The Annual Cycling of Nighttime Lights in India
2.1. Data Preparation
2.1.1. Monthly Nighttime Light
2.1.2. Masking for Lit Areas
2.1.3. Power Stability Estimates from Survey Data
2.1.4. Power Stability Estimates from Voltage Monitoring
2.2. Land Use/Land Cover
2.3. VNL Image Processing
2.3.1. Low Cloud Coverage Treatment
2.3.2. STL Decomposition and Detrending
2.3.3. Low-Pass Filtering
2.3.4. Autocorrelation Function
2.3.6. Festival Lighting
2.4. Classification of Periodic Signature
2.4.1. Rule Based Classification
- Stage (1): Take the ACF from Lag0 to Lag17, and apply the Gaussian filter to the selected section of the ACF profile.
- Stage (2): Calculate the ACF difference between the lags, then find the zero crossing lags of the difference profile (neglect the first one because it always starts from zero). This stage is essentially taking the second derivative of the ACF profile to find the inflection point.
- Stage (3): Determine the ACF type by counting the number of zero crossing lags. If there are 2 zero crossing lags, then it can be single; if there are 4, it can be dual; otherwise, it is acyclic.
- Stage (4): Double check the mean amplitude. If it is less then 0.05, then the profile is acyclic. This is to reject false counts of zero crossing caused by small oscillation in the profile.
2.4.2. Supervised Classification
2.4.3. Verifying Classification Results
- Randomly select 5000 samples from each class not masked by the lit area mask and examine if the underlying ACF profile agrees with the assigned class. This is shown in Figure 17.
3.1. Spatial Pattern of ACF Class
- Border between Madhya Pradesh and Maharashtra, showing the boundary of ACF class difference between northern and southern India (Figure 23a).
- Haryana and neighboring states. Displays the ACF class difference between west end of Uttar Pradesh and Haryana (Figure 23b).
- Meghalaya and neighboring areas. Displays the distinct differences of the ACF class for Meghalaya and Assam on its north and east, as well as Bangladesh to the south (Figure 23c).
- Tamil Nadu and neighboring states. Tamil Nadu displays a higher fraction of villages with the single peak ACF class than its neighboring southern states (Figure 23d).
- Gulf of Khambhat and coast of Maharashtra. Showing gas platforms in the Gulf of Khambhat and refineries near the mouth of Mahi River along the coast of Maharashtra, marked by a cluster of acyclic pixels (Figure 24a).
- Central Uttar Pradesh shows large areas of smaller villages marked by dual peak pixels. Lucknow and districts to the east and west have more villages with the single peak ACF class; the difference seems to follow the district boundaries (Figure 24b).
- Chattisgarh. The populated area in Chhattisgarh displays a very different ACF class in the north compared to the south (Figure 24c).
- Southwestern districts of Bihar. This scene displays a crisp difference of single peak grid cells stopping at the border between Uttar Pradesh and Bihar, while the southwestern districts of Bihar display a large portion of acyclic grid cells compared to their neighbors (Figure 24d).
3.2. ACF Class and LCLU Class
- Urban areas in most states are dominated by the single peak class, except Manipur in the northeastern region and the four large states (Karnataka, Kerala, Andhra Pradesh, and Tamil Nadu), which have the ACF class in urban areas dominated by acyclic pattern.
- Single peaks in the ACF class have a higher appearance in rural areas in northern states, while the four major states in the southern region (Karnataka, Andhra Pradesh, Kerala, Tami Nadu) and some from the northeast (Meghalaya, Manipur, Mazoram, Arunachal Pradesh) show a high proportion of the acyclic pattern.
- Cropland in northern states has ACF classes all below 50%. Some southern states (Karnataka, Kerala) and NE states (Arunachal Pradesh, Manipur) exhibit a higher proportion of the acyclic pattern in cropland.
- States exhibiting a higher acyclic pattern in cropland have mountainous terrain. This includes Karnataka, Kerala, and Andhra Pradesh from the southern region, Mizoram, Manipur, Meghalaya, and Arunachal Pradesh from the northeast, and Himachal Pradesh from the northern region.
- Tamil Nadu exhibits a distinct composition of ACF classes in rural and cropland compared to other larger southern states.
- Meghalaya exhibits a distinct composition of ACF classes in all three LCLU classes compared to Assam.
- Madhya Pradesh exhibits a distinct composition of ACF classes in all three LCLU classes compared to Maharashtra.
3.3. Power Stability and VNL Brightness
- How to prepare the monthly VNL profile for analysis,
- Defining three arch types of the ACF profile derived from the monthly VNL profile.
- Proposing two fundamentally different approaches to classify the ACF profile into one of the arch types. The two classification approaches were closely compared to verify whether their results agreed with each other.
Data Availability Statement
Conflicts of Interest
- Load Generation Balance Report. Available online: https://cea.nic.in/l-g-b-r-report/ (accessed on 5 November 2020).
- Garg, P. Energy scenario and vision 2020 in India. J. Sustain. Energy Environ. 2012, 3, 7–17. [Google Scholar]
- Electricity Demand Pattern Analysis (All India). 2016. Available online: https://posoco.in/reports/electricity-demand-pattern-analysis (accessed on 1 November 2020).
- Elvidge, C.D.; Baugh, K.E.; Sutton, P.C.; Bhaduri, B.; Tuttle, B.T.; Ghosh, T.; Ziskin, D.; Erwin, E.H. Who’s in the dark—satellite based estimates of electrification rates. Urban Remote Sens. 2011, 250, 211–224. [Google Scholar] [CrossRef][Green Version]
- Chand, T.K.; Badarinath, K.V.S.; Elvidge, C.D.; Tuttle, B.T. Spatial characterization of electrical power consumption patterns over India using temporal DMSP-OLS night-time satellite data. Int. J. Remote Sens. 2009, 30, 647–661. [Google Scholar] [CrossRef]
- Mann, M.L.; Melaas, E.K.; Malik, A. Using VIIRS day/night band to measure electricity supply reliability: Preliminary results from Maharashtra, India. Remote Sens. 2016, 8, 711. [Google Scholar] [CrossRef][Green Version]
- Elvidge, C.D.; Hsu, F.C.; Zhizhin, M.; Ghosh, T.; Taneja, J.; Bazilian, M. Indicators of Electric Power Instability from Satellite Observed Nighttime Lights. Remote Sens. 2020, 12, 3194. [Google Scholar] [CrossRef]
- Elvidge, C.D.; Baugh, K.E.; Zhizhin, M.; Hsu, F.C. Why VIIRS data are superior to DMSP for mapping nighttime lights. Proc. Asia-Pac. Adv. Netw. 2013, 35, 62. [Google Scholar] [CrossRef][Green Version]
- VIIRS Nighttime Light. Available online: https://eogdata.mines.edu/products/vnl (accessed on 15 October 2020).
- Elvidge, C.D.; Zhizhin, M.; Ghosh, T.; Hsu, F.C.; Taneja, J. Annual time series of global VIIRS nighttime lights derived from monthly averages 2012 to 2019. Remote Sens. 2021, 13, 922. [Google Scholar] [CrossRef]
- Indian Humanity Development Survey. Available online: https://ihds.umd.edu/about (accessed on 17 November 2020).
- Electricity Supply Monitoring Initiative (ESMI). Available online: https://www.prayaspune.org/peg/resources/electricity-supply-monitoring-initiative-esmi.html (accessed on 1 November 2020).
- Bhuvan Thematic Data Dissemination. Available online: https://bhuvan-app1.nrsc.gov.in/thematic/thematic/ (accessed on 15 November 2020).
- Robert, C.; William, C.; Irma, T. STL: A seasonal-trend decomposition procedure based on loess. J. Off. Stat. 1990, 6, 3–73. [Google Scholar]
- Box, G.E.P.; Jenkins, G.M.; Reinsel, G.C.; Ljung, G.M. Time Series Analysis, 5th ed.; John Wiley & Sons, Inc.: Hoboken, NJ, USA, 2016; ISBN 978-1-118-67502-1. [Google Scholar]
- Mahalanobis, A.; Kumar, B.V.; Sims, S.R.F. Distance-classifier correlation filters for multiclass target recognition. Appl. Opt. 1996, 35, 3127–3133. [Google Scholar] [CrossRef] [PubMed]
- Mahalanobis Distance. Available online: https://www.l3harrisgeospatial.com/docs/Mahalanobis.html (accessed on 3 November 2020).
- Velaga, N.R.; Kumar, A. Techno-economic evaluation of the feasibility of a smart street light system: A case study of rural India. Procedia-Soc. Behav. Sci. 2012, 62, 1220–1224. [Google Scholar] [CrossRef][Green Version]
- Bhoyar, R.R.; Bharatkar, S.S. Renewable energy integration in to microgrid: Powering rural Maharashtra State of India. In Proceedings of the Annual IEEE India Conference (INDICON), India, Mumbai, 13–15 December 2013; pp. 1–6. [Google Scholar] [CrossRef]
|IHDS-II (2011-12)||IHDS-I (2004-5)|
of Villages w/ power
of Villages w/ power
|Jammu and Kashmir||92.9||15.7||80.5||13.5|
|Color Band||Class Name|
|3||4||255||255||115||Agriculture, Crop land|
|8||7||255||201||176||Agriculture, Current Shifting Cultivation|
|11||10||121||200||0||Forest, Forest Plantation|
|12||11||150||231||138||Forest, Scrub Forest|
|16||14||25||171||242||Barren/Unculturable/Wastelands, Salt Affected Land|
|17||15||210||10||255||Barren/Unculturable/Wastelands, Gullied/Ravinous Land|
|18||16||255||30||250||Barren/Unculturable/Wastelands, Scrub land|
|19||17||229||207||255||Barren/Unculturable/Wastelands, Sandy area|
|20||18||255||150||232||Barren/Unculturable/Wastelands, Barren rocky|
|22||20||0||168||132||Wetlands/Water Bodies, Inland Wetland|
|23||21||0||255||181||Wetland/Water Bodies, Coastal Wetland|
|9||22||0||61||222||Wetland/Water Bodies, River/Stream/Canals|
|10||23||99||253||255||Wetlands/Water Bodies, Reservoir/Lands/Ponds|
|24||24||225||255||225||Snow and Glacier|
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Hsu, F.; Zhizhin, M.; Ghosh, T.; Elvidge, C.; Taneja, J. The Annual Cycling of Nighttime Lights in India. Remote Sens. 2021, 13, 1199. https://doi.org/10.3390/rs13061199
Hsu F, Zhizhin M, Ghosh T, Elvidge C, Taneja J. The Annual Cycling of Nighttime Lights in India. Remote Sensing. 2021; 13(6):1199. https://doi.org/10.3390/rs13061199Chicago/Turabian Style
Hsu, Fengchi, Mikhail Zhizhin, Tilottama Ghosh, Christopher Elvidge, and Jay Taneja. 2021. "The Annual Cycling of Nighttime Lights in India" Remote Sensing 13, no. 6: 1199. https://doi.org/10.3390/rs13061199