Environment data

Update Frequency:Twice a day Summary:The global Landslide Hazard Assessment for Situational Awareness (LHASA) model was developed to inform a wide range of users about landslide hazards. LHASA version 2.1 uses machine learning to show where and when landslides are most probable. Suggested Use:LHASA 2.1 was created to enable global monitoring of landslide hazard and is not a substitute for local site investigations. Red indicates high hazard and blue indicates low hazard.  LHASA nowcast "today": This map shows the relative probability of landslide occurrence today, based on rainfall estimates from GPM and forecasts from GEOS-FP. Satellite/Sensor:GPM, SMAP Resolution:30 arcseconds (~1 kilometer) Credits:NASA GSFC Users are requested to cite the following paper:Stanley, T. A., D. B. Kirschbaum, G. Benz, et al. 2021. "Data-Driven Landslide Nowcasting at the Global Scale." Frontiers in Earth Science, 9: [10.3389/feart.2021.640043]

This page provides data for the Carbon Neutrality performance measure.   The City of Tempe is committed to protecting the environment. Carbon emissions are a significant contributor to the pollution of our atmosphere. Sources of carbon emissions include the energy used in city buildings and facilities, and in the fuel used in transit, city vehicles, and employee commuting. This performance measure puts the City on a path to being a carbon-neutral city. The municipal carbon footprint includes buildings, streetlights, water treatment, electricity, and fuel usage for fleet, transit, solid waste, and employee commute. The performance measure dashboard is available at 4.19 Carbon Neutrality     Data Dictionary   Additional Information   Source: City pisions (is this word correct?): fleet, WUD, solid waste. APS, SRP, MAG, SHROG, and Valley Metro Contact:  Grace Kelly Contact E-Mail: Grace_Kelly@tempe.gov Data Source Type:  CSV Preparation Method:  It is part of a larger data set and uses proprietary software. Publish Frequency:  Every 5 years Publish Method: Manual

Raster data on natural radioactivity for the Umbria region, published by Regione Umbria SIAT as an image service.

Current and recent earthquakes alerts from the Global Disaster Alert and Coordination System (GDACS), a cooperation of the United Nations and the European Commission, with alert level, severity and affected population.

Date of Images: Post-Event: 8/13/2026, 8/19/2026 Pre-Event: July 2026 Summary: These Black Marble Day-Night Band (BRDF-Corrected) images were created by the NASA Black Marble Science team. The images are corrected for atmospheric, terrain, lunar BRDF, and straylight effects, and directly measures light intensity on the ground in units of nanowatts/(steradian centimeter squared). The images are scaled from 0 - 30. The baseline image is from July 2026, generated from all daily atmospheric and lunar-corrected data from that month. There is a layer to display where clouds are present. This comparison between the images is meant as a visual assessment of outage impacts to aid various partners who are working to deliver emergency aids to local communities. Power outage maps like these help disaster response efforts in the short-term as well as long-term monitoring during the crucial stages of disaster recovery. Suggested Use: NASA's Black Marble Nighttime Light product suite is a state-of-the-art daily global collection of standard products for monitoring nighttime lights (NTL). Utilizing the Visible Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB) aboard the Suomi-NPP, NOAA-20, and NOAA-21 satellites. Grey indicates cloud cover. Observations may be obscured by total or partial cloud cover. Other factors such as snow reflectance, moon phase, wildfires, and wildfire smoke may distort light radiance and require further analysis when assessing power outages. Satellite/Sensor: Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB) onboard the Suomi National Polar-orbiting Platform (SNPP), NOAA-20, and NOAA-21 Resolution: 15 arc-seconds (~500 meters) Credits: NASA Black Marble Science team Please cite the following two references when using this data: Román MO, Stokes EC, Shrestha R, Wang Z, Schultz L, Carlo EA, Sun Q, Bell J, Molthan A, Kalb V, Ji C. Satellite-based assessment of electricity restoration efforts in Puerto Rico after Hurricane Maria. PloS one. 2019 Jun 28;14(6):e0218883. Román MO, Wang Z, Sun Q, Kalb V, Miller SD, Molthan A, Schultz L, Bell J, Stokes EC, Pandey B, Seto KC. NASA's Black Marble nighttime lights product suite. Remote Sensing of Environment. 2018 Jun 1;210:113-43. Point of Contact: Ranjay Shrestha NASA Goddard Space Flight Center E-mail: ranjay.m.shrestha@nasa.gov Additional Links: NASA’s Black Marble Product Suite Román, M.O. et al. (2019) Satellite-based assessment of electricity restoration efforts in Puerto Rico after Hurricane Maria. PLoS One, 14 (6). Román, M.O. et al. (2018) NASA’s Black Marble nighttime lights product suite. Remote Sensing of Environment. 210, 113–143. Data Access: REST URL: https://gis.earthdata.nasa.gov/portal/home/item.html?id=a97341f5bb9143039769267a1f385dd6 WMS: https://gis.earthdata.nasa.gov/portal/services/DISASTERS_202608_FLOOD_OH/VIIRS_BRDF_cloudmask/ImageServer/WMSServer

Full Metadata This EnviroAtlas web service supports research and online mapping activities related to EnviroAtlas (https://www.epa.gov/enviroatlas). The layers in this web service depict ecosystem services at the census block group level for all EnviroAtlas communities. These layers illustrate the ecosystems and natural resources that are associated with clean air, clean and plentiful water, natural hazard mitigation, climate stabilization, recreation, culture, and aesthetics, and biodiversity conservation, and factors that place stress on those resources. EnviroAtlas allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the conterminous United States as well as detailed metrics for select communities. Additional descriptive information about each attribute in this web service can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).

Overview This layer shows the grasslands priority conservation areas (GPCAs) within North America's Central Grasslands, an ecosystem considered among the most threatened on the continent and the world. GPCAs are areas of tri-national importance due to their ecological significance and threatened nature, which require international cooperation for their successful conservation. This layer is part of the North American Environmental Atlas, produced through a collaboration between the national atlas programs of Canada, Mexico, and the United States, in partnership with the Commission for Environmental Cooperation (CEC). The 55 original GPCAs were identified in 2004 by 36 experts from Canada, Mexico, and the United States, during a workshop co-organized by the CEC and The Nature Conservancy (TNC). In 2010, the Rocky Mountain Bird Observatory (RMBO) updated the dataset using national and international land cover, elevation, habitat, species, and bird survey data, bringing the total to 59 GPCAs. Each area was scored and ranked based on its importance to key grassland species and the rate at which its habitat was being lost. Geographic Coverage This layer covers the Central Grasslands ecosystem of North America, spanning parts of Canada, Mexico, and the United States. Data Sources and Licensing This dataset draws on more than 120 physical, biological, and social data sets from federal, state, and provincial governments, engineering and environmental firms, universities, industries, and conservation organizations, including USGS land cover and elevation layers, USGS Breeding Bird Surveys and Christmas Bird Counts, RMBO bird and vegetation surveys, and Canadian Wildlife Service bird surveys. Further detail on the selection methodology is available in the North American Grassland Priority Conservation Areas: Technical Report and Documentation. This material is licensed under CC BY 4.0, allowing non-exclusive rights to distribute, remix, adapt, and build upon the material in any medium or format, including for commercial purposes, so long as attribution is given to the creator. Update frequency: Not planned Limitations of use: None. Acknowledgement of the Commission for Environmental Cooperation, the Rocky Mountain Bird Observatory, and (or) the Nature Conservancy would be appreciated in products derived from these data. Vintage: 2010 Applications (Intended Use) Conservation planning and priority-setting for grassland habitats across North America. Biodiversity research and monitoring of grassland-dependent species. Cross-border collaboration in the conservation of shared grassland ecosystems. Land use and natural resource management, including habitat protection. Integration with other thematic layers in the North American Environmental Atlas for continental-scale analysis. Supporting environmental cooperation and policy-making among Canada, Mexico, and the United States. Files Download

Date of Images: 10/9/2025 - 10/28/2025 Please Note: Images with majority cloud cover are not shown in the Map Viewer, but are available in the service. Summary: These Black Marble Day-Night Band (BRDF-Corrected) cloud masks were created by the NASA Black Marble Science team. This is the layer to displaying where clouds are present. This comparison between the images is meant as a visual assessment of outage impacts to aid various partners who are working to deliver emergency aids to local communities. Power outage maps like these help disaster response efforts in the short-term as well as long-term monitoring during the crucial stages of disaster recovery. Suggested Use: NASA's Black Marble Nighttime Light product suite is a state-of-the-art daily global collection of standard products for monitoring nighttime lights (NTL). Utilizing the Visible Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB) aboard the Suomi-NPP, NOAA-20, and NOAA-21 satellites. Grey represents cloud cover. Satellite/Sensor: Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB) onboard the Suomi National Polar-orbiting Platform (SNPP), NOAA-20, and NOAA-21 Resolution: 500 meters Credits: NASA Black Marble Science team Please cite the following two references when using this data: Román MO, Stokes EC, Shrestha R, Wang Z, Schultz L, Carlo EA, Sun Q, Bell J, Molthan A, Kalb V, Ji C. Satellite-based assessment of electricity restoration efforts in Puerto Rico after Hurricane Maria. PloS one. 2019 Jun 28;14(6):e0218883. Román MO, Wang Z, Sun Q, Kalb V, Miller SD, Molthan A, Schultz L, Bell J, Stokes EC, Pandey B, Seto KC. NASA's Black Marble nighttime lights product suite. Remote Sensing of Environment. 2018 Jun 1;210:113-43. Point of Contact: Ranjay Shrestha NASA Goddard Space Flight Center E-mail: ranjay.m.shrestha@nasa.gov Additional Links: NASA’s Black Marble Product Suite Román, M.O. et al. (2019) Satellite-based assessment of electricity restoration efforts in Puerto Rico after Hurricane Maria. PLoS One, 14 (6). Román, M.O. et al. (2018) NASA’s Black Marble nighttime lights product suite. Remote Sensing of Environment. 210, 113–143. Service URL: https://gis.earthdata.nasa.gov/portal/home/item.html?id=961f83b5aef64f75bc4caa9c4610f4ed WMS Endpoint: https://gis.earthdata.nasa.gov/portal/services/DISASTERS_202510_FLOOD_AK/2510_BlackMarble_brdf_cloud/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 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

Date of Images: 5/6/2024 Date of Next Image: Unknown Summary: The floodwater depth raster produced for the 06th May 2024 for long-term floods in southern Brazil was generated using the Flood Water Depth Estimation Tool (Cohen et al, 2018, 2019; Peter et al, 2020). The Floodwater Depth Estimation Tool (FwDET) is a solution for producing timely floodwater depth data during flood activations that require emergency response and post-flood assessment. FwDET is based solely on a flood extent layer and a digital elevation model (DEM). The DEM used here was the NASA SRTM data. The flood extent layer was derived from the OPERA Dynamic Surface Water eXtent (DSWx) from the Harmonized Landsat Sentinel-2 (HLS) product. The DEM used was the NASA Shuttle Radar Topography Mission (SRTM) data. The computation involves identifying elevations at the boundaries of the flood extent layer and using those elevations to assign floodwater surface elevation to each cell within the flooded domain by identifying its nearest boundary cell. The modeled flood surface elevation is then subtracted from the original DEM to retrieve depth. The results posted here are preliminary and unvalidated results, primarily intended to aid the field response and people who wanted to have a rough first look at the flood inundation depth. The flood depth map may contain errors due to inaccurate elevations in the NASA SRTM DEM or the water surface estimation. Data Sources: OPERA Dynamic Surface Water eXtent from Harmonized Landsat Sentinel-2 (DSWx-HLS) - based flood inundation extent raster generated by the ARIA/OPERA group at NASA JPL.  NASA SRTM (Shuttle Radar Topography Mission) Digital Elevation 30 m (openly available on Google Earth Engine) Suggested Use: The darkest shades of blue indicate where the product is estimating the largest floodwater depth, while lighter shades indicate shallower flooding Satellite/Sensor: Harmonized Landsat Sentinel-2 (HLS) MultiSpectral Instrument (MSI) on European Space Agency's (ESA) Copernicus Sentinel-2A/2B satellites NASA SRTM (Shuttle Radar Topography Mission) Digital Elevation 30 m (openly available on Google Earth Engine) Resolution: 30 meters Credits: Dinuke Munasinghe - NASA JPL Water and Ecosystems Team, NASA JPL ARIA/OPERA Team Product POCs: Dinuke Munasinghe (dinuke.nanayakkara.munasinghe@jpl.nasa.gov) FwDET Algorithm Documentation: Cohen, S., Brakenridge, G.R., Kettner, A., Bates, B., Nelson, J., McDonald, R., Huang, Y, Munasinghe, D., and J. Zhang (2018). Estimating Floodwater Depths from Flood Inundation Maps and Topography. Journal of the American Water Resources Association, 54(4), 847-858. DOI: https://doi.org/10.1111/1752-1688.12609 Cohen, S., Raney, A., Munasinghe D., Loftis, D., Molthan, A., Bell, J., Rogers, L., Galantowicz, J., Brakenridge, G.R., Kettner, A., Huang, Y., and Y. Tsang (2019). The Floodwater Depth Estimation Tool (FwDET v2.0) for Improved Remote Sensing Analysis of Coastal Flooding. Natural Hazards and Earth System Sciences, 19(9), 2053-2065. DOI: https://doi.org/10.5194/nhess-19-2053-2019 Farr, T.G., Rosen, P.A., Caro, E., Crippen, R., Duren, R., Hensley, S., Kobrick, M., Paller, M., Rodriguez, E., Roth, L., Seal, D., Shaffer, S., Shimada, J., Umland, J., Werner, M., Oskin, M., Burbank, D., and Alsdorf, D.E., 2007, The shuttle radar topography mission: Reviews of Geophysics, v. 45, no. 2, RG2004, at https://doi.org/10.1029/2005RG000183. Peter, B., Cohen, S., Lucey, R., Munasinghe, D., Raney, A., and G. Brakenridge (2020). "Google Earth Engine Implementation of the Floodwater Depth Estimation Tool (FwDET-GEE) for Rapid and Large Scale Flood Analysis". IEEE Geoscience and Remote Sensing Letters. DOI: https://doi.org/10.1109/lgrs.2020.3031190 OPERA. 2023. OPERA Dynamic Surface Water Extent from Harmonized Landsat Sentinel-2 CalVal Database (Version 1). Ver. 1.0. PO.DAAC, CA, USA. Dataset accessed [2024-05-09] at https://doi.org/10.5067/OPDSW-PCVV1 Esri REST Endpoint: See URL section on right side of page WMS Endpoint: https://gis.earthdata.nasa.gov/portal/services/DISASTERS_202405_FLOOD_BRAZIL/2407_opera_fwdet_hls/ImageServer/WMSServer

Date of Image(s): 9/5/24, 1/16/25 Summary: The normalized burn ratio difference (dNBR) are generated from the NASA AVIRIS-3 (Airborne Visible/Infrared Imaging Spectrometer -3). They are generated from differencing images from September 5, 2024 and January 16, 2025 over Palisades and Eaton. 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. Suggested Usage: NBR is commonly used as a proxy to indicate areas which have charred vegetation. 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. Sensor: Airborne Visible/Infrared Imaging Spectrometer 3rd-Generation (AVIRIS-3) The AVIRIS-3 wavelengths are 866nm and 2198nm. Credits: Rob Green (JPL), Phil Brodrick (JPL), David Thompson (JPL), John Chapman (JPL), Michael Eastwood (JPL), and Adam Chlus (JPL). Service URL: https://gis.earthdata.nasa.gov/portal/home/item.html?id=61f84f97df684f8ba5446c82d881b960 WMS Endpoint: https://gis.earthdata.nasa.gov/portal/services/DISASTERS_202501_FIRE_CA/2501_aviris_dnbr_color/ImageServer/WMSServer

NOTE: The Disasters Team is currently revising the legend on this dataset. Visualization properties are not finalized. Please consult the metadata on this page for reference. Date of Images Pre-event: 2025-10-08, 2025-10-10 Co and Post-event: 2025-10-12, 2025-10-13, 2025-10-15 Summary: 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 (JPL) and the California Institute of Technology produced maps showing areas affected by flooding in western Alaska in October 2025 using the OPERA Dynamic Surface Water Extent from Sentinel-1 (DSWx-S1) product. The flooding was triggered by the remnants of Typhoon Halong, which brought hurricane-force winds and record-breaking storm surge to the Yukon-Kuskokwim Delta region. The event caused catastrophic damage across multiple Alaska Native coastal villages, including Kipnuk and Kwigillingok, where floodwaters swept homes from their foundations, destroyed infrastructure, and disrupted access to essential services. These maps provide an initial view of the surface water extent observed following the flood event. The results presented here are preliminary and unvalidated, intended primarily to support situational awareness and field response efforts. The ARIA-Share platform emphasizes rapid dissemination of satellite-based products for disaster response. All information is provisional and should be used under emergency response guidelines. These data are provided without warranty of any kind. Use at your own risk. OPERA DSWx-S1 The OPERA DSWx-S1 was used to identify surface water for the flooding event. The images provided are a mosaic of OPERA DSWx-S1 products, producing a composite image for each date. The mosaicking applied prioritized pixels classified as open surface water to preserve water pixels in region of product granule overlap. The images are provided as GeoTIFF files. Water (WTR) Layer Description: Masked interpreted water classification layer. This represents pixel-wise classification into one of three water classes (not water, open water and inundated vegetation), masks (HAND mask and layover/shadow mask), or no data classes. Water (WTR) Layer Values: 0: Not Water - an area with valid data that is not open water (class 1), inundated vegetation (class 3), height above nearest drainage masked (class 250), or layover/shadow masked (class 251). Masking can result in "not water" (class 0) where land cover masking is applied (suggested color: #ffffff) 1: Open Water - an area that is entirely water and unobstructed to the sensor, including obstructions by vegetation, terrain, and buildings. (suggested color: #0000ff) 3: Inundated vegetation - an area that is considered inundated, extracted from the high value in dual polarization ratio and the wetland class in land cover map. (suggested color: #00ff00) 250: Height Above Nearest Drainage (HAND) masked - an area where topographic height is higher than the HAND threshold (suggested color: #c8c8c8) 251: Layover/shadow masked - an area identified as layover or shadow computed from the geometry of the digital elevation model and sensor. The area is directly copied from input burst RTC-S1 products (suggested color: #808080) 255: No data (suggested color: transparent) Satellite/Sensor: Synthetic Aperture Radar (SAR) instrument on European Space Agency's (ESA) Sentinel-1A satellite. Resolution: 30 meters OPERA DSWx-S1 data availability The post-processed products are available to download at https://aria-share.jpl.nasa.gov/20251012_Alaska_Floods/DSWx-S1/. The OPERA DSWx-S1 products have been in production since September 2024, are freely distributed to the public via NASA's Physical Oceanography Distributed Active Archive Center (PO.DAAC), and can be downloaded through NASA's Earthdata search. For more information about the Dynamic Surface Water eXtent product suite, please refer to the DSWx Product page: https://www.jpl.nasa.gov/go/opera/products/dswx-product-suite. For more information about the Caltech-JPL ARIA project, visit https://aria.jpl.nasa.gov. For more information about the JPL OPERA project, visit https://www.jpl.nasa.gov/go/opera/. REST Endpoint: See URL on the right. WMS Endpoint: https://gis.earthdata.nasa.gov/portal/services/DISASTERS_202510_FLOOD_AK/202510_opera_dswx_s1/ImageServer/WMSServer Product POCs: Cole Speed (cole.speed@jpl.nasa.gov) Mary Grace Bato (mary.grace.p.bato@jpl.nasa.gov) Ines Fenni (ines.fenni@jpl.nasa.gov) Alexander Handwerger (alexander.handwerger@jpl.nasa.gov) Renato Frasson (renato.prata.de.moraes.frasson@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 (2025), 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. NASA JPL-Caltech ARIA/OPERA Team ==================

Date of Image: Pre-Event: March 2024 Post-Event: 5/7/2024, 5/8/2024, 5/9/2024, 5/10/2025, 5/11/2024, 5/12/2024, 5/15/2024 Date of Next Image: Unknown Summary: These Black Marble High-Definition (BMHD) images were created by the NASA Black Marble Science team, with directed funding the NASA-Google Partnership program. The images map the impact of the major flooding in Rio Grande do Sul on electric grids in Porto Alegre. The baseline image (before the floods) was from May 2024, a cloud-free, moon-free composite, and the “after" image was from May 7, 2024. There is a layer to display where clouds are present in the "after" images. This comparison between the two images is meant as a visual assessment of outage impacts from the flooding to aid various partners who are working to deliver emergency aids to local communities. Power outage maps like these help disaster response efforts in the short-term as well as long-term monitoring during the crucial stages of disaster recovery. Suggested Use: NOTE: Black Marble HD images are downscaled from NASA’s Black Marble nighttime lights product (VNP46), and as such are a “modelled” or “best guess” estimate of how lights are distributed at a 30m resolution. These images should be used for visualization purposes, not for quantitative analysis. The image is in inferno color scale. Yellow represents presence of more light; dark blue less lights. Satellite/Sensor: The primary data source, NASA’s Black Marble nighttime lights product suite (VNP46), utilized to generate this product is derived from the Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB) onboard the Suomi National Polar-orbiting Platform (SNPP) along with high resolution base layers - Landsat derived normalized index products (NDVI and NDWI) and OpenStreetMap (OSM) derived road layer Resolution: Scaled resolution of 30 meters Credits: NASA Black Marble Science team Please cite the following two references when using this data: Román MO, Stokes EC, Shrestha R, Wang Z, Schultz L, Carlo EA, Sun Q, Bell J, Molthan A, Kalb V, Ji C. Satellite-based assessment of electricity restoration efforts in Puerto Rico after Hurricane Maria. PloS one. 2019 Jun 28;14(6):e0218883. Román MO, Wang Z, Sun Q, Kalb V, Miller SD, Molthan A, Schultz L, Bell J, Stokes EC, Pandey B, Seto KC. NASA's Black Marble nighttime lights product suite. Remote Sensing of Environment. 2018 Jun 1;210:113-43. Point of Contact: Ranjay Shrestha NASA Goddard Space Flight Center E-mail: ranjay.m.shrestha@nasa.gov Additional Links: NASA’s Black Marble Product Suite Román, M.O. et al. (2019) Satellite-based assessment of electricity restoration efforts in Puerto Rico after Hurricane Maria. PLoS One, 14 (6). Román, M.O. et al. (2018) NASA’s Black Marble nighttime lights product suite. Remote Sensing of Environment. 210, 113–143. Esri REST Endpoint: See URL section on right side of page. WMS Endpoint: https://maps.disasters.nasa.gov/ags03/services/brasil_flood_202405/blackmarble_hd/MapServer/WMSServe

Overview This map layer prepared by the Commission for Environmental Cooperation (CEC) shows the direct influence of humans on terrestrial ecosystems across North America. The Human Influence Index (HII) is based on population density, built-up areas, roads, railroads, navigable rivers, coastlines, land use/land cover, and nighttime lights. HII values range from 0 to 64, with 0 representing no human influence and 64 representing maximum human influence, based on all eight measures of human influence. The data layer was compiled by the Wildlife Conservation Society (WCS) and the Center for International Earth Science Information Network (CIESIN).  Geographic Coverage The coverage spans terrestrial areas of North America, including Canada, Mexico, and the United States. Data Sources and Licensing Commission for Environmental Cooperation (CEC). The Last of the Wild, Version Two. 2005. Global human influence index (HII). Wildlife Conservation Society (WCS), and Center for International Earth Science Information Network (CIESIN). The data is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0), allowing redistribution and adaptation with appropriate attribution. Update frequency: Not planned Limitations of use: Acknowledgment of the Wildlife Conservation Society (WCS) and the Center for International Earth Science Information Network (CIESIN) is appreciated in products derived from these data. Vintage: 2005 Applications (Intended Use) Conservation planning and identification of low-impact/wilderness areas. Assessing human pressure on biomes and ecoregions across North America. Supporting natural resource management and sustainability research. Cross-border environmental cooperation and policy-making among North American nations. Files Download

Holdings of the Sonoma County Agricultural Preservation and Open Space District. Includes conservation easements, open space easements conveyed to Ag + Open Space through the County of Sonoma planning process, and Ag + Open Space fee properties. This dataset represents Ag + Open Space's current portfolio of protected lands. Superseded projects (e.g. former fee properties since transferred, easements since amended or rescinded and replaced) are housed in a separate dataset.

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/

Date of Images: Post-Event: 8/13/2026, 8/19/2026 Pre-Event: July 2026 Summary: These Black Marble Day-Night Band (BRDF-Corrected) images were created by the NASA Black Marble Science team. The images are corrected for atmospheric, terrain, lunar BRDF, and straylight effects, and directly measures light intensity on the ground in units of nanowatts/(steradian centimeter squared). The images are scaled from 0 - 30. The baseline image is from July 2026, generated from all daily atmospheric and lunar-corrected data from that month. There is a layer to display where clouds are present. This comparison between the images is meant as a visual assessment of outage impacts to aid various partners who are working to deliver emergency aids to local communities. Power outage maps like these help disaster response efforts in the short-term as well as long-term monitoring during the crucial stages of disaster recovery. Suggested Use: NASA's Black Marble Nighttime Light product suite is a state-of-the-art daily global collection of standard products for monitoring nighttime lights (NTL). Utilizing the Visible Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB) aboard the Suomi-NPP, NOAA-20, and NOAA-21 satellites. The image is in inferno color scale. Yellow represents presence of more light; dark blue less lights. Observations may be obscured by total or partial cloud cover. Other factors such as snow reflectance, moon phase, wildfires, and wildfire smoke may distort light radiance and require further analysis when assessing power outages. Satellite/Sensor: Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB) onboard the Suomi National Polar-orbiting Platform (SNPP), NOAA-20, and NOAA-21 Resolution: 15 arc-seconds (~500 meters) Credits: NASA Black Marble Science team Please cite the following two references when using this data: Román MO, Stokes EC, Shrestha R, Wang Z, Schultz L, Carlo EA, Sun Q, Bell J, Molthan A, Kalb V, Ji C. Satellite-based assessment of electricity restoration efforts in Puerto Rico after Hurricane Maria. PloS one. 2019 Jun 28;14(6):e0218883. Román MO, Wang Z, Sun Q, Kalb V, Miller SD, Molthan A, Schultz L, Bell J, Stokes EC, Pandey B, Seto KC. NASA's Black Marble nighttime lights product suite. Remote Sensing of Environment. 2018 Jun 1;210:113-43. Point of Contact: Ranjay Shrestha NASA Goddard Space Flight Center E-mail: ranjay.m.shrestha@nasa.gov Additional Links: NASA’s Black Marble Product Suite Román, M.O. et al. (2019) Satellite-based assessment of electricity restoration efforts in Puerto Rico after Hurricane Maria. PLoS One, 14 (6). Román, M.O. et al. (2018) NASA’s Black Marble nighttime lights product suite. Remote Sensing of Environment. 210, 113–143. Data Access: REST URL: https://gis.earthdata.nasa.gov/portal/home/item.html?id=a83088fd0a0940cda0770b46e9222acf WMS: https://gis.earthdata.nasa.gov/portal/services/DISASTERS_202608_FLOOD_OH/202620_VIIRS_BRDF/ImageServer/WMSServer

Dates of Images: July 9, 2025 Date of Next Image: None Expected Summary: These maps show the results of a machine learning classification applied to UAVSAR data to identify open water flooding and inundation under tree canopy.  Colored areas in the image show detected flooding, where the color shows the type of landcover which was flooded (see legend below).  Non-flooded areas are transparent.  The classification uses a U-Net machine learning algorithm which was trained using UAVSAR data from previous flood events.  UAVSAR provides polarimetric synthetic aperture radar data from which different types of scattering mechanisms can be observed, which provides information on the type of flooding.  For example, flooding underneath vegetation produces strong double-bounce scattering from the water surface and tree trunks.  However, strong double-bounce scattering from urban areas aligned with the radar viewing direction, such as in Austin, TX, can produce erroneous classifications.  Non-flooded vegetation is generally dominated by volume scattering from the forest canopy.  In comparison, open water flooding exhibits weak radar returns in all polarizations. Suggested Use: Classified Images: Blue   = Open Water Red    = Flooded Developed Green  = Flooded Vegetation Orange = Flooded Crop Satellite/Sensor: UAVSAR airborne L-band synthetic aperture radar (SAR) aboard a NASA Gulfstream C-20A jet. Resolution: 10 meters Credits: UAVSAR data courtesy of NASA/JPL-Caltech Esri REST Endpoint: See URL section on right side of the page WMS Endpoint: https://maps.disasters.nasa.gov/ags03/services/texas_flood_202507/uavsar/MapServer/WMSServer Data Download: https://uavsar.jpl.nasa.gov/cgi-bin/download-files.pl

Temporal coverage: Current calendar year - Today Data Sources: VIIRS Sensor on NOAA-20 Time Interval: Every 12 Hours Data Latency: ~ 9 - 12 hours from satellite overpass Projection: EPSG:4326 Spatial Extent: Contiguous United States, Alaska, and Canada. Portions of Alaska west of 169 Degrees west and portions of Canada north of 75 degrees north are excluded. Resolution: Vector data derived from the 375 m nadir resolution of the VIIRS sensors Purpose / use case: The FEDS algorithm is an “event-based” approach that tracks the growth and intensity of individual fires every 12 hours and treats them as discrete objects that persist, expand, and evolve together. This tracking strategy allows for detailed monitoring of large fires in space and time as they spread across a landscape. The algorithm clusters VIIRS active fire observations into specific fire events, and then estimates a fire perimeter polygon, the active segment of the perimeter, and metrics for each fire, such as the total estimated area (km^2) and length of the active fire front (km). Each 12-hour increment also includes information on a fire’s mean Fire Radiative Power (FRP), which is a metric that describes fire intensity as estimated by the satellite sensor. This dataset is best used for near real-time situational awareness and retrospective scientific analysis of fire behavior. Notes on Interpretation of the Data: As a satellite-based data product, FEDS has lower spatial resolution and accuracy than fire perimeters derived in operational contexts, such as those from airborne imagers. However, FEDS can provide information when other sources are unavailable, and the algorithm runs automatically in fixed 12-hr intervals for all clusters of fire activity greater than 4 km^2. The FEDS algorithm inherits the same uncertainties associated with the VIIRS active fire product. These include potential geolocation errors that can be relevant for interpreting fire locations and fire spread. For example, hot plumes of smoke can generate false positive detections that overestimate the size of the fire at that time step. In addition, active fire detections may be clustered into events that do not align directly with operational definitions of fire events (i.e., FEDS may group multiple agency-designated fires into a single fire event, or consider a single agency-designated fire to be multiple fire events). Finally, clouds or thick smoke may lead to the omission of VIIRS active fire detections, leading to an underestimate of fire activity and fire spread. More information on the interpretation of the VIIRS Active Fire Dataset for fires can be found in the NASA Fire Information for Resource Management System (FIRMS) FAQ. Source information/ Program Information: This dataset was developed as part of the NASA Earth Information Systems (EIS) project. This dataset was generated through the Wildfire Tracking Lab –a collaboration between scientists at NASA Goddard, NASA Langley, NASA Marshall, University of California Irvine, and University of Maryland. Citation: Chen et al. “California wildfire spread derived using VIIRS satellite observations and an object-based tracking system.” Scientific data 9.1 (2022): 249 https://doi.org/10.1038/s41597-022-01343-0. Repository: The underlying algorithm that generates these data layers is hosted and developed at the Fire Events Data Suite repository. Parameters: Column Name: t Description: Time of VIIRS detection. Data collected during approximately 1:30 AM and 1:30 PM local solar time overpasses. The time component of the datetime is assigned a local time of 00:00:00 for 1:30 AM overpasses, and 12:00:00 for 1:30 PM overpasses. Unit is Datetime. yyyy-mm-ddThh:mm:ss. Time component is in local solar time. Date component is the UTC date. UTC date will match local date for all of CONUS and Canada, but will not match for Alaska. Column Name: region Description: Descriptive name of larger region where tracking is occurring. Column Name: fireid Description: Fire ID. Unique for each fire within a given region. Unit is Numeric ID Column: geometry Description: The shape of the Fire Segment. Unit is Geometry. CRS is EPSG:4326. Column: Primary Key Description: Globally unique identifier for a given layer entry. This key is formatted by [region]|[fireid]|[timestamp]. Unitless. Alternate Forms Of Data Access: This data can also be accessed through an API. Documentation for the API can be found here. The API distributes data in open source data formats. Contact: For inquiries about service performance please contact support@earthdata.nasa.gov or post/view questions on the Earthdata Forum. For questions about the data, please contact the science team with this form.

The tsunami hazard map indicates the mean wave run-up height on the coast for a return period of 475 years. The map has coastal coverage with points in the countries with tsunami hazard. Run-up values below 0.1 m are considered as “Low”; run-up values between 0.1 m and 2 m (inclusive) are considered as “Moderate”; run-up values greater than 2 m are considered as “High”. Background paper: Multi-hazard Disaster Risk Model of Infrastructure and Buildings at the Global Level (2023). Cardona, O.D., Bernal, G.A., Villegas, C.P., Molina, J.F., Herrera, S.A., Marulanda, M.C., Rincón, D.F., Grajales, S., Marulanda, P.M., Gonzalez, D., Maskrey, A. (2023). https://giri.unepgrid.ch/sites/default/files/2023-11/2.4-INGENIAR-CDRI-Background-Report-Risk-model.pdf Release date: Fri Jun 30 2023 The layer was clipped to the geographic boundaries of 1) IDB Borrowing Member Countries, 2) Caribbean Development Bank (CDB) Borrowing Countries, 3) Organization of Eastern Caribbean States (OECS) Member Countries.

Date of Image(s): January 11, 2025 Summary: This product shows the char/ash fraction of each pixel based on airborne imaging spectroscopy measurements from NASA JPL AVIRIS-3. The surface reflectance was first estimated using ISOFIT, after which a Spectral Unmixing algorithm was used to determine the amount of char and ash within each pixel, based on the MESMA approach. See credits for citations and more information on ISOFIT, Spectral Unmixing, and the MESMA approach. The char/ash fractional cover value is displayed as a single band pseudocolor grayscale, with thresholds at minimum detectable bounds of 50% and 80% char/ash fractional cover for Palisades and Eaton Fires, respectively. Suggested Use: The product describes the relative amount of char and ash (0-1) within each pixel. In order to deliver a product with low latency, the endmember library used was constrained to that common to a Southern California urban environment, and manual masking was also used. Locations that are heavily shaded may have errant solutions. The product also only indicates the char/ash fraction of the exposed surface - not what lies underneath. Areas covered by ash, for example, may express as having a fraction despite having only a thin coating of ash on top. This is a research grade product with known false positives and false negatives. Improvements to product accuracy are ongoing and linear spectral mixture model was used to approximate char/ash fractional cover; however, due to multiple scattering effects of complex surfaces, actual the relationship between observed char/ash reflectance and actual char/ash abundance may be non-linear. Resolution: The product ground spatial resolution is approximately 2.8 m, and flights occurred between 19:40 and 21:00 UTC on Jan 11th 2025. Raw data are available for download here: https://popo.jpl.nasa.gov/pub/LA_Fires/working/eaton_1.tif Point of Contact: Philip G. Brodrick (JPL) and David R. Thompson (JPL) Credits: AVIRIS-3 Radiance Data: Eckert, R., D.R. Thompson, A.M. Chlus, J.W. Chapman, M. Eastwood, M. Bernas, S. Geier, M. Helmlinger, D. Keymeulen, E. Liggett, S. Nadgauda, L.M. Rios, L.A. Shaw, W. Olson-Duvall, P.G. Brodrick, and R.O. Green. 2024. AVIRIS-3 L1B Calibrated Radiance, Facility Instrument Collection. ORNL DAAC, Oak Ridge, Tennessee, USA. https://doi.org/10.3334/ORNLDAAC/2356 AVIRIS-3 Reflectance Data: Brodrick, P.G., A.M. Chlus, U.N. Bohn, E. Greenberg, J. Montgomery, J.W. Chapman, M. Eastwood, S.R. Lundeen, R. Eckert, W. Olson-Duvall, D.R. Thompson, and R.O. Green. 2025. AVIRIS-3 L2A Orthocorrected Surface Reflectance, Facility Instrument Collection. ORNL DAAC, Oak Ridge, Tennessee, USA. https://doi.org/10.3334/ORNLDAAC/2357 ISOFIT: Thompson, D.R., Natraj, V., Green, R.O., Helmlinger, M.C., Gao, B.C. and Eastwood, M.L., 2018. Optimal estimation for imaging spectrometer atmospheric correction. Remote sensing of environment, 216, pp.355-373. Philip G Brodrick, David R Thompson, Niklas Bohn, Nimrod Carmon, Regina Eckert, James Montgomery, Kevin Wurster, & Nicholas Vaughn. (2024). isofit (v3.2.2). Zenodo. https://doi.org/10.5281/zenodo.14135462 Spectral Unmixing: https://github.com/emit-sds/SpectralUnmixing MESMA Approach: Quintano, C., Fernández-Manso, A. and Roberts, D.A., 2013. Multiple Endmember Spectral Mixture Analysis (MESMA) to map burn severity levels from Landsat images in Mediterranean countries. Remote Sensing of Environment, 136, pp.76-88. Service URL:https://gis.earthdata.nasa.gov/portal/home/item.html?id=f08f896fe8ce488fa66e6ab25b1963be WMS Endpoint:https://gis.earthdata.nasa.gov/portal/services/DISASTERS_202501_FIRE_CA/2501_aviris_CharAsh/ImageServer/WMSServer

Current and recent floods alerts from the Global Disaster Alert and Coordination System (GDACS), a cooperation of the United Nations and the European Commission, with alert level, severity and affected population.

Date of Images: 2/4/2023, 2/5/2023, 2/9/2023, 2/10/2023, 2/16/2023, 2/17/2023 Date of Next Image: None Expected Summary: The Alaska Satellite Facility has developed water extent images using the Sentinel-1A/B Synthetic Aperture Radar (SAR) instrument. These images can be used to see where water is located at the time of the satellite overpass. This product shows all water detected and does not differentiate between normal water and flood water. To determine where flooding may have occurred, combine this layer with a reference water layer. Additional SAR Information Suggested Use: This product shows all water detected and does not differentiate between normal water and flood water. To determine where flooding may have occurred, combine this layer with a reference water layer. Satellite/Sensor: Synthetic Aperture Radar on European Space Agency's (ESA) Copernicus Sentinel-1A satellite Resolution: 30 meters Credits: Sentinel data used in this derived product, contains modified Copernicus Sentinel data (2023), processed by ESA, Alaska Satellite Facility, NASA MSFC Service URL: https://gis.earthdata.nasa.gov/portal/home/item.html?id=c9225c80d734497a8a8509383feb65b8 WMS Endpoint: https://gis.earthdata.nasa.gov/portal/services/DISASTERS_202302_EARTHQUAKE_TURKIYE/2302_sentinel1_water/ImageServer/WMSServer

This data and mapping details the Bring Bank Locations within Co. Monaghan. If the banks are full, please contact the Environment Section of Monaghan County Council on 042 9661240 or email envservices@monaghancoco.ie and we will arrange for the banks to be emptied as soon as is possible. Monaghan County Council provides this information with the understanding that it is not guaranteed to be accurate, correct or complete. Monaghan County Council accepts no liability for any loss or damage suffered by those using this data for any purpose. The Projection of this dataset is WGS84.

Location of wastewater treatment plants in East Libya

**ADAM ID: FL-20231121-SOM-00** Flood covering 922555.0 sq m on Nov 21 2023 in Somalia. It impacted 450428 people.

**ADAM ID: FL-20231109-ETH-00** Flood covering 205753.0 sq m on Nov 09 2023 in Ethiopia. It impacted 95847 people.

Community Townwatching (Tamsui District) for pre-disaster preparation at December 2nd, 2023

Community twon-watching walkthrough (Zhonghe District) for pre-disaster preparation, April 7th, 2023.

This data relates to an aerial survey conducted of Mozambique from the 24-29 March 2019 following tropical cyclone Idai. Data was collected by INGC (National Disaster Management Authority of Mozambique), IFRC, UNDAC, MSF, DFID, Save the Children, and MapAction. Enumerators flew in squirrel helicopters over priority areas (usually at 500 feet) and some fixed wing aircraft for confirmation on outlying areas. Data contains location, points of interest, severity and estimated population. The survey used Kobo for data collection. Processing was conducted in R and then imported into ArcGIS for final map products. Data is presented in .xlsx (with and without hxl tags) and .geojson formats.

**ADAM ID: FL-20231121-COG-02** Flood covering 346099.0 sq m on Nov 21 2023 in Congo. It impacted 43821 people.

Tracks of Tropical Cyclones that entered the Philippine Area of Responsibility in 2024. Observed positions were provided by DOST-PAGASA. (https://www.pagasa.dost.gov.ph/)

**ADAM ID: FL-20231108-SOM-00** Flood covering 393749.0 sq m on Nov 08 2023 in Somalia. It impacted 248941 people.

**ADAM ID: FL-20231114-SOM-00** Flood covering 576683.0 sq m on Nov 14 2023 in Somalia. It impacted 320755 people.

Índice de peligro por inundaciones a nivel municipal, elaborado por la Subdirección de Riesgos Hidrometeorológicos del CENAPRED.

For the floods in Southern Malawi of March 2019, we have combined flood extent maps (Sentinel) with HRSL settlement/population grid. This results in a calculation of # of affected buildings/people per district. The results is shared through maps and in a shapefile. ### 1. Data sources ## Sentinel 1 Imagery from 7th of March 2017 ## Sentinel 2 Imagery from 10th/12th/14th of March 2017 ## HRSL population data Facebook Connectivity Lab and Center for International Earth Science Information Network - CIESIN - Columbia University. 2016. High Resolution Settlement Layer (HRSL). Source imagery for HRSL © 2016 DigitalGlobe. Accessed 9 March 2019. ### 2. Good to know The flood extent for Nsanje district was separately added on March 14th, to the existing flood extent for the main area from March 12th. ### 3. Methodology ## A. Flood Extent Based on SAR The following steps were used to detect flood extent(water/no water). In SNAP tool the raw data downloaded from sci-hub Copernicus was processed to calibrate image for atmospheric correction, spike filter and terrain correction(This is mainly for Sentinel 1 data). Finally defining water no water based on a threshold applied on the corrected image. Defining a threshold is always a challenge in SAR image analysis for flood detection, we collected data from the field to define this threshold. For Sentinel 2 as a first step cloud filter was calculated by applying a combined threshold on Band 2 and Band 10. The cloud mask shown in the figure below didn’t capture shadows of clouds, these were miss interpreted by the flood algorithm as water/flood. To correct this areas with more cloud cover were clipped out with a polygon. To define water no water based on sentinel data we used NDWI index, the treshold is adjusted based on data collected from the field Validation points were collected by Field team tested different values and check if the threshold identified fits with observation. The complete methodology how to detect flooding based on Sentinel 1 data and SNAP toolbox is documented in ESA website. ## B. Affected People To calculate number of affected people per each admin level, flood extent map is combined with HRSL population data. This is done in two steps: First, in step 1, we calculate a raster, which multiplies the population grid with the flood grid, such that we are left with only "population in flooded area". This is done using raster calculator where population density raster was multiplied by flood extent raster, which has a value of 0 for no flood and 1 for flood areas. Note that the flood extent grid was first resampled to match it to the population grid. This whole exercise is repeated for settlement/buildings instead of population. Step 2: We apply zonal statistics per TA to calculate total number of buildings/people affected in each admin level. For each Admin level2 estimated number of affected people and affected houses are plotted in the map. The zonal statistics data used for plotting can be found in the shape file.