Americas GIS data

# 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:**

# EOS-RS Damage Proxy Map: Ecuador, Earthquake, 19-26 Mar 2023, v0.8 The Earth Observatory of Singapore - Remote Sensing Lab (EOS-RS) created this preliminary Damage Proxy Map (DPM) depicting areas that are likely damaged in southern Ecuador and northern Peru due to the M6.8 earthquake that occurred on 18 Mar 2023. 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 (7 Nov 2022 to 14 Mar 2023) and after (19-26 Mar 2023) the event. The image covers an area indicated by the large white polygon. Each pixel measures about 30 meters across. The colour variation from yellow to red indicates increasingly more significant surface change. Preliminary validation was done by comparing with media reports. This map could be used as a guidance to identify damaged areas, and may be less reliable over vegetated areas. Scattered pixels over vegetated areas may be false positives, and a lack of colored pixels over vegetated areas may not mean no damage. Some pixels may be due to changes in vegetation cover. The product contains modified Copernicus Sentinel data (2022-2023), processed by ESA and analyzed by the Earth Observatory of Singapore - Remote Sensing Lab (EOS-RS). **More map details and files:** **More map details and files:** **Credits:** Earth Observatory of Singapore - Remote Sensing Lab (EOS-RS), Contains modified Copernicus Sentinel data (2022-2023).

The Hand-in-Hand Initiative (HiH) agricultural typologies Regional Initiative for the Amazon, was prepared for the Amazon Cooperation Treaty Organization (ACTO), which includes 8 countries: Bolivia, Brazil, Colombia, Ecuador, Peru, Guyana, Suriname, and Venezuela. The HiH Agricultural Typology analysis targets the identification of micro-regional level innovation opportunities, bottlenecks, and investment gaps. This concept is based on the production possibilities frontier applied to farm activities. It draws on household-level surveys and geospatial data on agroecological conditions, accessibility, and poverty. The final Agricultural Typology consists of a classification representing a combination of agricultural potential, agricultural efficiency, and priority (poverty) components in each region. The 7 classes are as follows: • Critical with moderate agricultural opportunities. • Medium priority with moderate agricultural opportunities. • Low priority. • High priority. • Medium priority with high agricultural opportunities. • Low priority with high agricultural opportunities. • High-performance. **Data Components** The Agricultural Potential component provides the maximum agricultural income smallholder farmers can attain if performing at maximum capacity (their own, as well as of the markets, productive infrastructure, and basic services surrounding them). It is determined by both the biophysical and economic factors. The interaction of these two sets of elements establishes the maximum income a farmer can earn from agricultural activities. The Agricultural Efficiency component defines how much of the potential is attained by farmers in a region under current conditions. To increase their efficiency, farmers need to reduce transaction costs in agricultural production and marketing through improved infrastructure (such as roads) and services (such as market information), overcome market failures (access to credit, insurance, land markets, etc.), and receive better access to basic services (such as education and extension services). The Priority component reveals a region’s degree of urgency for investments in development, measured in terms of the wellbeing of the local population, the target beneficiaries of agricultural innovation efforts. **Supplemental Information** The Agricultural Typology analysis is a dataset developed in the framework of the HiH, which is an evidence-based, country-led, and country-owned initiative of the Food and Agriculture Organization of the United Nations (FAO) aiming to accelerate the agricultural transformation and sustainable rural development to eradicate poverty, and end hunger and all forms of malnutrition.

The Hand-in-Hand Initiative (HiH) agricultural typologies Regional Initiative for the Amazon, was prepared for the Amazon Cooperation Treaty Organization (ACTO), which includes 8 countries: Bolivia, Brazil, Colombia, Ecuador, Peru, Guyana, Suriname, and Venezuela. The HiH Agricultural Typology analysis targets the identification of micro-regional level innovation opportunities, bottlenecks, and investment gaps. This concept is based on the production possibilities frontier applied to farm activities. It draws on household-level surveys and geospatial data on agroecological conditions, accessibility, and poverty. The final Agricultural Typology consists of a classification representing a combination of agricultural potential, agricultural efficiency, and priority (poverty) components in each region. The 7 classes are as follows: • Critical with moderate agricultural opportunities. • Medium priority with moderate agricultural opportunities. • Low priority. • High priority. • Medium priority with high agricultural opportunities. • Low priority with high agricultural opportunities. • High-performance. **Data Components** The Agricultural Potential component provides the maximum agricultural income smallholder farmers can attain if performing at maximum capacity (their own, as well as of the markets, productive infrastructure, and basic services surrounding them). It is determined by both the biophysical and economic factors. The interaction of these two sets of elements establishes the maximum income a farmer can earn from agricultural activities. The Agricultural Efficiency component defines how much of the potential is attained by farmers in a region under current conditions. To increase their efficiency, farmers need to reduce transaction costs in agricultural production and marketing through improved infrastructure (such as roads) and services (such as market information), overcome market failures (access to credit, insurance, land markets, etc.), and receive better access to basic services (such as education and extension services). The Priority component reveals a region’s degree of urgency for investments in development, measured in terms of the wellbeing of the local population, the target beneficiaries of agricultural innovation efforts. **Supplemental Information** The Agricultural Typology analysis is a dataset developed in the framework of the HiH, which is an evidence-based, country-led, and country-owned initiative of the Food and Agriculture Organization of the United Nations (FAO) aiming to accelerate the agricultural transformation and sustainable rural development to eradicate poverty, and end hunger and all forms of malnutrition.

The Hand in Hand Initiative (HiH) - Building Resilience in the Dry Corridor and Arid Zones of the Central American Economic Integration System (SICA) regional initiative, is being developed since 2022, involves eight countries: Belize, Costa Rica, El Salvador, Honduras, Guatemala, Nicaragua, Panama and the Dominican Republic. The HiH Agricultural Typology analysis targets the identification of micro-regional level innovation opportunities, bottlenecks, and investment gaps. This concept is based on the production possibilities frontier applied to farm activities. It draws on household-level surveys and geospatial data on agroecological conditions, accessibility, and poverty. The final Agricultural Typology consists of a classification representing a combination of agricultural potential, agricultural efficiency, and priority (poverty) components in each region. The 7 classes are as follows: • Critical with moderate agricultural opportunities. • Medium priority with moderate agricultural opportunities. • Low priority. • High priority. • Medium priority with high agricultural opportunities. • Low priority with high agricultural opportunities. • High-performance. **Data Components** The Agricultural Potential component provides the maximum agricultural income smallholder farmers can attain if performing at maximum capacity (their own, as well as of the markets, productive infrastructure, and basic services surrounding them). It is determined by both the biophysical and economic factors. The interaction of these two sets of elements establishes the maximum income a farmer can earn from agricultural activities. The Agricultural Efficiency component defines how much of the potential is attained by farmers in a region under current conditions. To increase their efficiency, farmers need to reduce transaction costs in agricultural production and marketing through improved infrastructure (such as roads) and services (such as market information), overcome market failures (access to credit, insurance, land markets, etc.), and receive better access to basic services (such as education and extension services). The Priority component reveals a region’s degree of urgency for investments in development, measured in terms of the wellbeing of the local population, the target beneficiaries of agricultural innovation efforts. **Supplemental Information** The Agricultural Typology analysis is a dataset developed in the framework of the HiH, which is an evidence-based, country-led, and country-owned initiative of the Food and Agriculture Organization of the United Nations (FAO) aiming to accelerate the agricultural transformation and sustainable rural development to eradicate poverty, and end hunger and all forms of malnutrition.

South America Seasonal Full 1995-2021 IR Anvil Day Count

The landslide susceptibility map for current climate conditions highlights regions vulnerable to landslides caused by earthquakes or precipitation. This map was derived by combining the two susceptibility layers described below: for each cell, the highest value between the two was selected. Classes 2 and 3 are categorized as "Moderate", and classes 4 and 5 as "High". Class 1 denotes no susceptibility and is not shown. 1) The earthquake-induced landslide susceptibility map indicates the areas of the world that are more prone to landslides triggered by earthquakes. The landslide susceptibility is based on the model developed by NGI (Nadim et al., 2006, 2013; Jaedicke et al., 2013) but with improvements and refinements. The earthquake-induced landslides susceptibility map classifies the terrain into five susceptibility classes by combining slope, vegetation, lithology, and soil moisture information from global datasets. The weights of different susceptibility factors were calibrated to the information available in landslide inventories and physical processes. 2) The precipitation-induced landslide susceptibility map for the existing climate indicates the areas of the world that are more prone to landslides in the current climate conditions. The landslide susceptibility is based on the model developed by NGI (Nadim et al., 2006, 2013; Jaedicke et al., 2013) but with improvements and refinements. The precipitation-induced landslides susceptibility map for the current climate classifies the terrain into five susceptibility classes by combining slope, vegetation, lithology, and antecedent rainfall information from global datasets. Antecedent rainfall information has been obtained from the W5E5 dataset for the period 1979-2016. The weights of different susceptibility factors were calibrated to the information available in landslide inventories and physical processes. Note: This layer represents landslide susceptibility and not landslide hazard. It does not incorporate triggering factors (e.g., earthquake ground motion or rainfall intensity/return periods). Hazard can only be derived by combining susceptibility with a defined trigger scenario. The map has a resolution of 3 arc-sec. (~93 meters at the equator; east–west distance decreases with latitude). Background paper: A new model for global landslide susceptibility assessment and scenario-based hazard assessment (2003) Rosa M. Palau, Farrokh Nadim, Eivind Paulsen, Erlend Storrøsten https://giri.unepgrid.ch/sites/default/files/2023-06/20230615-NGI_manuscript_GIRI_landlside_hazard_model.pdf Release date: Sun Apr 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. VECTORIZED VERSION AVAILABLE HERE: Landslide Susceptibility

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.

Overview This Feature Service presents comprehensive bathymetric data for ocean areas within North America, resulting from a collaborative effort between the national atlas programs of Canada, Mexico, and the United States, in partnership with the North American Commission for Environmental Cooperation (CEC). The service provides standardized geospatial data at a 1:10,000,000 scale, forming part of the North American Environmental Atlas. Feature Layers NA Bathymetry Lines: This layer depicts isobaths, or lines of equal ocean depth, providing detailed measurements at key depths including sea level (coastline), 200, 500, and 2500 meters. It helps visualize depth contours relevant for oceanographic studies and marine navigation. NA Bathymetry Polygons: This layer complements the lines by delineating polygonal areas that represent depth ranges such as 0-200, 200-500, 500-2500, and depths greater than 2500 meters. These polygons assist in understanding the distribution of ocean depths in the region. Geographic Coverage The data covers oceanic and coastal regions of Canada, Mexico, and the United States, with supplementary contextual data for adjacent areas beyond these countries to complete the geographic context. Data Sources and Licensing The data is a product of joint efforts from federal agencies including Natural Resources Canada, Instituto Nacional de Estadística y Geografía in Mexico, and the U.S. Geological Survey. It is licensed under Creative Commons Attribution 4.0 International License (CC BY 4.0), allowing redistribution and adaptation with appropriate attribution. Update frequency: Irregular Limitations of use: Intended for continental-scale reference. Vintage: The information presented in the map layer represents the available input data in 2010 Applications (Intended Use) Environmental planning and management in coastal and marine ecosystems. Marine navigation and oceanographic research. 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

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

Hurricane Maria peak 3-sec gust footprints in kml format (through 9UT 21st Sept 2017) Credit - Tropical Storm Risk/UCL

Tropical Storm Risk - University College London - gust footprint for Matthew in kml format. If you zoom in (using Google Earth) the footprint shows the towns and villages in western Haiti which UCL model predicts will be worst affected by wind damage (with peak gusts of 160 mph (260 kmph)).

This Archive contains shapefiles for FEWS NET Food Security Outlook for **Central America and The Caribbean**. It was last updated on January 14, 2013. The classification used is IPC V2.0 Compatible, aimed to address acute food insecurity. The two shapefiles represent the two analytic periods: * xx201304_ML1 Most likely food security outcome for January-March 2013 * xx201304_ML2 Most likely food security outcome for April-June 2013 Where xx is one of the region codes listed above. Within the shapefiles, the food security outlook is contained in a field named as ML1 or ML2 according to the outlook period. The code itself is the IPC phase. Two additional codes are used: * 66 = water * 88 = parks, forests, reserves * 99 = missing data (usually urban centers)

This Archive contains shapefiles for FEWS NET Food Security Outlook for **Central America and The Caribbean**. It was last updated on October 08, 2015. The classification used is IPC V2.0 Compatible, aimed to address acute food insecurity. The two shapefiles represent the two analytic periods: * LAC201304_ML1 Most likely food security outcome for July-September 2015 * LAC201304_ML2 Most likely food security outcome for October-December 2015 Within the shapefiles, the food security outlook is contained in a field named as ML1 or ML2 according to the outlook period. The code itself is the IPC phase. Two additional codes are used: * 66 = water * 88 = parks, forests, reserves * 99 = missing data (usually urban centers)

This Archive contains shapefiles for FEWS NET Food Security Outlook for **Central America and The Caribbean**. It was last updated on January 19, 2014. The classification used is IPC V2.0 Compatible, aimed to address acute food insecurity. The two shapefiles represent the two analytic periods: * xx201401_ML1 Most likely food security outcome for January-March 2014 * xx201401_ML2 Most likely food security outcome for April-June 2014 Within the shapefiles, the food security outlook is contained in a field named as ML1 or ML2 according to the outlook period. The code itself is the IPC phase. Two additional codes are used: * 66 = water * 88 = parks, forests, reserves * 99 = missing data (usually urban centers)

This Archive contains shapefiles for FEWS NET Food Security Outlook for **Central America and The Caribbean**. It was last updated on May 15, 2014. The classification used is IPC V2.0 Compatible, aimed to address acute food insecurity. The two shapefiles represent the two analytic periods: * LAC201304_ML1 Most likely food security outcome for April-June 2013 * LAC201304_ML2 Most likely food security outcome for July-September 2013 Within the shapefiles, the food security outlook is contained in a field named as ML1 or ML2 according to the outlook period. The code itself is the IPC phase. Two additional codes are used: * 66 = water * 88 = parks, forests, reserves * 99 = missing data (usually urban centers)

This Archive contains shapefiles for FEWS NET Food Security Outlook for **Central America and The Caribbean**. It was last updated on November 13, 2014. The classification used is IPC V2.0 Compatible, aimed to address acute food insecurity. The two shapefiles represent the two analytic periods: * LAC201304_ML1 Most likely food security outcome for October-December 2014 * LAC201304_ML2 Most likely food security outcome for January-March 2015 Within the shapefiles, the food security outlook is contained in a field named as ML1 or ML2 according to the outlook period. The code itself is the IPC phase. Two additional codes are used: * 66 = water * 88 = parks, forests, reserves * 99 = missing data (usually urban centers)

This Archive contains shapefiles for FEWS NET Food Security Outlook for **East Africa**. It was last updated on July 14, 2013. The classification used is IPC V2.0 Compatible, aimed to address acute food insecurity. The two shapefiles represent the two analytic periods: * EA201304_ML1 Most likely food security outcome for October-December 2014 * EA201304_ML2 Most likely food security outcome for January-March 2015 Within the shapefiles, the food security outlook is contained in a field named as ML1 or ML2 according to the outlook period. The code itself is the IPC phase. Two additional codes are used: * 66 = water * 88 = parks, forests, reserves * 99 = missing data (usually urban centers)

This Archive contains shapefiles for FEWS NET Food Security Outlook for **Central America and The Caribbean**. It was last updated on April 2014. The classification used is IPC V2.0 Compatible, aimed to address acute food insecurity. The two shapefiles represent the two analytic periods: * xx201404_ML1 Most likely food security outcome for April-June 2014 * xx201404_ML2 Most likely food security outcome for July-September 2014 Within the shapefiles, the food security outlook is contained in a field named as ML1 or ML2 according to the outlook period. The code itself is the IPC phase. Two additional codes are used: * 66 = water * 88 = parks, forests, reserves * 99 = missing data (usually urban centers)

This Archive contains shapefiles for FEWS NET Food Security Outlook for **Central America and The Caribbean**. It was last updated on March 31, 2015. The classification used is IPC V2.0 Compatible, aimed to address acute food insecurity. The two shapefiles represent the two analytic periods: * LAC201304_ML1 Most likely food security outcome for January-March 2015 * LAC201304_ML2 Most likely food security outcome for April-June 2015 Within the shapefiles, the food security outlook is contained in a field named as ML1 or ML2 according to the outlook period. The code itself is the IPC phase. Two additional codes are used: * 66 = water * 88 = parks, forests, reserves * 99 = missing data (usually urban centers)

This Archive contains shapefiles for FEWS NET Food Security Outlook for **Central America and The Caribbean**. It was last updated on November 14, 2013. The classification used is IPC V2.0 Compatible, aimed to address acute food insecurity. The two shapefiles represent the two analytic periods: * xx201304_ML1 Most likely food security outcome for October-December 2013 * xx201304_ML2 Most likely food security outcome for January-March 2014 Within the shapefiles, the food security outlook is contained in a field named as ML1 or ML2 according to the outlook period. The code itself is the IPC phase. Two additional codes are used: * 66 = water * 88 = parks, forests, reserves * 99 = missing data (usually urban centers)

This Archive contains shapefiles for FEWS NET Food Security Outlook for **Central America and The Caribbean**. It was last updated on July 01, 2015. The classification used is IPC V2.0 Compatible, aimed to address acute food insecurity. The two shapefiles represent the two analytic periods: * LAC201304_ML1 Most likely food security outcome for April-June 2015 * LAC201304_ML2 Most likely food security outcome for July-September 2015 Within the shapefiles, the food security outlook is contained in a field named as ML1 or ML2 according to the outlook period. The code itself is the IPC phase. Two additional codes are used: * 66 = water * 88 = parks, forests, reserves * 99 = missing data (usually urban centers)

This Archive contains shapefiles for FEWS NET Food Security Outlook for **Central America and The Caribbean**. It was last updated on September 30, 2014. The classification used is IPC V2.0 Compatible, aimed to address acute food insecurity. The two shapefiles represent the two analytic periods: * LAC201304_ML1 Most likely food security outcome for July-September 2014 * LAC201304_ML2 Most likely food security outcome for October-December 2014 Where xx is one of the region codes listed above. Within the shapefiles, the food security outlook is contained in a field named as ML1 or ML2 according to the outlook period. The code itself is the IPC phase. Two additional codes are used: * 66 = water * 88 = parks, forests, reserves * 99 = missing data (usually urban centers)

**UNOSAT code: TC20240702JAM, GDACS ID: 1001067** This map illustrates satellite-detected water extent in Kingston, Saint Catherine and Saint Andrew Parishes as observed from a TerraSAR-X image acquired at 06:03 local time. Within the analysed area of about 1,450 km², a limited extent of 5 km² of lands appear to be affected with flood waters. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to the United Nations Satellite Centre (UNOSAT). Important note: Flood analysis from radar images may underestimate the presence of standing waters in built-up areas and densely vegetated areas due to backscattering properties of the radar signal.

**UNOSAT code: TC20240702JAM, GDACS ID: 1001067** This map illustrates satellite-detected water extent in Saint Elizabeth Parish as observed from a Sentinel-2 image acquired at 10:48 local time. Within the analysed area of about 1,200 km², a limited extent of 13 km² of lands appear to be affected with flood waters. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to the United Nations Satellite Centre (UNOSAT).

This is a point dataset of road blockages in Jamaica and Haiti following Hurricane Melissa. The road blockages are marked by a single point, alongside the type of damage e.g. debris, flooding or coastal damage. The data is held in a shapefile format using the WGS 84 EPSG: 4326 projection. The Hurricane Melissa 2025 project data was collected via crowdsourcing on the Zooniverse platform.

Goddard’s LiDAR, Hyperspectral, and Thermal Imagery (G-LiHT) mission is a portable, airborne imaging system that aims to simultaneously map the composition, structure, and function of terrestrial ecosystems. G-LiHT primarily focuses on a broad diversity of forest communities and ecoregions in North America, mapping aerial swaths over Conterminous United States (CONUS), Alaska, Puerto Rico, and Mexico. The purpose of G-LiHT’s Trajectory data product (GLTRAJECTORY) is to provide aircraft location and orientation to support and supplement other G-LiHT data products. GLTRAJECTORY data are processed as a Google Earth overlay Keyhole Markup Language (KML) file over the extent of an entire flight path. A low resolution browse is also provided to show the flight path.

Goddard’s LiDAR, Hyperspectral, and Thermal Imagery (G-LiHT) mission is a portable, airborne imaging system that aims to simultaneously map the composition, structure, and function of terrestrial ecosystems. G-LiHT primarily focuses on a broad diversity of forest communities and ecoregions in North America, mapping aerial swaths over the Conterminous United States (CONUS), Alaska, Puerto Rico, and Mexico. The purpose of G-LiHT’s Digital Surface Model data product (GLDSMT) is to provide LiDAR-derived visualizations of elevation above bare earth. GLDSMT data is offered in multiple formats, including Digital Surface Model, Mean, Aspect, Rugosity, and Slope. GLDSMT data are processed as multiple raster data products (GeoTIFFs) at a nominal 1 meter spatial resolution over locally defined areas. A low resolution browse is also provided showing the digital surface model with a color map applied in PNG format.

Includes Assessment, two shapefiles: basemaps

Date of Images: Post-Event: May 3, 2022-May 12, 2022 (Daily) Pre-Event: April 2022 (Composite) 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 September 2025, 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: 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: See REST Endpoint on the right. WMS Endpoint: https://gis.earthdata.nasa.gov/portal/services/DISASTERS_202510_HURRICANE_MELISSA/BlackMarble_BRDF/ImageServer/WMSServer

Dates of Images: 5/3/2022, 5/10/2022 Summary: MODIS Global Flood Product, MCDWD, showing flooding observed on 5/3 and 5/10/2022 along the Red river in North Dakota and Manitoba. Product is based on optical imagery from the MODIS sensor, on both Terra and Aqua spacecraft. Observation times are approximately 10:30 am and 1:30 pm local time; flood extents at other times may not be observed. The 2-day composite product is delivered here; this uses observations from the given date and one day previous (eg 5/2 and 5/3 for the 5/3 product). For this event, 5/3 product was the first available after the clouds cleared, and the 2-day product shows reasonable flood coverage. The 1-day product shows more coverage, but also more false-positives; it may be of interest to some users. One week later, much of the flooding persisted (5/10). Caveats: Flooding is determined by comparing observed water extent to a reference water map, which may be inaccurate in fine details; some "flood" may be expected surface water, and some "surface water" may actually be flood, but both indicate areas where water was detected. The 2-day product has many fewer cloud-shadow false-positives than 1-day products, but these may still occur. Users are advised to review the source optical imagery (Terra and Aqua) in Worldview to confirm (https://go.nasa.gov/globalflood). Worldview also hosts the 2- and 3-day composite products, and allows rapid examination of all daily products. At 250 m resolution, this product will not reliably capture flood extents smaller than 250 m. As this is optical imagery, flooding under clouds will not be detected. Suggested Use: Red in the displayed image indicates flood; blue indicates expected surface water. Satellite/Sensor/Resolution: Terra and Aqua satellites, MODIS sensor, daily 250m resolution data (10:30 am observation for Terra, 1:30 pm for Aqua) Credits: NRT MODIS/Aqua+Terra Global Flood Product MCDWD_L3_NRT distributed from NASA LANCE. Available online [https://www.earthdata.nasa.gov/global-flood-product]. doi:10.5067/MODIS/MCDWD_L3_NRT.061 Project website includes FAQs and contacts for support: https://www.earthdata.nasa.gov/global-flood-product

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

Goddard’s LiDAR, Hyperspectral, and Thermal Imager (G-LiHT) mission is a portable, airborne imaging system that aims to simultaneously map the composition, structure, and function of terrestrial ecosystems. G-LiHT primarily focuses on a broad diversity of forest communities and ecoregions in North America, mapping aerial swaths over the Conterminous United States (CONUS), Alaska, Puerto Rico, and Mexico. The purpose of G-LiHT’s Hyperspectral Ancillary data product (GLHYANC) is to provide information related to aircraft attitude and altitude, view and solar angles, and other ancillary reflectance and radiance data. GLHYANC data are processed as a zipped raster data product (GeoTIFF) with associated header file (.hdr) at 1 meter spatial resolution over locally defined areas. Some data files may be in ENVI format.

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.

Goddard’s LiDAR, Hyperspectral, and Thermal Imager (G-LiHT) mission utilizes a portable, airborne imaging system that aims to simultaneously map the composition, structure, and function of terrestrial ecosystems. G-LiHT primarily focuses on a broad diversity of forest communities and ecoregions in North America, mapping aerial swaths over the Conterminous United States (CONUS), Alaska, Puerto Rico, and Mexico. The purpose of G-LiHT’s Canopy Height Model data product (GLCHMT) is to provide LiDAR-derived maximum canopy height and canopy variability information to aid in the study and analysis of biodiversity and climate change. Scientists at NASA’s Goddard Space Flight Center began collecting data over locally-defined areas in 2011 and that the collection will continue to grow as aerial campaigns are flown and processed. GLCHMT data are processed as a raster data product (GeoTIFF) at a nominal 1 meter spatial resolution over locally-defined areas. A low resolution browse is also provided showing the canopy height with a color map applied in JPEG format.