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Remote Sens. 2015, 7(9), 11887-11913; doi:10.3390/rs70911887

Building a Better Urban Picture: Combining Day and Night Remote Sensing Imagery

1
Center for Geospatial Information, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, 1068 Xueyuan Avenue, Shenzhen University Town, Shenzhen, Guangdong 518055, China
2
School of Forestry and Environmental Studies, Yale University, 195 Prospect Street, New Haven, CT 06511, USA
3
Department of Geography, Central Michigan University, Mount Pleasant, MI 48859, USA
4
Google Earth Engine, Google Inc., 1600 Amphitheatre Pkwy, Mountain View, CA 94043, USA
*
Author to whom correspondence should be addressed.
Academic Editors: Janet Nichol, Parth Sarathi Roy and Prasad S. Thenkabail
Received: 30 June 2015 / Revised: 31 August 2015 / Accepted: 1 September 2015 / Published: 16 September 2015
View Full-Text   |   Download PDF [3557 KB, uploaded 17 September 2015]   |  

Abstract

Urban areas play a very important role in global climate change. There is increasing need to understand global urban areas with sufficient spatial details for global climate change mitigation. Remote sensing imagery, such as medium resolution Landsat daytime multispectral imagery and coarse resolution Defense Meteorological Satellite Program/Operational Linescan System (DMSP/OLS) nighttime light imagery, has provided a powerful tool for characterizing and mapping cities, with advantages and disadvantages. Here we propose a framework to merge cloud and cloud shadow-free Landsat Normalized Difference Vegetation Index (NDVI) composite and DMSP/OLS Night Time Light (NTL) to characterize global urban areas at a 30 m resolution, through a Normalized Difference Urban Index (NDUI) to make full use of them while minimizing their limitations. We modify the maximum NDVI value multi-date image compositing method to generate the cloud and cloud shadow-free Landsat NDVI composite, which is critical for generating a global NDUI. Evaluation results show the NDUI can effectively increase the separability between urban areas and bare lands as well as farmland, capturing large scale urban extents and, at the same time, providing sufficient spatial details inside urban areas. With advanced cloud computing facilities and the open Landsat data archives available, NDUI has the potential for global studies at the 30 m scale. View Full-Text
Keywords: climate mitigation; multi-temporal image compositing; land use land cover; cloud computing; urban geography climate mitigation; multi-temporal image compositing; land use land cover; cloud computing; urban geography
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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

Zhang, Q.; Li, B.; Thau, D.; Moore, R. Building a Better Urban Picture: Combining Day and Night Remote Sensing Imagery. Remote Sens. 2015, 7, 11887-11913.

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