34°37΄05″ and 35°09΄25″ E and 0°02΄53″ S and 0°32΄23″ N and encompasses an area of 60 km by 65 km around the Kakamega-Nandi forest complex (see Fig. 1). While the forests are considered the easternmost relict of the Guineo-Congolian rainforest belt (Wagner, Köhler, Schmitz, & Böhme, 2008) known for their unique biodiversity and species composition (e.g. Althof, 2005; Mitchell, Schaab, & Wägele, 2009), their surrounding areas are densely
Redistributed population density
In order to allow discerning the modelling results with sufficient spatial detail, for this paper two subsets (see Fig. 1 for their location) are visualised instead of the entire study area. The two subsets are considered to be representative in terms of characteristics of the modelling outcomes. Showing pronounced differences in population density between adjacent sublocations, the first subset (Fig. 4) is employed for a comparative presentation of the two modelling approaches A and B. The
QuickBird image classification results
Many studies on extracting buildings from VHR satellite imagery are known (e.g. Bhaskaran, Paramananda, & Ramnarayan, 2013; Haverkamp, 2004; Lhomme, He, Weber, & Morin, 2009). However, most research has been conducted for urban areas in Europe, the U.S. and Australia, whereas for developing countries detailed information on the location of individual housing is scarce (Devaux, Fotsing, & Chéry, 2007). Therefore, and since ancillary data such as from Lidar surveys (cp. Weidner, 1997) was not
Conclusions
This study has demonstrated that results obtained through object-based image analysis (OBIA) of VHR satellite imagery can provide a suitable means for implementing regional scale population distribution models, in particular for areas where sufficiently detailed land use/cover data is absent, a situation typically found in developing countries. For calculating an overall probability coefficient, the use of houses as classified from VHR imagery for a subset of the study area for establishing
Acknowledgements
The study was carried out within the research framework BIOTA East Africa (subproject E02, 01LC0625D1), funded by the German Federal Ministry of Education and Research (BMBF). We would also like to thank Eric Nyadimo from the Kenyan School Mapping Project for providing the GIS data on schools, as well as Uwe Deichmann (The World Bank) for providing a C code to implement the pycnophylactic interpolation approach.
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Present address: European Commission – Joint Research Centre, Institute for Environment & Sustainability (IES), Via E. Fermi, 27439, TP 272, 21027 Ispra (VA), Italy.
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Present address: Bundesamt für Naturschutz, AS Insel Vilm, 18581 Putbus, Germany.
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Present address: FAO-SWALIM, UN Somalia, Ngecha Road, Off Lower Kabete Road, P.O. Box 30470-00100, Nairobi, Kenya.