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Document Type

Thesis - University Access Only

Award Date

2015

Degree Name

Master of Science (MS)

Department

Geography

First Advisor

David Roy

Keywords

Urban census, Remote sensing, Linear spectral mixing analysis, Dasymetric mapping, Landsat-8, LandScan, Kinshasa.

Abstract

Population census is the universal way of gathering population information and is usually undertaken every decade. However, in many African nations censuses are held irregularly and may have questionable accuracy. Census information are conventionally made available in aggregated units and are therefore less useful in localities when population densities vary rapidly, particularly in cities. Spatially explicit maps of urban population are needed for many urban planning and assessment applications. Gathering accurate information on the size and spatial location of urban populations, particularly in developing countries, can be challenging due to a number of factors including lack of resources, institutional weakness, extensive informal settlements, and literacy issues. Remotely sensed data provides a spatially explicit synoptic mapping capability and the opportunity to map urban land cover and a potential means to estimate residential area extent and density. Established spatial statistical methods such as dasymetric mapping enable the spatial redistribution (termed spatial disaggregation) of quantitative data from spatial units for which they exist to spatial units for which they are required with the aid of ancillary data. The current thesis aims to implement and apply a study that uses census data, remotely sensed satellite data, and dasymetric mapping techniques to generate spatially xiii explicit disaggregated population maps. Specifically, a local census collected by conventional ground based techniques by municipal authorities in 2013 for Lemba, a 14.3 km2 commune of Kinshasa, the capital of the Democratic Republic of Congo, is compared to a publically available satellite based 1km2 global population data set named LandScan that is generated by the U.S. Oak Ridge National Laboratory (ORNL) (Rose and Bright 2014). The study refines the census and the LandScan data using Landsat and GeoEye-1 imagery as sources of ancillary data to spatially disaggregate the population data to Landsat scale population density (people/30m2) estimates. First, a three residential density zone (low, medium, high) 30m image was generated from the average impervious surface fraction of two 30m Landsat-8 images. The impervious surface fractions were generated using Linear Spectral Mixing Analysis (LSMA) with four endmembers (Impervious, Vegetation, Soil, and Shade). A fourth uninhabited zone was digitalized visually from a higher spatial resolution 0.5-2m GeoEye-1 image. The residential density zone map was used as ancillary data with dasymetric mapping to spatially disaggregate the census and LandScan population data. The two resulting spatially explicit population density maps were then compared. The described research suggests a relatively quick and inexpensive, way to frequently map the spatial distribution of urban populations using satellite data and Geographic Information System (GIS) techniques. The thesis concludes with a summary and discussion of the limitations of the current work and suggestions for future research.

Library of Congress Subject Headings

Kinshasa (Congo)--Population--Remote sensing Kinshasa (Congo)--Population--Maps
Kinshasa (Congo)--Census
Cities and towns--Congo (Democratic Republic)--Kinshasa--Remote sensing.

Description

Includes bibliographical references (pages 72-88)

Format

application/pdf

Number of Pages

101

Publisher

South Dakota State University

Rights

In Copyright - Educational Use Permitted
http://rightsstatements.org/vocab/InC-EDU/1.0/

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