Sentinel-1 Likely Damaged Areas for the Southern California Wildfires, January 2025

Date of Image(s): January 9, 2025, January 13, 2025, January 21, 2025 Summary: This raster dataset (TIF) represents likely damaged/destroyed urban areas in Los Angeles, California, during the fires of January 2025. The map is the result of a coherent change detection analysis of interferometric Sentinel-1 radar (InSAR) images from January 2025 in comparison with pre-disaster reference images from 2022-2025. Coherent change detection analysis is used to show change across the entire domain, which includes vegetation and forested areas. This analysis is constrained to a snapshot of the FIRIS perimeter domain to highlight the damage to built up areas only. Potentially damaged areas denoted in orange represent data collected on January 9, 2025 from a Sentinel-1 pass over the Altadena/Eaton area. Potentially damaged areas denoted in red represent data collected on January 13, 2025 from a Sentinel-1 pass over the Palisades area. Potentially damaged structures denoted in red represent data collected on January 21, 2025. Date(s) and time(s) covered: Swaths from 9 January 2025, 14 January 2025, and January 21, 2025, with 188 images collected from 2022-12-20 to 2025-01-02 for a pre-disaster reference dataset. Suggested Usage: The map is meant for landscape-level geospatial overlay and visualization purposes of fire damage in urban/built-up regions. For example, the map is useful for tabulating estimates of the number of likely damaged or destroyed structures. Pixel values of 1 represent a region likely damaged or destroyed; pixel values of 0 represent all other regions. Satellite/Sensor: European Space Agency Copernicus Sentinel-1/10 meter spatial resolution processed to 40 meters for interferometric coherence change analysis Resolution: 40 meters Credits: Damage analysis of Copernicus Sentinel-1 satellite data by Corey Scher of CUNY Graduate Center and Jamon Van Den Hoek of Oregon State University. Contact: Jamon Van Den Hoek (jamon.vandenhoek@oregonstate.edu). Data sources: Sentinel-1 https://asf.alaska.edu/datasets/daac/sentinel-1/ Alaska Satellite Facility https://hyp3-docs.asf.alaska.edu/usage_guidelines/ CA Perimeters NIFC FIRIS data (gis.data.cnra.ca.gov/datasets/CALFIRE-Forestry::ca-perimeters-nifc-firis-public-view/) Access and Use Constraints: Likely Damaged Areas: The damage map has not been field-validated and omission/commission errors are likely present. Regions that changed over the course of the reference period or were otherwise dynamic (such as places of construction or destruction or vegetated areas) are likely excluded from the analysis. Service URL: WMS Endpoint:

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. --------------------------------- 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. Service URL: https://gis.earthdata.nasa.gov/portal/home/item.html?id=24269306876346f689860d3e7a67da0a WMS Endpoint: https://gis.earthdata.nasa.gov/portal/services/DISASTERS_202501_FIRE_CA/2501_opera_dist_s1/ImageServer/WMSServer Data Download: https://aria-share.jpl.nasa.gov/20250113-GreaterLosAngeles_Fires/

# ARIA DPM Sentinel-1 v1.0, Hurricane Ida The Advanced Rapid Imaging and Analysis (ARIA) team at NASA's Jet Propulsion Laboratory and California Institute of Technology in Southern California, in collaboration with the Earth Observatory of Singapore (EOS), created this Damage Proxy Map (DPM) depicting areas that are likely damaged by the Hurricane Ida (August 29, 2021) in central and western Louisiana, United States. The map was derived from synthetic aperture radar (SAR) images on Sep 03, 2021 by the Copernicus Sentinel-1 satellites operated by the European Space Agency (ESA). The pre-event images were taken before (Aug 10, 2021 and Aug 22, 2021) the hurricane hit land. The image covers an area of 337 by 250 kilometers (210 by 155 miles), shown by the large red polygon. Each pixel measures about 30 meters across. The color variation from yellow to red indicates increasingly more significant surface change. Preliminary validation was done by comparing with the Media reports. This damage proxy map should be used as guidance to identify damaged areas, and may be less reliable over vegetated or flooded areas. For example, the scattered colored pixels over vegetated areas and flooded fields may be false positives, and the lack of colored pixels over vegetated areas does not necessarily mean no damage. Some additional false positives in the middle of the image are likely due to the stitching of Sentinel-1 frames as a thin line running across the map at a ENE-WSW angle. This map is most sensitive to building damage, but small-scale change or partial structural damage may not be detected by this map. Sentinel-1 data were accessed through the Copernicus Open Hub and the Alaska Satellite Facility server. The product contains modified Copernicus Sentinel data (2021), processed by ESA and analyzed by the NASA-JPL/Caltech ARIA team and EOS team. Part of the funding was provided by NASA's Earth Applied Sciences Disasters Program. The products (KMZ and GeoTIFF files) are available to download at For more information about ARIA, visit: For more information about EOS, visit: **More map details and files:** **NOTE:** If you are loading the KMZ file in Google Earth Pro, to avoid slight color degradation, please make sure to uncheck the "Compress" option in the Preference > 3D View > Texture Colors (for Mac) or Tools > Options > 3D View > Texture Colors (for Windows). Google Earth may need to be restarted afterwards.

# EOS-RS Damage Proxy Map: Ofunato, Japan, Wildfire, 08 Mar 2025, v0.9 The Earth Observatory of Singapore - Remote Sensing Lab (EOS-RS) created this preliminary Damage Proxy Map (DPM) depicting areas that are likely damaged in Ofunato, Japan, due to several wildfires. This map was derived from synthetic aperture radar (SAR) images acquired by the Copernicus Sentinel-1 satellite operated by the European Space Agency (ESA) before (11 Aug 2024 to 24 Feb 2025) and during (08 Mar 2025) the event. The map covers an area indicated by the white polygon. Damage is shown by colored pixels of 30m in size, where yellow to red indicates increasingly significant ground surface change before and after the event. Preliminary validation was conducted using news reports and ground-level plus aerial imagery and videos in selected areas. This map should be used as a guidance to identify damaged areas, and may be less reliable over vegetated or mountainous areas. Scattered pixels over vegetated or mountainous areas may be false positives, and a lack of colored pixels over such areas may not mean no damage. The product contains modified Copernicus Sentinel data (2024-2025), processed by ESA and analyzed by the Earth Observatory of Singapore - Remote Sensing Lab (EOS-RS). **More map details and files:** **More map details and files:** EOS-RS_202503_Japan_Ofunato_Wildfires/ **Legal Disclaimer:** **Credits:** Earth Observatory of Singapore - Remote Sensing Lab (EOS-RS), Contains modified Copernicus Sentinel data (2024-2025)

Dates of Images Sentinel-1 and Sentinel-2 pre-fire collection: January 2018 to November 2024 Sentinel-1 post-fire image: 21st January 2025 Sentinel-2 post-fire image: 12th January 2025 Date of Next Image None Summary Fire severity refers to the degree of change caused by fire in the state of vegetation, encompassing both aboveground and belowground impacts. It reflects the extent of fuel consumption, vegetation mortality, and structural damage caused by the fire. Accurately mapping fire severity is critical for understanding ecological impacts, informing post-fire recovery strategies, and supporting emergency response efforts. The 2025 Southern California fire episode marked one of the most severe global fire events in recent history. It resulted in extensive ecological and structural damage, particularly within the wildland-urban interface (WUI), where anthropogenic development interacts with fire-prone vegetation. The Palisades and Eaton fires were among the most severe, driven by prolonged drought, anomalously high temperatures, and strong synoptic-scale wind patterns. Given the large-scale ecosystem disturbances and infrastructural losses, an independent fire severity assessment for each fire is essential to quantify post-fire ecological impacts and inform resilience strategies. Traditionally, fire severity has been assessed both qualitatively and quantitatively through field surveys conducted by forest professionals. These assessments provide detailed on-the-ground observations but are often constrained by logistical challenges, such as less frequency of visit and limited spatial coverage. As a result, satellite remote sensing has become the preferred approach for fire severity mapping due to its ability to provide high-frequency, high-resolution, and continuous data over large spatial extents. To address this need, this study employs the “OptiSAR” framework, a multi-sensor remote sensing approach, for enhanced fire severity assessment by integrating Synthetic Aperture Radar (SAR; Sentinel-1) and multispectral optical imagery (Sentinel-2). By leveraging SAR’s all-weather and day/night capability and its sensitivity to structural changes in vegetation, along with optical indices' ability to capture spectral responses of burned landscapes, the “OptiSAR” enables a comprehensive and multi-dimensional assessment of fire-induced transformations. Satellite/Sensor & Resolution Sentinel-1/ C-Band /10m spatial resolution. Sentinel-2/ MSI /resampled spatial resolution from 20m to 10m. Methodology Independent fire severity analyses were conducted for the Palisades and Eaton fires due to their distinct vegetation characteristics and fire dynamics. The Palisades and Eaton fires burned under different conditions, each with unique fire behavior and impacts. The Palisades fire spread rapidly due to strong coastal winds and dry fuels, leading to high-intensity burns. In contrast, the Eaton fire, with more varied terrain and denser fuel accumulation, burned at a different pace, resulting in distinct structural and spectral fire effects. The study area consists of chaparral, and coastal sage scrub, with chaparral being the dominant fire-prone vegetation type. It comprises dense, drought-adapted shrubs which accumulate dry biomass, contributing to high fire intensity. Coastal sage scrub and oak woodlands are also present, with varying degrees of fire adaptation. The fire severity was divided into five classes and color-coded for intuitive visualization. Pixels in green show ‘unburnt’ representing undisturbed vegetation, yellow depict ‘low’ indicating areas with surface fuel depletion but an intact canopy. Orange represents ‘moderate’, signifying partial canopy scorch with some structural damage. Red denotes ‘high’, where complete canopy scorch and significant structural damage have occurred. Black indicates ‘extreme’, describing areas with total canopy consumption and collapse, resulting in extensive structural destruction. To quantify changes in vegetation, two advanced change detection indices were employed: the RADAR-Vegetation Structural Perpendicular Index (RADAR-VSPI; Chhabra et al., 2022) and the Vegetation Structural Perpendicular Index (VSPI; Massetti et al., 2019). These indices represent a significant advancement over conventional vegetation characterization and burn severity indices by providing more detailed insights into disturbance-induced transformations as a function of their undisturbed state from the pre-fire period. To compute RADAR-VSPI and VSPI, a pixel-wise baseline was established using a time series of Sentinel-1 and Sentinel-2 data from January 2018 to November 2024. This baseline represented the spatiotemporal characteristics of undisturbed vegetation, identified from periods with no observed disturbances in both SAR and optical datasets. Post-fire vegetation changes were then quantified as an orthogonal distance from this baseline. Independent fire severity classifications were performed separately on the post-fire RADAR-VSPI (21st January 2025) and VSPI (12th January 2025) acquisitions using K-means clustering. This unsupervised classification method was chosen due to its ability to objectively segment the data into distinct severity levels based on natural groupings within the distribution of RADAR-VSPI and VSPI values. Unlike threshold-based approaches, K-means clustering allows for a data-driven classification, reducing subjective biases in defining severity categories while ensuring adaptability across different fire-affected landscapes. From the clustering results, the severity class ranges for RADAR-VSPI and VSPI were derived, establishing quantitative thresholds that delineate the gradient of fire-induced vegetation and structural changes. Higher negative decibel values of RADAR-VSPI indicate greater structural destruction, whereas higher values in VSPI indicate greater vegetation damage. To systematically classify fire severity based on the synergies between RADAR-VSPI and VSPI, a high-level decision-tree algorithm was developed, enabling a data-driven approach to delineate fire impacts across varying burn intensities. Suggested Use The Palisades Fire severity classification map highlights widespread ‘high’ to ‘extreme’ severity burns across the Santa Monica Mountains and surrounding areas, particularly in Topanga State Park, Temescal Gateway Park, and Monte Nido. These areas, dominated by chaparral and coastal sage scrub, experienced intense fire behavior, resulting in significant vegetation loss. The western sections, including areas near Saddle Peak Trailhead, also show severe burns following ridgelines and steep slopes, reinforcing the role of topography in fire intensity. In contrast, ‘moderate’ and ‘low’ severity burns appear in scattered patches, particularly near Topanga, Sylvia Park, and portions of Mandeville and Rustic Canyons, where surface fuels burned but some canopy remained intact. Additionally, vegetation in green ‘unburnt’ is mapped along the Pacific Coast Highway, near Malibu, Pacific Palisades, and Brentwood, suggesting fire suppression efforts or natural firebreaks may have limited spread in these areas. Terms of Use & Limitations A key limitation of the fused fire severity product is its dependence on the quality of pre-disturbance baseline data. Inconsistencies or gaps in the baseline—particularly in areas with frequent vegetation changes or limited temporal coverage—can impact classification accuracy. Additionally, in highly mixed vegetation landscapes such as chaparral shrublands, RADAR-VSPI’s structural sensitivity may dominate, potentially masking subtle spectral changes captured by VSPI. These challenges emphasize the need for ongoing refinement, improved temporal consistency, and ground-truth validation to enhance classification reliability. Despite its limitations, the fire severity classification provides a general assessment of fire perimeters and vegetation damage. It can aid in post-fire recovery planning, such as soil stabilization in affected areas. The severity map also distinguishes between areas of structural collapse and regions where surface burns occurred while vegetation structure remained intact, offering insights for targeted mitigation efforts. Additionally, it may support long-term monitoring in the WUI, helping assess ecosystem recovery and fire behavior trends over time. This enhanced fire severity product should be used with caution as a qualitative guide rather than an absolute measure of fire impacts. The classification is based on satellite-derived indices and may be influenced by factors such as sensor limitations, baseline data availability, and local vegetation variability. NASA data and products are freely available to federal, state, public, non-profit, and commercial users. However, these datasets are primarily experimental or research-grade and may not be suitable for operational applications requiring real-time accuracy or consistency. The NASA Disasters Mapping Portal and associated data products are designed to support decision-making and enhance situational awareness, but availability and updates are not guaranteed on a routine basis. Credits 1. Aakash Chhabra (NASA Ames/NEX; aakash.chhabra7489@gmail.com), 2. Taejin Park (NASA Ames/NEX), 3. Ian Brosnan (NASA Ames/NEX) Data Sources a . Sentinel-1 (GRD pre-processed product): https://developers.google.com/earthengine/datasets/catalog/COPERNICUS_S1_GRD#description b. Sentinel-2 (2A atmospherically corrected product): https://developers.google.com/earthengine/datasets/catalog/COPERNICUS_S2_SR_HARMONIZED c. ESA Landcover: https://developers.google.com/earthengine/datasets/catalog/ESA_WorldCover_v200 d. Fire Perimeter: https://www.nifc.gov/fire-information/maps

# USA California Wildfires: Damage Proxy Maps, Jan 2025 The **Earth Observatory of Singapore – Remote Sensing Lab (EOS-RS)** produced these damage proxy maps for USA California Wildfires (Jan 2025) from synthetic aperture radar (SAR) satellite images. Each map is a separate ZIP below with its GeoTIFF, a KMZ for Google Earth, the PNG figures and a text file describing how it was made and how to read it. ## Maps in this dataset - **EOS-RS Damage Proxy Map: California, USA, Eaton Fire, 19 Jan 2025, v0.9 ** - **EOS-RS Damage Proxy Map: California, USA, Wildfires, 9 Jan 2025, v2.5 ** - **EOS-RS Damage Proxy Map: California, USA, Wildfires, 9 Jan 2025, v0.9 ** (superseded) **More map details and files:** **More map details and files:** **Legal Disclaimer:** **Credits:** Earth Observatory of Singapore - Remote Sensing Lab (EOS-RS), Original data ALOS-2 PALSAR-2 Product - JAXA (2023-2025)