Doll, Christopher Nicholas Hideo; (2003) Understanding the information content of night-time light satellite data for modelling socio-economic dimensions of global change. Doctoral thesis (Ph.D), UCL (University College London).

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Abstract

This thesis explores the ways in which Earth Observation data can be used to provide information about the human dimensions of global change. After setting out the key themes in global change research, attention is focused on the contribution data from satellite remote sensing systems can make to the detection of human impacts on the Earth's surface. Data sources include night-time light imagery from the Defense Meteorological Satellite Program's Operational Linescan System (DMSP-OLS), radar interferometry and new data products from the Moderate Resolution Imaging Spectroradiometer (MODIS), part of the National Aeronautic and Space Administration's Earth Observing System. Night-time light imagery was analysed with respect to its capability to provide information on population, economic activity (via the Gross Domestic Product - GDP) and carbon dioxide emissions. Using different levels of sub-national economic data from the European Commission's statistical organisation Eurostat, relationships between radiance and GDP were established. This introduced issues concerning the Modifiable Areal Unit Problem and the Ecological Fallacy, which have been discussed in relation to the problem. These correlations were very high and anomalies could be categorized into three different types. Disaggregated maps of economic activity at 5km resolution have been produced for 11 countries in the European Union as well as for the conterminous United States using these relationships. The results gained from this exercise are highly encouraging with most countries being mapped to within 5% of the published national GDP figure. This thesis provides a timely contribution to the debate on the human dimensions of global change at a time when international organisations are in the process of identifying the best ways to proceed with the monitoring and modelling of its impacts. The results and recommendations offer a great deal of encouragement for further research and sets the scene for the explicit monitoring of human attributes from remote sensing data.

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