Synergistic use of nighttime satellite data, electric utility infrastructure, and ambient population to improve power outage detections in urban areas

Remote Sensing

Volumn 9, Issue 3, 2017, Pages

  Cole, Tony A ^a   Wanik, David W ^b   Molthan, Andrew L ^c   Román, Miguel O ^d   Griffin, Robert E ^a  

^a UNIVERSITY OF ALABAMA IN HUNTSVILLE   (United States)

^b UNIVERSITY OF CONNECTICUT   (United States)

^c NASA MARSHALL SPACE FLIGHT CENTER   (United States)

^d NASA GODDARD SPACE FLIGHT CENTER   (United States)

Author keywords

Day night band; Hurricane sandy; Lunar BRDF; Natural hazards; Nighttime light; NPP VIIRS; Power outages; Societal impact; Suomi NPP

Indexed keywords

DISASTERS; ELECTRIC UTILITIES; FEEDFORWARD NEURAL NETWORKS; HAZARDS; HURRICANES; INFRARED IMAGING; ORBITS; POPULATION STATISTICS; RESTORATION; SATELLITE IMAGERY; SATELLITES; THERMOGRAPHY (IMAGING);

DAY/NIGHT BAND; LUNAR BRDF; NATURAL HAZARD; NIGHT-TIME LIGHTS; NPP-VIIRS; POWER OUTAGE; SOCIETAL IMPACTS; SUOMI-NPP;

OUTAGES;

EID: 85019359446     PISSN: None     EISSN: 20724292     Source Type: Journal    
DOI: 10.3390/rs9030286     Document Type: Article

References (42)

1. IFRC What Is a Disaster? Available online: http://www.ifrc.org/en/what-we-do/disaster-management/ about-disasters/what-is-a-disaster/ (accessed on 1 January 2016).
2. Guikema, S.D.; Quiring, S.M.; Han, S.-R. Prestorm Estimation of Hurricane Damage to Electric Power Distribution Systems. Risk Anal. 2010, 30, 1744-1752.
3. Wanik, D.W.; Anagnostou, E.N.; Hartman, B.M.; Frediani, M.E.B.; Astitha, M. Storm outage modeling for an electric distribution network in Northeastern USA. Nat. Hazards 2015, 79, 1359-1384.
4. He, J.; Wanik, D.W.; Hartman, B.M.; Anagnostou, E.N.; Astitha, M.; Frediani, M.E.B. Nonparametric Tree-Based Predictive Modeling of Storm Outages on an Electric Distribution Network. Risk Anal. 2016.
5. Manoj, B.S.; Baker, A.H. Communication Challenges in Emergency Response. Commun. ACM 2007, 50, 51-53.
6. Miller, S.D.; Mills, S.P.; Elvidge, C.D.; Lindsey, D.T.; Lee, T.F.; Hawkins, J.D. Suomi satellite brings to light a unique frontier of nighttime environmental sensing capabilities. Proc. Natl. Acad. Sci. USA 2012, 109, 15706-15711.
7. Lee, T.E.; Miller, S.D.; Turk, F.J.; Schueler, C.; Julian, R.; Deyo, S.; Dills, P.; Wang, S. The NPOESS VIIRS Day/Night visible sensor. Bull. Am. Meteorol. Soc. 2006, 87, 191-199.
8. Cao, C.; Bai, Y. Quantitative analysis of VIIRS DNB nightlight point source for light power estimation and stability monitoring. Remote Sens. 2014, 6, 11915-11935.
9. Elvidge, C.D.; Baugh, K.E.; Kihn, E.A.; Kroehl, H.W.; Davis, E.R.; Davis, C.W. Relation between satellite observed visible-near infrared emissions, population, economic activity and electric power consumption. Int. J. Remote Sens. 1997, 18, 1373-1379.
10. Wei, Y.; Liu, H.; Song, W.; Yu, B.; Xiu, C. Normalization of time series DMSP-OLS nighttime light images for urban growth analysis with Pseudo Invariant Features. Landsc. Urban Plan. 2014, 128, 1-13.
11. Min, B.; Gaba, K. Tracking electrification in Vietnam using nighttime lights. Remote Sens. 2014, 6, 9511-9529.
12. Min, B.; Gaba, K.M.; Sarr, O.F.; Agalassou, A. Detection of rural electrification in Africa using DMSP-OLS night lights imagery. Int. J. Remote Sens. 2013, 34, 8118-8141.
13. He, C.; Ma, Q.; Liu, Z.; Zhang, Q. Modeling the spatiotemporal dynamics of electric power consumption in Mainland China using saturation-corrected DMSP/OLS nighttime stable light data. Int. J. Digit. Earth 2014, 7, 993-1014.
14. Elvidge, C.D.; Tuttle, B.T.; Sutton, P.C.; Baugh, K.E.; Howard, A.T.; Milesi, C.; Bhaduri, B.; Nemani, R. Global distribution and density of constructed impervious surfaces. Sensors 2007, 7, 1962.
15. Elvidge, C.D.; Baugh, K.E.; Hobson, V.R.; Kihn, E.A.; Kroehl, H.W. Detection of fires and power outages using DMSP-OLS data. In Remote Sensing Change Detection: Environmental Monitoring Methods and Applications; Ann Arbor Press: Chelsea, MI, USA, 1999; pp. 123-135.
16. Aubrecht, C.; Elvidge, C.D.; Ziskin, D.; Baugh, K.E.; Tuttle, B.T.; Erwin, E.; Kerle, N. Observing power blackouts from space -A disaster related study. In EGU General Assembly: Geophysical Research Abstracts; European Geosciences Union: Vienna, Austria, 2009; pp. 1-2.
17. Kohiyama, M.; Hayashi, H.; Maki, N.; Higashida, M.; Kroehl, H.W.; Elvidge, C.D.; Hobson, V.R. Early damaged area estimation system using DMSP-OLS night-time imagery. Int. J. Remote Sens. 2004, 25, 2015-2036.
18. Elvidge, C. VIIRS DNB Cloud Free Composites; NGDC NOAA: Boulder, CO, USA, 2014.
19. Baugh, K. Nighttime lights compositing using the VIIRS day-night band: Preliminary Results. Proc. Asia-Pacific Adv. Netw. 2013, 35, 70-86.
20. Li, X.; Zhang, R.; Huang, C.; Li, D. Detecting 2014 Northern Iraq Insurgency using night-time light imagery. Int. J. Remote Sens. 2015, 36, 3446-3458.
21. Shi, K.; Yu, B.; Huang, Y.; Hu, Y.; Yin, B.; Chen, Z.; Chen, L.; Wu, J. Evaluating the ability of NPP-VIIRS nighttime light data to estimate the gross domestic product and the electric power consumption of China at multiple scales: A Comparison with DMSP-OLS data. Remote Sens. 2014, 6, 1705-1724.
22. Li, X.; Xu, H.; Chen, X.; Li, C. Potential of NPP-VIIRS Nighttime Light Imagery for Modeling the Regional Economy of China. Remote Sens. 2013, 5, 3057-3081.
23. Shi, K.; Huang, C.; Yu, B.; Yin, B.; Huang, Y.; Wu, J. Evaluation of NPP-VIIRS night-time light composite data for extracting built-up urban areas. Remote Sens. Lett. 2014, 5, 358-366.
24. Hu, C.; Chen, S.;Wang, M.; Murch, B.; Taylor, J. Detecting surface oil slicks using VIIRS nighttime imagery under moon glint: A case study in the Gulf of Mexico. Remote Sens. Lett. 2015, 6, 295-301.
25. Cao, C.; Shao, X.; Uprety, S. Detecting light outages after severe storms using the S-NPP/VIIRS Day/Night band radiances. IEEE Geosci. Remote Sens. Lett. 2013, 10, 1582-1586.
26. Molthan, A.; Jedlovec, G. Satellite observations monitor outages from Superstorm Sandy. Eos Trans. Am. Geophys. Union 2013, 94, 53-54.
27. Miller, S.D.; Turner, R.E. A dynamic lunar spectral irradiance data set for NPOESS/VIIRS Day/Night band nighttime environmental applications. IEEE Trans. Geosci. Remote Sens. 2009, 47, 2316-2329.
28. Román, M.O.; Stokes, E.C. Holidays in lights: Tracking cultural patterns in demand for energy services. Earth's Futur. 2015, 3, 182-205.
29. NASA LAADS DAAC. Available online: https://ladsweb.nascom.nasa.gov/ (accessed on 17 March 2017).
30. Shuai, Y.; Schaaf, C.B.; Strahler, A.H.; Liu, J.; Jiao, Z. Quality assessment of BRDF/albedo retrievals in MODIS operational system. Geophys. Res. Lett. 2008, 35.
31. ORNL Landscan Documentation. Available online: http://web.ornl.gov/sci/landscan/landscan_ documentation.shtml (accessed on 2 February 2016).
32. Census, U.S. Metropolitan and Micropolitan. Available online: http://www.census.gov/population/metro/ about/ (accessed on 2 February 2016).
33. NHC Hurricane Sandy. Available online: http://www.nhc.noaa.gov/archive/2012/al18/al182012.discus. 019.shtml (accessed on 9 June 2016).
34. Halverson, J.B.; Rabenhorst, T. Hurricane Sandy: The science and impacts of a superstorm. Weatherwise 2013, 66, 14-23.
35. NHC Post-Tropical Cyclone Sandy. Available online: http://www.nhc.noaa.gov/archive/2012/al18/ al182012.update.10300002.shtml (accessed on 9 June 2016).
36. Kunz, M.; Mühr, B.; Kunz-Plapp, T.; Daniell, J.E.; Khazai, B.;Wenzel, F.; Vannieuwenhuyse, M.; Comes, T.; Elmer, F.; Schröter, K.; et al. Investigation of superstorm Sandy 2012 in a multi-disciplinary approach. Nat. Hazards Earth Syst. Sci. 2013, 13, 2579-2598.
37. United States Department of Energy. Hurricane Sandy Situation Reports; doe.gov: Washington, DC, USA, 2012.
38. Gunther, F.; Fritsch, S. neuralnet: Training of Neural Networks. R J. 2010, 2, 30-38.
39. Clifton, C. Change Detection in Overhead Imagery Using Neural Networks. Appl. Intell. 2003, 18, 215-234.
40. Chini, M.; Pacifici, F.; Emery, W.J.; Pierdicca, N.; Frate, F. Del Comparing Statistical and Neural Network Methods Applied to Very High Resolution Satellite Images Showing Changes in Man-Made Structures at Rocky Flats. IEEE Trans. Geosci. Remote Sens. 2008, 46, 1812-1821.
41. Ito, Y.; Hosokawa, M.; Lee, H.; Liu, J.G. Extraction of damaged regions using SAR data and neural networks. In Proceedings of the 19th ISPRS Congress, Amsterdam, The Netherlands, 16-23 July 2000; pp. 156-163.
42. Mann, L.M.; Melaas, K.E.; Malik, A. Using VIIRS Day/Night Band to Measure Electricity Supply Reliability: Preliminary Results from Maharashtra, India. Remote Sens. 2016, 8, 711.