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

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

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The EnMAP-Box—A Toolbox and Application Programming Interface for EnMAP Data Processing

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Time-domain Statistics of the Electromagnetic Bias in GNSS-Reflectometry

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Organic Matter Modeling at the Landscape Scale Based on Multitemporal Soil Pattern Analysis Using RapidEye Data

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

Content Provider	MDPI
Author	Zhang, Qingling Li, Bin Thau, David Moore, Rebecca
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.
File Size	3642368
Ending Page	11913
Page Count	27
Starting Page	11887
File Format	PDF
e-ISSN	20724292
DOI	10.3390/rs70911887
Journal	Remote Sensing
Issue Number	9
Volume Number	7
Language	English
Publisher Date	2015-09-16
Access Restriction	Open
Subject Keyword	climate mitigation multi-temporal image compositing land use land cover cloud computing urban geography
Content Type	Text
Resource Type	Article

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