202611_sentinel1_landslide_heatmap

Dates of Images: Post-Event: 06/25/2026 Pre-Event: 04/07/2026-06/13/2026 (8 images total) Summary: The NASA GSFC landslides team produced this landslide proxy heatmap from Sentinel-1 imagery. Potential landslides were detected by calculating the change in the backscatter coefficient before and after the triggering event using the log ratio approach (Iratio). The Iratio is generated by subtracting median pre- and post-event Radiometrically Terrain Corrected (RTC) gamma0 backscatter in decibel scale at 10 m resolution obtained from the Alaska Sar Facility’s Hybrid Pluggable Processing Pipeline (HyP3). This map should be used as a guide to identify areas likely affected by landslides. This is a rapid response product. We have not done any form of manual verification or corrections to remove false positives. The likely landslide pixels are identified using 99th percentile Iratio threshold considering areas greater than 10-degree in slope and not affected by radar shadow and layover effects. A heatmap is generated using identified landslide pixels for visualization ease. Pre-event imagery = median imagery generated using images from 04/07/2026-06/13/2026 (8 images total) Post-event imagery = 06/25/2026 Suggested Use: The red and yellow areas indicate potential zones of dense landsliding. False positives related to natural and human-made ground surface change, including but not limited to deformation from mining, construction, deforestation, agriculture, flooding, snow cover, and changes in reservoir water levels can also be within the red and yellow areas. Satellite/Sensor/Resolution: Synthetic Aperture Radar on European Space Agency's (ESA) Copernicus Sentinel-1 satellite, 10 meters Credits: NASA GSFC landslides team, Copernicus Sentinel-1 data Citation: Handwerger AL, Huang M-H, Jones SY, Amatya P, Kerner HR, Kirschbaum DB. 2022. Generating landslide density heatmaps for rapid detection using open-access satellite radar data in Google Earth Engine. Nat. Hazards Earth Syst. Sci. Copernicus Publications, 22(3): 753–773. https://doi.org/10.5194/nhess-22-753-2022. Esri REST Endpoint: See URL to the right. WMS Endpoint: https://gis.earthdata.nasa.gov/portal/services/DISASTERS_202606_EARTHQUAKE_VENEZUELA/202611_sentinel1_landslide_heatmap/ImageServer/WMSServer

Dates of Images: 5/11/2024 Summary: The NASA GSFC landslides team produced this landslide proxy heatmap from Sentinel-1 imagery. Potential landslides were detected by calculating the change in the backscatter coefficient before and after the triggering event using the log ratio approach (Iratio). The Iratio is generated by subtracting median pre- and post-event Radiometrically Terrain Corrected (RTC) gamma0 backscatter in decibel scale at 10 m resolution obtained from the Alaska Sar Facility’s Hybrid Pluggable Processing Pipeline (HyP3). The likely landslide pixels are identified using 99th percentile Iratio threshold considering areas greater than 10-degree in slope and not affected by radar shadow and layover effects. A heatmap is generated using identified landslide pixels for visualization ease. Pre-event imagery = median imagery generated using images from 09/14/2023, 09/26/2023 Post-event imagery = 05/11/2024 This map should be used as a guide to identify areas likely affected by landslides. This is a rapid response product. We have not done any form of manual verification or corrections to remove false positives. Please see the slope and shadow layover mask item for areas not considered in this analysis. Suggested Use: The red and yellow areas indicate potential zones of dense landsliding. False positives related to natural and human-made ground surface change, including but not limited to deformation from mining, construction, deforestation, agriculture, flooding, snow cover, and changes in reservoir water levels can also be within the red and yellow areas. Satellite/Sensor/Resolution: Synthetic Aperture Radar on European Space Agency's (ESA) Copernicus Sentinel-1 satellite, 10 meters Credits: NASA GSFC landslides team, Copernicus Sentinel-1 data Esri REST Endpoint: See URL to the right. WMS Endpoint: https://gis.earthdata.nasa.gov/portal/services/DISASTERS_EX2602_202405_FLOOD_BRAZIL/ex2602_sentinel1_landslide_heatmap/ImageServer/WMSServer

Dates of Images: 5/11/2024 Summary: The NASA GSFC landslides team produced this landslide proxy heatmap from Sentinel-1 imagery. Potential landslides were detected by calculating the change in the backscatter coefficient before and after the triggering event using the log ratio approach (Iratio). The Iratio is generated by subtracting median pre- and post-event Radiometrically Terrain Corrected (RTC) gamma0 backscatter in decibel scale at 10 m resolution obtained from the Alaska Sar Facility’s Hybrid Pluggable Processing Pipeline (HyP3). The likely landslide pixels are identified using 99th percentile Iratio threshold considering areas greater than 10-degree in slope and not affected by radar shadow and layover effects. A heatmap is generated using identified landslide pixels for visualization ease. Pre-event imagery = median imagery generated using images from 09/14/2023, 09/26/2023 Post-event imagery = 05/11/2024 This map should be used as a guide to identify areas likely affected by landslides. This is a rapid response product. We have not done any form of manual verification or corrections to remove false positives. Please see the slope and shadow layover mask item for areas not considered in this analysis. Suggested Use: The red and yellow areas indicate potential zones of dense landsliding. False positives related to natural and human-made ground surface change, including but not limited to deformation from mining, construction, deforestation, agriculture, flooding, snow cover, and changes in reservoir water levels can also be within the red and yellow areas. Satellite/Sensor/Resolution: Synthetic Aperture Radar on European Space Agency's (ESA) Copernicus Sentinel-1 satellite, 10 meters Credits: NASA GSFC landslides team, Copernicus Sentinel-1 data Citation: Handwerger AL, Huang M-H, Jones SY, Amatya P, Kerner HR, Kirschbaum DB. 2022. Generating landslide density heatmaps for rapid detection using open-access satellite radar data in Google Earth Engine. Nat. Hazards Earth Syst. Sci. Copernicus Publications, 22(3): 753–773. https://doi.org/10.5194/nhess-22-753-2022. Esri REST Endpoint: See URL to the right. WMS Endpoint: https://gis.earthdata.nasa.gov/portal/services/DISASTERS_EX2602_202405_FLOOD_BRAZIL/ex2602_sentinel1_landslide_heatmap/ImageServer/WMSServer

Date of Image: 9/28/2024, 10/3/2024, 10/5/2024, 10/10/2024, 10/15/2024, 10/17/2024 Date of Next Image: Unknown Summary: NASA used a Sentinel-1 SAR backscatter change approach developed in GEE (Handwerger et al., 2022) to detect areas with high landslide density. This approach detects potential landslides by calculating the change in the backscatter coefficient before and after the triggering event using the log ratio approach. False positives such as backscatter change due to flooding, agriculture, and more, are removed by using threshold-based masks made from the topographic slope from the 1 arcsec (∼30 m) resolution NASADEM (NASA JPL, 2020). Using stacks of SAR data reduces noise, and furthermore, the pre-event stack provides backscatter data that is more representative of the pre-event ground surface properties. Finally, to detect landslide areas, the backscatter change raster was thresholded using the 99th percentile to highlight strong signals, and the heatmap was calculated. Pre-event time frame: 2024-01-10 to 2024-09-26 Post-event time frame: 2024-09-28 to 2024-10-17 This map should be used as a guidance to identify areas likely affected by landslides. This is a rapid response product. We have not done any form of manual corrections to remove false positives. Suggested Use: The red and yellow areas indicate potential zones of dense landsliding. Disclaimer: not verified in field and optical imagery has clouds preventing verification. Satellite/Sensor: Copernicus Sentinel-1 Synthetic Aperture Radar (SAR) Resolution: 10 meters Credits: NASA GSFC Landslides Team, Copernicus Sentinel-1 data Handwerger Al, Huang M-H, Jones SY, Amatya P, Kerner HR, Kirschbaum DB. 2022. Generating landslide density heatmaps for rapid detection using open-access satellite radar data in Google Earth Engine. Nat. Hazards Earth Syst. Sci. Copernicus Publications, 22(3): 753–773. https://doi.org/10.5194/nhess-22-753-2022. Esri REST Endpoint: See URL section on right side of page

Displacement maps from Sentinel-1 ascending track 033, acquired on 2026/06/25 Dates of Images: June 24, 2026; June 25, 2026 Summary: EARTHQUAKE OVERVIEW (Source: USGS): On June 24, 2026, northwestern and central Venezuela was struck by two large strike-slip earthquakes occurring just 39 seconds apart: Foreshock: Magnitude 7.2 at 22:04:33 (UTC), located 23 km SE of Yumare, Venezuela Mainshock: Magnitude 7.5 at 22:05:11 (UTC), located 28 km SE of Yumare, Venezuela (10.435°N, 68.472°W) Scientists working with the Advanced Rapid Imaging and Analysis (ARIA) team at NASA's Jet Propulsion Laboratory and California Institute of Technology, both in Pasadena, California, created these displacement maps with measurements of surface displacements caused by the earthquake sequence. The maps were derived from synthetic aperture radar (SAR) images from the Copernicus Sentinel-1 satellites, operated by the European Space Agency (ESA). The team computed the phase difference between a post-event image acquired on June 25, 2026 (reference scene) with a pre-event image acquired on June 18, 2026 (secondary scene), on Sentinel-1 ascending (satellite moving north) track 033. Interferometric Synthetic Aperture Radar (InSAR) measures ground movement toward or away from the satellite. Sentinel-1 views the ground from the side at about 40° from vertical. On ascending passes, it travels slightly west of north (heading ≈ 348° or −12°) and looks to the east. On descending passes, it travels slightly southwest (heading ≈ 192°) and looks to the west. The full dataset covers an area centered on the earthquake epicenter. Each pixel measures approximately 30 meters across. Suggested Use: Line-of-Sight Displacement (Venezuela_S1_A033_20260618_20260625_UNW.tif): Unwrapped phase data converted to metric displacement along the radar line-of-sight. Positive values (blue) indicate ground movement towards from the satellite (uplift and/or westward motion), while negative values (red) indicate movement away from the satellite (subsidence and/or eastward motion). Units: meters. The dataset has been re-referenced to a stable point at coordinates (-68.5435°, 10.9696°). Areas of low coherence and water bodies have been masked. Wrapped Phase (Venezuela_S1_A033_20260618_20260625_WRP.tif): Wrapped phase map showing relative ground deformation where each "fringe" or complete color cycle represents approximately 2.8 cm of displacement along the line-of-sight (half the Sentinel-1 C-band wavelength of 5.56 cm). The color progression from (outward to inward) red, orange, yellow, green, and blue indicates ground is moving away from the satellite, blue, green,yellow, orange, and red means ground is moving towards the satellite. This visualization is useful for identifying the spatial pattern and magnitude of deformation. Range Offset (Venezuela_S1_A033_20260618_20260625_RNG.tif): Across-track (range direction) pixel offsets measured through amplitude cross-correlation. This represents ground displacement perpendicular to the satellite flight path, predominantly capturing east-west motion. Positive values (blue) indicate westward movement, negative values (red) indicate eastward movement. Units: meters. Water bodies and areas outside the valid data frame have been masked. Interferometric Coherence (Venezuela_S1_A033_20260618_20260625_COH.tif): Measures the similarity of the radar phase between the two SAR acquisitions, indicating the reliability of the interferometric phase used to estimate ground displacement. Values range from 0 (decorrelated, unreliable) to 1 (highly correlated, reliable). Data Sources and Satellite Specifications: Water masking: ESA World Cover 2021 land cover dataset was used to mask permanent water bodies Orbit direction: Ascending (A) - satellite moving north Track number: 033 Wavelength: 5.56 cm (C-band) Spatial resolution: 30 meters Reference point: -68.5435°E, 10.9696°N Temporal baseline: 7 days (June 18-25, 2026) Caveats: The InSAR ground displacement results posted here are preliminary results, primarily intended to aid field response and provide rapid assessment of earthquake-induced surface deformation. These preliminary products may or may not be suitable for detailed scientific analysis without further validation. This displacement map should be used as guidance to identify areas of significant ground displacement, and may be less reliable over snow-covered and vegetated areas, steep terrain, or regions that experienced surface changes between acquisitions. The Sentinel-1 data products contain modified Copernicus Sentinel data (2026), processed by the European Space Agency and analyzed at the NASA Jet Propulsion Laboratory. For more information about ARIA, visit: http://aria.jpl.nasa.gov Credits: Sentinel-1 data were accessed through the Copernicus Open Hub and the Alaska Satellite Facility server. The product contains modified Copernicus Sentinel data (2026), processed by the European Space Agency and analyzed by the NASA-JPL/Caltech ARIA and OPERA team. The products are produced as part of the OPERA project, which is funded by NASA to address remote sensing needs identified by the Satellite Needs Working Group, and managed by NASA's Jet Propulsion Laboratory. Grace Bato, Cole Speed, Eric Fielding Esri REST Endpoint: See URL to the right

Date of Images: Post-Event: June 26, 2025 at 00:59 UTC (about 6:59PM MT) Pre-Event: June 20, 2025 Summary: We developed a rapid-response workflow to map flooded areas during this emergency event using multi-temporal Sentinel-1 SAR data. Sentinel-1’s SAR backscatter characteristics are particularly effective for detecting surface water: water surfaces typically exhibit low backscatter, whereas non-water areas show higher backscatter. Leveraging this physical property, we distinguished between water and non-water surfaces from both pre- and post-flood observations. To highlight flood-induced surface changes, we computed the differenced co-polarized VV backscatter (dVV = VV_post – VV_pre). This change detection technique enhances the visibility of newly inundated areas. We then applied a simple threshold to the dVV image to separate flooded from non-flooded areas. Suggested Use: The final output is a binary raster product, where pixels with a value of 1 represent flooded areas (dark blue color). Satellite/Sensor: Sentinel-1 /Synthetic Aperture Radar (SAR) Resolution: 10 meters Credits: Dr. Khuong H. Tran (Khuong.tran@nasa.gov) Service URL: https://gis.earthdata.nasa.gov/portal/home/item.html?id=87d3edc8e19d437da1336481506ff377 WMS Endpoint: https://gis.earthdata.nasa.gov/portal/services/DISASTERS_202506_FIRE_NM/2506_sentinel1_flood/ImageServer/WMSServer