HEPGIS is a web-based interactive geographic map server that allows users to navigate and view geo-spatial data, print maps, and obtain data on specific features using only a web browser. It includes geo-spatial data used for transportation planning. HEPGIS previously received ARRA funding for development of Economically distressed Area maps. It is also being used to demonstrate emerging trends to address MPO and statewide planning regulations/requirements , enhanced National Highway System, Primary Freight Networks, commodity flows and safety data . HEPGIS has been used to help implement MAP-21 regulations and will help implement the Grow America Act, particularly related to Ladder of Opportunities and MPO reforms.

This is a bike sharing data for a fictitious Cyclistic company. Actual data is based on Divvy, a Chicago bike sharing. The original data was cleaned using Postgres and Google Sheet This data has been cleaned to exclude data that's missing the station IDs and trips that has duration over 24 hours. Several columns was created to calculate trip durations and day of the week.

--- Finding duplicate , assumed duplicated ID means duplicated data. Result = no duplicated data found---

SELECT ride_id, COUNT(ride_id) AS ride_id_count FROM "Cyclistic" GROUP BY ride_id HAVING COUNT(ride_id)>1

--- Extract station table for data cleaning ----

SELECT DISTINCT start_station_name, start_station_id, end_station_id, end_station_name FROM "Cyclistic" ORDER BY start_station_name ;

Using Google Sheet Clean start_station_id code, clean missing station name, clean station id with extra .0, Assign id to NULL station data

---- Update main table with cleaned station name and id ---- UPDATE "Cyclistic" SET end_lng = lng FROM "cleaned_station_info" WHERE start_station_id = id;

---- Original data has latitude and longitude data that varies by small amount of decimal points. To make the data more uniform, the latitude and longitude were averaged out based on the station ID and use 8 decimal points for location accuracy. Data was then checked using Google Maps to make sure data is accurate to the nearest Divvy location in Chicago. ---

SELECT DISTINCT start_station_id, start_station_name, ROUND(AVG(start_lat)::DECIMAL,8) lat, ROUND(AVG(start_lng)::DECIMAL,8) lng FROM "Cyclistic" GROUP BY start_station_id, start_station_name ORDER BY start_station_id

--- Create a cleaned table for export excluding data that are less than 2 minutes and more than 24 hours. Based on data where duration less than 2 minutes, the ride always ends up at the same station. It is assumed that the rider canceled the ride or had trouble using the bike, therefore this data is excluded. For data more than 24 hours it's assumed that there's an error in docking in the bicycle or other problem with logging out of the ride. New table also exclude NULL data where start_station_name and end_station_name is missing ---

SELECT
*
FROM (
SELECT
ride_id, member_casual, rideable_type,
start_station_id, start_station_name,
end_station_id, end_station_name,
started_at, ended_at,
ended_at - started_at as duration,
start_lat, start_lng, end_lat, end_lng\

FROM "Cyclistic"\

WHERE start_station_name IS NOT NULL AND end_station_name IS NOT NULL ) AS duration_tbl\

WHERE duration >= '00:02' and duration <= '24:00' \

This map displays projected visible surface smoke across the contiguous United States for the next 48 hours in 1 hour increments. It is updated every 24 hours by NWS. Concentrations are reported in micrograms per cubic meter.Where is the data coming from?The National Digital Guidance Database (NDGD) is a sister to the National Digital Forecast Database (NDFD). Information in NDGD may be used by NWS forecasters as guidance in preparing official NWS forecasts in NDFD. The experimental/guidance NDGD data is not an official NWS forecast product.Source: https://tgftp.nws.noaa.gov/SL.us008001/ST.opnl/DF.gr2/DC.ndgd/GT.aq/AR.conus/ds.smokes01.binSource data archive can be found here: https://www.ncei.noaa.gov/products/weather-climate-models/national-digital-guidance-database look for 'LXQ...' files by date. These are the Binary GRIB2 files that can be decoded via DeGRIB tool.Where can I find other NDGD data?The Source data is downloaded and parsed using the Aggregated Live Feeds methodology to return information that can be served through ArcGIS Server as a map service or used to update Hosted Feature Services in Online or Enterprise.What can you do with this layer?This map service is suitable for data discovery and visualization. Identify features by clicking on the map to reveal the pre-configured pop-ups. View the time-enabled data using the time slider by Enabling Time Animation.RevisionsJuly 11, 2022: Feed updated to leverage forecast model change by NOAA, whereby the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) forecast model system was replaced with the Rapid Refresh (RAP) forecast model system. Key differences: higher accuracy with RAP now concentrated at 0-8 meter detail vs HYSPLIT at 0-100 meter; earlier data delivery by 6 hrs; forecast output extended to 51 hrs.This map is provided for informational purposes and is not monitored 24/7 for accuracy and currency.If you would like to be alerted to potential issues or simply see when this Service will update next, please visit our Live Feed Status Page!

The Time Zones dataset was compiled on October 04, 2019 and was updated January 05, 2023 from the Bureau of Transportation Statistics (BTS) and is part of the U.S. Department of Transportation (USDOT)/Bureau of Transportation Statistics (BTS) National Transportation Atlas Database (NTAD). This layer is a digital representation of the geographic boundaries of the nine time zones that cover the United States and its territories (the Atlantic, Eastern, Central, Mountain, Pacific, Alaska, Hawaii–Aleutian, Samoa, and Chamorro time zones). The U.S. Department of Transportation (DOT) oversees the Nation's time zones and the uniform observance of Daylight-Saving Time. The oversight of time zones was assigned to DOT due to the importance of time coordination for transportation related activities. The time zones were established by the Standard Time Act of 1918 and amended by the Uniform Time Act of 1966. Time zones in the U.S. are defined in the U.S. Code, Title 15, Chapter 6, Subchapter IX - Standard Time. The time zone boundaries are defined in the Code of Federal Regulations (CFR), Title 49, Subtitle A, Part 71 - Standard Time Zone Boundaries. Segments used to compile the geospatial layer were derived from the CFR’s time zone descriptions (https://www.ecfr.gov/current/title-49/subtitle-A/part-71). Descriptions consist of segments referencing administrative boundaries, infrastructure, natural features, and geodesic lines. These segments are contained in various data layers in the National Geospatial Data Asset (NGDA) portfolio, the federal government’s authoritative geospatial data repository. Referenced segments were extracted from their NGDA and then merged to form continuous boundaries. In instances where there were multiple scales for a given dataset, the largest scale or most detailed layer was used. The standard time of the Atlantic zone is the Coordinated Universal Time (UTC) minus 4 hours; Eastern zone is UTC minus 5 hours; Central zone is UTC minus 6 hours; Mountain zone is UTC minus 7 hours; Pacific zone is UTC minus 8 hours; Alaska zone is UTC minus 9 hours; Hawaii–Aleutian zone is UTC minus 10 hours; Samoa zone is UTC minus 11 hours; and Chamorro zone is UTC plus 10 hours. For more information, please visit: https://doi.org/10.21949/1519143.

It starts with a simple habit: you open your browser and type a question. A few keystrokes later, Google gives you answers, videos, maps, and suggestions before you even finish your thought. For billions of people around the world, this daily interaction is second nature. But behind that blinking cursor...

This map contains continuously updated U.S. tornado reports, wind storm reports and hail storm reports. Click each feature to receive information about the specific location and read a short description about the issue. Now contains ALL available Incident Report types, for a total of 15, not just Hail; Wind; and Tornados.See new layer for details or Feature Layer Item with exclusive Past 24-Hour ALL Storm Reports Layer. Each layer is updated 4 times hourly from data provided by NOAA’s National Weather Service Storm Prediction Center. A full archive of storm events can be accessed from the NOAA National Centers for Environmental Information. SourceNOAA Storm Prediction Center https://www.spc.noaa.gov/climo/reportsNOAA ALL Storm Reports layer https://www.spc.noaa.gov/exper/reports Sample DataSee Sample Layer Item for sample data during inactive periods! Update FrequencyThe service is updated every 15 minutes using the Aggregated Live Feeds Methodology Area CoveredCONUS (Contiguous United States) What can you do with this layer? This map service is suitable for data discovery and visualization.Change the symbology of each layer using single or bi-variate smart mapping. For instance, use size or color to indicate the intensity of a tornado.Click each feature to receive information about the specific location and read a short description about the issue.Query the attributes to show only specific event types or locations. RevisionsAug 10, 2021: Updated Classic Layers to use new Symbols. Corrected Layer Order Presentation. Updated Thumbnail.Aug 8, 2021: Update to layer-popups, correcting link URLs. Expanded length of 'Comment' fields to 1kb of text. New Layer added that includes ALL available Incident Types and Age in 'Hours Old'. This map is provided for informational purposes and is not monitored 24/7 for accuracy and currency.If you would like to be alerted to potential issues or simply see when this service will update next, please visit our Live Feed Status Page.

A global data set of soil types is available at 0.5-degree latitude by 0.5-degree longitude resolution. There are 106 soil units, based on Zobler?s (1986) assessment of the FAO/UNESCO Soil Map of the World. This data set is a conversion of the Zobler 1-degree resolution version to a 0.5-degree resolution. The resolution of the data set was not actually increased. Rather, the 1-degree squares were divided into four 0.5-degree squares with the necessary adjustment of continental boundaries and islands. The computer code used to convert the original 1-degree data to 0.5-degree is provided as a companion file. A JPG image of the data is provided in this document. The Zobler data (1-degree resolution) as distributed by Webb et al. (1993) [http://www.ngdc.noaa.gov/seg/eco/cdroms/gedii_a/datasets/a12/wr.htm#top] contains two columns, one column for continent and one column for soil type. The Soil Map of the World consists of 9 maps that represent parts of the world. The texture data that Webb et al.(1993) provided allowed for the fact that a soil type in one part of the world may have different properties than the same soil in a different part of the world. This continent-specific information is retained in this 0.5-degree resolution data set, as well as the soil type information which is the second column. A code was written (one2half.c) to take the file CONTIZOB.LER distributed by Webb et al. (1993) [http://www.ngdc.noaa.gov/seg/eco/cdroms/gedii_a/datasets/a12/wr.htm#top] and simply divide the 1-degree cells into quarters. This code also reads in a land/water file (land.wave) that specifies the cells that are land at 0.5 degrees. The code checks for consistency between the newly quartered map and the land/water map to which the quartered map is to be registered. If there is a discrepancy between the two, an attempt was made to make the two consistent using the following logic. If the cell is supposed to be water, it is forced to be water. If it is supposed to be land but was resolved to water at 1 degree, the code looks at the surrounding 8 cells and picks the most frequent soil type and assigns it to the cell. If there are no surrounding land cells then it is kept as water in the hopes that on the next pass one or more of the surrounding cells might be converted from water to a soil type. The whole map is iterated 5 times. The remaining cells that should be land but couldn't be determined from surrounding cells (mostly islands that are resolved at 0.5 degree but not at 1 degree) are printed out with coordinate information. A temporary map is output with -9 indicating where data is required. This is repeated for the continent code in CONTIZOB.LER as well. A separate map of the temporary continent codes is produced with -9 indicating required data. A nearly identical code (one2half.c) does the same for the continent codes. The printout allows one to consult the printed versions of the soil map and look up the soil type with the largest coverage in the 0.5-degree cell. The program manfix.c then will go through the temporary map and prompt for input to correct both the soil codes and the continent codes for the map. This can be done manually or by preparing a file of changes (new_fix.dat) and redirecting stdin. A new complete version of the map is outputted. This is in the form of the original CONTIZOB.LER file (contizob.half) but four times larger. Original documentation and computer codes prepared by Post et al. (1996) are provided as companion files with this data set. Image of 106 global soil types available at 0.5-degree by 0.5-degree resolution. Additional documentation from Zobler?s assessment of FAO soil units is available from the NASA Center for Scientific Information.

Multispectral remote sensing data acquired by Landsat 8 Operational Land Imager (OLI) sensor were analyzed using an automated technique to generate surficial mineralogy and vegetation maps of the conterminous western United States. Six spectral indices (e.g. band-ratios), highlighting distinct spectral absorptions, were developed to aid in the identification of mineral groups in exposed rocks, soils, mine waste rock, and mill tailings across the landscape. The data are centered on the Western U.S. and cover portions of Texas, Oklahoma, Kansas, the Canada-U.S. border, and the Mexico-U.S. border during the summers of 2013 – 2014. Methods used to process the images and algorithms used to infer mineralogical composition of surficial materials are detailed in Rockwell and others (2021) and were similar to those developed by Rockwell (2012; 2013). Final maps are provided as ERDAS IMAGINE (.img) thematic raster images and contain pixel values representing mineral and vegetation group classifications. Rockwell, B.W., 2012, Description and validation of an automated methodology for mapping mineralogy, vegetation, and hydrothermal alteration type from ASTER satellite imagery with examples from the San Juan Mountains, Colorado: U.S. Geological Survey Scientific Investigations Map 3190, 35 p. pamphlet, 5 map sheets, scale 1:100,000, http://doi.org/10.13140/RG.2.1.2769.9365. Rockwell, B.W., 2013, Automated mapping of mineral groups and green vegetation from Landsat Thematic Mapper imagery with an example from the San Juan Mountains, Colorado: U.S. Geological Survey Scientific Investigations Map 3252, 25 p. pamphlet, 1 map sheet, scale 1:325,000, http://doi.org/10.13140/RG.2.1.2507.7925. Rockwell, B.W., Gnesda, W.R., and Hofstra, A.H., 2021, Improved automated identification and mapping of iron sulfate minerals, other mineral groups, and vegetation from Landsat 8 Operational Land Imager Data: San Juan Mountains, Colorado, and Four Corners Region: U.S. Geological Survey Scientific Investigations Map 3466, scale 1:325,000, 51 p. pamphlet, https://doi.org/10.3133/sim3466/.

The AQS Data Mart is a database containing all of the information from AQS. It has every measured value the EPA has collected via the national ambient air monitoring program. It also includes the associated aggregate values calculated by EPA (8-hour, daily, annual, etc.). The AQS Data Mart is a copy of AQS made once per week and made accessible to the public through web-based applications. The intended users of the Data Mart are air quality data analysts in the regulatory, academic, and health research communities. It is intended for those who need to download large volumes of detailed technical data stored at EPA and does not provide any interactive analytical tools. It serves as the back-end database for several Agency interactive tools that could not fully function without it: AirData, AirCompare, The Remote Sensing Information Gateway, the Map Monitoring Sites KML page, etc.

AQS must maintain constant readiness to accept data and meet high data integrity requirements, thus is limited in the number of users and queries to which it can respond. The Data Mart, as a read only copy, can allow wider access.

The most commonly requested aggregation levels of data (and key metrics in each) are:

Sample Values (2.4 billion values back as far as 1957, national consistency begins in 1980, data for 500 substances routinely collected) The sample value converted to standard units of measure (generally 1-hour averages as reported to EPA, sometimes 24-hour averages) Local Standard Time (LST) and GMT timestamps Measurement method Measurement uncertainty, where known Any exceptional events affecting the data NAAQS Averages NAAQS average values (8-hour averages for ozone and CO, 24-hour averages for PM2.5) Daily Summary Values (each monitor has the following calculated each day) Observation count Observation per cent (of expected observations) Arithmetic mean of observations Max observation and time of max AQI (air quality index) where applicable Number of observations > Standard where applicable Annual Summary Values (each monitor has the following calculated each year) Observation count and per cent Valid days Required observation count Null observation count Exceptional values count Arithmetic Mean and Standard Deviation 1st - 4th maximum (highest) observations Percentiles (99, 98, 95, 90, 75, 50) Number of observations > Standard Site and Monitor Information FIPS State Code (the first 5 items on this list make up the AQS Monitor Identifier) FIPS County Code Site Number (unique within the county) Parameter Code (what is measured) POC (Parameter Occurrence Code) to distinguish from different samplers at the same site Latitude Longitude Measurement method information Owner / operator / data-submitter information Monitoring Network to which the monitor belongs Exemptions from regulatory requirements Operational dates City and CBSA where the monitor is located Quality Assurance Information Various data fields related to the 19 different QA assessments possible

Querying BigQuery tables

You can use the BigQuery Python client library to query tables in this dataset in Kernels. Note that methods available in Kernels are limited to querying data. Tables are at bigquery-public-data.epa_historical_air_quality.[TABLENAME]. Fork this kernel to get started.

Acknowledgements

Data provided by the US Environmental Protection Agency Air Quality System Data Mart.

Building footprint and height data were obtained from the latest global 3D building database. The building footprint data originated from Microsoft and Google datasets. Building height information was estimated using an XGBoost machine learning regression model that integrates multi-source remote sensing features. The height estimation model was trained using datasets from ONEGEO Map, Microsoft, Baidu, and EMU Analytics, utilizing 2020 data for the final estimations. Validation of this database demonstrates that the height estimation models perform exceptionally well at a global scale across both the Northern and Southern Hemispheres. The estimated heights closely match reference height data, especially for landmark buildings, showcasing superior accuracy compared to other global height products. The 3D building data that support this dataset are available in Zenodo DOI:10.5194/essd-16-5357-2024 (Che, Y., Li, X., Liu, X., Wang, Y., Liao, W., Zheng, X., Zhang, X., Xu, X., Shi, Q., Zhu, J., Yuan, H., and Dai, Y. 3D-GloBFP: the first global three-dimensional building footprint dataset. Earth System Science Data)Based on the 3D building database, we verify the locations and boundaries of individual cultural heritage sites and their buffer zones using UNESCO's heritage map platform (https://whc.unesco.org/), and categorize heritage into three groups for data extraction:Broad Scale Sites: For sites encompassing continuous building clusters or portions of cities (e.g., City of Bath), we extract buildings within the designated buffer zones provided by the UNESCO platform.Single Building Sites: For individual monuments or structures (e.g., Tower of London), we precisely extract the building footprints based on their exact coordinates.Multiple Dispersed Buildings: For sites consisting of multiple, non-contiguous structures (e.g., Wooden Churches of Southern Małopolska, Poland), we identify each location using the platform’s data and verify them through Google Maps before extracting the relevant buildings.A few linear heritage sites, such as extensive archaeological routes spanning over a thousand kilometers, are excluded due to the complexities associated with their vast spatial extent and the variability of climate conditions across different segments.The effective data coverage varies across continents: Europe and North America have an effective rate of 82.5%, Asia and the Pacific 68.3%, Latin America and the Caribbean 75.7%, Arab States 76.5%, and Africa 49.2%. This variability reflects differences in data availability. In less developed regions, remote sensing data tends to overlook non-urban heritage sites, and soil and rock structures common in Africa and Southeast Asia are more difficult to detect using satellite remote sensing techniques, leading to lower effective data coverage in these regions.This dataset accompanies the following published article:Chen, Zihua, Gao, Qian, Wu, Yajing, Li, Jiaxin, Li, Xiaowei, Li, Xiao, Wang, Zhenbo, & Cui, Haiyang (2025). World Cultural Heritage sites are under climate stress and no emissions mitigation pathways can uniformly protect them. Communications Earth & Environment, 6:628. https://doi.org/10.1038/s43247-025-02603-8

Last Revised: February 2016

Map Information

This nowCOAST™ time-enabled map service provides maps of lightning strike density data from the NOAA/National Weather Service/NCEP's Ocean Prediction Center (OPC) which emulate (simulate) data from the future NOAA GOES-R Global Lightning Mapper (GLM). The purpose of this product is to provide mariners and others with enhanced "awareness of developing and transitory thunderstorm activity, to give users the ability to determine whether a cloud system is producing lightning and if that activity is increasing or decreasing..." Lightning Strike Density, as opposed to display of individual strikes, highlights the location of lightning cores and trends of increasing and decreasing activity. The maps depict the density of lightning strikes during a 15 minute time period at an 8 km x 8 km spatial resolution. The lightning strike density maps cover the geographic area from 25 degrees South to 80 degrees North latitude and from 110 degrees East to 0 degrees West longitude. The map units are number of strikes per square km per minute multiplied by a scaling factor of 10^3. The strike density is color coded using a color scheme which allows the data to be easily seen when overlaid on GOES imagery and to distinguish areas of low and high density values. The maps are updated on nowCOAST™ approximately every 15 minutes. The latest data depicted on the maps are approximately 12 minutes old (or older). Given the spatial resolution and latency of the data, the data should NOT be used to activite your lightning safety plans. Always follow the safety rule: when you first hear thunder or see lightning in your area, activate your emergency plan. If outdoors, immediately seek shelter in a substantial building or a fully enclosed metal vehicle such as a car, truck or van. Do not resume activities until 30 minutes after the last observed lightning or thunder. For more detailed information about layer update frequency and timing, please reference the
nowCOAST™ Dataset Update Schedule.

Background Information

The source for the data is OPC's gridded lightning strike density data on an 8x8 km grid. The gridded data emulate the spatial resolution of the future Global Lightning Mapper (GLM) instrument to be flown on the NOAA GOES-R series of geostationary satellites, with the first satellite scheduled for launch in late 2016.

The gridded data is based on data from Vaisala's ground based U.S. National Lightning Detection Network (NLDN) and its global lightning detection network referred to as the Global Lightning Dataset (GLD360). These networks are capable of detecting cloud-to-ground strikes, cloud-to-ground flash information and survey level cloud lightning information. According to the National Lightning Safety Institute, NLDN uses radio frequency detectors in the spectrum 1.0 kHz through 400 kHz to measure energy discharges from lightning as well as approximate distance and direction. According to Vaisala, the GLD360 network is capable of a detection efficiency greater than 70% over most of the Northern Hemisphere with a median location accuracy of 5 km or better. OPC's gridded data are coarser than the original source data from Vaisala's networks. The 15-minute gridded source data are updated at OPC every 15 minutes at 10 minutes past the valid time.

The lightning strike density product from NWS/NCEP/OPC is considered a derived product or Level 5 product ("NOAA-generated products using lightning data as input but not displaying the contractor transmitted/provided lightning data") and is appropriate for public distribution.

Time Information

This map service is time-enabled, meaning that each individual layer contains time-varying data and can be utilized by clients capable of making map requests that include a time component.

In addition to ArcGIS Server REST access, time-enabled OGC WMS 1.3.0 access is also provided by this service.

This particular service can be queried with or without the use of a time component. If the time parameter is specified in a request, the data or imagery most relevant to the provided time value, if any, will be returned. If the time parameter is not specified in a request, the latest data or imagery valid for the present system time will be returned to the client. If the time parameter is not specified and no data or imagery is available for the present time, no data will be returned.

This service is configured with time coverage support, meaning that the service will always return the most relevant available data, if any, to the specified time value. For example, if the service contains data valid today at 12:00 and 12:10 UTC, but a map request specifies a time value of today at 12:07 UTC, the data valid at 12:10 UTC will be returned to the user. This behavior allows more flexibility for users, especially when displaying multiple time-enabled layers together despite slight differences in temporal resolution or update frequency.

When interacting with this time-enabled service, only a single instantaneous time value should be specified in each request. If instead a time range is specified in a request (i.e. separate start time and end time values are given), the data returned may be different than what was intended.

Care must be taken to ensure the time value specified in each request falls within the current time coverage of the service. Because this service is frequently updated as new data becomes available, the user must periodically determine the service's time extent. However, due to software limitations, the time extent of the service and map layers as advertised by ArcGIS Server does not always provide the most up-to-date start and end times of available data. Instead, users have three options for determining the latest time extent of the service:

  Issue a returnUpdates=true request (ArcGIS REST protocol only)
  for an individual layer or for the service itself, which will return
  the current start and end times of available data, in epoch time format
  (milliseconds since 00:00 January 1, 1970). To see an example, click on
  the "Return Updates" link at the bottom of the REST Service page under
  "Supported Operations". Refer to the
  ArcGIS REST API Map Service Documentation
  for more information.


  Issue an Identify (ArcGIS REST) or GetFeatureInfo (WMS) request against
  the proper layer corresponding with the target dataset. For raster
  data, this would be the "Image Footprints with Time Attributes" layer
  in the same group as the target "Image" layer being displayed. For
  vector (point, line, or polygon) data, the target layer can be queried
  directly. In either case, the attributes returned for the matching
  raster(s) or vector feature(s) will include the following:


      validtime: Valid timestamp.


      starttime: Display start time.


      endtime: Display end time.


      reftime: Reference time (sometimes referred to as
      issuance time, cycle time, or initialization time).


      projmins: Number of minutes from reference time to valid
      time.


      desigreftime: Designated reference time; used as a
      common reference time for all items when individual reference
      times do not match.


      desigprojmins: Number of minutes from designated
      reference time to valid time.




  Query the nowCOAST™ LayerInfo web service, which has been created to
  provide additional information about each data layer in a service,
  including a list of all available "time stops" (i.e. "valid times"),
  individual timestamps, or the valid time of a layer's latest available
  data (i.e. "Product Time"). For more information about the LayerInfo
  web service, including examples of various types of requests, refer to
  the 
  nowCOAST™ LayerInfo Help Documentation

References

Kithil, 2015: Overview of Lightning Detection Equipment, National
Lightning Safety Institute, Louisville, CO. (Available from
http://www.lightningsafety.com/nsli_ihm/detectors.html).


NASA and NOAA, 2014: Geostationary Lightning Mapper (GLM). (Available at
http://www.goes-r.gov/spacesegment/glm.html).


NWS, 2013: Lightning Strike Density Product Description Document.
NOAA/NWS/NCEP/Ocean Prediction Center, College Park, MD (Available at
http://www.opc.ncep.noaa.gov/lightning/lightning_pdd.php
and http://products.weather.gov/PDD/Experimental%20Lightning%20Strike%20Density%20Product%2020130913.pdf).


NOAA Knows Lightning. NWS, Silver Spring, MD (Available at
http://www.lightningsafety.noaa.gov/resources/lightning3_050714.pdf).


Siebers, A., 2013: Soliciting Comments until June 3, 2014 on an
Experimental Lightning Strike Density product (Offshore Waters). Public
Information Notice, NOAA/NWS Headquarters, Washington, DC (Available at
http://www.nws.noaa.gov/om/notification/pns13lightning_strike_density.htm).

This dataset is linked to a list of KML (Keyhole Markup Language) files. This format is a markup language based on the XML (eXtensible Markup Language) syntax standard, using a tagging structure with nested elements and attributes. Developed and maintained by Keyhole, a company under Google, it is used to express geographic annotations. Documents written in KML language are KML files, which use the XML file format and are used in Google Earth-related software (such as Google Earth, Google Map, Google Maps for mobile) to display geographic data, including points, lines, areas, polygons, polyhedra, and models. Many GIS-related systems now also adopt this format to exchange geographic data. The fields and encoding of this KML data are all in UTF-8. For more details, please visit the "Geographic Information Center" (http://gic.wra.gov.tw/).

1: The Interstellar Boundary Explorer (IBEX) has operated in space since 2008 updating our knowledge of the outer heliosphere and its interaction with the local interstellar medium. Start-time: 2008-12-25. There are currently 16 releases of IBEX-HI and/or IBEX-LO data covering 2009-2019. 2: This data set is from the Release 17 (1 year-cadence) IBEX-Lo map data for the years 2009-2019 in the form of omnidirectional ENA (hydrogen) fluxes with Compton-Getting correction (cg) of flux spectra for spacecraft motion and no correction for ENA survival probability (nosp) between 1 and 100 AU. 3. The data consist of all-sky maps in Solar Ecliptic Longitude (east and west) and Latitude angles for ENA (hydrogen) fluxes from IBEX-Lo energy bands 1-8 in numerical data form. Energy channels 1-8 have FWHM center-point energies at 0.015, 0.029, 0.055, 0.11, 0.209, 0.439, 0.872, 1.821 keV, respectively. 4: Details of the data and enabled science from Release 10 are given in the following journal publication: McComas, D. J., et al. (2017), Seven Years of Imaging the Global Heliosphere with IBEX, Astrophys. J. Supp. Ser., 229(2), 41 (32 pp.), 5: http://doi.org/10.3847/1538-4365/aa66d8 6. The following codes are used to define dataset types:- cg = Compton-Getting corrections have been applied to the data to account for the speed of the spacecraft relative to the direction of arrival of the ENAs.- nocg = no Compton-Getting corrections- sp = survival probability corrections have been applied to the data to account for the loss of ENAs due to radiation pressure, photoionization and ionization via charge exchange with solar wind protons as they stream through the heliosphere. This correction scales the data out from IBEX at 1 AU to ~100 AU. In the original data this mode is denoted as Tabular.- noSP - no survival probability corrections have been applied to the data.- omni = data from all directions.- ram = data was collected when the spacecraft was ramming into the incoming ENAs.- antiram = data was collected when the spacecraft was moving away from the incoming ENAs. 7. The following list associates Release 17 map numbers (1-22) with mission year (1-9), orbits (11-471b), and dates (12/25/2008-12/26/2019):- Map 1: Map2009A, year 1, orbits 11-34, dates 12/25/2008-06/25/2009- Map 2: Map2009B, year 1, orbits 35-58, dates 06/25/2009-12/25/2009- Map 3: Map2010A, year 2, orbits 59-82, dates 12/25/2009-06/26/2010- Map 4: Map2010B, year 2, orbits 83-106, dates 06/26/2010-12/26/2010- Map 5: Map2011A, year 3, orbits 107-130a, dates 12/26/2010-06/25/2011- Map 6: Map2011B, year 3, orbits 130b-150a, dates 06/25/2011-12/24/2011- Map 7: Map2012A, year 4, orbits 150b-170a, dates 12/24/2011-06/22/2012- Map 8: Map2012B, year 4, orbits 170b-190b, dates 06/22/2012-12/26/2012- Map 9: Map2013A, year 5, orbits 191a-210b, dates 12/26/2012-06/26/2013- Map 10: Map2013B, year 5, orbits 211a-230b, dates 06/26/2013-12/26/2013- Map 11: Map2014A, year 6, orbits 231a-250b, dates 12/26/2013-06/26/2014- Map 12: Map2014B, year 6, orbits 251a-270b, dates 06/26/2014-12/24/2014- Map 13: Map2015A, year 7, orbits 271a-290b, dates 12/24/2014-06/24/2015- Map 14: Map2015B, year 7, orbits 291a-310b, dates 06/24/2015-12/23/2015- Map 15: Map2016A, year 8, orbits 311a-330b, dates 12/24/2015-06/23/2016- Map 16: Map2016B, year 8, orbits 331a-351a, dates 06/24/2016-12/26/2016- Map 17: Map2017A, year 9, orbits 351b-371a, dates 12/26/2016-06/24/2017- Map 18: Map2017B, year 9, orbits 371b-391a, dates 06/25/2017-12/25/2017- Map 19: Map2018A, year 10, orbits 391b-411b, dates 12/25/2017-06/28/2018- Map 20: Map2018B, year 10, orbits 412a-431b, dates 06/29/2018-12/26/2018- Map 21: Map2019A, year 11, orbits 432a-451b, dates 12/27/2018-06/27/2019- Map 22: Map2019B, year 11, orbits 452a-471b, dates 06/28/2019-12/26/2019 8: The energy resolution is delta-E/E = 0.8 for all channels:Energy channel 1: center energy = 0.015 keVEnergy channel 2: center energy = 0.029 keVEnergy channel 3: center energy = 0.055 keVEnergy channel 4: center energy = 0.11 keVEnergy channel 5: center energy = 0.209 keVEnergy channel 6: center energy = 0.439 keVEnergy channel 7: center energy = 0.872 keVEnergy channel 8: center energy = 1.821 keV 9: This particular data set, denoted in the original ascii files as lvset_h_cg_hb_N for N=2009-2019, includes pixel map data from all directions (omnidirectional), CG, no SP, 1 year cadence.

This preliminary version is based on all available satellite data until August 2025 (*_2025_08). The map will be updated when more data are available.

The dataset contains a map of the main classes of agricultural land use (dominant crop types and other land use types) in Germany for the year 2025. It complements a series of maps that are produced annually at the Thünen Institute beginning with the year 2017 on the basis of satellite data. The maps cover the entire open landscape, i.e., the agriculturally used area (UAA) and e.g., uncultivated areas.

Data and methods used to create map versions v301/2 differ from those used in previous versions. The v301/2 maps were derived from a time series of Sentinel-2 and Landsat 8/9 images. Map production is based on the methods described in Pham et al. (2024).

All optical satellite data were managed, pre-processed and structured in an analysis-ready data (ARD) cube using the open-source software FORCE - Framework for Operational Radiometric Correction for Environmental monitoring (Frantz, D., 2019), in which SAR and environmental data were integrated.

The map extent covers all areas in Germany that are defined as agricultural land, grassland, small woody features, heathland, peatland or unvegetated areas according to ATKIS Basis-DLM (Geobasisdaten: © GeoBasis-DE / BKG, 2020).

Version v301:
Post-processing of the maps included a sieve filter as well as a ruleset for the reduction of non-plausible areas using the Basis-DLM and the digital terrain model of Germany (Geobasisdaten: © GeoBasis-DE / BKG, 2015).

Version v302:
Additional post-processing was performed to detect and mask additional non-plausible areas that were not adequately covered by the first post-processing (e.g., areas with sparse vegetation, montane forests) based on the „Ökosystematlas Deutschland“ (© Statistisches Bundesamt, Deutschland, 2024). As a consequence, the current version includes a new class “Small woody features on other land”. Furthermore, the class "permanent grassland" was refinded. Each pixel that was classified as "cultivated grassland" in at least five years (between 2017 and 2022) was translated to "permanent grassland" in the annual maps.

Validation:
The final maps were validated using all pixels of the publicly available IACS parcels from the federal states of Brandenburg, Lower Saxony, and North Rhine-Westphalia that were not used for model training. Classes that are underrepresented in these federal states could therefore not be adequately evaluated (e.g., hops and grapevines). We provide this dataset "as is" without any warranty regarding the accuracy or completeness and exclude all liability.

The maps are available as cloud optimized GeoTiffs, which makes downloading the full dataset optional. All data can directly be accessed in QGIS, R, Python or any supported software of your choice using the URL that will be provided on request. By doing so the entire map area or only the regions of interest can be accessed. QGIS legend files for data visualization can be downloaded separately.

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References:

Pham, V.-D., Tetteh, G., Thiel, F., Erasmi, S., Schwieder, M., Frantz, D., & van der Linden, S. (2024). Temporally transferable crop mapping with temporal encoding and deep learning augmentations. International Journal of Applied Earth Observation and Geoinformation, 129, 103867.

BKG, Bundesamt für Kartographie und Geodäsie (2015). Digitales Geländemodell Gitterweite 10 m. DGM10. https://sg.geodatenzentrum.de/web_public/gdz/dokumentation/deu/dgm10.pdf (last accessed: 28. April 2022).

BKG, Bundesamt für Kartographie und Geodäsie (2020). Digitales Basis-Landschaftsmodell.
https://sg.geodatenzentrum.de/web_public/gdz/dokumentation/deu/basis-dlm.pdf (last accessed: 28. April 2022).

Frantz, D. (2019). FORCE—Landsat + Sentinel-2 Analysis Ready Data and Beyond. Remote Sensing, 11, 1124.

Statistisches Bundesamt, Deutschland (2024). Ökosystematlas Deutschland
https://oekosystematlas-ugr.destatis.de/ (last accessed: 08.02.2024).

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National-scale crop type maps for Germany from combined time series of Sentinel-2 and Landsat data (2025) © 2025 by Tetteh, Gideon Okpoti; Schwieder, Marcel; Blickensdörfer, Lukas; Gocht, Alexander; Erasmi, Stefan; licensed under CC BY 4.0.

Funding was provided by the German Federal Ministry of Food and Agriculture as part of the joint project “Monitoring der biologischen Vielfalt in Agrarlandschaften” (MonViA, Monitoring of biodiversity in agricultural landscapes).

The IBEX ENA-Hi data sets are from Release 13 of all-sky map data for the first ten years, 2009-2018, in the form of ram direction Hydrogen, H, energetic neutral atom fluxes with Compton-Getting corrections for spacecraft motion and with no corrections for ENA survival probability between 1 and 100 AU. All-sky maps have been compiled for the whole 1 yr time interval. The Interstellar Boundary Explorer, IBEX, has operated in space since 2008 updating our knowledge of the outer heliosphere and its interaction with the local interstellar medium. Start-time: 2008-12-25. There are currently 14 releases of IBEX ENA-Hi and/or IBEX ENA-Lo data covering 2009-2018. The data consist of all-sky maps in Solar Ecliptic Longitude, east and west, and Latitude angles for Energetic Neutral Atom, ENA, Hydrogen fluxes from either IBEX ENA-Hi from energy band 2 through energy band 6, see the first table below, or from IBEX ENA-Lo from energy band 5 through energy band 8, see the second table below. Details of the data and enabled science from Release 13 are given in the following journal publications that describe the 1-yr data results and the IBEX-Hi and IBEX-Lo Instruments: McComas, D.J., et al. (2018), Heliosphere Responds to a Large Solar Wind Intensification: Decisive Observations from IBEX, Astrophys. J. Lett., 856(1), L10, (6 pp.), http://doi.org/10.3847/2041-8213/aab611 Funnsten, H.O., et al. (2009), The Interstellar Boundary Explorer High Energy (IBEX-Hi) Neutral Atom Imager, Space Sci. Rev., 146, 75-103, https://doi.org/10.1007/s11214-009-9504-y Fuselier, S.A., et al. (2009), The IBEX-Lo Sensor, Space Sci. Rev., 146, 117-147, https://doi.org/10.1007/s11214-009-9495-8 The IBEX ENA-Hi band/channel center energies and full width half maximum, FWHM, energy ranges are listed in a table below: +-----------------------------------------------------+ Energy Band Center Energy Energy Range ----------------------------------------------------- Channel 2 ~0.71 keV 0.52 keV to 0.95 keV Channel 3 ~1.11 keV 0.84 keV to 1.55 keV Channel 4 ~1.74 keV 1.36 keV to 2.50 keV Channel 5 ~2.73 keV 1.99 keV to 3.75 keV Channel 6 ~4.29 keV 3.13 keV to 6.00 keV +-----------------------------------------------------+ The IBEX ENA-Lo band/channel center energies are listed in a table below: +-----------------------------+ Energy Band Center Energy ----------------------------- Channel 1 0.015 keV Channel 2 0.029 keV Channel 3 0.055 keV Channel 4 0.110 keV Channel 5 0.209 keV Channel 6 0.439 keV Channel 7 0.872 keV Channel 8 1.821 keV +-----------------------------+ This particular IBEX-Hi CDF data product was constructed from the original ascii files named using the pattern hvset_noSP_ram_cg_yearN for N=1,10, includes pixel map data from the ram direction, with corrections, cg, for the Compton-Getting effect no corrections, nosp, for ENA survival probability between 1 AU and 100 AU, and a map compilation cadence equal to one year. In all, there are two IBEX ENA-Hi Release 13 CDF data products with one Compton-Getting correction setting, two survival probability settings, and one directional setting: ram. The table below defines how the file naming pattern is constructed for the two data products. Note that "ibex_h3_ena_hi_r13" is the file naming pattern root for these IBEX ENA-Hi CDF data products. The asterisk symbols in the last column of the table shows the line corresponding to this CDF data product within the expanded file naming pattern schema. +---------------------------------------------------------------------------------------------------+ C-G Corr. SP Corr. Dir. Acronym ENA Hi/Lo File Naming Pattern for 1 yr Skymaps --------------------------------------------------------------------------------------------------- cg nosp ram ENA Hi ibex_h3_ena_hi_r13_cg_nosp_ram_1yr *** cg sp ram ENA Hi ibex_h3_ena_hi_r13_cg_sp_ram_1yr +---------------------------------------------------------------------------------------------------+ The first column in the above table shows whether Compton-Getting, C-G, corrections have been applied to the data. C-G corrections account for how ENA measurements are affected by the the orientation of the IBEX spacecraft velocity vector relative to the arrival direction of the ENAs. * cg: Compton-Getting corrections applied * nocg: Compton-Getting corrections not applied The second column in the above table shows whether Survival Probability, SP, corrections have been applied to the data. SP corrections account for the loss of ENAs due to radiation pressure, photoionization and ionization via charge exchange with solar wind protons as they stream through the heliosphere. This correction scales the data out from IBEX at 1 AU to ~100 AU. In the original data this mode is denoted as Tabular. * sp: Survival Probability corrections applied * nosp: Survival Probability corrections not applied The third column in the above table shows the constraint placed on the ENA arrival direction relative to spacecraft motion used in the construction of each of the various IBEX ENA-Hi and IBEX ENA-Lo Skymaps. * omni: All data, no Constraint on the IBEX velocity vector relative to the ram direction of incoming ENAs * ram: Data constrained to times when the IBEX velocity vector pointed into the ram direction of the incoming ENAs * antiram: Data constrained to times when the IBEX velocity vector pointed away from the ram direction of the incoming ENAs The data in IBEX Release 13 are separated into 1 year segments. The following table shows the association between Release 13 map numbers from 1 to 20 with mission year from 1 to 10, orbits from 11 to 431b, and dates from 2008-12-25 to 2018-12-26. +-------------------------------------------------------------------------+ Skymap # Year Start-End of Orbit or Arcs Start Date to Stop Date ------------------------------------------------------------------------- 1 1 11-34 2008-12-25 to 2009-06-25 2 1 35-58 2009-06-25 to 2009-12-25 3 2 59-82 2009-12-25 to 2010-06-26 4 2 83-106 2010-06-26 to 2010-12-26 5 3 107-130a 2010-12-26 to 2011-06-25 6 3 130b-150a 2011-06-25 to 2011-12-24 7 4 150b-170a 2011-12-24 to 2012-06-22 8 4 170b-190b 2012-06-22 to 2012-12-26 9 5 191a-210b 2012-12-26 to 2013-06-26 10 5 211a-230b 2013-06-26 to 2013-12-26 11 6 231a-250b 2013-12-26 to 2014-06-26 12 6 251a-270b 2014-06-26 to 2014-12-24 13 7 271a-290b 2014-12-24 to 2015-06-24 14 7 291a-310b 2015-06-24 to 2015-12-23 15 8 311a-330b 2015-12-24 to 2016-06-23 16 8 331a-351a 2016-06-24 to 2016-12-26 17 9 351b-371a 2016-12-26 to 2017-06-25 18 9 371b-391a 2017-06-25 to 2017-12-25 19 10 391a-411b 2017-12-25 to 2018-06-28 20 10 412a-431b 2018-06-29 to 2018-12-26 +-------------------------------------------------------------------------+

The soil mechanical maps were drawn up by the Centre for Soil Mechanical Mapping of Ghent University and the Working Group or Commission for Soil Mechanical Mapping (several authors) and published under the auspices of the National Institute for Soil Mechanics. Quote from the explanatory texts to the ground mechanical maps: "The soil mechanical maps respond to a need for a summary of those components of the geological environment that play a role in land use and influence the design, construction and maintenance of buildings. However, the data provided should not be given absolute accuracy due to the interpolations made when compiling them. The maps provide information on the general geological and soil mechanical condition of the subsoil as it can be deduced from the tests available at the time of the mapping. They are therefore only guiding documents and the authors cannot be held responsible for their possible applications. The soil-mechanical maps cannot in any case exempt the user from carrying out additional tests in function of well-defined projects." The soil-mechanical map 7.7.5-8 Ekeren-Zuid, Plate VI: Joint thickness of the Scaldisian a sand complex and the Antwerpian sand complex, scale 1:10000. Explanatory text Grondmechanische kaart 7.7.5-8 Ekeren-Zuid (I. Bolle and I. Meyus under the leadership of E. De Beer, W. De Breuck and W. Van Impe).