Global environment data
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Date of Image: 4/14/2026, 4/16/2026, 4/18/2026 Summary ICEYE US Backscatter is derived from scaling factors provided with ICEYE Single Look Complex (SLC) and/or Ground Range Detected (GRD) products. In the SLC product all original sensor data is retained and offers full resolution in the azimuth and range directions. Data in the GRD product is oriented in the natural range-azimuth orientation and projected to the ground surface using an Earth ellipsoid mode and may be multi-looked to reduced speckle. The units for this product are decibels (dB). Resolution: Sub 1 meter. Credits NASA Disasters Program, Processing from MSFC, ICEYE US. For derived products: “Includes copyrighted material of ICEYE US. All rights reserved.” This work utilized data made available through the NASA Commercial Satellite Data Acquisition (CSDA) program.
This table describes each metric in the Brisbane City Council dataset called Flood — Awareness — Property Parcel Metrics. Definitions for this dataset: AEP – Annual Exceedance Probability (AEP) is the probability of a flood event of a given size occurring in any one year, usually expressed as a percentage annual chance. FWPR – Floodwise Property Report (FWPR), Council's FloodWise Property Reports provide property specific flood information to enable you to plan and build in accordance with the correct requirements. Holding – A property holding is a Council-defined and managed information entity. Its boundaries are generally based on land parcels. A property holding may consist of one or multiple land parcels. Parcel – Land Parcels are the building blocks of Council properties. Land parcels (also called lots) are mapped and the title details shown on a Plan of Subdivision. The parcel is a graphical representation of surveyed boundaries together with identifiers such as Lot/Plan description and house numbers. Information about flood types can be found in open data by using this search on the Brisbane City Council open data website and opening the required dataset.
Current and recent droughts 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.
Current and recent wildfires 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.
Current and recent volcanic eruptions 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.
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 "tomorrow": This map shows the relative probability of landslide occurrence tomorrow, 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]
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 "yesterday": This map shows the relative probability of landslide occurrence within the last day, based on rainfall estimates from GPM. 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]
Current and recent tropical cyclones 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.
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]
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.
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.
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.
# EOS-RS Flood Proxy Map: Chiayi, Taiwan, Floods, 28 July 2025, v0.9 The Earth Observatory of Singapore - Remote Sensing Lab (EOS-RS) created this preliminary Flood Proxy Map (FPM) depicting areas that are likely flooded in Chiayi, Taiwan, due to heavy rainfall. 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 (12 Mar 2024 to 22 Jul 2025) and after (28 Jul 2025) the event. The map covers an area indicated by the white polygon. The preliminary map shows areas which are likely flooded (shown by light blue pixels of approximately 30m in size) due to heavy rainfall. Preliminary validation was conducted using news reports and ground level imagery in selected areas. This map should be used as a guide to identify areas that are likely flooded, and may be less reliable over urban and vegetated areas. This product contains modified Copernicus Sentinel data (2024-2025), 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:** **Legal Disclaimer:** **Credits:** Earth Observatory of Singapore - Remote Sensing Lab (EOS-RS), Contains modified Copernicus Sentinel data (2024-2025)
The National Flood Hazard Layer (NFHL) data incorporates all Flood Insurance Rate Map (FIRM) databases published by the Federal Emergency Management Agency (FEMA), and any Letters of Map Revision (LOMRs) that have been issued against those databases since their publication date. It is updated on a monthly basis. The FIRM Database is the digital, geospatial version of the flood hazard information shown on the published paper FIRMs. The FIRM Database depicts flood risk information and supporting data used to develop the risk data. The primary risk classifications used are the 1-percent-annual-chance flood event, the 0.2-percent-annual-chance flood event, and areas of minimal flood risk. The FIRM Database is derived from Flood Insurance Studies (FISs), previously published FIRMs, flood hazard analyses performed in support of the FISs and FIRMs, and new mapping data, where available. The FISs and FIRMs are published by FEMA. The NFHL is available as State or US Territory data sets. Each State or Territory data set consists of all FIRM Databases and corresponding LOMRs available on the publication date of the data set. The specification for the horizontal control of FIRM Databases is consistent with those required for mapping at a scale of 1:12,000. This file is georeferenced to the Earth's surface using the Geographic Coordinate System (GCS) and North American Datum of 1983.
Global burned area polygons derived from MODIS satellite imagery, providing information on spatial and temporal attributes of areas affected by fires. Fires are mapped using a semi-automatic procedure. Provided as GeoJSON. Note: the dates reported as Start date and Last update may not correspond to the date of ignition and extinction of the fire.
GLIDEnumbers are GLobal unique IDEntifiers for Disasters. Each disaster is registered in a record that contains a GLIDEnumber that uniquely identifies it and allows information to be interoperable across multiple disaster information sources, both structured and textual. Accessing disaster information can be a time consuming and laborious task. Not only is data scattered but frequently identification of the disaster can be confusing in countries with many disaster events and across borders. To address both of these issues, Asian Disaster Reduction Center (ADRC) proposed a globally common Unique ID code for disasters. Among the institutions using actively GLIDEnumbers are UN-OCHA (ReliefWeb, FSCC, HDX), UNDRR, UNDP, WMO, IFRC, FAO, the European Commission and the Centre for Research on the Epidemiology of Disasters (CRED), among many others. Country datasets are only produced for countries with at least one GLIDE event since 1 January 2022. The global dataset contains all events available from the GLIDE API, including those not assigned to a country.
THIS LAYER IS HOSTED BY FEMA, not City of Santa Monica. You can view this information in a standalone viewer here: https://hazards-fema.maps.arcgis.com/apps/webappviewer/index.html?id=8b0adb51996444d4879338b5529aa9cd
Contains data from the National Flood Hazard Layer, a GIS database of flood risks and regulatory flood determination data.
Contains authoritative data from the National Flood Hazard Layer, a GIS database of flood risks and regulatory flood determination data.
Contains data from the National Flood Hazard Layer, a GIS database of flood risks and regulatory flood determination data.
Map Panel Border - FEDERAL EMERGENCY MANAGEMENT AGENCY
This layer shows the Digital FIRMs layer from FEMA focused on Rock County, WI. The intent is to help citizens and those doing businesses in Rock County determine risk due to flooding on properties within the county. Information and metadata pertaining to the Digital Firms can be found on FEMA's National Flood Hazard Layer (Official) Map. The Rock County Planning, Economic and Community Development Agency enforces Floodplain Zoning for unincorporated areas of Rock County. The County Floodplain Zoning Ordinance is available for viewing on the Ordinances and Plans web page.
Soil organic carbon content (fine earth fraction) in dg/kg at 6 standard depths. Predictions were derived using a digital soil mapping approach based on Quantile Random Forest, drawing on a global compilation of soil profile data and environmental layers. To visualize these layers please use www.soilgrids.org.
Sand content (50/63-2000 micro meter) mass fraction in ‰ at 6 standard depths. Predictions were derived using a digital soil mapping approach based on Quantile Random Forest, drawing on a global compilation of soil profile data and environmental layers. To visualize these layers please use www.soilgrids.org.
Flood Hazard Mapping is an important part of the U.S. National Flood Insurance Program. The Federal Emergency Management Agency publishes Flood Insurance Rate Maps that show areas where the annual likelihood of a flood is 1% or greater (Special Flood Hazard Areas). Because standard homeowners insurance does not cover flood damage the congress created the National Flood Insurance Program in 1968. In participating communities homeowners and renters are able to by federally-backed flood insurance. Dataset Summary This layer's data represents Federal Emergency Management Agency's (FEMA) Flood Insurance Rate Map (FIRM) data. This data is based upon the original vector data from FIRM. These data are important for floodplain management, mitigation, and insurance activities for the National Flood Insurance Program (NFIP). The National Flood Hazard Layer (NFHL) data present the flood risk information depicted on the FIRM in a digital format suitable for use in electronic mapping applications. The NFHL database is a subset of the information created for the Flood Insurance Studies (FIS) and serves as a means to archive a portion of the information collected during the FIS. The NFHL data incorporates Digital Flood Insurance Rate Map (DFIRM) databases published by Federal Emergency Management Agency (FEMA). This layer initially shows the flood zones by FEMA Flood Zone Designations with this symbology: The layer covers the continental U.S. Link to source metadata What can you do with this layer? The vector features for this layer can be used for visualization and analysis in ArcGIS. The features in this layer are only visible at scales larger than 1:1,155,582. The features can also be downloaded for use with ArcGIS desktop by with the Extract Landscape Source Data tool. This layer is part of a larger collection of landscape layers that you can use to perform a wide variety of mapping and analysis tasks. This collection includes a raster layer that was derived from these data.
Coarse fragments volumetric in ‱ at 6 standard depths. Predictions were derived using a digital soil mapping approach based on Quantile Random Forest, drawing on a global compilation of soil profile data and environmental layers. To visualize these layers please use www.soilgrids.org.
Silt content (2-50/63 micro meter) mass fraction in ‰ at 6 standard depths. Predictions were derived using a digital soil mapping approach based on Quantile Random Forest, drawing on a global compilation of soil profile data and environmental layers. To visualize these layers please use www.soilgrids.org.
Cation exchange capacity (buffered at pH 7) of fine earth fraction in cmolc/kg at 6 standard depths. Predictions were derived using a digital soil mapping approach based on Quantile Random Forest, drawing on a global compilation of soil profile data and environmental layers. To visualize these layers please use www.soilgrids.org.
Soil organic carbon stock in t/ha for 0-30 cm depth interval. Predictions were derived using a digital soil mapping approach based on Quantile Random Forest, drawing on a global compilation of soil profile data and environmental layers. To visualize these layers please use www.soilgrids.org.
These datasets were prepared within the scope of the Holisoils (Holistic management practices, modelling & monitoring for European Forest Soils) project (EU Horizon 2020 Grant agreement ID: 101000289). The soil property maps were generated at a resolution of 100m, and created with LUCAS and other continental level data using digital soil mapping. Users should assess the local ''predictive'' accuracy of the maps prior to using them for making recommendations at local (or field) level. The datasets contain the following soil properties (for top soil (0-30 cm) depth): Soil organic carbon content, pH in water, Total nitrogen, Bulk density (oven dry), Coarse fragments (volumetric), Soil inorganic carbon content, Sand, Silt, Clay, as well as Soil organic carbon stocks, Soil inorganic carbon stocks and Soil nitrogen stocks. Measurement units and web links for the above properties can be found be below. The maps are provided as Web Map Service (WMS) layers which load dynamically in a GIS software. For instructions see: https://www.isric.org/instruction-wms .
Bulk density (fine earth) in cg/cm³ at 6 standard depths. Predictions were derived using a digital soil mapping approach based on Quantile Random Forest, drawing on a global compilation of soil profile data and environmental layers. To visualize these layers please use www.soilgrids.org.
WRB classes and probabilites. Predictions were derived using a digital soil mapping approach based on Random Forest, drawing on a global compilation of soil profile data and environmental layers. To visualize these layers please use www.soilgrids.org. Legend: 0 = Acrisols 1 = Albeluvisols 2 = Alisols 3 = Andosols 4 = Arenosols 5 = Calcisols 6 = Cambisols 7 = Chernozems 8 = Cryosols 9 = Durisols 10 = Ferralsols 11 = Fluvisols 12 = Gleysols 13 = Gypsisols 14 = Histosols 15 = Kastanozems 16 = Leptosols 17 = Lixisols 18 = Luvisols 19 = Nitisols 20 = Phaeozems 21 = Planosols 22 = Plinthosols 23 = Podzols 24 = Regosols 25 = Solonchaks 26 = Solonetz 27 = Stagnosols 28 = Umbrisols 29 = Vertisols
Global Human Footprint Index is the relative human influence in each terrestrial biome expressed as a percentage. The purpose is to provide an updated map of anthropogenic impacts on the environment in geographic projection which can be used in wildlife conservation planning, natural resource management, and research on human-environment interactions. Dataset Summary The Global Human Footprint Index Dataset of the Last of the Wild Project, Version 2, 2005 (LWP-2) is the Human Influence Index (HII) normalized by biome and realm. The HII is a global dataset of 1-kilometer grid cells, created from nine global data layers covering human population pressure (population density), human land use and infrastructure (built-up areas, nighttime lights, land use/land cover), and human access (coastlines, roads, railroads, navigable rivers). A value of zero represents the least influenced- the “most wild” part of the biome with value of 100 representing the most influenced (least wild) part of the biome. The dataset is produced by the Wildlife Conservation Society (WCS) and the Columbia University Center for International Earth Science Information Network (CIESIN). Recommended Citation Wildlife Conservation Society - WCS, and Center for International Earth Science Information Network - CIESIN - Columbia University. 2005. Last of the Wild Project, Version 2, 2005 (LWP-2): Global Human Footprint Dataset (Geographic). Palisades, NY: NASA Socioeconomic Data and Applications Center (SEDAC). http://dx.doi.org/10.7927/H4M61H5F. Accessed DAY MONTH YEAR.
Global Human Footprint Index is the relative human influence in each terrestrial biome expressed as a percentage. The purpose is to provide an updated map of anthropogenic impacts on the environment in geographic projection which can be used in wildlife conservation planning, natural resource management, and research on human-environment interactions. Dataset Summary The Global Human Footprint Index Dataset of the Last of the Wild Project, Version 2, 2005 (LWP-2) is the Human Influence Index (HII) normalized by biome and realm. The HII is a global dataset of 1-kilometer grid cells, created from nine global data layers covering human population pressure (population density), human land use and infrastructure (built-up areas, nighttime lights, land use/land cover), and human access (coastlines, roads, railroads, navigable rivers). A value of zero represents the least influenced- the “most wild” part of the biome with value of 100 representing the most influenced (least wild) part of the biome. The dataset is produced by the Wildlife Conservation Society (WCS) and the Columbia University Center for International Earth Science Information Network (CIESIN). Recommended Citation Wildlife Conservation Society - WCS, and Center for International Earth Science Information Network - CIESIN - Columbia University. 2005. Last of the Wild Project, Version 2, 2005 (LWP-2): Global Human Footprint Dataset (Geographic). Palisades, NY: NASA Socioeconomic Data and Applications Center (SEDAC). http://dx.doi.org/10.7927/H4M61H5F. Accessed DAY MONTH YEAR.