Name
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. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.3390/rs70911887 | REMOTE SENSING |
Keywords | Field | DocType |
climate mitigation,multi-temporal image compositing,land use land cover,cloud computing,urban geography | Meteorology,Global warming,Multispectral image,Remote sensing,Normalized Difference Vegetation Index,Geology,Compositing,Global studies,Defense Meteorological Satellite Program,Urban geography,Cloud computing | Journal |
Volume | Issue | ISSN |
7 | 9 | 2072-4292 |
Citations | PageRank | References |
5 | 0.60 | 11 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Qingling Zhang | 1 | 17 | 3.01 |
| Bin Li | 2 | 25 | 3.03 |
| David Thau | 3 | 27 | 4.06 |
| rebecca moore | 4 | 7 | 1.31 |