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Researchers working with the Advanced Rapid Imaging and Analysis (ARIA) and Observational Products for End-Users from Remote Sensing Analysis (OPERA) teams at NASA's Jet Propulsion Laboratory, Pasadena, California, created the OPERA Surface Disturbance Alert from Sentinel-1 (DIST-S1-ALERT) prototype product suite to create maps of surface disturbance due to ongoing wildfires in Los Angeles County in January 2025.
The OPERA DIST-S1 prototype results posted here are preliminary results, primarily intended to aid the field response and people who wanted to have a rough first look area impacted by wildfire. The ARIA-share website has always focused on posting preliminary results as fast as possible for urgent response. All information is provisional for use under emergency response guidelines. These data are provided with absolutely no warranty of any kind. Use at your own risk.
Suggested Use:
The DIST-S1 product shows disturbance of each pixel with a corresponding level of confidence:
0: No disturbance
1: Moderate Confidence Disturbance (more than 2.5 standard deviations from mean estimated from imagery prior to event)
2: High Confidence Disturbance (more than 4.5 standard deviations from mean estimated from imagery prior to event)
Date of most recent image used:
Ascending track 64: 2025-01-09T01:50:51 (UTC) or 2025-01-08T17:50:51 (PST).
Descending track 71: 2025-01-09T13:53:03 (UTC) or 2025-01-09 05:53:03 (PST)
Dates of other used images to define pre-fire baseline:
2024-09-11, 2024-09-23, 2024-10-05, 2024-10-17, 2024-10-29, 2024-11-10, 2024-11-22, 2024-12-04, 2024-12-16, 2024-12-28
Processing Details:
The OPERA DIST-S1-ALERT prototype product provides a picture of surface disturbance (e.g., vegetation loss, structure change, damage) outside a historical norm using Sentinel-1A images. The products are distributed as GeoTIFFs. Pixels capturing disturbances are further differentiated as moderate confidence disturbance (pixel value = 1) and high confidence disturbance (pixel value = 2). These labels represent pixels that deviate more than 2.5 and 4.5 standard deviations from the mean, respectively, estimated from the 10 SAR acquisitions collected prior to the event. Statistics are estimated using a deep-learning model with spatiotemporal encoding (via the vision transformer).
Files:
disturbance_track64_2025-01-09_LA.tif (geotiff) shows surface disturbance as of January 9, 2025 2025-01-09T01:50:51 (UTC) or 2025-01-08T17:50:51 (PST). Note there are artifacts in this file that result from issue with Sentinel-1 burst overlap areas. Further investigation is needed in these areas.
disturbance_track71_2025-01-09_LA.tif (geotiff) shows surface disturbance as of January 9, 2025 2025-01-09T13:53:03 (UTC) or 2025-01-09 05:53:03 (PST)
Released January 13, 2025.
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Data Availability:
The OPERA DIST-S1 products produced by ARIA/OPERA - JPL, contain modified Copernicus data (2024). The products are available to download at https://aria-share.jpl.nasa.gov/20250113-GreaterLosAngeles_Fires/
Product POCs:
Alexander L. Handwerger (alexander.handwerger@jpl.nasa.gov)
Charlie Marshak (charlie.z.marshak@jpl.nasa.gov)
Harris Hardiman-Mostow (hhm@math.ucla.edu)
Steven Chan (steventsz.k.chan@jpl.nasa.gov)
David Bekaert (david.bekaert@jpl.nasa.gov)
References/Acknowledgement:
https://aria.jpl.nasa.gov/ (ARIA project)
https://www.jpl.nasa.gov/go/opera/ (OPERA Project)
This prototype product was developed by Harris Hardiman-Mostow (UCLA) under the guidance of Charlie Marshak and Al Handwerger. Further product development has been led by the JPL OPERA DIST-S1 team (Charlie Marshak, Talib Oliver Cabrera, Jungkyo Jung, Richard West)
Satellite/Sensor:
Sentinel-1A
Resolution:
30 meters
Disclaimer:
The California Institute of Technology (''Caltech'') makes these data available to the public ''as is'' for informational purposes only. The methods used and data collected have not been reviewed, cleared, or validated. Caltech, including its operating division the Jet Propulsion Laboratory, its employees, and agents make no representation or warranty, express or implied, as to the merchantability, non-infringement, or fitness for a particular purpose.