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Remote Sens. 2017, 9(2), 175; doi:10.3390/rs9020175

Urban Land Extraction Using VIIRS Nighttime Light Data: An Evaluation of Three Popular Methods

1
Center for Human-Environment System Sustainability (CHESS), State Key Laboratory of Earth Surface Processes and Resource Ecology (ESPRE), Beijing Normal University, 19 Xinjiekouwai Street, Beijing 100875, China
2
Academy of Disaster Reduction and Emergency Management, Faculty of Geographical Science, Beijing Normal University, 19 Xinjiekouwai Street, Beijing 100875, China
*
Author to whom correspondence should be addressed.
Academic Editors: Bailang Yu, Yuyu Zhou, Xiaofeng Li, James Campbell and Prasad S. Thenkabail
Received: 15 December 2016 / Revised: 29 January 2017 / Accepted: 15 February 2017 / Published: 20 February 2017
(This article belongs to the Collection Editor's Choice)
View Full-Text   |   Download PDF [3711 KB, uploaded 20 February 2017]   |  

Abstract

Timely and accurate extraction of urban land area using the Suomi National Polar-orbiting Partnership Visible Infrared Imaging Radiometer Suite (VIIRS) nighttime light data is important for urban studies. However, a comprehensive assessment of the existing methods for extracting urban land using VIIRS nighttime light data remains inadequate. Therefore, we first reviewed the relevant methods and selected three popular methods for extracting urban land area using nighttime light data. These methods included local-optimized thresholding (LOT), vegetation-adjusted nighttime light urban index (VANUI), integrated nighttime lights, normalized difference vegetation index, and land surface temperature support vector machine classification (INNL-SVM). Then, we assessed the performance of these methods for extracting urban land area based on the VIIRS nighttime light data in seven evaluation areas with various natural and socioeconomic conditions in China. We found that INNL-SVM had the best performance with an average kappa of 0.80, which was 6.67% higher than the LOT and 2.56% higher than the VANUI. The superior performance of INNL-SVM was mainly attributed to the integration of information on nighttime light, vegetation cover, and land surface temperature. This integration effectively reduced the commission and omission errors arising from the overflow effect and low light brightness of the VIIRS nighttime light data. Additionally, INNL-SVM can extract urban land area more easily. Thus, we suggest that INNL-SVM has great potential for effectively extracting urban land with VIIRS nighttime light data at large scales. View Full-Text
Keywords: VIIRS nighttime light data; urban land extraction; normalized difference vegetation index; land surface temperature; support vector machine; local-optimized thresholding VIIRS nighttime light data; urban land extraction; normalized difference vegetation index; land surface temperature; support vector machine; local-optimized thresholding
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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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Dou, Y.; Liu, Z.; He, C.; Yue, H. Urban Land Extraction Using VIIRS Nighttime Light Data: An Evaluation of Three Popular Methods. Remote Sens. 2017, 9, 175.

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