Americas environment data

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 location of the pixels. 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.

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 Name: pixden Description: Number of total pixels (n_pixels) divided by area of fire perimeter. Unit is pixels/Km^2 Column Name: duration Description: Number of days since first observation of fire. Fires with a single observation have a duration of zero. Unit is Days. Column Name: flinelen Description: Length of active fire segment, based on new pixels. If no new pixels are detected, flinelen is set to zero. Unit is Km. Column: fperim Description: Length of fire perimeter. Unit is Km. Column: farea Description: Area within fire perimeter. Unit is Km^2 Column Name: meanfrp Description: Mean fire radiative power for detected active fire pixels. The sum of the fire radiative power divided by the number of newly detected pixels at each time step. If no new pixels are detected, meanfrp is set to zero for that timestep. Units are MW/pixel. Column: n_newpixels Description: Number of pixels newly detected since last overpass. Unit is pixels. Column: n_pixels Description: Number of total pixel-detections in history of fire. Unit is pixels. Column: isactive Description: Boolean indicator of recent fire activity. If no new detections occur within N days, this is marked as “False” The number of days is defined regionally, and has a default of 5. See repository information. Unit is Boolean. Column: geometry Description: The shape of the perimeter. Unit is Geometry. Can be a Polygon or Multipolygon. 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.

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

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.

# Grenada St Vincent and the Grenadines Hurricane Beryl: Damage Proxy Maps, Jul 2024 The **Earth Observatory of Singapore – Remote Sensing Lab (EOS-RS)** produced these damage proxy maps for Grenada St Vincent and the Grenadines Hurricane Beryl (Jul 2024) from synthetic aperture radar (SAR) satellite images. Each map is a separate ZIP below with its GeoTIFF, a KMZ for Google Earth, the PNG figures and a text file describing how it was made and how to read it. ## Maps in this dataset - **EOS-RS Damage Proxy Map: Grenada & St. Vincent and The Grenadines, Hurricane Beryl, 7 July 2024, v0.9 wbd** (superseded) - **EOS-RS Damage Proxy Map: Grenada & St. Vincent and The Grenadines, Hurricane Beryl, 7 July 2024, v0.9 wbd** (superseded) - **EOS-RS Damage Proxy Map: Grenada & St. Vincent and The Grenadines, Hurricane Beryl, 7 July 2024, v0.9 ** - **EOS-RS Damage Proxy Map: Grenada & St. Vincent and The Grenadines, Hurricane Beryl, 7 July 2024, v0.9 ** **More map details and files:** **More map details and files:** **Legal Disclaimer:** **Credits:** Earth Observatory of Singapore - Remote Sensing Lab (EOS-RS), Contains modified Copernicus Sentinel data (2024)

# EOS-RS Flood Proxy Map: Dominican Republic, Floods, 26 Aug 2023, v0.4 The Earth Observatory of Singapore - Remote Sensing Lab (EOS-RS) created this preliminary Flood Proxy Map (FPM) depicting areas that are likely flooded in the Dominican Republic and Haiti due to the tropical storm Franklin. This map was derived from synthetic aperture radar (SAR) images acquired by the Copernicus Sentinel-1 satellite operated by the European Space Agency (ESA) before (14 Aug 2023) and during (26 Aug 2023) the event. Â The map covers an area indicated by the large white polygon. Each light blue pixel measures about 30 meters across. Preliminary validation was done in selected areas using optical imagery and news reports. This flood proxy map should be used as a guide to identify areas that are likely flooded, and is less reliable over urban and vegetated areas. Â The product contains modified Copernicus Sentinel data (2023), processed by ESA and analyzed by the Earth Observatory of Singapore - Remote Sensing Lab (EOS-RS), using the Advanced Rapid Imaging and Analysis (ARIA) system originally developed at NASA's Jet Propulsion Laboratory, California Institute of Technology, and modified at EOS-RS. Â **More map details and files:** Â **Credits:** Earth Observatory of Singapore - Remote Sensing Lab (EOS-RS), Advanced Rapid Imaging and Analysis (ARIA), NASA-JPL/Caltech, Contains modified Copernicus Sentinel data (2023) **More map details and files:**

Overview This layer maps the ecological scorecard sites for marine protected areas (MPAs) across North America as part of the Commission for Environmental Cooperation (CEC) North American Environmental Atlas. To assess the environmental conditions of MPAs, experts developed an Ecological Assessment Scorecard methodology that distills large amounts of complex technical and traditional/local ecological knowledge about MPA conditions. These scorecards are an easy yet powerful tool for MPA managers to share evidence-based ecological information about water, habitat, and living resources. The scorecard for each MPA is based on standard questions about three key themes (water, habitat, and living resources) to describe environmental health. Conditions for each ecosystem element addressed by the questions are defined on a five-point scale, ranging from superior (best it could be) to critical (worst it could be). Trends in conditions are likewise defined in five categories, ranging from rapidly improving to rapidly diminishing (likely to reach a different state in five years), with stable indicating conditions unlikely to change beyond normal variation. Geographic Coverage This layer covers the ecological scorecard sites for marine protected areas in the Bering Sea to Baja California (B2B) region, spanning Canada, the United States, and Mexico. Data Sources and Licensing Commission for Environmental Cooperation (CEC) and NOAA Marine Protected Areas Center. 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: Irregular Limitations of use: None. Acknowledgement of the Commission for Environmental Cooperation and NOAA Marine Protected Areas Center would be appreciated in products derived from these data. Vintage: 2008 Applications (Intended Use) Sharing evidence-based ecological information about water, habitat, and living resources conditions within marine protected areas. Supporting MPA managers in tracking ecological conditions and trends over time. Research, education, monitoring, and reporting on the ecological health of North American marine protected areas. Reference framework for comparing environmental conditions across MPAs in the Bering Sea to Baja California (B2B) region. Integration with other thematic layers in the North American Environmental Atlas for comprehensive continental analysis. Supporting cross-border environmental cooperation and policy-making among North American nations. Files Download

Note: This layer is produced using a different data source and modeling methodology than the climate‑change riverine flooding layer (CDRI-GIRI), which means there are areas identified as hazardous by one model but not the other; using both layers together is good practice to capture all areas that may be potentially affected by riverine flooding across the two modeling frameworks. This layer represents an aggregated riverine flood hazard classification derived from seven original flood‑inundation depth rasters corresponding to return periods (RP) of 10, 20, 50, 75, 100, 200, and 500 years. Each RP layer was classified as either Moderate or High hazard using the matrix shown below, following a hazard‑ranking approach derived from the Nicaragua MET‑ALARN thresholds. This classification was applied independently to the seven RP layers. A combination rule was then applied, assigning to each pixel the highest hazard class observed across all seven RPs. Intensity / Frequency RP ≤ 10 10 H ≥ 1.0 m High High 0.5 m ≤ H High Moderate 0.1 m ≤ H Moderate Moderate The original rasters come from the Global River Flood Hazard (GLOFAS) Maps dataset, a gridded global product representing simulated inundation depths along the global river network. The underlying flow simulations are produced with the open‑source LISFLOOD hydrological model, while inundation extents are simulated using the LISFLOOD‑FP hydrodynamic model. Each cell in the source rasters represents water depth in meters, and the dataset was developed under the Copernicus Emergency Management Service (CEMS) to support disaster‑risk management and infrastructure‑resilience planning. The map has a resolution of 3 arc-sec. (~93 meters at the equator; east–west distance decreases with latitude). The layers were clipped to the geographic boundaries of the IDB borrowing countries. Links to the original layers: 1‑in‑10 yr riverine flooding hazard 1‑in‑20 yr riverine flooding hazard 1‑in‑50 yr riverine flooding hazard 1‑in‑75 yr riverine flooding hazard 1‑in‑100 yr riverine flooding hazard 1‑in‑200 yr riverine flooding hazard 1‑in‑500 yr riverine flooding hazard VECTORIZED VERSION AVAILABLE HERE: Riverine Flooding Hazard. Disclaimer on Coastal Coverage Limitations The spatial dataset does not provide complete coverage in coastal areas. The data were clipped using the World Countries Generalized polygon, which does not fully capture detailed coastal geometries. As a result, certain coastal zones fall outside the clipping boundary and are therefore not represented in the dataset. This limitation should be taken into account when interpreting results for coastal or near‑shore areas. This will be corrected soon.

Population exposure estimates based on tropical cyclone IRMA-17 path observed and predicted at 7 September 2017.

Includes Assessment, two shapefiles: basemaps

The Soil and Terrain database for Latin America and the Caribbean (SOTERLAC), version 2.0, at scale 1:5 million, replaces version 1.02. The update includes changes in the GIS file and in the attributes database. The topographic base of the SOTERLAC map was adapted to a version congruent to the Digital Chart of the World. The SOTERLAC attribute database has changed in respect to the number of ... pedon attributes that can be stored. Contrary to the preceding, compact version, version 2.0 can accommodate all pedon attributes considered in a 1:1 million scale SOTER database. SOTERLAC forms a part of the ongoing activities of ISRIC, FAO and UNEP to update the world's baseline information on natural resources.The project involved collaboration with national soil institutes from the countries in the region as well as individual experts

This harmonized set of soil parameter estimates for Latin America and the Caribbean was derived from a revised version of the 1:5M Soil and Terrain Database for the region (SOTERLAC, ver. 2.0) and the ISRIC-WISE soil profile database. The land surface of Latin America and the Caribbean has been characterized using 1585 unique SOTER units, corresponding with 5855 polygons. The major soils have been described using 1660 profiles, selected by national soil experts as being representative for these units. The associated soil analytical data have been derived from soil survey reports. These sources seldom hold all the physical and chemical attributes ideally required by SOTER. Gaps in the measured soil profile data have been filled using a step-wise procedure that uses taxotransfer rules, based on about 9600 soil profiles held in the WISE database, complemented with expert-rules. Parameter estimates are presented by soil unit for fixed depth intervals of 0.2 m to 1 m depth for: organic carbon, total nitrogen, pH(H2O), CECsoil, CECclay, base saturation, effective CEC, aluminium saturation, CaCO3 content, gypsum content, exchangeable sodium percentage (ESP), electrical conductivity of saturated paste (ECe), bulk density, content of sand, silt and clay, content of coarse fragments (less than 2 mm), and available water capacity (-33 to -1500 kPa). These attributes have been identified as being useful for agro-ecological zoning, land evaluation, crop growth simulation, modelling of soil carbon stocks and change, and analyses of global environmental change. The current parameter estimates should be seen as best estimates based on the current selection of soil profiles and data clustering procedure. Taxotransfer rules have been flagged to provide an indication of the possible confidence in the derived data. Results are presented as summary files and can be linked to the 1:5M scale SOTERLAC map in a GIS, through the unique SOTER-unit code. The secondary data set is considered appropriate for studies at the continental scale (greater than 1:5M). Correlation of soil analytical data should be done more rigorously when more detailed scientific work is considered.