{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T02:41:27Z","timestamp":1760150487089,"version":"build-2065373602"},"reference-count":59,"publisher":"MDPI AG","issue":"23","license":[{"start":{"date-parts":[[2023,11,23]],"date-time":"2023-11-23T00:00:00Z","timestamp":1700697600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Deriving timely natural disaster information is critical in emergency risk management and disaster recovery efforts. Due to the limitation of data availability, such information is difficult to obtain in a timely manner. In this research, VIIRS nighttime light (NTL) image time series from January 2014 to July 2019 were employed to reflect key changes between pre- and post-disasters. The Automated Valley Detection (AVD) model was proposed and applied to derive critical disaster indicators in the 2017 Hurricane Maria event in Puerto Rico. Critical disaster indicators include outage duration, damage degree, and recovery level. Two major findings can be concluded. First, the AVD model is a robust and useful approach to detecting sudden changes in NTL in terms of their location and duration at the census tract level. Second, the AVD-estimated disaster metrics are able to capture disaster information successfully and match with two types of reference data. These findings will be valuable for emergency planning and the energy industry to monitor and restore power outages in future natural disasters.<\/jats:p>","DOI":"10.3390\/rs15235471","type":"journal-article","created":{"date-parts":[[2023,11,23]],"date-time":"2023-11-23T06:06:23Z","timestamp":1700719583000},"page":"5471","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Critical Disaster Indicators (CDIs): Deriving the Duration, Damage Degree, and Recovery Level from Nighttime Light Image Time Series"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6046-7487","authenticated-orcid":false,"given":"Weiying","family":"Lin","sequence":"first","affiliation":[{"name":"Department of Geography, Texas A&M University, College Station, TX 77843, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5459-5586","authenticated-orcid":false,"given":"Chengbin","family":"Deng","sequence":"additional","affiliation":[{"name":"Center for Spatial Analysis, University of Oklahoma, Norman, OK 73019, USA"},{"name":"Department of Geography and Environmental Sustainability, University of Oklahoma, Norman, OK 73019, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5825-0630","authenticated-orcid":false,"given":"Burak","family":"G\u00fcneralp","sequence":"additional","affiliation":[{"name":"Department of Geography, Texas A&M University, College Station, TX 77843, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6206-3558","authenticated-orcid":false,"given":"Lei","family":"Zou","sequence":"additional","affiliation":[{"name":"Department of Geography, Texas A&M University, College Station, TX 77843, USA"}]}],"member":"1968","published-online":{"date-parts":[[2023,11,23]]},"reference":[{"key":"ref_1","unstructured":"Climate Central (2022, September 14). Surging Power Outages and Climate Change. Available online: https:\/\/assets.ctfassets.net\/cxgxgstp8r5d\/73igUswSfOhdo7DUDVLwK7\/bb0a4e95e1d04457e56106355a1f74b9\/2022PowerOutages.pdf."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1582","DOI":"10.1109\/LGRS.2013.2262258","article-title":"Detecting light outages after severe storms using the S-NPP\/VIIRS day\/night band radiances","volume":"10","author":"Cao","year":"2013","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_3","unstructured":"DOE (2017, May 13). Hurricanes Nate, Maria, Irma, and Harvey Situation Reports, Available online: https:\/\/www.energy.gov\/ceser\/articles\/hurricanes-nate-maria-irma-and-harvey-situation-reports."},{"key":"ref_4","unstructured":"King, C.W., Rhodes, J.D., Zarnikau, J., Lin, N., Kutanoglu, E., Leibowicz, B., Niyogi, D., Rai, V., Santoso, S., and Spence, D. (2021). The Timeline and Events of the February 2021 Texas Electric Grid Blackouts, The University of Texas Energy Institute."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Papic, M., Clemons, M., Ekisheva, S., Langthorn, J., Ly, T., Pakeltis, M., Quest, R., Schaller, J., Till, D., and Weisman, K. (2016, January 16\u201320). Transmission availability data system (TADS) reporting and data analysis. Proceedings of the 2016 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS), Beijing, China.","DOI":"10.1109\/PMAPS.2016.7764059"},{"key":"ref_6","unstructured":"Eaton, Powering Business Worldwide (2015, February 26). Power Outage Annual Report: Blackout Tracker. Available online: http:\/\/www.eaton.com\/blackouttracker."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"9037","DOI":"10.1038\/s41598-022-12871-5","article-title":"Scale, context, and heterogeneity: The complexity of the social space","volume":"12","author":"Menendez","year":"2022","journal-title":"Sci. Rep."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Rom\u00e1n, M.O., Stokes, E.C., Shrestha, R., Wang, Z., Schultz, L., Carlo, E.A.S., Sun, Q., Bell, J., Molthan, A., and Kalb, V. (2019). Satellite-based assessment of electricity restoration efforts in Puerto Rico after Hurricane Maria. PLoS ONE, 14.","DOI":"10.1371\/journal.pone.0218883"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Zhao, X., Yu, B., Liu, Y., Yao, S., Lian, T., Chen, L., Yang, C., Chen, Z., and Wu, J. (2018). NPP-VIIRS DNB daily data in natural disaster assessment: Evidence from selected case studies. Remote Sens., 10.","DOI":"10.3390\/rs10101526"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1843","DOI":"10.1109\/TGRS.2019.2949797","article-title":"Building a series of consistent night-time light data (1992\u20132018) in Southeast Asia by integrating DMSP-OLS and NPP-VIIRS","volume":"58","author":"Zhao","year":"2019","journal-title":"IEEE Trans. GeoSci. Remote Sens."},{"key":"ref_11","first-page":"11","article-title":"LiDAR for management in natural disasters and catastrophes","volume":"Volume 1","author":"Greene","year":"2019","journal-title":"Government Briefing Book: Emerging Technology & Human Rights"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"417","DOI":"10.1016\/j.ijdrr.2018.09.015","article-title":"Satellite remote sensing for disaster management support: A holistic and staged approach based on case studies in Sentinel Asia","volume":"33","author":"Kaku","year":"2019","journal-title":"Int. J. Disaster Risk Reduct."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"30","DOI":"10.1016\/j.scijus.2021.11.002","article-title":"Applications of drone in disaster management: A scoping review","volume":"62","author":"Daud","year":"2022","journal-title":"Sci. Justice"},{"key":"ref_14","first-page":"62","article-title":"Why VIIRS data are superior to DMSP for mapping nighttime lights","volume":"35","author":"Elvidge","year":"2013","journal-title":"Proc. Asia-Pac. Adv. Netw."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"358","DOI":"10.1080\/2150704X.2014.905728","article-title":"Evaluation of NPP-VIIRS night-time light composite data for extracting built-up urban areas","volume":"5","author":"Shi","year":"2014","journal-title":"Remote Sens. Lett."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"165","DOI":"10.1080\/2150704X.2014.890758","article-title":"Responses of Suomi-NPP VIIRS-derived nighttime lights to socioeconomic activity in China\u2019s cities","volume":"5","author":"Ma","year":"2014","journal-title":"Remote Sens. Lett."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Wu, R., Yang, D., Dong, J., Zhang, L., and Xia, F. (2018). Regional inequality in China based on NPP-VIIRS night-time light imagery. Remote Sens., 10.","DOI":"10.3390\/rs10020240"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"205","DOI":"10.1080\/2150704X.2018.1538582","article-title":"Use of smart meter readings and nighttime light images to track pixel-level electricity consumption","volume":"10","author":"Deng","year":"2019","journal-title":"Remote Sens. Lett."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1077","DOI":"10.1177\/0265813516658477","article-title":"A comparison of nighttime lights data for urban energy research: Insights from scaling analysis in the US system of cities","volume":"44","author":"Fragkias","year":"2017","journal-title":"Environ. Plan. B Urban Anal. City Sci."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"3020","DOI":"10.3390\/rs70303020","article-title":"Automatic boat identification system for VIIRS low light imaging data","volume":"7","author":"Elvidge","year":"2015","journal-title":"Remote Sens."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Geronimo, R.C., Franklin, E.C., Brainard, R.E., Elvidge, C.D., Santos, M.D., Venegas, R., and Mora, C. (2018). Mapping fishing activities and suitable fishing grounds using nighttime satellite images and maximum entropy modelling. Remote Sens., 10.","DOI":"10.3390\/rs10101604"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.apgeog.2018.03.001","article-title":"Utilizing remote sensing and big data to quantify conflict intensity: The Arab Spring as a case study","volume":"94","author":"Levin","year":"2018","journal-title":"Appl. Geogr."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Li, X., Liu, S., Jendryke, M., Li, D., and Wu, C. (2018). Night-time light dynamics during the Iraqi civil war. Remote Sens., 10.","DOI":"10.3390\/rs10060858"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"97","DOI":"10.1016\/j.gloenvcha.2019.02.001","article-title":"World Heritage in danger: Big data and remote sensing can help protect sites in conflict zones","volume":"55","author":"Levin","year":"2019","journal-title":"Glob. Environ. Chang."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Cole, T.A., Wanik, D.W., Molthan, A.L., Rom\u00e1n, M.O., and Griffin, R.E. (2017). Synergistic use of nighttime satellite data, electric utility infrastructure, and ambient population to improve power outage detections in urban areas. Remote Sens., 9.","DOI":"10.3390\/rs9030286"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1002\/2013EO050001","article-title":"Satellite observations monitor outages from Superstorm Sandy","volume":"94","author":"Molthan","year":"2013","journal-title":"Eos Trans. Am. Geophys. Union"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1853","DOI":"10.5194\/isprs-archives-XLII-3-1853-2018","article-title":"Monitoring disaster-related power outages using NASA black marble nighttime light product","volume":"42","author":"Wang","year":"2018","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"303","DOI":"10.1111\/j.1467-7717.2009.01130.x","article-title":"Urban disaster recovery: A measurement framework and its application to the 1995 Kobe earthquake","volume":"34","author":"Chang","year":"2010","journal-title":"Disasters"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"035029","DOI":"10.1088\/1748-9326\/8\/3\/035029","article-title":"Changing the spatial location of electricity generation to increase water availability in areas with drought: A feasibility study and quantification of air quality impacts in Texas","volume":"8","author":"Pacsi","year":"2013","journal-title":"Environ. Res. Lett."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1109\/JPETS.2019.2900293","article-title":"Hurricane Maria effects on Puerto Rico electric power infrastructure","volume":"6","author":"Kwasinski","year":"2019","journal-title":"IEEE Power Energy Technol. Syst. J."},{"key":"ref_31","unstructured":"Corps, M. (2019, July 03). Quick Facts: Hurricane Maria\u2019s Effect on Puerto Rico. Available online: https:\/\/www.mercycorps.org\/blog\/facts-hurricane-maria-puerto-rico#:~:text=Electricity%20was%20cut%20off%20to,into%20a%20desperate%20humanitarian%20crisis."},{"key":"ref_32","unstructured":"Irfan, U. (2018, December 08). It\u2019s Been More than 100 Days and Puerto Rico is Still in the Longest Blackout in US History. Vox. Available online: https:\/\/www.vox.com\/energy-and-environment\/2017\/10\/30\/16560212\/puerto-rico-longest-blackout-in-us-history-hurricane-maria-grid-electricity."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1627","DOI":"10.1021\/ac60214a047","article-title":"Smoothing and differentiation of data by simplified least squares procedures","volume":"36","author":"Savitzky","year":"1964","journal-title":"Anal. Chem."},{"key":"ref_34","first-page":"1","article-title":"Moving average and Savitzki-Golay smoothing filters using Mathcad","volume":"2007","author":"Ortega","year":"2007","journal-title":"Papers ICEE"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"332","DOI":"10.1016\/j.rse.2004.03.014","article-title":"A simple method for reconstructing a high-quality NDVI time-series data set based on the Savitzky\u2013Golay filter","volume":"91","author":"Chen","year":"2004","journal-title":"Remote Sens. Environ."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"669","DOI":"10.1063\/1.4822961","article-title":"Savitzky-Golay smoothing filters","volume":"4","author":"Press","year":"1990","journal-title":"Comput. Phys."},{"key":"ref_37","unstructured":"van Brakel, J.P. (2016, June 17). Smoothed z-Score Algorithm (Stack Overflow). Available online: https:\/\/stackoverflow.com\/questions\/22583391\/peak-signal-detection-in-realtime-timeseries-data."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"424","DOI":"10.1016\/j.atmosenv.2019.06.035","article-title":"Transport most likely to cause air pollution peak exposures in everyday life: Evidence from over 2000 days of personal monitoring","volume":"213","author":"Dons","year":"2019","journal-title":"Atmos. Environ."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Perkins, P., and Heber, S. (2018, January 18\u201320). Identification of ribosome pause sites using a z-score based peak detection algorithm. Proceedings of the 2018 IEEE 8th International Conference on Computational Advances in Bio and Medical Sciences (ICCABS), Las Vegas, NV, USA.","DOI":"10.1109\/ICCABS.2018.8541902"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"1300","DOI":"10.1080\/17538947.2018.1545878","article-title":"Social and geographical disparities in Twitter use during Hurricane Harvey","volume":"12","author":"Zou","year":"2019","journal-title":"Int. J. Digit. Earth"},{"key":"ref_41","unstructured":"(2018, March 21). Har\u00e1n \u201cReclamo Masivo\u201d en Humacao por el Servicio El\u00e9ctrico. Redacci\u00f3n EL Oriental. Available online: http:\/\/periodicoeloriental.com\/noticias\/haran-reclamo-masivo-en-humacao-por-el-servicio-electrico\/."},{"key":"ref_42","unstructured":"Mill\u00e1n, R. (2018, January 10). Justo Gonz\u00e1lez Admite Siente la Presi\u00f3n de Restablecer Servicio. El Regional. Available online: https:\/\/www.elregionalpr.com\/justo-gonzalez-admite-siente-la-presion-de-restablecer-servicio\/."},{"key":"ref_43","unstructured":"(2018, March 20). Mayita Mel\u00e9ndez Habla de la Situaci\u00f3n de Ponce tras seis Meses del Hurac\u00e1n Mar\u00eda. Per\u00edod\u00edco El Solpr. 20 March 2018. Available online: https:\/\/periodicoelsolpr.com\/2018\/03\/20\/mayita-melendez-habla-de-la-situacion-de-ponce-tras-seis-meses-del-huracan-maria\/."},{"key":"ref_44","unstructured":"(2018, March 27). Alcalde Busca Alternativas para Acelerar Restablecimiento de Electricidad en Guayanilla. Voces del Sur. Available online: https:\/\/vocesdelsurpr.com\/2018\/03\/alcalde-busca-alternativas-para-acelerar-restablecimiento-de-electricidad-en-guayanilla\/."},{"key":"ref_45","unstructured":"Rodr\u00edguez, D.J. (2017, December 19). El Oeste Tendr\u00e1 luz Total Despu\u00e9s de Enero. Redacci\u00f3n One Red Media. Available online: https:\/\/laislaoeste.com\/el-oeste-tendra-luz-total-despues-de-enero\/."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.cities.2018.11.023","article-title":"Resilient urban forms: A macro-scale analysis","volume":"85","author":"Sharifi","year":"2019","journal-title":"Cities"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"32772","DOI":"10.1073\/pnas.2001671117","article-title":"Quantifying the dynamics of migration after Hurricane Maria in Puerto Rico","volume":"117","author":"Acosta","year":"2020","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Lin, J., and Shi, W. (2020). Statistical correlation between monthly electric power consumption and VIIRS nighttime light. ISPRS Int. J. Geo-Inf., 9.","DOI":"10.3390\/ijgi9010032"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"994","DOI":"10.1257\/aer.102.2.994","article-title":"Measuring economic growth from outer space","volume":"102","author":"Henderson","year":"2012","journal-title":"Am. Econ. Rev."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"2320","DOI":"10.1016\/j.rse.2011.04.032","article-title":"Mapping urbanization dynamics at regional and global scales using multi-temporal DMSP\/OLS nighttime light data","volume":"115","author":"Henderson","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_51","unstructured":"United States Census Bureau (2022, September 13). QuickFacts: Puerto Rico, Available online: https:\/\/www.census.gov\/quickfacts\/fact\/table\/PR\/PST045222."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"14653","DOI":"10.1073\/pnas.0605726103","article-title":"Reconstruction of New Orleans after Hurricane Katrina: A research perspective","volume":"103","author":"Kates","year":"2006","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"2561","DOI":"10.1175\/BAMS-D-17-0097.1","article-title":"The dark side of hurricane matthew: Unique perspectives from the VIIRS day\/night band","volume":"99","author":"Miller","year":"2018","journal-title":"Bull. Am. Meteorol. Soc."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"689","DOI":"10.1007\/s11069-018-3413-x","article-title":"Socioeconomic vulnerability and electric power restoration timelines in Florida: The case of Hurricane Irma","volume":"94","author":"Miller","year":"2018","journal-title":"Nat. Hazards"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"28","DOI":"10.1007\/s11111-020-00339-5","article-title":"Out-migration from and return migration to Puerto Rico after Hurricane Maria: Evidence from the consumer credit panel","volume":"42","author":"DeWaard","year":"2020","journal-title":"Popul. Environ."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1007\/s11111-020-00338-6","article-title":"Using geotagged tweets to track population movements to and from Puerto Rico after Hurricane Maria","volume":"42","author":"Cutter","year":"2020","journal-title":"Popul. Environ."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s11111-020-00356-4","article-title":"Puerto Rico\u2019s population before and after Hurricane Maria","volume":"42","author":"Rivera","year":"2020","journal-title":"Popul. Environ."},{"key":"ref_58","unstructured":"Schachter, J., and Bruce, A. (2021, November 08). Revising Methods to Better Reflect the Impact of Disaster, Available online: https:\/\/www.census.gov\/library\/stories\/2020\/08\/estimating-puerto-rico-population-after-hurricane-maria.html."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"299","DOI":"10.1504\/IJGE.2007.013061","article-title":"Urbanisation and global environmental change: New intergenerational challenges","volume":"1","author":"Simon","year":"2007","journal-title":"Int. J. Green Econ."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/23\/5471\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T21:28:13Z","timestamp":1760131693000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/23\/5471"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,11,23]]},"references-count":59,"journal-issue":{"issue":"23","published-online":{"date-parts":[[2023,12]]}},"alternative-id":["rs15235471"],"URL":"https:\/\/doi.org\/10.3390\/rs15235471","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2023,11,23]]}}}