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Remote Sens. 2016, 8(2), 114; doi:10.3390/rs8020114

Evaluating Multi-Sensor Nighttime Earth Observation Data for Identification of Mixed vs. Residential Use in Urban Areas

1
Social, Urban, Rural & Resilience (GSURR), The World Bank, Washington, DC 20006, USA
2
Institute of Engineering, National Autonomous University of Mexico (UNAM), Mexico City 04510, Mexico
These authors contributed equally to this work.
*
Author to whom correspondence should be addressed.
Academic Editors: Ioannis Gitas and Prasad Thenkabail
Received: 28 November 2015 / Revised: 13 January 2016 / Accepted: 25 January 2016 / Published: 4 February 2016
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Abstract

This paper introduces a novel top-down approach to geospatially identify and distinguish areas of mixed use from predominantly residential areas within urban agglomerations. Under the framework of the World Bank’s Central American Country Disaster Risk Profiles (CDRP) initiative, a disaggregated property stock exposure model has been developed as one of the key elements for disaster risk and loss estimation. Global spatial datasets are therefore used consistently to ensure wide-scale applicability and transferability. Residential and mixed use areas need to be identified in order to spatially link accordingly compiled property stock information. In the presented study, multi-sensor nighttime Earth Observation data and derivative products are evaluated as proxies to identify areas of peak human activity. Intense artificial night lighting in that context is associated with a high likelihood of commercial and/or industrial presence. Areas of low light intensity, in turn, can be considered more likely residential. Iterative intensity thresholding is tested for Cuenca City, Ecuador, in order to best match a given reference situation based on cadastral land use data. The results and findings are considered highly relevant for the CDRP initiative, but more generally underline the relevance of remote sensing data for top-down modeling approaches at a wide spatial scale. View Full-Text
Keywords: top-down modeling; urban areas; nighttime lights; DMSP; VIIRS; human activity; residential use; mixed use; global spatial data; CDRP top-down modeling; urban areas; nighttime lights; DMSP; VIIRS; human activity; residential use; mixed use; global spatial data; CDRP
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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).

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MDPI and ACS Style

Aubrecht, C.; León Torres, J.A. Evaluating Multi-Sensor Nighttime Earth Observation Data for Identification of Mixed vs. Residential Use in Urban Areas. Remote Sens. 2016, 8, 114.

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