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Remote Sens. 2015, 7(2), 1422-1440; doi:10.3390/rs70201422

Integrating Multiple Source Data to Enhance Variation and Weaken the Blooming Effect of DMSP-OLS Light

State Key Laboratory of Earth Surface Processes and Resource Ecology/Center for Human-Environment System Sustainability (CHESS), Beijing Normal University, No. 19, Xinjiekouwai Street, Haidian District, Beijing 100875, China
Author to whom correspondence should be addressed.
Academic Editors: Christopher D. Elvidge, Janet Nichol and Prasad S. Thenkabail
Received: 25 October 2014 / Accepted: 22 January 2015 / Published: 29 January 2015
(This article belongs to the Special Issue Remote Sensing with Nighttime Lights)
View Full-Text   |   Download PDF [42643 KB, uploaded 29 January 2015]   |  


Defense Meteorological Satellite Program/Operational Linescan System (DMSP-OLS) nighttime light has proved to be an effective tool to monitor human activities, especially in mapping urban areas. However, the inherent defects of DMSP-OLS light including saturation and blooming effects remain to be tackled. In this study, the Normalized Difference Vegetation Index (NDVI) product of the Moderate-resolution Imaging Spectroradiometer/Normalized Difference Vegetation Index 1-Month (MODND1M), the temperature product of Moderate-resolution Imaging Spectroradiometer/Land Surface Temperature 1-Month (MODLT1M) and DMSP-OLS light were integrated to establish the Vegetation Temperature Light Index (VTLI), aiming at weakening the saturation and blooming effects of DMSP-OLS light. In comparison with DMSP-OLS nighttime light, this new methodology achieved the following improvements: (1) the high value (30%–100%) range of VTLI was concentrated in the urban areas; (2) VTLI could effectively enhance the variation of DMSP-OLS light, especially in the urban center; and (3) VTLI reached convergence faster than Vegetation Adjusted Normalized Urban Index (VANUI). Results showed that the urban areas extracted by VTLI were closer to those from Landsat TM images with the accuracy of kappa coefficients in Beijing (0.410), Shanghai (0.718), Lanzhou (0.483), and Shenyang (0.623), respectively. Thus, it can be concluded that the proposed index is able to serve as a favorable option for urban areas mapping. View Full-Text
Keywords: NDVI; urban heat island; urbanization area; landscape ecology; land use; land cover NDVI; urban heat island; urbanization area; landscape ecology; land use; land cover

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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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Hao, R.; Yu, D.; Sun, Y.; Cao, Q.; Liu, Y.; Liu, Y. Integrating Multiple Source Data to Enhance Variation and Weaken the Blooming Effect of DMSP-OLS Light. Remote Sens. 2015, 7, 1422-1440.

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