Next Article in Journal
Comment on Tompalski et al. Combining Multi-Date Airborne Laser Scanning and Digital Aerial Photogrammetric Data for Forest Growth and Yield Modelling. Remote Sens. 2018, 10, 347
Previous Article in Journal
A Microwave Tomography Strategy for Underwater Imaging via Ground Penetrating Radar
Article Menu
Issue 9 (September) cover image

Export Article

Open AccessArticle
Remote Sens. 2018, 10(9), 1409; https://doi.org/10.3390/rs10091409

Mapping Population Distribution from High Resolution Remotely Sensed Imagery in a Data Poor Setting

1
Department of Geography, Cartography and GIS Research Group, Physical Geography, Vrije Universiteit Brussel, Pleinlaan 2, 1050 Brussels, Belgium
2
Department of Geography, Physical Geography, Vrije Universiteit Brussel, Pleinlaan 2, 1050 Brussels, Belgium
3
Centre National de Documentation et de Recherche Scientifique, Observatoire Volcanologique du Karthala, 169 Moroni, Comoros
4
Department of Geography, Cartography and GIS Research Group, Vrije Universiteit Brussel, Pleinlaan 2, 1050 Brussels, Belgium
*
Author to whom correspondence should be addressed.
Received: 13 August 2018 / Revised: 2 September 2018 / Accepted: 3 September 2018 / Published: 5 September 2018
(This article belongs to the Section Remote Sensing Image Processing)
Full-Text   |   PDF [7443 KB, uploaded 18 September 2018]   |  

Abstract

Accurate mapping of population distribution is essential for policy-making, urban planning, administration, and risk management in hazardous areas. In some countries, however, population data is not collected on a regular basis and is rarely available at a high spatial resolution. In this study, we proposed an approach to estimate the absolute number of inhabitants at the neighborhood level, combining data obtained through field work with high resolution remote sensing. The approach was tested on Ngazidja Island (Union of the Comoros). A detailed survey of neighborhoods at the level of individual dwellings, showed that the average number of inhabitants per dwelling was significantly different between buildings characterized by a different roof type. Firstly, high spatial resolution remotely sensed imagery was used to define the location of individual buildings, and second to determine the roof type for each building, using an object-based classification approach. Knowing the location of individual houses and their roof type, the number of inhabitants was estimated at the neighborhood level using the data on house occupancy of the field survey. To correct for misclassification bias in roof type discrimination, an inverse calibration approach was applied. To assess the impact of variations in average dwelling occupancy between neighborhoods on model outcome, a measure of the degree of confidence of population estimates was calculated. Validation using the leave-one-out approach showed low model bias, and a relative error at the neighborhood level of 17%. With the increasing availability of high resolution remotely sensed data, population estimation methods combining data from field surveys with remote sensing, as proposed in this study, hold great promise for systematic mapping of population distribution in areas where reliable census data are not available on a regular basis. View Full-Text
Keywords: population estimation; data-poor regions; roof type mapping; high resolution remote sensing; Union of the Comoros population estimation; data-poor regions; roof type mapping; high resolution remote sensing; Union of the Comoros
Figures

Graphical abstract

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
SciFeed

Share & Cite This Article

MDPI and ACS Style

Mossoux, S.; Kervyn, M.; Soulé, H.; Canters, F. Mapping Population Distribution from High Resolution Remotely Sensed Imagery in a Data Poor Setting. Remote Sens. 2018, 10, 1409.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Remote Sens. EISSN 2072-4292 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top