On January 25, 2018 FEMA replaced this map with a new NFHL map with additional functionality which allows users to print official flood maps. On April 1, 2018 this map and NFHL link will no longer function. Please update your bookmark to https://hazards-fema.maps.arcgis.com/apps/webappviewer/index.html?id=8b0adb51996444d4879338b5529aa9cd. For more information on NFHL data availability, please visit the NFHL GIS Services page at https://hazards.fema.gov/femaportal/wps/portal/NFHLWMSAs of August 1, 2017 all FEMA systems will require the use of the “https” protocol, and “http” links will no longer function. This may impact NFHL web services. The FEMA GeoPlatform (including this map) will not be affected by this change. For more information on how NFHL GIS services will be impacted, please visit the NFHL GIS Services page at https://hazards.fema.gov/femaportal/wps/portal/NFHLWMS.An NFHL FIRMette print service is now available HERE. (For a video tutorial, click here.)OverviewThe National Flood Hazard Layer (NFHL) dataset represents the current effective flood data for the country, where maps have been modernized. It is a compilation of effective Flood Insurance Rate Map (FIRM) databases and Letters of Map Change (LOMCs). The NFHL is updated as studies go effective. For more information, visit FEMA's Map Service Center (MSC). Base Map ConsiderationsThe default base map is from a USGS service and conforms to FEMA's specification for horizontal accuracy. This base map from The National Map (TNM) consists of National Agriculture Imagery Program (NAIP) and high resolution orthoimagery (HRO) that combine the visual attributes of an aerial photograph with the spatial accuracy and reliability of a map. This map should be considered the best online resource to use for official National Flood Insurance Program (NFIP) purposes when determining locations in relation to regulatory flood hazard information. If a different base map is used with the NFHL, the accuracy specification may not be met and the resulting map should be used for general reference only, and not official NFIP purposes. Users can download a simplified base map from the USGS service via: https://viewer.nationalmap.gov/services/ For the specifics of FEMA’s policy on the use of digital flood hazard data for NFIP purposes see: http://www.fema.gov/library/viewRecord.do?id=3235Letter of Map Amendment (LOMA) pointsLOMA point locations are approximate. The location of the LOMA is referenced in the legal description of the letter itself. Click the LOMA point for a link to the letter (use the arrows at the top of the popup window to bring up the LOMA info, if needed).This LOMA database may include LOMAs that are no longer effective. To be certain a particular LOMA is currently valid, please check relevant documentation at https://msc.fema.gov/ . Relevant documents can be found for a particular community by choosing to "Search All Products", and finding the community by State and County. Documents include LOMAs found in the "Effective Products" and "LOMC" folders, as well as Revalidations (those LOMAs which are still considered to be effective after a map is revised).Updates3/27/2017 - Updated all references to https to prevent issues with mixed content.5/11/2016 - Added link to NFHL FIRMette Print Service. Updated LOMA and CBRS popup notes.2/20/2014 - Created a General Reference map for use when the USGS base map service is down. Renamed this map to "Official".Further InformationSpecific questions about FEMA flood maps can be directed to FEMAMapSpecialist@riskmapcds.comFor more flood map data, tool, and viewing options, visit the FEMA NFHL page. Information about connecting to web map services (REST, WMS, WFS) can be found here.Several fact sheets are available to help you learn more about FEMA’s NFHL utility: National Flood Hazard Layer (NFHL) GIS Services Users GuideNational Flood Hazard Layer (NFHL): New Products and Services for FEMA's Flood Hazard Map DataMoving to Digital Flood Hazard Information Standards for Flood Risk Analysis and MappingNFHL GIS Data: Perform Spatial Analyses and Make Custom Maps and Reports

Street Noise-Level Dataset — Regulatory & Governmental Use

Silencio’s Street Noise-Level Dataset provides regulatory bodies, governmental agencies, and public health authorities with the most reliable and detailed data on environmental noise worldwide. Built from over 35 billion datapoints, collected via our mobile app and enhanced through AI-powered interpolation, this dataset covers hyper-local average noise levels (dBA) across streets, neighborhoods, and cities in over 200 countries.

Our dataset is specifically suited for noise regulation, environmental impact assessments, policy-making, and compliance monitoring. It offers objective, real-world acoustic data that goes beyond traditional noise models by combining actual user-collected measurements with AI-predicted values. Authorities can use the data to map noise pollution hotspots, monitor changes over time, enforce regulations, and inform sustainable urban and environmental strategies.

Silencio also operates the world’s largest noise complaint database, giving policymakers a unique tool to correlate objective noise exposure with subjective community reports for more people-focused decision-making.

Delivery options are flexible, including: • CSV exports • S3 bucket access • High-resolution image maps suitable for integration into reports, GIS platforms, or public communication.

The dataset is available both as historical records and continuously updated data. An API is currently in development, and we are open to early access discussions and custom integrations tailored to government and regulatory workflows.

Fully anonymized and fully GDPR-compliant, Silencio’s data supports transparent, evidence-based environmental and public health decision-making.

Methods

The dataset is the output of a comprehensive literature-based search that aims to collate all the evidence on where ES relationships have been mentioned and addressed. We applied systematic mapping which is based on the “Guidelines for Systematic Review in Environmental Management” developed by the Centre for Evidence-Based Conservation at Bangor University (Pullin and Stewart 2006).

The methodological framework followed the standard stages outlined for systematic mapping in environmental sciences (James et al. 2016). Briefly, we defined the scope and objectives:

· We comprehensively review and further explore the global evidence of ES trade-offs and synergies focusing on all systems including terrestrial, freshwater, and marine.

· We compiled the evidence on trade-offs and synergies among multiple ES interacting across various ecosystems.

· We performed a geographical and temporal trend analysis exploring the distribution of studies across the world examining how the focus on various ecosystem types and ES categories has evolved to highlight gaps and biases.

Then we set the criteria for study inclusion (Table 1), searched the evidence, coded, and produced the database. Extracted article information including the specific criteria is detailed in Table 1.

The first step was to search the ISI Web of Knowledge core collection (http://apps.webofknowledge.com) database, targeting the search on the ecosystem services literature and studies dealing with trade-offs/synergies, win-win outcomes or bundles when managing different ecosystem services in the landscape/seascape. All peer-reviewed journal articles written in English and Spanish have been considered for review.

The peer-reviewed literature from 2005 to 2021 was reviewed identifying relevant studies according to specific search terms. The relevant search terms and descriptive words derived from (Howe et al. 2014) adding “bundles” and “co-benefits”. Boolean nomenclatures ‘*’ = all letters were allowed after the *, were used on the root of words where several different endings applied (Figure 1). Search terms used were:

(“*ecosystem service*” OR “environment* service*” OR “ecosystem* approach*” OR “ecosystem good*” OR “environment* good*”)

AND

(“*trade-off*” OR “tradeoff*” OR “synerg*” OR “win-win*” OR “bundle*” OR “cost*and benefit*” OR “co-benefit*”) n=5194

Papers were preliminarily coded with a semantic analysis using the R package Bibliometrix (http://www.bibliometrix.org).

In the second step (Figure 1) papers were preliminarily coded with a semantic analysis using the R package Bibliometrix (http://www.bibliometrix.org). Papers were classified according to three systems: terrestrial, marine, and freshwater (Table 1). Papers with multiple systems, transitional habitats or those that could not be classified were classified as “other” (Mazor et al. 2018). Articles were classified based on the occurrence of the most frequent system words in their title, keywords, and abstract (Mazor et al. 2018). The set of system-specific words was determined by extracting the 250 most frequently used keywords from all considered articles and assigning each word to either system (articles could fall into just one of the four categories). Using this technique, we managed to classify 100% of the papers. To further enrich the dataset and make it a useful repository for science and policy, an additional sub-classification was performed, categorizing papers into the following categories: Coastal, Urban, Wetlands, Forest, Mountain, Freshwater, Agroecosystems, and Others that mainly represented multiple ecosystems (Table S1). This comprehensive classification approach enhances the dataset’s utility for various scientific and policy-making applications.

In the third step (Figure 1), applying the same technique, we classified the papers into four ES categories: habitat (supporting biodiversity related), provisioning, regulating, and cultural services (De Groot et al. 2010; MEA 2005; Sukhdev 2010; Wallace 2007). For the classification into ES categories, articles could fall into one or more of the four categories (see Table 1 for example the keywords used to classify ecosystems, ES categories, and countries). Applying this technique, we excluded 2149 papers that weren’t classified in any of the ecosystem services types categories resulting in 3629 papers (see Figure 1).

In the fourth step (Figure 1), an initial screening was conducted to identify papers that did not align with the review objectives of assessing ecosystem services trade-offs and synergies to inform policy and management decisions. We manually reviewed the titles of each paper in the dataset, excluding those that were from other fields or did not align with the review objectives. In this initial assessment, we excluded 347 papers, leaving a total of 3,286 papers for further review. A descriptive analysis of this 3286 article dataset was performed to examine the distribution of ES categories within each ecosystem type over the specified period. This analysis allowed us to conclude the prevalence of each ecosystem service category in different ecosystem types, identifying temporal trends and patterns. The number of occurrences was calculated for each ES category within each ecosystem type, expressed as counts. This allowed for the comparison of ecosystem service distributions across the selected ecosystem types.

In the fifth step (Figure 1), we employed an approach to visually represent the geographical distribution and focus of ES studies across the world. With the classification of studies in ES categories and the types of ecosystems, the papers were coded according to the country where the study was performed. It was possible to assign a specific country to 2636 studies, removing 650 studies that did not specify the country of study. From these 2636 papers classified, a proportion were global studies that consider several countries under study (499 global studies).

We developed global maps (Figure 1), each offering a unique perspective on the ES research landscape. The first map presents the total number of ES trade-off studies conducted worldwide, illustrating the geographical spread and concentration of research efforts to provide a clear overview of regions that have been extensively studied and those that may require more attention in future research. Additionally, we calculated two key metrics to assess research productivity more comprehensively: the number of research papers per capita and the number of research papers relative to Gross Domestic Product (GDP). For population and GDP, we used the most recent available data from the World Bank (https://data.worldbank.org). These alternative metrics normalize the data based on economic output and population size, providing a more balanced view of research activity across different countries (Figures S3).

Detailed maps were created featuring pie charts that highlight the different categories of ES and ecosystem types addressed for each country. These charts offer an understanding of how various ES categories and ecosystems are represented in different parts of the world. Finally, we assessed ES trade-off studies to world regions (Africa, Antarctica, Asia, Australasia, Europe, Latin America, and North America) looking at the relationships between the categories of ES. We considered papers that evaluated more than one category of ES and the papers that considered only one category of ES. This country-level analysis offers insights into regional research trends and priorities, contributing to a more localized understanding of ES studies.

In the sixth step (Figure 1), each publication in this review was critically appraised to evaluate the quality of the papers included in the review. The foundation for our critical appraisal stems from the comprehensive and multidimensional approach of Belcher et al. (2016) that is framed to evaluate research quality, which aligns well with the interdisciplinary nature of our study. Belcher et al. (2016) developed a robust framework that incorporates essential principles and criteria for assessing the quality of transdisciplinary research. This is particularly relevant for ecosystem services science and our review that contributes to advancing current knowledge by systematically synthesizing evidence on relationships among various ES across these diverse systems.

The Belcher et al. (2016) framework emphasizes four main principles: relevance, credibility (which we have adapted as methodological transparency), legitimacy (generalizability in our context), and effectiveness (significance). A continuous scoring system (ranging from 0 to 1) was applied for the four main criteria to maintain simplicity and consistency across the large number of studies. In this system, a value closer to 0 indicates that the criteria are not met, while a value closer to 1 indicates that the criteria are more closely met. This scoring method was a useful indicator of the overall quality of the paper and how well the article met the review's goals overall.

Methodological Transparency was assessed based on the clarity and completeness of methodological descriptions, including data availability, the rigor of statistical analyses, methodological detail, and reproducibility of the findings. This criterion assesses the transparency and rigor of the study's methodology, including data collection, analysis, and reporting (Belcher et al. 2016). Relevance was evaluated by the study's alignment with the review's objectives, its importance to the field, and its practical applicability. This includes the extent to which the study addresses pertinent research

The Global Administrative Unit Layers (GAUL) compiles and disseminates the best available information on administrative units for all the countries in the world, providing a contribution to the standardization of the spatial dataset representing administrative units. The GAUL always maintains global layers with a unified coding system at country, first (e.g. departments), and second administrative levels (e.g. districts). Where data is available, it provides layers on a country by country basis down to third, fourth, and lowers levels.

GAUL 2015 is the eighth and latest release of the GAUL Set.

Contact points:

Metadata Contact: FAO-Data

Data lineage:

The overall methodology consists in a) collecting the best available data from most reliable sources, b) establishing validation periods of the geographic features (when possible), c) adding selected data to the global layer based on the last country boundaries map provided by the UN Cartographic Unit (UNCS), d) generating codes using GAUL Coding System, and e) distribute data to the users (see Technical Aspects of the GAUL Distribution Set. Note that some administrative units are multi-polygon features.

Resource constraints:

The GAUL license is also subject to the constraints and limitations of the data license of the International Boundary Dataset of the UN Cartographic Section and the one of the Second Administrative Level Boundary (SALB) data (for the countries that use this dataset).

Terms of Use The GAUL dataset is distributed to the United Nations and other authorized international and national institutions/agencies. FAO grants a license to use, download and print the materials contained in the GAUL dataset solely for non-commercial purposes and in accordance with the conditions specified in the data license. The full GAUL Data License document is available for downloading. See also the disclaimer.

Online resources:

GAUL Disclaimer

GAUL Data License

GAUL Documentation

GAUL Additional Attributes

What is SEPAL?

SEPAL (https://sepal.io/) is a free and open source cloud computing platform for geo-spatial data access and processing. It empowers users to quickly process large amounts of data on their computer or mobile device. Users can create custom analysis ready data using freely available satellite imagery, generate and improve land use maps, analyze time series, run change detection and perform accuracy assessment and area estimation, among many other functionalities in the platform. Data can be created and analyzed for any place on Earth using SEPAL.

https://data.apps.fao.org/catalog/dataset/9c4d7c45-7620-44c4-b653-fbe13eb34b65/resource/63a3efa0-08ab-4ad6-9d4a-96af7b6a99ec/download/cambodia_mosaic_2020.png" alt="alt text" title="Figure 1: Best pixel mosaic of Landsat 8 data for 2020 over Cambodia">

Figure 1: Best pixel mosaic of Landsat 8 data for 2020 over Cambodia

SEPAL reaches over 5000 users in 180 countries for the creation of custom data products from freely available satellite data. SEPAL was developed as a part of the Open Foris suite, a set of free and open source software platforms and tools that facilitate flexible and efficient data collection, analysis and reporting. SEPAL combines and integrates modern geospatial data infrastructures and supercomputing power available through Google Earth Engine and Amazon Web Services with powerful open-source data processing software, such as R, ORFEO, GDAL, Python and Jupiter Notebooks. Users can easily access the archive of satellite imagery from NASA, the European Space Agency (ESA) as well as high spatial and temporal resolution data from Planet Labs and turn such images into data that can be used for reporting and better decision making.

National Forest Monitoring Systems in many countries have been strengthened by SEPAL, which provides technical government staff with computing resources and cutting edge technology to accurately map and monitor their forests. The platform was originally developed for monitoring forest carbon stock and stock changes for reducing emissions from deforestation and forest degradation (REDD+). The application of the tools on the platform now reach far beyond forest monitoring by providing different stakeholders access to cloud based image processing tools, remote sensing and machine learning for any application. Presently, users work on SEPAL for various applications related to land monitoring, land cover/use, land productivity, ecological zoning, ecosystem restoration monitoring, forest monitoring, near real time alerts for forest disturbances and fire, flood mapping, mapping impact of disasters, peatland rewetting status, and many others.

The Hand-in-Hand initiative enables countries that generate data through SEPAL to disseminate their data widely through the platform and to combine their data with the numerous other datasets available through Hand-in-Hand.

https://data.apps.fao.org/catalog/dataset/9c4d7c45-7620-44c4-b653-fbe13eb34b65/resource/868e59da-47b9-4736-93a9-f8d83f5731aa/download/probability_classification_over_zambia.png" alt="alt text" title="Figure 2: Image classification module for land monitoring and mapping. Probability classification over Zambia">

Figure 2: Image classification module for land monitoring and mapping. Probability classification over Zambia

To create this app:Make a map of the AfDB projects CSV file in the Training Materials group.Download the CSV file, click Map (at the top of the page), and drag and drop the file onto your mapFrom the layer menu on your Projects layer choose Change Symbols and show the projects using Unique Symbols and the Status of field.Make a second map of the AfDB projects shown using Unique Symbols and the Sector field.HINT: Create a copy of your first map using Save As... and modify the copy.Assemble your story map on the Esri Story Maps websiteGo to storymaps.arcgis.comAt the top of the site, click AppsFind the Story Map Tabbed app and click Build a Tabbed Story MapFollow the instructions in the app builder. Add the maps you made in previous steps and copy the text from this sample app to your app. Explore and experiment with the app configuration settings.=============OPTIONAL - Make a third map of the AFDB projects summarized by country and add it to your story map.Add the World Countries layer to your map (Add > Search for Layers)From the layer menu on your Projects layer choose Perform Analysis > Summarize Data > Aggregate Points and run the tool to summarize the projects in each country.HINT: UNCHECK "Keep areas with no points"Experiment with changing the symbols and settings on your new layer and remove other unnecessary layers.Save AS... a new map.At the top of the site, click My Content.Find your story map application item, open its Details page, and click Configure App.Use the builder to add your third map and a description to the app and save it.

THIS LAYER IS HOSTED BY FEMA, not NAPSG Foundation. We are simply pointing to their layer with this ArcGIS Online item. The National Flood Hazard Layer (NFHL) dataset represents the current effective flood data for the country, where maps have been modernized. It is a compilation of effective Flood Insurance Rate Map (FIRM) databases and Letters of Map Change (LOMCs). The NFHL is updated as studies go effective. For more information, visit FEMA's Map Service Center (MSC). You can view this information in a standalone viewer here: https://hazards-fema.maps.arcgis.com/apps/webappviewer/index.html?id=8b0adb51996444d4879338b5529aa9cdREST URL: https://hazards.fema.gov/gis/nfhl/rest/services/public/NFHL/MapServerBase Map ConsiderationsThe default base map is from an ESRI service and conforms to FEMA's specification for horizontal accuracy. This base map is composed of the orthoimagery used when the Flood Insurance Rate Maps (FIRMs) were initially created combined with standard imagery products managed by ESRI. This map should be considered the best online resource to use for official National Flood Insurance Program (NFIP) purposes when determining locations in relation to regulatory flood hazard information. If a different base map is used with the NFHL, the accuracy specification may not be met and the resulting map should be used for general reference only, and not official NFIP purposes.Further InformationFor more flood map data, tool, and viewing options, visit the FEMA NFHL page.Several fact sheets are available to help you learn more about FEMA’s NFHL utility: National Flood Hazard Layer (NFHL) GIS Services Users GuideNational Flood Hazard Layer (NFHL): New Products and Services for FEMA's Flood Hazard Map DataNFHL GIS Data: Perform Spatial Analyses and Make Custom Maps and Reports

THIS LAYER IS HOSTED BY FEMA, not NAPSG Foundation. We are simply pointing to their layer with this ArcGIS Online item. The National Flood Hazard Layer (NFHL) dataset represents the current effective flood data for the country, where maps have been modernized. It is a compilation of effective Flood Insurance Rate Map (FIRM) databases and Letters of Map Change (LOMCs). The NFHL is updated as studies go effective. For more information, visit FEMA's Map Service Center (MSC). You can view this information in a standalone viewer here: https://hazards-fema.maps.arcgis.com/apps/webappviewer/index.html?id=8b0adb51996444d4879338b5529aa9cdREST URL: https://hazards.fema.gov/gis/nfhl/rest/services/public/NFHL/MapServerBase Map ConsiderationsThe default base map is from an ESRI service and conforms to FEMA's specification for horizontal accuracy. This base map is composed of the orthoimagery used when the Flood Insurance Rate Maps (FIRMs) were initially created combined with standard imagery products managed by ESRI. This map should be considered the best online resource to use for official National Flood Insurance Program (NFIP) purposes when determining locations in relation to regulatory flood hazard information. If a different base map is used with the NFHL, the accuracy specification may not be met and the resulting map should be used for general reference only, and not official NFIP purposes.Further InformationFor more flood map data, tool, and viewing options, visit the FEMA NFHL page.Several fact sheets are available to help you learn more about FEMA’s NFHL utility: National Flood Hazard Layer (NFHL) GIS Services Users GuideNational Flood Hazard Layer (NFHL): New Products and Services for FEMA's Flood Hazard Map DataNFHL GIS Data: Perform Spatial Analyses and Make Custom Maps and Reports

On January 25, 2018 FEMA replaced this map with a new NFHL map with additional functionality which allows users to print official flood maps. On April 1, 2018 this map and NFHL link will no longer function. Please update your bookmark to https://hazards-fema.maps.arcgis.com/apps/webappviewer/index.html?id=8b0adb51996444d4879338b5529aa9cd. For more information on NFHL data availability, please visit the NFHL GIS Services page at https://hazards.fema.gov/femaportal/wps/portal/NFHLWMSAs of August 1, 2017 all FEMA systems will require the use of the “https” protocol, and “http” links will no longer function. This may impact NFHL web services. The FEMA GeoPlatform (including this map) will not be affected by this change. For more information on how NFHL GIS services will be impacted, please visit the NFHL GIS Services page at https://hazards.fema.gov/femaportal/wps/portal/NFHLWMS.An NFHL FIRMette print service is now available HERE. (For a video tutorial, click here.)OverviewThe National Flood Hazard Layer (NFHL) dataset represents the current effective flood data for the country, where maps have been modernized. It is a compilation of effective Flood Insurance Rate Map (FIRM) databases and Letters of Map Change (LOMCs). The NFHL is updated as studies go effective. For more information, visit FEMA's Map Service Center (MSC). Base Map ConsiderationsThe default base map is from a USGS service and conforms to FEMA's specification for horizontal accuracy. This base map from The National Map (TNM) consists of National Agriculture Imagery Program (NAIP) and high resolution orthoimagery (HRO) that combine the visual attributes of an aerial photograph with the spatial accuracy and reliability of a map. This map should be considered the best online resource to use for official National Flood Insurance Program (NFIP) purposes when determining locations in relation to regulatory flood hazard information. If a different base map is used with the NFHL, the accuracy specification may not be met and the resulting map should be used for general reference only, and not official NFIP purposes. Users can download a simplified base map from the USGS service via: https://viewer.nationalmap.gov/services/ For the specifics of FEMA’s policy on the use of digital flood hazard data for NFIP purposes see: http://www.fema.gov/library/viewRecord.do?id=3235Letter of Map Amendment (LOMA) pointsLOMA point locations are approximate. The location of the LOMA is referenced in the legal description of the letter itself. Click the LOMA point for a link to the letter (use the arrows at the top of the popup window to bring up the LOMA info, if needed).This LOMA database may include LOMAs that are no longer effective. To be certain a particular LOMA is currently valid, please check relevant documentation at https://msc.fema.gov/ . Relevant documents can be found for a particular community by choosing to "Search All Products", and finding the community by State and County. Documents include LOMAs found in the "Effective Products" and "LOMC" folders, as well as Revalidations (those LOMAs which are still considered to be effective after a map is revised).Updates3/27/2017 - Updated all references to https to prevent issues with mixed content.5/11/2016 - Added link to NFHL FIRMette Print Service. Updated LOMA and CBRS popup notes.2/20/2014 - Created a General Reference map for use when the USGS base map service is down. Renamed this map to "Official".Further InformationSpecific questions about FEMA flood maps can be directed to FEMAMapSpecialist@riskmapcds.comFor more flood map data, tool, and viewing options, visit the FEMA NFHL page. Information about connecting to web map services (REST, WMS, WFS) can be found here.Several fact sheets are available to help you learn more about FEMA’s NFHL utility: National Flood Hazard Layer (NFHL) GIS Services Users GuideNational Flood Hazard Layer (NFHL): New Products and Services for FEMA's Flood Hazard Map DataMoving to Digital Flood Hazard Information Standards for Flood Risk Analysis and MappingNFHL GIS Data: Perform Spatial Analyses and Make Custom Maps and Reports

The California Department of Forestry and Fire Protection's Fire and Resource Assessment Program (FRAP) annually maintains and distributes an historical wildland fire perimeter dataset from across public and private lands in California. The GIS data is developed with the cooperation of the United States Forest Service Region 5, the Bureau of Land Management, California State Parks, National Park Service and the United States Fish and Wildlife Service and is released in the spring with added data from the previous calendar year. Although the dataset represents the most complete digital record of fire perimeters in California, it is still incomplete, and users should be cautious when drawing conclusions based on the data. This data should be used carefully for statistical analysis and reporting due to missing perimeters (see Use Limitation in metadata). Some fires are missing because historical records were lost or damaged, were too small for the minimum cutoffs, had inadequate documentation or have not yet been incorporated into the database. Other errors with the fire perimeter database include duplicate fires and over-generalization. Additionally, over-generalization, particularly with large old fires, may show unburned "islands" within the final perimeter as burned. Users of the fire perimeter database must exercise caution in application of the data. Careful use of the fire perimeter database will prevent users from drawing inaccurate or erroneous conclusions from the data. This data is updated annually in the spring with fire perimeters from the previous fire season. This dataset may differ in California compared to that available from the National Interagency Fire Center (NIFC) due to different requirements between the two datasets. The data covers fires back to 1878. As of May 2025, it represents fire24_1. Please help improve this dataset by filling out this survey with feedback:Historic Fire Perimeter Dataset Feedback (arcgis.com)Current criteria for data collection are as follows:CAL FIRE (including contract counties) submit perimeters ≥10 acres in timber, ≥50 acres in brush, or ≥300 acres in grass, and/or ≥3 impacted residential or commercial structures, and/or caused ≥1 fatality.All cooperating agencies submit perimeters ≥10 acres.Version update:Firep24_1 was released in April 2025. Five hundred forty-eight fires from the 2024 fire season were added to the database (2 from BIA, 56 from BLM, 197 from CAL FIRE, 193 from Contract Counties, 27 from LRA, 8 from NPS, 55 from USFS and 8 from USFW). Six perimeters were added from the 2025 fire season (as a special case due to an unusual January fire siege). Five duplicate fires were removed, and the 2023 Sage was replaced with a more accurate perimeter. There were 900 perimeters that received updated attribution (705 removed “FIRE” from the end of Fire Name field and 148 replaced Complex IRWIN ID with Complex local incident number for COMPLEX_ID field). The following fires were identified as meeting our collection criteria but are not included in this version and will hopefully be added in a future update: Addie (2024-CACND-002119), Alpaugh (2024-CACND-001715), South (2024-CATIA-001375). One perimeter is missing containment date that will be updated in the next release.Cross checking CALFIRS reporting for new CAL FIRE submissions to ensure accuracy with cause class was added to the compilation process. The cause class domain description for “Powerline” was updated to “Electrical Power” to be more inclusive of cause reports.Includes separate layers filtered by criteria as follows:California Fire Perimeters (All): Unfiltered. The entire collection of wildfire perimeters in the database. It is scale dependent and starts displaying at the country level scale. Recent Large Fire Perimeters (≥5000 acres): Filtered for wildfires greater or equal to 5,000 acres for the last 5 years of fires (2020-January 2025), symbolized with color by year and is scale dependent and starts displaying at the country level scale. Year-only labels for recent large fires.California Fire Perimeters (1950+): Filtered for wildfires that started in 1950-January 2025. Symbolized by decade, and display starting at country level scale.Detailed metadata is included in the following documents:Wildland Fire Perimeters (Firep24_1) MetadataFor any questions, please contact the data steward:Kim Wallin, GIS SpecialistCAL FIRE, Fire & Resource Assessment Program (FRAP)kimberly.wallin@fire.ca.gov

The major source of geo-referenced soil data of Latin America and the Caribbean at a scale of 1:5 M is the Soil Map of the World (SMW) of FAO/Unesco (1974-1981). For this part of the globe the information was collected before 1974, the year of publication of the Latin American map sheets. Collection of soil survey information by the national institutes responsible for soil survey continued after publication and a large amount of new data is available at the national level. Since 1991 the FAO is actualizing the SMW information of Latin America with support from national soils institutes in the concerned countries. This has resulted in the acquisition of new 1:5 M soil maps of most Latin American countries. New soil maps with a revised soil classification legend (FAO, 1990) of Argentina, Brazil, Chile, Colombia, Ecuador, Mexico, Paraguay, Peru, Uruguay and Venezuela were obtained by FAO through subcontracts with the national soil institutes. Since 1988 the World Soils and Terrain Database Programme (SOTER) is operational in various Latin American countries at a scale of 1:1 M, in particular in Argentina, Brazil and Uruguay with UNEP funding. The major objective of the SOTER methodology is to use information technology, like geographic information systems and database management systems, for the creation of a world soils and terrain database with digital maps and attributes and their interpretations. At the moment SOTER databases at scale 1:1 M are available for the whole of Uruguay, the northern part of Argentina (460,000 km2) and the south of Brazil (100,000 km2). In 1992 FAO formally endorsed SOTER and decided to use the methodology to store and update soils and terrain data at a global level. The SOTER Procedures Manual was published jointly by FAO, ISRIC, ISSS and UNEP in 1993 and in the following year also as No. 74 in the series of World Soil Resources Bulletins. During the same year an agreement was signed between FAO and UNEP to develop a soils and terrain database of Latin America at scale 1:5 M, jointly with the updating of the SMW. ISRIC was asked to coordinate the activities of the SOTERLAC 1:5 M project in the countries to be included.

The Country boundaries at level 0 (National level dataset) is part of the Global Administrative Unit Layers (GAUL) dataset series which includes information on administrative units for all the countries in the world, providing a contribution to the standardization of the spatial dataset representing administrative units.

The GAUL maintains global layers with a unified coding system at country, first (e.g. departments), and second administrative levels (e.g. districts). Where data is available, it provides layers on a country by country basis down to third, fourth, and lowers levels. Please read the GAUL Documentation for more information.

Data publication: 2014-12-19

Supplemental Information:

The Global Administrative Unit Layers (GAUL) is an initiative that was originally implemented by FAO within the Bill & Melinda Gates Foundation, Agricultural Market Information System (AMIS) and AfricaFertilizer.org projects.

Citation:

© 1990-2014 GAUL

Contact points:

Resource Contact: FAO-Data

Data lineage:

The overall GAUL methodology consists in a) collecting the best available data from most reliable sources, b) establishing validation periods of the geographic features (when possible), c) adding selected data to the global layer based on the last country boundaries map provided by the UN Cartographic Unit (UNCS), d) generating codes using GAUL Coding System, and e) distribute data to the users (see "Technical Aspects of the GAUL Distribution Set" in the GAUL 2015 Documentation). Note that some administrative units are multi-polygon features.

Because GAUL works at global level, unsettled territories are reported. The approach of GAUL is to deal with these areas in such a way to preserve national integrity for all disputing countries (see G2015_DisputedAreas.dbf).

5 territories have been updated in the GAUL 2015 with respect to the previous release and the coastline of American countries and other special areas have been updated using Open Street Map (see ReleaseNoteGAUL2015.pdf).

GAUL keeps track of administrative units that has been changed, added or dismissed in the past for political causes. Changes implemented in different years are recorded in GAUL on different layers. For this reason the GAUL product is not a single layer but a group of layers, named "GAUL Set" (see "ReleaseNoteGAUL2015" in the GAUL 2015 Documentation).

Resource constraints:

The GAUL license is also subject to the constraints and limitations of the data license of the International Boundary Dataset of the UN Cartographic Section and the one of the Second Administrative Level Boundary (SALB) data (for the countries that use this dataset).

Terms of Use

The GAUL dataset is distributed to the United Nations and other authorized international and national institutions/agencies. FAO grants a license to use, download and print the materials contained in the GAUL dataset solely for non-commercial purposes and in accordance with the conditions specified in the data license. The full GAUL Data License document is available for downloading.

Data might not be officially validated by authoritative national sources and cannot be distributed to the general public. A disclaimer should always accompany any use of GAUL data. More info on the disclaimer content here.

Online resources:

GAUL Data License

GAUL Disclaimer

GAUL Documentation

GAUL Additional Attributes

Downolad: Country boundaries (level 0) - GAUL 2015

FEMA is the author and publisher of this content. City of Las Cruces Enterprise GIS provides this content for informational purposes only. This view is filtered to only display flood hazard areas from the official FEMA 2016 flood certification file. Layer Type: PolygonData Owner: FEMAAuthoritative: YesDownloadable: YesInitial Dataset Creation: UnknownLast update: N/AUpdate Frequency: N/AReason for Update: City does not maintainFilters Applied: Flood hazard areasSource data: https://las-cruces.maps.arcgis.com/home/item.html?id=5f1906fd247244e8b1eef0212d0fa290Projected Coordinate System: N/ALimitations: OverviewThe National Flood Hazard Layer (NFHL) dataset represents the current effective flood data for the country, where maps have been modernized. It is a compilation of effective Flood Insurance Rate Map (FIRM) databases and Letters of Map Change (LOMCs). The NFHL is updated as studies go effective. For more information, visit FEMA's Map Service Center (MSC). You can view this information in a standalone viewer here: https://hazards-fema.maps.arcgis.com/apps/webappviewer/index.html?id=8b0adb51996444d4879338b5529aa9cdREST URL: https://hazards.fema.gov/gis/nfhl/rest/services/public/NFHL/MapServerBase Map ConsiderationsThe default base map is from an ESRI service and conforms to FEMA's specification for horizontal accuracy. This base map is composed of the orthoimagery used when the Flood Insurance Rate Maps (FIRMs) were initially created combined with standard imagery products managed by ESRI. This map should be considered the best online resource to use for official National Flood Insurance Program (NFIP) purposes when determining locations in relation to regulatory flood hazard information. If a different base map is used with the NFHL, the accuracy specification may not be met and the resulting map should be used for general reference only, and not official NFIP purposes.Further InformationFor more flood map data, tool, and viewing options, visit the FEMA NFHL page. National Flood Hazard Layer (NFHL) GIS Services Users GuideNational Flood Hazard Layer (NFHL): New Products and Services for FEMA's Flood Hazard Map Data

OverviewThis Web Mapping Application provides the ability to print maps based on FEMA's National Flood Hazard Layer (NFHL) dataset. This application should only be used for areas where digital Flood Insurance Rate Map (FIRM) data is available; for other areas it is recommended that users use printing tools available at the MSC.FEMA's National Flood Hazard LayerThe National Flood Hazard Layer (NFHL) dataset represents the current effective flood data for the country, where maps have been modernized. It is a compilation of effective Flood Insurance Rate Map (FIRM) databases and Letters of Map Change (LOMCs). The NFHL is updated as studies go effective. For more information, visit FEMA's Map Service Center (MSC). Base Map ConsiderationsThe default base map is from a USGS service and conforms to FEMA's specification for horizontal accuracy. This base map from The National Map (TNM) consists of National Agriculture Imagery Program (NAIP) and high resolution orthoimagery (HRO) that combine the visual attributes of an aerial photograph with the spatial accuracy and reliability of a map. This map should be considered the best online resource to use for official National Flood Insurance Program (NFIP) purposes when determining locations in relation to regulatory flood hazard information. If a different base map is used with the NFHL, the accuracy specification may not be met and the resulting map should be used for general reference only, and not official NFIP purposes.Users can download a simplified base map from the USGS service via: https://viewer.nationalmap.gov/services/ For the specifics of FEMA’s policy on the use of digital flood hazard data for NFIP purposes see standards 605 and 606 in FEMA Policy: Standards for Flood Risk Analysis and Mapping available from Guidelines and Standards for Flood Risk Analysis and Mapping Activities Under the Risk MAP Program | FEMA.govFurther InformationFor more flood map data, tool, and viewing options, visit National Flood Hazard Layer | FEMA.gov

The administrative boundaries dataset at level 1 (Sub-national level) is part of the Global Administrative Unit Layers (GAUL) dataset series which includes information on administrative units for all the countries in the world, providing a contribution to the standardization of the spatial dataset representing administrative units.

The administrative boundaries at the level 1 dataset distinguishes States, Provinces, Departments and equivalent.

The GAUL maintains global layers with a unified coding system at country, first (e.g. departments), and second administrative levels (e.g. districts). Where data is available, it provides layers on a country by country basis down to third, fourth, and lowers levels. Please read the GAUL Documentation for more information.

Data publication: 2014-12-19

Supplemental Information:

The Global Administrative Unit Layers (GAUL) is an initiative that was originally implemented by FAO within the Bill & Melinda Gates Foundation, Agricultural Market Information System (AMIS) and AfricaFertilizer.org projects.

Citation:

© 1990-2014 GAUL

Contact points:

Resource Contact: FAO-Data

Data lineage:

The overall GAUL methodology consists in a) collecting the best available data from most reliable sources, b) establishing validation periods of the geographic features (when possible), c) adding selected data to the global layer based on the last country boundaries map provided by the UN Cartographic Unit (UNCS), d) generating codes using GAUL Coding System, and e) distribute data to the users (see "Technical Aspects of the GAUL Distribution Set" in the GAUL 2015 Documentation). Note that some administrative units are multi-polygon features.

Because GAUL works at global level, unsettled territories are reported. The approach of GAUL is to deal with these areas in such a way to preserve national integrity for all disputing countries (see G2015_DisputedAreas.dbf).

5 territories have been updated in the GAUL 2015 with respect to the previous release and the coastline of American countries and other special areas have been updated using Open Street Map (see ReleaseNoteGAUL2015.pdf).

GAUL keeps track of administrative units that has been changed, added or dismissed in the past for political causes. Changes implemented in different years are recorded in GAUL on different layers. For this reason the GAUL product is not a single layer but a group of layers, named "GAUL Set" (see "ReleaseNoteGAUL2015" in the GAUL 2015 Documentation).

Resource constraints:

The GAUL license is also subject to the constraints and limitations of the data license of the International Boundary Dataset of the UN Cartographic Section and the one of the Second Administrative Level Boundary (SALB) data (for the countries that use this dataset).

Terms of Use The GAUL dataset is distributed to the United Nations and other authorized international and national institutions/agencies. FAO grants a license to use, download and print the materials contained in the GAUL dataset solely for non-commercial purposes and in accordance with the conditions specified in the data license. The full GAUL Data License document is available for downloading.

Data might not be officially validated by authoritative national sources and cannot be distributed to the general public. A disclaimer should always accompany any use of GAUL data. More info on the disclaimer content here.

Online resources:

GAUL Data License

GAUL Disclaimer

GAUL Documentation

GAUL Additional Attributes

Downolad: Country boundaries (level 1) - GAUL 2015

Kenya’s most recent national land cover map emanates from the Africover (FAO Project funded by the Government of Italy and Country programmes) and was based on the interpretation of Landsat imagery from the 1990s released in 2000 by Kenya’s Department of Resource Surveys and Remote Sensing (DRSRS) and the Food and Agriculture Organization of the United Nations (FAO). The update of the database was undertaken by using most recent available ASTER imagery (2005-2010) for the majority of the areas, complemented by Landsat 2005/2007 or newer imagery as the source for the update. In addition where appropriate similar resolution and sensor was used.

This App Gallery contains a collection web apps created as part of Esri's Green Infrastructure Infrastructure Initiative. They can be used to explore, investigate, and analyze landscapes and support GI planning workflows and engage stakeholders.The apps Include:Intact Habitat Near Me:Explore your Community’s Potential for Green Infrastructure. View the remaining intact habitat near you, and other measures of natural and man-made assets that connect us.Land cover Change App:This application compares changes between aggregated 2011 National Land Cover Database land cover categories with similarly aggregated land cover categories from The Clark Labs 2050 Conterminous US Land Cover Prediction.Select Your Intact Landscape Cores App:Explore and filter a national database if Intact Landscape Cores to identify areas most relevant to your organization, local area, or region.Prioritize Your Intact Landscape Cores App:Rank and score your Intact Landscape Cores by weighting relevant landscape characteristics of importance to you.Investigate Core Weighting:Experiment with weighting landscape characteristics that contribute to the ranking of Intact Landscape Cores.Conduct Landscape Analysis App:Identify and evaluate areas that exhibit landscape characteristics you are interested in protecting.