Rapid Conflict Monitoring of Israeli Strikes on Iran in June, 2025 using VIIRS Nightfire Data
Rapid Conflict Monitoring of Israeli Strikes on Iran in June, 2025 using VIIRS Nightfire Data
PAYNE INSTITUTE COMMENTARY SERIES: COMMENTARY
June 18, 2025
This study investigates thermal anomalies detected by the VIIRS Nightfire (VNF) algorithm in relation to reported Israeli military strikes on Iranian territory beginning June 13, 2025. Leveraging VNF’s ability to identify high-temperature combustion sources such as industrial flares, wildfires, and crucially, explosions and fires in conflict zones, this analysis aims to provide an independent assessment of the recent events.
Our objective is to build a geotemporal understanding of the strikes and their immediate aftermath by comparing VNF detections with open-source intelligence, satellite imagery, and verified news reports. This analysis focuses on installations of significant interest, including the nuclear enrichment facilities at Natanz, Fordow, and Isfahan, military bases in Kermanshah and Tabriz, and critical energy infrastructure across Iran. Where available, we utilize known coordinates and installation details to link VNF thermal signatures to specific impact sites.
This work builds on established methodologies [1] developed for oil and gas flaring and industrial thermal monitoring, adapting them to a conflict-intelligence context. Key modifications include the incorporation of short-duration, high-intensity thermal signatures, the exclusion of VNF detections within known persistent infrared emitter sites, and the application of spatial clustering to infer the approximate location and scale of each thermal event.
By integrating VNF data with independent reporting and geospatial information, this study seeks to enhance our understanding of the scale, timing, and thermal characteristics of the strikes that occurred starting from June 13, 2025. The findings serve both as an independent validation of reported events and as a demonstration of VNF data’s capacity for timely conflict zone monitoring.
Our methodology involves the following key steps:
- Initial Data Filtering: We collected all near-real-time, temperature-fitted VNF detections within Iran from June 13 to the present. These detections were then filtered to exclude those located around the known persistent infrared emitters in the EOG catalog [2], which primarily include upstream and downstream gas flares and steel mills.
- Space-Time Clustering: The remaining VNF detections were clustered based on their spatial and temporal proximity to identify distinct “fire” events. For each cluster, we derived the centroid (geographic center) and the concave hull (outer boundary).
- Thermal Signature Analysis: We estimated the mean temperature and the total radiative heat emitted by each identified cluster.
- Population Density Overlay: The centroid of each cluster was cross-referenced with the World population grid for 2023 to determine the population count at that location.
- Fire Type Classification: Based on the population count, each fire cluster was categorized as “populated” (>100 people), “industrial” (> 10 people), or “unpopulated” (< 10 people).
- News Report Correlation: We compiled news reports from reliable sources detailing the time, location, and type of installations reportedly attacked within Iran.
- Interactive Web Mapping: An interactive web map was created using OpenStreetMap and Google Maps satellite imagery as background layers. This map displays pushpins indicating the geolocated news reports, the individual pixel centers of the VNF detections, the polygon boundaries of the fire clusters, and circle markers representing the fire type (colored) and the total emitted radiative heat (size).
- Optional Animation: An animated version of the map, showing daily intervals, was generated to illustrate the timeline of the conflict.
The interactive map (Figure 1) for the period of June 13-17 is available for download [here]. The animated map can be downloaded [here].
Our analysis reveals a confirmation of fires at several military installations in Iran through the cross-matching of news reports and satellite-observed infrared detection clusters. For example, we observed thermal signatures consistent with reported attacks on air defense systems in Fordow [3]. However, based on the VNF data within the timeframe of this analysis (June 13-17), we have been unable to independently confirm the reported attacks and/or destruction of the Iranian nuclear facilities in Natanz [4] and Isfahan [5], as these sites did not exhibit significant VNF detections during this period. This does not necessarily negate the reports but indicates a lack of high-intensity thermal emissions detectable by VNF at these specific locations during the analyzed timeframe. Further analysis, including assessment of subsequent VNF data and other remote sensing modalities, may provide additional insights.
Interactive Web Map: VNF Detections and Media Reports of Strikes in Iran (June 12-17, 2025)
Figure 1. Interactive web map showing VIIRS Nightfire (VNF) thermal anomaly detection clusters (red: populated, orange: industrial, blue: unpopulated) on OpenStreetMap and Google Satellite imagery. Yellow exclusion buffers surround known persistent oil & gas and industrial emitters. Media reports of strikes are located via newspaper pushpins. The map also displays concave hull boundaries of VNF detection clusters with individual detections as dots. Metadata for each map object is available upon clicking.
References
[1] Zhizhin, M., Matveev, A., Ghosh, T., Hsu, F.-C., Howells, M., & Elvidge, C. (2021). Measuring Gas Flaring in Russia with Multispectral VIIRS Nightfire. Remote Sensing, 13(16), 3078. https://doi.org/10.3390/rs13163078
[2] Elvidge, C. D., Zhizhin, M., Sparks, T., Ghosh, T., Pon, S., Bazilian, M., Sutton, P. C., & Miller, S. D. (2023). Global Satellite Monitoring of Exothermic Industrial Activity via Infrared Emissions. Remote Sensing, 15(19), 4760. https://doi.org/10.3390/rs15194760
[4] https://apnews.com/article/iran-nuclear-israel-radiation-natanz-a58c0e06ad30b3a8bf21159a4f54c35a
ABOUT THE AUTHORS
Mikhail Zhizhin
Research Associate, Earth Observation Group, Payne Institute for Public Policy, Colorado School of Mines
Mikhail Zhizhin, M.Science in mathematics from the Moscow State University in 1984, Ph.D. in computational seismology and pattern recognition from the Russian Acad. Sci. in 1992. Research positions from 1987 to 2012 in geophysics, space research and nuclear physics at Russian Acad. Sci., later at NOAA and CU Boulder. Currently he is a researcher at the Earth Observation Group at Colorado School of Mines. His applied research fields evolved from high performance computing in seismology, geodynamics, terrestrial and space weather to deep learning in remote sensing. He is developing new machine learning algorithms to better understand the Nature with Big Data.
Christopher Elvidge
Senior Research Associate, Director of Earth Observation Group
Christopher D. Elvidge has decades of experience with satellite low light imaging data, starting in 1994. He pioneered nighttime satellite observation on visible lights, heat sources including gas flares and wildfires, as well as bright lit fishing vessels. He led the development of these nighttime remote sensed products with images from DMSP, JPSS, and Landsat satellites. These data are very popular and used globally in both public and private sectors. As of February 2018, he has more than 11,000 scholarly publication citations.
Morgan Bazilian
Director, Payne Institute and Professor of Public Policy
Morgan Bazilian is the Director of the Payne Institute and a Professor of public policy at the Colorado School of Mines. Previously, he wD.as lead energy specialist at the World Bank. He has over two decades of experience in the energy sector and is regarded as a leading expert in international affairs, policy and investment. He is a Member of the Council on Foreign Relations.
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