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

• Published in: Remote sensing (Basel, Switzerland) Vol. 9; no. 2; p. 175
• Main Authors: Dou, Yinyin, Liu, Zhifeng, He, Chunyang, Yue, Huanbi
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.02.2017
• Subjects: Extraction; Infrared imaging; Land; Land area; Land surface temperature; Light; local-optimized thresholding; Meteorological satellites; Night; normalized difference vegetation index; Performance indices; Radiometry; Remote sensing; Socioeconomic factors; support vector machine; Support vector machines; Urban areas; urban land extraction; Urban studies; Vegetation; VIIRS nighttime light data; Beijing China; United States > US; China
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs9020175

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.