Settlement location and population density estimation in rugged terrain using information derived from Landsat ETM and SRTM data |
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| Authors: | Sarah Mubareka Daniele Ehrlich Ferdinand Bonn Francois Kayitakire |
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| Affiliation: | 1. Centre d'Applications et de Recherches en Télédétection , Université de Sherbrooke , 2500 Boulevard Université, Sherbrooke (QC), J1K 2R1, Canada sarah.mubareka@usherbrooke.ca;3. European Commission Joint Research Centre , Institute for the Protection and the Security of the Citizen , TP 267, via Enrico Fermi, 1 Ispra, (VA) 21020, Italy;4. Centre d'Applications et de Recherches en Télédétection , Université de Sherbrooke , 2500 Boulevard Université, Sherbrooke (QC), J1K 2R1, Canada |
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| Abstract: | It is useful to have a disaggregated population database at uniform grid units in disaster situations. This study presents a method for settlement location probability and population density estimations at a 90 m resolution for northern Iraq using the Shuttle Radar Topographic Mission (SRTM) digital terrain model and Landsat Enhanced Thematic Mapper satellite imagery. A spatial model each for calculating the probability of settlement location and for estimating population density is described. A randomly selected subset of field data (equivalent to 50%) is first analysed for statistical links between settlement location probability and population density; and various biophysical features which are extracted from Landsat or SRTM data. The model is calibrated using this subset. Settlement location probability is attributed to the distance from roads and water bodies and land cover. Population density can be estimated based upon land cover and topographic features. The Landsat data are processed using a segmentation and subsequent feature–based classification approach making this method robust to seasonal variations in imagery and therefore applicable to a time series of images regardless of acquisition date. The second half of the field data is used to validate the model. Results show a reasonable estimate of population numbers (r = 0.205, p<0.001) for both rural and urban settlements. Although there is a strong overall correlation between the results of this and the LandScan model (r = 0.464, p<0.001), this method performs better than the 1 km resolution LandScan grid for settlements with fewer than 1000 people, but is less accurate for estimating population numbers in urban areas (LandScan rural r = 0.181, p<0.001; LandScan urban r = 0.303, p<0.001). The correlation between true urban population numbers is superior to that of LandScan however when the 90 m grid values are summed using a filter which corresponds to the LandScan spatial resolution (r = 0.318, p<0.001). |
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