2501_s1_s2_burnseverity

Date of Image(s) Sentinel-1 Synthetic Aperture Radar (SAR): 12/28/2024 (Pre-fire) 01/09/2025 (Called post-fire, but fires may still burning) Sentinel-2 MultiSpectral Instrument (MSI): 01/02/2025 (Pre-fire) 01/12/2025 (Called post-fire, but fires may still burning) Ancillary data ESA Global Land Cover in 2021 01/23/2025 Summary The burned areas and burn severity levels for vegetation and urban areas were generated separately based on an integration of Sentinel-2 and Sentinel-1 data. Specifically, the Sentinel-2 MSI data provide large changes in vegetation reflectance after burning due to changes in near-infrared (NIR) and shortwave-infrared (SWIR) bands, but changes in these spectral ranges were less obvious or nonexistent in affected urban environments. In a complementary sense, Sentinel-1 synthetic aperture radar (SAR) is sensitive to surface roughness changes, a signal which is more apparent (usually rougher or higher backscatter) due to the fire destruction of built infrastructure. Thus, we leveraged these two key physical characteristics together to distinguish the urban-burned areas and vegetation-burned areas. Note that while the magnitude of calculated changes in NIR, SWIR, and SAR backscatter in pre-and post-event imagery imply a range of burn severity, these metrics are still considered proxies for actual burn severity and should be interpreted as such. We first calculated the differenced Normalized Burn Ratio (dNBR = NBR_pre – NBR_post) from Sentinel-2 data in which the NBR = (NIR – SWIR)/(NIR+SWIR). This provides critical changes in vegetation due to burning. Second, we calculated the differenced vertical/horizontal polarization (VH) backscatter (dVH = VH_post – VH_pre) from Sentinel-1 SAR data. The ESA Land Cover classification map and the relationship between dNBR and dVH were used to distinguish urban and vegetation-burned areas. The generated dNBR was used to divide the vegetation-burned areas into 4 groups: Low (values ≤0.25), Moderate (0.250.45). The generated dVH was used to divide the urban-burned areas into 3 groups: Low (values ≤1), Moderate (14). Notes that these thresholds were defined for contrast visualization of severity levels across four southern California fires and may not reflect other regions. All analysis was performed in Google Earth Engine. From these layers, we can clearly see the vegetation and urban-burned areas and their burn levels. The structure damage could be overlayed on top of burn maps generated by the integration of Sentinel-1 and -2. The maps were preliminary results only and no ground-validation has been considered at the time of posting. To separate vegetation and urban-burned areas, we applied the empirical thresholds of Sentinel-2 dNBR and Sentinel-1 dVH, which could be imperfect in some locations on a large scale. Sentinel-1 SAR data was subject to signal noise in some areas likely due to terrain and speckle noise, which could lead to incorrect severity levels in urban-burned areas to some extents. Refinement of the results is in progress. Suggested Usage Despite the rawer form of this quick-look product, it is still useful for distinguishing vegetation and urban-burned areas and severity assessment. Resolution Sentinel-1A SAR C-band, 10m spatial resolution Sentinel-2 MSI, 10-60m spatial resolution Credits Khuong Tran (ARC), Taejin Park (ARC), Aakash Chhabra (ARC), Weile Wang (ARC), Kyle Kabasares (ARC). Service URL https://gis.earthdata.nasa.gov/portal/home/item.html?id=88fe789c74f9459392d293931ac7aac5 WMS Endpoint https://gis.earthdata.nasa.gov/portal/services/DISASTERS_202501_FIRE_CA/2501_s1_s2_burnseverity/ImageServer/WMSServer

Date of Image(s): 12/18/2024; UTC: 183709 1/12/25; UTC: 183731 Summary: These pre-fire and post-fire images pair were analyzed for normalized burn ratio (NBR). The input imagers were generated from the ESA’s Sentinel-2’s Multi-Spectral imager. They were created from the Level 2A products which have been atmospherically corrected and orthorectified. NBR is defined mathematically as (NIR – SWIR)/(NIR + SWIR) where NIR is near-infrared and SWIR is short-wave infrared. For Sentinel 2, NIR = Band 8 and SWIR = Band 12. Using these definitions, the 20-meter resolution Band 8 and Band 12 images were used to generate these products. Sentinel-2 NBR methods: https://custom-scripts.sentinel-hub.com/custom-scripts/sentinel-2/nbr/ Finally dNBR is simply the difference between the pre-fire NBR and the post-fire NBR. Thus, we calculated dNBR as dNBR = NBR(12/18/2024) - NBR(01/12/2025). Suggested Use: NBR is commonly used as a proxy to indicate areas which have charred vegetation. Darker areas (more negative values) in the NBR image more strongly represent the presence of burned vegetation. Since the dNBR considers the condition of the scene before the fire occurred, the resulting value has been used as a proxy for burn severity. More positive values in the dNBR analysis represent a proxy for greater burn severity. Negative values in dNBR represent a re-greening of or growth of vegetation in between pre and post imagery. The use of this dNBR product as a quantitative metric of burn severity of the Los Angeles Fires at the time of posting this dataset should be strongly caveated. This is due to several dNBR Limitations: The spectral band selections used in the NBR and dNBR calculations, and the implication of changes observed following fire in those wavelengths, primarily pertain to how vegetation spectral signatures change in NIR and SWIR wavelengths following charring. Because of this, dNBR may not accurately describe burned surfaces that are not vegetation (e.g. human built infrastructure). This dataset has not been validated by independent burn severity assessments. The degree to which dNBR is accurately determined depends on careful selection of pre and post event imagery. An effort was made to use the highest quality imagery (i.e. cloud free) with the shortest time difference possible in the image pair; however, it is unknown at the time of this posting how selection of different pre/post image pairs could affect the derived dNBR values. Satellite/Sensor: ESA Copernicus Sentinel-2 Resolution: 20 m in infrared bands Credits: Copernicus Sentinel-2 data was downloaded from the Sentinel-2 data explorer (https://dataspace.copernicus.eu/explore-data/data-collections/sentinel-data/sentinel-2) Data processed by Kyle Kabasares (ARC; kyle.k.kabasares@nasa.gov) References: Normalize Burn Ratio theory: https://un-spider.org/advisory-support/recommended-practices/recommended-practice-burn-severity/in-detail/normalized-burn-ratio Service URL: https://gis.earthdata.nasa.gov/portal/home/item.html?id=3bc872886af24b799acd2ef881691a6a WMS Endpoint: https://gis.earthdata.nasa.gov/portal/services/DISASTERS_202501_FIRE_CA/SENTINEL2_DNBR/ImageServer/WMSServer

Date of Image(s): 12/18/2024; UTC: 183709 1/12/25; UTC: 183731 Summary: These pre-fire and post-fire images pair were analyzed for normalized burn ratio (NBR). The input imagers were generated from the ESA’s Sentinel-2’s Multi-Spectral imager. They were created from the Level 2A products which have been atmospherically corrected and orthorectified. NBR is defined mathematically as (NIR – SWIR)/(NIR + SWIR) where NIR is near-infrared and SWIR is short-wave infrared. For Sentinel 2, NIR = Band 8 and SWIR = Band 12. Using these definitions, the 20-meter resolution Band 8 and Band 12 images were used to generate these products. Sentinel-2 NBR methods: https://custom-scripts.sentinel-hub.com/custom-scripts/sentinel-2/nbr/ Finally dNBR is simply the difference between the pre-fire NBR and the post-fire NBR. Thus, we calculated dNBR as dNBR = NBR(12/18/2024) - NBR(01/12/2025). Suggested Use: NBR is commonly used as a proxy to indicate areas which have charred vegetation. Darker areas (more negative values) in the NBR image more strongly represent the presence of burned vegetation. Since the dNBR considers the condition of the scene before the fire occurred, the resulting value has been used as a proxy for burn severity. More positive values in the dNBR analysis represent a proxy for greater burn severity. Negative values in dNBR represent a re-greening of or growth of vegetation in between pre and post imagery. The use of this dNBR product as a quantitative metric of burn severity of the Los Angeles Fires at the time of posting this dataset should be strongly caveated. This is due to several dNBR Limitations: The spectral band selections used in the NBR and dNBR calculations, and the implication of changes observed following fire in those wavelengths, primarily pertain to how vegetation spectral signatures change in NIR and SWIR wavelengths following charring. Because of this, dNBR may not accurately describe burned surfaces that are not vegetation (e.g. human built infrastructure). This dataset has not been validated by independent burn severity assessments. The degree to which dNBR is accurately determined depends on careful selection of pre and post event imagery. An effort was made to use the highest quality imagery (i.e. cloud free) with the shortest time difference possible in the image pair; however, it is unknown at the time of this posting how selection of different pre/post image pairs could affect the derived dNBR values. Satellite/Sensor: ESA Copernicus Sentinel-2 Resolution: 20 m in infrared bands Credits: Copernicus Sentinel-2 data was downloaded from the Sentinel-2 data explorer (https://dataspace.copernicus.eu/explore-data/data-collections/sentinel-data/sentinel-2) Data processed by Kyle Kabasares (ARC; kyle.k.kabasares@nasa.gov) References: Normalize Burn Ratio theory: https://un-spider.org/advisory-support/recommended-practices/recommended-practice-burn-severity/in-detail/normalized-burn-ratio Service URL: https://gis.earthdata.nasa.gov/portal/home/item.html?id=3bc872886af24b799acd2ef881691a6a WMS Endpoint: https://gis.earthdata.nasa.gov/portal/services/DISASTERS_202501_FIRE_CA/2501_sentinel2_dnbr/ImageServer/WMSServer

Dates of Images: Post-Event: May 20, 2026; May 19, 2026 Pre-Event: May 7, 2026; April 20, 2026 Date of Next Image: Unknown Summary: These pre-fire and post-fire images pair were analyzed for normalized burn ratio (NBR). The input images were generated from the Sentinel2- dataset at 10m resolution. NBR is defined mathematically as (NIR – SWIR)/(NIR + SWIR) where NIR is near-infrared and SWIR is short-wave infrared. dNBR is computed by the difference between the pre-fire NBR and the post-fire NBR. More information on dNBR can be found here: https://un-spider.org/advisory-support/recommended-practices/recommended-practice-burn-severity/in-detail/normalized-burn-ratio. dNBR data may be computed while the fire is in progress. This is intentionally done to prioritize rapid data availability for proactive disaster response but means data can change over the course of the fire. Check fire containment and image dates for further context on image timing. Suggested Use: NBR is commonly used as a proxy to indicate areas which have charred vegetation. Darker areas (more negative values) in the NBR image more strongly represent the presence of burned vegetation. Since the dNBR considers the condition of the scene before the fire occurred, the resulting value has been used as a proxy for burn severity. Higher dNBR values represent a proxy for greater burn severity. Negative dNBR values may represent a re-greening of or growth of vegetation in between pre and post imagery. The use of this dNBR product as a quantitative metric of burn severity at the time of posting this dataset should be strongly caveated. This is due to several dNBR limitations: The spectral band selections used for dNBR calculations, and the implication of changes observed following fire in those wavelengths, primarily pertain to how vegetation spectral signatures change in NIR and SWIR wavelengths following charring. Because of this, dNBR may not accurately describe burned surfaces that are not vegetation (e.g. human built infrastructure). This dataset has not been validated by independent burn severity assessments. The degree to which dNBR is accurately determined depends on careful selection of pre and post event imagery. An effort was made to use the highest quality imagery (i.e. cloud free) with representative conditions for each scene; however, it is unknown at the time of this posting how selection of different pre/post image pairs could affect the derived dNBR values. Satellite/Sensor: Copernicus Sentinel-2 Resolution: 10 meters Credits: NASA/MSFC, USGS Service URL: See REST Endpoint to the right. WMS Endpoint: https://gis.earthdata.nasa.gov/gis05/services/DISASTERS_202605_FIRE_CA/202609_sentinel2_nbr/ImageServer/WMSServer

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

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 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. Table 1: Fire severity classification ranges for the Palisades fire Fire Severity Class RADAR-VSPI [dB] VSPI [-] Unburnt (green) 0.107 (-68.804) Low (yellow) (-1.027) 142.064 Moderate (orange) (-1.992) 406.705 High (red) (-3.138) 700.930 Extreme (black) (-5.472) 920.930 Table 2: Fire severity classification ranges for the Eaton fire Fire Severity Class RADAR-VSPI [dB] VSPI [-] Unburnt (green) 0.895 (-49.594) Low (yellow) (-0.834) 116.854 Moderate (orange) (-2.227) 392.053 High (red) (-3.503) 572.801 Extreme (black) (-5.945) 794.519 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. The Eaton Fire severity classification map shows extensive ‘high’ to ‘extreme’ severity burns in the San Gabriel Mountains, particularly around Mount Wilson Observatory, Big Santa Anita Canyon, Sturtevant Falls, and Echo Mountain. These regions, characterized by steep terrain and mixed chaparral-woodland vegetation, experienced severe fire effects, leading to complete canopy consumption. The northwestern portions, including Dawn Mine and sections of the San Gabriel Mountains, also exhibit intense burns along ridgelines, aligning with expected fire spread dynamics in mountainous landscapes. In contrast, ‘moderate’ and ‘low’ severity burns are more scattered, particularly in Kinneloa Mesa and sections of Santa Anita Canyon, where some structural elements of the vegetation were retained despite surface fuel consumption. At the southern edge, near Altadena, Hastings Ranch, and Sierra Madre, ‘unburnt’ vegetation is observed, marking areas that remained unaffected within the fire perimeter. Terms of Use & Limitations This product is only relevant to fire impacts to vegetation and should not be used to understand impacts to built infrastructure. 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 Service URL https://gis.earthdata.nasa.gov/portal/home/item.html?id=d5cfe94588854455a443980c5bd52e3a WMS Endpoint https://gis.earthdata.nasa.gov/portal/services/DISASTERS_202501_FIRE_CA/2501_optisar_FireSeverity/ImageServer/WMSServer