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Open AccessArticle

Filtering the NPP-VIIRS Nighttime Light Data for Improved Detection of Settlements in Africa

1
State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China
2
University of Chinese Academy of Sciences, Beijing 100049, China
3
Department of Geoscience and Remote Sensing, Delft University of Technology, 2628 CN Delft, The Netherlands
4
Key Laboratory for Geographical Process Analysis & Simulation of Hubei Province, College of Urban and Environmental Sciences, Central China Normal University, Wuhan 430079, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(24), 3002; https://doi.org/10.3390/rs11243002
Received: 1 November 2019 / Revised: 27 November 2019 / Accepted: 5 December 2019 / Published: 13 December 2019
Observing and understanding changes in Africa is a hotspot in global ecological environmental research since the early 1970s. As possible causes of environmental degradation, frequent droughts and human activities attracted wide attention. Remote sensing of nighttime light provides an effective way to map human activities and assess their intensity. To identify settlements more effectively, this study focused on nighttime light in the northern Equatorial Africa and Sahel settlements to propose a new method, namely, the patches filtering method (PFM) to identify nighttime lights related to settlements from the National Polar-orbiting Partnership Visible Infrared Imaging Radiometer Suite (NPP-VIIRS) monthly nighttime light data by separating signal components induced by biomass burning, thereby generating a new annual image in 2016. The results show that PFM is useful for improving the quality of NPP-VIIRS monthly nighttime light data. Settlement lights were effectively separated from biomass burning lights, in addition to capturing the seasonality of biomass burning. We show that the new 2016 nighttime light image can very effectively identify even small settlements, notwithstanding their fragmentation and unstable power supply. We compared the image with earlier NPP-VIIRS annual nighttime light data from the National Oceanic and Atmospheric Administration (NOAA) National Center for Environmental Information (NCEI) for 2016 and the Sentinel-2 prototype Land Cover 20 m 2016 map of Africa released by the European Space Agency (ESA-S2-AFRICA-LC20). We found that the new annual nighttime light data performed best among the three datasets in capturing settlements, with a high recognition rate of 61.8%, and absolute superiority for settlements of 2.5 square kilometers or less. This shows that the method separates biomass burning signals very effectively, while retaining the relatively stable, although dim, lights of small settlements. The new 2016 annual image demonstrates good performance in identifying human settlements in sparsely populated areas toward a better understanding of human activities. View Full-Text
Keywords: human activity; nighttime light; NPP-VIIRS; biomass burning; patches filtering method; African settlement human activity; nighttime light; NPP-VIIRS; biomass burning; patches filtering method; African settlement
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MDPI and ACS Style

Yuan, X.; Jia, L.; Menenti, M.; Zhou, J.; Chen, Q. Filtering the NPP-VIIRS Nighttime Light Data for Improved Detection of Settlements in Africa. Remote Sens. 2019, 11, 3002.

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