Catholic Carbon Footprint Story Map Map:DataBurhans, Molly A., Cheney, David M., Gerlt, R.. . “PerCapita_CO2_Footprint_InDioceses_FULL”. Scale not given. Version 1.0. MO and CT, USA: GoodLands Inc., Environmental Systems Research Institute, Inc., 2019.Map Development: Molly BurhansMethodologyThis is the first global Carbon footprint of the Catholic population. We will continue to improve and develop these data with our research partners over the coming years. While it is helpful, it should also be viewed and used as a "beta" prototype that we and our research partners will build from and improve. The years of carbon data are (2010) and (2015 - SHOWN). The year of Catholic data is 2018. The year of population data is 2016. Care should be taken during future developments to harmonize the years used for catholic, population, and CO2 data.1. Zonal Statistics: Esri Population Data and Dioceses --> Population per dioceses, non Vatican based numbers2. Zonal Statistics: FFDAS and Dioceses and Population dataset --> Mean CO2 per Diocese3. Field Calculation: Population per Diocese and Mean CO2 per diocese --> CO2 per Capita4. Field Calculation: CO2 per Capita * Catholic Population --> Catholic Carbon FootprintAssumption: PerCapita CO2Deriving per-capita CO2 from mean CO2 in a geography assumes that people's footprint accounts for their personal lifestyle and involvement in local business and industries that are contribute CO2. Catholic CO2Assumes that Catholics and non-Catholic have similar CO2 footprints from their lifestyles.Derived from:A multiyear, global gridded fossil fuel CO2 emission data product: Evaluation and analysis of resultshttp://ffdas.rc.nau.edu/About.htmlRayner et al., JGR, 2010 - The is the first FFDAS paper describing the version 1.0 methods and results published in the Journal of Geophysical Research.Asefi et al., 2014 - This is the paper describing the methods and results of the FFDAS version 2.0 published in the Journal of Geophysical Research.Readme version 2.2 - A simple readme file to assist in using the 10 km x 10 km, hourly gridded Vulcan version 2.2 results.Liu et al., 2017 - A paper exploring the carbon cycle response to the 2015-2016 El Nino through the use of carbon cycle data assimilation with FFDAS as the boundary condition for FFCO2."S. Asefi‐Najafabady P. J. Rayner K. R. Gurney A. McRobert Y. Song K. Coltin J. Huang C. Elvidge K. BaughFirst published: 10 September 2014 https://doi.org/10.1002/2013JD021296 Cited by: 30Link to FFDAS data retrieval and visualization: http://hpcg.purdue.edu/FFDAS/index.phpAbstractHigh‐resolution, global quantification of fossil fuel CO2 emissions is emerging as a critical need in carbon cycle science and climate policy. We build upon a previously developed fossil fuel data assimilation system (FFDAS) for estimating global high‐resolution fossil fuel CO2 emissions. We have improved the underlying observationally based data sources, expanded the approach through treatment of separate emitting sectors including a new pointwise database of global power plants, and extended the results to cover a 1997 to 2010 time series at a spatial resolution of 0.1°. Long‐term trend analysis of the resulting global emissions shows subnational spatial structure in large active economies such as the United States, China, and India. These three countries, in particular, show different long‐term trends and exploration of the trends in nighttime lights, and population reveal a decoupling of population and emissions at the subnational level. Analysis of shorter‐term variations reveals the impact of the 2008–2009 global financial crisis with widespread negative emission anomalies across the U.S. and Europe. We have used a center of mass (CM) calculation as a compact metric to express the time evolution of spatial patterns in fossil fuel CO2 emissions. The global emission CM has moved toward the east and somewhat south between 1997 and 2010, driven by the increase in emissions in China and South Asia over this time period. Analysis at the level of individual countries reveals per capita CO2 emission migration in both Russia and India. The per capita emission CM holds potential as a way to succinctly analyze subnational shifts in carbon intensity over time. Uncertainties are generally lower than the previous version of FFDAS due mainly to an improved nightlight data set."Global Diocesan Boundaries:Burhans, M., Bell, J., Burhans, D., Carmichael, R., Cheney, D., Deaton, M., Emge, T. Gerlt, B., Grayson, J., Herries, J., Keegan, H., Skinner, A., Smith, M., Sousa, C., Trubetskoy, S. “Diocesean Boundaries of the Catholic Church” [Feature Layer]. Scale not given. Version 1.2. Redlands, CA, USA: GoodLands Inc., Environmental Systems Research Institute, Inc., 2016.Using: ArcGIS. 10.4. Version 10.0. Redlands, CA: Environmental Systems Research Institute, Inc., 2016.Boundary ProvenanceStatistics and Leadership DataCheney, D.M. “Catholic Hierarchy of the World” [Database]. Date Updated: August 2019. Catholic Hierarchy. Using: Paradox. Retrieved from Original Source.Catholic HierarchyAnnuario Pontificio per l’Anno .. Città del Vaticano :Tipografia Poliglotta Vaticana, Multiple Years.The data for these maps was extracted from the gold standard of Church data, the Annuario Pontificio, published yearly by the Vatican. The collection and data development of the Vatican Statistics Office are unknown. GoodLands is not responsible for errors within this data. We encourage people to document and report errant information to us at data@good-lands.org or directly to the Vatican.Additional information about regular changes in bishops and sees comes from a variety of public diocesan and news announcements.GoodLands’ polygon data layers, version 2.0 for global ecclesiastical boundaries of the Roman Catholic Church:Although care has been taken to ensure the accuracy, completeness and reliability of the information provided, due to this being the first developed dataset of global ecclesiastical boundaries curated from many sources it may have a higher margin of error than established geopolitical administrative boundary maps. Boundaries need to be verified with appropriate Ecclesiastical Leadership. The current information is subject to change without notice. No parties involved with the creation of this data are liable for indirect, special or incidental damage resulting from, arising out of or in connection with the use of the information. We referenced 1960 sources to build our global datasets of ecclesiastical jurisdictions. Often, they were isolated images of dioceses, historical documents and information about parishes that were cross checked. These sources can be viewed here:https://docs.google.com/spreadsheets/d/11ANlH1S_aYJOyz4TtG0HHgz0OLxnOvXLHMt4FVOS85Q/edit#gid=0To learn more or contact us please visit: https://good-lands.org/Esri Gridded Population Data 2016DescriptionThis layer is a global estimate of human population for 2016. Esri created this estimate by modeling a footprint of where people live as a dasymetric settlement likelihood surface, and then assigned 2016 population estimates stored on polygons of the finest level of geography available onto the settlement surface. Where people live means where their homes are, as in where people sleep most of the time, and this is opposed to where they work. Another way to think of this estimate is a night-time estimate, as opposed to a day-time estimate.Knowledge of population distribution helps us understand how humans affect the natural world and how natural events such as storms and earthquakes, and other phenomena affect humans. This layer represents the footprint of where people live, and how many people live there.Dataset SummaryEach cell in this layer has an integer value with the estimated number of people likely to live in the geographic region represented by that cell. Esri additionally produced several additional layers World Population Estimate Confidence 2016: the confidence level (1-5) per cell for the probability of people being located and estimated correctly. World Population Density Estimate 2016: this layer is represented as population density in units of persons per square kilometer.World Settlement Score 2016: the dasymetric likelihood surface used to create this layer by apportioning population from census polygons to the settlement score raster.To use this layer in analysis, there are several properties or geoprocessing environment settings that should be used:Coordinate system: WGS_1984. This service and its underlying data are WGS_1984. We do this because projecting population count data actually will change the populations due to resampling and either collapsing or splitting cells to fit into another coordinate system. Cell Size: 0.0013474728 degrees (approximately 150-meters) at the equator. No Data: -1Bit Depth: 32-bit signedThis layer has query, identify, pixel, and export image functions enabled, and is restricted to a maximum analysis size of 30,000 x 30,000 pixels - an area about the size of Africa.Frye, C. et al., (2018). Using Classified and Unclassified Land Cover Data to Estimate the Footprint of Human Settlement. Data Science Journal. 17, p.20. DOI: http://doi.org/10.5334/dsj-2018-020.What can you do with this layer?This layer is unsuitable for mapping or cartographic use, and thus it does not include a convenient legend. Instead, this layer is useful for analysis, particularly for estimating counts of people living within watersheds, coastal areas, and other areas that do not have standard boundaries. Esri recommends using the Zonal Statistics tool or the Zonal Statistics to Table tool where you provide input zones as either polygons, or raster data, and the tool will summarize the count of population within those zones. https://www.esri.com/arcgis-blog/products/arcgis-living-atlas/data-management/2016-world-population-estimate-services-are-now-available/

World Imagery provides one meter or better satellite and aerial imagery in many parts of the world and lower resolution satellite imagery worldwide. The map includes 15-meter TerraColor imagery at small and mid-scales (~1:591M down to ~1:288k) for the world. The map features Vantor imagery at 0.3-meter resolution for select metropolitan areas around the world, 0.5-meter resolution across the United States and parts of Western Europe, and 0.6-meter resolution imagery across the rest of the world. In addition to commercial sources, the World Imagery map features high-resolution aerial photography contributed by the GIS User Community. This imagery ranges from 0.3-meter to 0.03-meter resolution, down to ~1:280 in select communities. You can contribute your imagery to this map and have it served by Esri via the Community Maps Program.Updates and CoverageYou can use the World Imagery Updates app to learn more about recent updates and map coverage. UseYou can add this layer to the ArcGIS Online Map Viewer, ArcGIS Desktop, or ArcGIS Pro. To view this layer with a useful reference overlay, open the Imagery Hybrid (WGS84) web map. FeedbackHave you ever seen a problem in the Esri World Imagery Map that you wanted to report? You can use the Imagery Map Feedback web map to provide comments on issues. The feedback will be reviewed by the ArcGIS Online team and considered for one of our updates. Precise Tile RegistrationThe World Imagery map uses the improved tiling scheme “WGS84 Geographic, Version 2” to ensure proper tile positioning at higher resolutions (neighborhood level and beyond). The new tiling scheme is much more precise than tiling schemes of the legacy basemaps Esri released years ago. We recommend that you start using this new basemap for any new web maps in WGS84 that you plan to author. Due to the number of differences between the old and new tiling schemes, some web clients will not be able to overlay tile layers in the old and new tiling schemes in one web map.

Mature Support Notice: This item is in mature support as of December 2024. A new version of this item is available for your use. Esri recommends updating your maps and apps to use the new version. See blog for more information.This web map presents a vector basemap of OpenStreetMap (OSM) data hosted by Esri. This version of the map is rendered in a style similar to the Esri Street Map (with Relief). It includes the World Hillshade layer. Created from the sunsetted Daylight map distribution, data updates supporting this layer are no longer available. OpenStreetMap is an open collaborative project to create a free editable map of the world. Volunteers gather location data using GPS, local knowledge, and other free sources of information and upload it. The resulting free map can be viewed and downloaded from the OpenStreetMap site: www.OpenStreetMap.org. Esri is a supporter of the OSM project. Precise Tile Registration: The web map uses the improved tiling scheme “WGS84 Geographic, Version 2” to ensure proper tile positioning at higher resolutions (neighborhood level and beyond). The new tiling scheme is much more precise than tiling schemes of the legacy basemaps Esri released years ago. We recommend that you start using this new basemap for any new web maps in WGS84 that you plan to author. Due to the number of differences between the old and new tiling schemes, some web clients will not be able to overlay tile layers in the old and new tiling schemes in one web map.

Landforms are large recognizable features such as mountains, hills and plains; they are an important determinant of ecological character, habitat definition and terrain analysis. Landforms are important to the distribution of life in natural systems and are the basis for opportunities in built systems, and therefore landforms play a useful role in all natural science fields of study and planning disciplines. Dataset SummaryPhenomenon Mapped: LandformsGeographic Extent: GlobalProjection: WGS 1984Mosaic Projection: Web Mercator Auxiliary SphereUnits: MetersCell Size: 231.91560581932 metersPixel Depth: 8-bit unsigned integerAnalysis: Restricted single source analysis. Maximum size of analysis is 30,000 x 30,000 pixels.Source: EsriPublication Date: May 2016ArcGIS Server URL: https://landscape7.arcgis.com/arcgis/ In February 2017, Esri updated the World Landforms - Improved Hammond Method service with two display functions: Ecological Land Units landform classes and Ecological Facets landform classes. This layer represents Ecological Facets landform classes. You can view the Ecological Land Units landform classes by choosing Image Display, and changing the Renderer. This layer was produced using the Improved Hammond Landform Classification Algorithm produced by Esri in 2016. This algorithm published and described by Karagulle et al. 2017: Modeling global Hammond landform regions from 250-m elevation data in Transactions in GIS. The algorithm, which is based on the most recent work in this area by Morgan, J. & Lesh, A. 2005: Developing Landform Maps Using Esri’s Model Builder., Esri converted Morgan’s model into a Python script and revised it to work on global 250-meter resolution GMTED2010 elevation data. Hammond’s landform classification characterizes regions rather than identifying individual features, thus, this layer contains sixteen classes of landforms:Nearly flat plains Smooth plains with some local relief Irregular plains with moderate relief Irregular plains with low hills Scattered moderate hills Scattered high hills Scattered low mountains Scattered high mountains Moderate hills High hills Tablelands with moderate relief Tablelands with considerable relief Tablelands with high relief Tablelands with very high relief Low mountains High mountains To produce these classes, Esri staff first projected the 250-meter resolution GMTED elevation data to the World Equidistant Cylindrical coordinate system. Each cell in this dataset was assigned three characteristics: slope based on 3-km neighborhood, relief based on 6 km neighborhood, and profile based on 6-km neighborhood. The last step was to overlay the combination of these three characteristics with areas that are exclusively plains. Slope is the percentage of the 3-km neighborhood occupied by gentle slope. Hammond specified 8% as the threshold for gentle slope. Slope is used to define how flat or steep the terrain is. Slope was classified into one of four classes: Percent of neighborhood over 8% of slopeSlope Classes0 - 20%40021% -50%30051% - 80%200>81% 100Local Relief is the difference between the maximum and minimum elevation within in the 6-km neighborhood. Local relief is used to define terrain how rugged or the complexity of the terrain"s texture. Relief was assigned one of six classes:Change in elevationRelief Class ID0 – 30 meters1031 meter – 90 meters2091 meter – 150 meters30151 meter – 300 meters40301 meter – 900 meters50>900 meters60The combination of slope and relief begin to define terrain as mountains, hills and plains. However, the difference between mountains or hills and tablelands cannot be distinguished using only these parameters. Profile is used to determine tableland areas. Profile identifies neighborhoods with upland and lowland areas, and calculates the percent area of gently sloping terrain within those upland and lowland areas. A 6-km circular neighborhood was used to calculate the profile parameter. Upland/lowland is determined by the difference between average local relief and elevation. In the 6-km neighborhood window, if the difference between maximum elevation and cell’s elevation is smaller than half of the local relief it’s an upland. If the difference between maximum elevation and cell’s elevation is larger than half of the local relief it’s a lowland. Profile was assigned one of five classes:Percent of neighborhood over 8% slope in upland or lowland areasProfile ClassLess than 50% gentle slope is in upland or lowland0More than 75% of gentle slope is in lowland150%-75% of gentle slope is in lowland250-75% of gentle slope is in upland3More than 75% of gentle slope is in upland4Early reviewers of the resulting classes noted one confusing outcome, which was that areas were classified as "plains with low mountains", or "plains with hills" were often mostly plains, and the hills or mountains were part of an adjacent set of exclusively identified hills or mountains. To address this areas that are exclusively plains were produced, and used to override these confusing areas. The hills and mountains within those areas were converted to their respective landform class. The combination of slope, relief and profile merged with the areas of plains, can be better understood using the following diagram, which uses the colors in this layer to show which classes are present and what parameter values produced them: What can you do with this layer?This layer is suitable for both visualization and analysis. It can be used in ArcGIS Online in web maps and applications and can be used in ArcGIS Desktop. Restricted single source analysis means this layer has size constraints for analysis and it is not recommended for use with other layers in multisource analysis. This layer is part of a larger collection of landscape layers that you can use to perform a wide variety of mapping and analysis tasks. The Living Atlas of the World provides an easy way to explore the landscape layers and many other beautiful and authoritative maps on hundreds of topics. Geonet is a good resource for learning more about landscape layers and the Living Atlas of the World. To get started see the Living Atlas Discussion Group. The Esri Insider Blog provides an introduction to the Ecophysiographic Mapping project.

Impervious surfaces are surfaces that do not allow water to pass through. Examples of these surfaces include highways, parking lots, rooftops, and airport runways. Instead of allowing rain to pass into the soil, impervious surfaces cause water to collect at the surface, then run off. An increase in impervious surface area causes an increase of water volume which needs to be managed by stormwater systems. With the flow come pollutants, which collect on impervious surfaces then discharge with the runoff into streams and the ocean. Runoff water does not enter the water table, and that can cause other management issues, such as interruptions in baseline stream flow.The NLCD imperviousness layer represents urban impervious surfaces as a percentage of developed surface over every 30-meter pixel in the United States. Phenomenon Mapped: The proportion of the landscape that is impervious to water.Time Extent: 2001, 2004, 2006, 2008, 2011, 2013, 2016, 2019, and 2021 for the lower 48 conterminous US states. A small portion of Alaska around Anchorage displays a time series of 2001, 2011, and 2016. Hawaii, Puerto Rico, and the US Virgin Islands unfortunately only have data for 2001 so there is only one image in the series. This information may be used in conjunction with the USA NLCD Land Cover layer.Units: PercentCell Size: 30 metersSource Type: DiscretePixel Type: Unsigned integerData Coordinate System: North America Albers Equal Area Conic (102008)Mosaic Projection: North America Albers Equal Area Conic (102008)Extent: CONUS, Hawaii, A portion of Alaska around Anchorage, District of Columbia, Puerto RicoNoData Value: 127Source: Multi-Resolution Land Characteristics ConsortiumPublication Date: June 30, 2023ArcGIS Server URL: https://landscape10.arcgis.com/arcgis/Time SeriesBy default, this layer will appear in your client with a time slider which allows you to play the series as an animation. The animation will advance year by year, but the layer only changes appearance every few years in the lower 48 states, in 2001, 2004, 2006, 2008, 2011, 2013, 2016, 2019, and 2021. To select just one year in the series, first turn the time series off on the time slider, then create a definition query on the layer which selects only the desired year.Time Series DescriptorMRLC issued a set of companion rasters with this impervious surface layer showing the reason why each pixel is impervious. This companion layer, called the Developed Imperviousness Descriptor, is not currently available in this map service. The descriptor layer identifies types of roads, core urban areas, and energy production sites for each impervious pixel to allow deeper analysis of developed features. The descriptor layer may be downloaded directly from MRLC and added to ArcGIS Pro.Alaska, Hawaii, and Puerto RicoAt this time Alaska, Hawaii, and Puerto Rico are produced with a different methodology, and are not set up to be directly compared the way the CONUS time series is. To analyze change between the latest two data years for this portion of Alaska, be sure to use the NLCD 2011 to 2016 Developed Impervious Change raster. For Hawaii and Puerto Rico, only the year 2001 is available for download at the MRLC.North America Albers ProjectionAll NLCD layers in the Living Atlas are projected into the North America Albers Projection before serving in the Living Atlas. This allows the coterminous USA, Puerto Rico, Hawaii, and Alaska to be served from a common projection and analyzed together. In tests performed by esri, the NLCD land cover classes after projection to North America Albers had the exact same number of pixels in input as output, but pixels had been slightly rearranged after projection. Processing TemplatesThis layer comes with two color schemes, cool and warm. The default is a cool gray color scheme, designed to look good on light and dark gray web maps. To choose a warm color scheme which was the default until 2021, change the processing template to the Impervious Surface Warm Renderer in your map client.Dataset SummaryThe National Land Cover Database products are created through a cooperative project conducted by the Multi-Resolution Land Characteristics Consortium (MRLC). The MRLC Consortium is a partnership of federal agencies, consisting of the U.S. Geological Survey, the National Oceanic and Atmospheric Administration, the U.S. Environmental Protection Agency, the U.S. Department of Agriculture, the U.S. Forest Service, the National Park Service, the U.S. Fish and Wildlife Service, the Bureau of Land Management and the USDA Natural Resources Conservation Service.What can you do with this layer?This layer can be used to create maps and to visualize the underlying data. This layer can be used as an analytic input in ArcGIS Desktop.This layer is part of a larger collection of landscape layers that you can use to perform a wide variety of mapping and analysis tasks.The Living Atlas of the World provides an easy way to explore the landscape layers and many other beautiful and authoritative maps on hundreds of topics.

The California State Water Resources Control Board is currently in the process of improving the functionality and accessibility of information residing in their Water Quality Control Plans (aka Basin Plans). In order to achieve this, the data (i.e. statewide water quality objectives, beneficial uses, applicable TMDLs, etc.), are being transferred to a standardized digital format and linked to applicable surface water features. This dataset is limited to the beneficial uses data, while the water quality objectives, applicable TMDLs, etc. will be released at a later date. Data formats will include GIS data layers and numerous nonspatial data tables. The GIS layers contain hydrography features derived from a 2012 snapshot of the high-resolution (1:24000 scale) National Hydrography Dataset with added attribution. Nonspatial tables will contain various textual and numeric data from the Regional Basin and State Plans. The extent of the dataset covers the state of California and the non-spatial tables reflect the information and elements from the various plans used up to 2020. The GIS layers and associated attribution will enable the future integration of the various elements of the Basin Plans to ensure that all applicable Basin Plan requirements for a particular waterbody can be determined in a quick and precise manner across different modern mediums. The data are being managed and the project implemented by State and Regional Water Board staff using ESRI's ArcGIS Server and ArcSDE technology.The statewide layer is only provided as a map image layer service. The data is available as feature layer services by Regional Board extract. To view all regional board feature layer extracts go to the Basin Plan GIS Data Library Group here.

This app is part of Indicators of the Planet. Please see https://livingatlas.arcgis.com/indicatorsThis layer presents detectable thermal activity from VIIRS satellites for the last 7 days. VIIRS Thermal Hotspots and Fire Activity is a product of NASA’s Land, Atmosphere Near real-time Capability for EOS (LANCE) Earth Observation Data, part of NASA's Earth Science Data.Source: NASA LANCE - VNP14IMG_NRT active fire detection - WorldScale/Resolution: 375-meterUpdate Frequency: Hourly using the aggregated live feed methodologyArea Covered: WorldWhat can I do with this layer?This layer represents the most frequently updated and most detailed global remotely sensed wildfire information. Detection attributes include time, location, and intensity. It can be used to track the location of fires from the recent past, a few hours up to seven days behind real time. This layer also shows the location of wildfire over the past 7 days as a time-enabled service so that the progress of fires over that timeframe can be reproduced as an animation.The VIIRS thermal activity layer can be used to visualize and assess wildfires worldwide. However, it should be noted that this dataset contains many “false positives” (e.g., oil/natural gas wells or volcanoes) since the satellite will detect any large thermal signal.Fire points in this service are generally available within 3 1/4 hours after detection by a VIIRS device. LANCE estimates availability at around 3 hours after detection, and esri livefeeds updates this feature layer every 15 minutes from LANCE.Even though these data display as point features, each point in fact represents a pixel that is >= 375 m high and wide. A point feature means somewhere in this pixel at least one "hot" spot was detected which may be a fire.VIIRS is a scanning radiometer device aboard the Suomi NPP and NOAA-20 satellites that collects imagery and radiometric measurements of the land, atmosphere, cryosphere, and oceans in several visible and infrared bands. The VIIRS Thermal Hotspots and Fire Activity layer is a livefeed from a subset of the overall VIIRS imagery, in particular from NASA's VNP14IMG_NRT active fire detection product. The downloads are automatically downloaded from LANCE, NASA's near real time data and imagery site, every 15 minutes.The 375-m data complements the 1-km Moderate Resolution Imaging Spectroradiometer (MODIS) Thermal Hotspots and Fire Activity layer; they both show good agreement in hotspot detection but the improved spatial resolution of the 375 m data provides a greater response over fires of relatively small areas and provides improved mapping of large fire perimeters.Attribute informationLatitude and Longitude: The center point location of the 375 m (approximately) pixel flagged as containing one or more fires/hotspots.Satellite: Whether the detection was picked up by the Suomi NPP satellite (N) or NOAA-20 satellite (1). For best results, use the virtual field WhichSatellite, redefined by an arcade expression, that gives the complete satellite name.Confidence: The detection confidence is a quality flag of the individual hotspot/active fire pixel. This value is based on a collection of intermediate algorithm quantities used in the detection process. It is intended to help users gauge the quality of individual hotspot/fire pixels. Confidence values are set to low, nominal and high. Low confidence daytime fire pixels are typically associated with areas of sun glint and lower relative temperature anomaly (<15K) in the mid-infrared channel I4. Nominal confidence pixels are those free of potential sun glint contamination during the day and marked by strong (>15K) temperature anomaly in either day or nighttime data. High confidence fire pixels are associated with day or nighttime saturated pixels.Please note: Low confidence nighttime pixels occur only over the geographic area extending from 11 deg E to 110 deg W and 7 deg N to 55 deg S. This area describes the region of influence of the South Atlantic Magnetic Anomaly which can cause spurious brightness temperatures in the mid-infrared channel I4 leading to potential false positive alarms. These have been removed from the NRT data distributed by FIRMS.FRP: Fire Radiative Power. Depicts the pixel-integrated fire radiative power in MW (MegaWatts). FRP provides information on the measured radiant heat output of detected fires. The amount of radiant heat energy liberated per unit time (the Fire Radiative Power) is thought to be related to the rate at which fuel is being consumed (Wooster et. al. (2005)).DayNight: D = Daytime fire, N = Nighttime fireNote about near real time data:Near real time data is not checked thoroughly before it's posted on LANCE or downloaded and posted to the Living Atlas. NASA's goal is to get vital fire information to its customers within three hours of observation time. However, the data is screened by a confidence algorithm which seeks to help users gauge the quality of individual hotspot/fire points. Low confidence daytime fire pixels are typically associated with areas of sun glint and lower relative temperature anomaly (<15K) in the mid-infrared channel I4. Medium confidence pixels are those free of potential sun glint contamination during the day and marked by strong (>15K) temperature anomaly in either day or nighttime data. High confidence fire pixels are associated with day or nighttime saturated pixels.This layer is provided for informational purposes and is not monitored 24/7 for accuracy and currency.

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).

The California State Water Resources Control Board is currently in the process of improving the functionality and accessibility of information residing in their Water Quality Control Plans (aka Basin Plans). In order to achieve this, the data (i.e. statewide water quality objectives, beneficial uses, applicable TMDLs, etc.), are being transferred to a standardized digital format and linked to applicable surface water features. This dataset is limited to the beneficial uses data, while the water quality objectives, applicable TMDLs, etc. will be released at a later date. Data formats will include GIS data layers and numerous nonspatial data tables. The GIS layers contain hydrography features derived from a 2012 snapshot of the high-resolution (1:24000 scale) National Hydrography Dataset with added attribution. Nonspatial tables will contain various textual and numeric data from the Regional Basin and State Plans. The extent of the dataset covers the state of California and the non-spatial tables reflect the information and elements from the various plans used up to 2020. The GIS layers and associated attribution will enable the future integration of the various elements of the Basin Plans to ensure that all applicable Basin Plan requirements for a particular waterbody can be determined in a quick and precise manner across different modern mediums. The data are being managed and the project implemented by State and Regional Water Board staff using ESRI's ArcGIS Server and ArcSDE technology.The statewide layer is only provided as a map image layer service. The data is available as feature layer services by Regional Board extract. To view all regional board feature layer extracts go to the Basin Plan GIS Data Library Group here.

The USDA-ARS Southwest Watershed Research Center (SWRC) operates the Walnut Gulch Experimental Watershed (WGEW) in southeastern Arizona as an outdoor laboratory for studying semiarid rangeland hydrologic, ecosystem, climate, and erosion processes. Since its establishment in 1953, the SWRC in Tucson, Arizona, has collected, processed, managed, and disseminated high-resolution, spatially distributed hydrologic data in support of the center’s mission. Data management at the SWRC has evolved through time in response to new computing, storage, and data access technologies. In 1996, the SWRC initiated a multiyear project to upgrade rainfall and runoff sensors and convert analog systems to digital electronic systems supported by data loggers. This conversion was coupled with radio telemetry to remotely transmit recorded data to a central computer, thus greatly reducing operational overhead by reducing labor, maintenance, and data processing time. A concurrent effort was initiated to improve access to SWRC data by creating a system based on a relational database supporting access to the data via the Internet. An SWRC team made up of scientists, IT specialists, programmers, hydrologic technicians, and instrumentation specialists was formed. This effort is termed the Southwest Watershed Research Center Data Access Project (DAP). The goal of the SWRC DAP is to efficiently disseminate data to researchers; land owners, users, and managers; and to the public. Primary access to the data is provided through a Web-based user interface. In addition, data can be accessed directly from within the SWRC network. The first priority for the DAP was to assimilate and make available rainfall and runoff data collected from two instrumented field sites, the WGEW near Tombstone, Arizona, and the Santa Rita Experimental Range (SRER) south of Tucson, Arizona. This web map describes the associated GIS layers. Resources in this dataset:Resource Title: GeoData catalog record. File Name: Web Page, url: https://geodata.nal.usda.gov/geonetwork/srv/eng/catalog.search#/metadata/fe4ac74f13484a169899b166159e0bb5

This layer is subset of World Ecological Facets Landform Classes Image Layer. Landforms are large recognizable features such as mountains, hills and plains; they are an important determinant of ecological character, habitat definition and terrain analysis. Landforms are important to the distribution of life in natural systems and are the basis for opportunities in built systems, and therefore landforms play a useful role in all natural science fields of study and planning disciplines.Dataset SummaryPhenomenon Mapped: LandformsUnits: MetersCell Size: 231.91560581932 metersSource Type: ThematicPixel Type: 8-bit unsigned integerData Coordinate System: WGS 1984Mosaic Projection: Web Mercator Auxiliary SphereExtent: GlobalSource: EsriPublication Date: May 2016ArcGIS Server URL: https://landscape7.arcgis.com/arcgis/In February 2017, Esri updated the World Landforms - Improved Hammond Method service with two display functions: Ecological Land Units landform classes and Ecological Facets landform classes. This layer represents Ecological Facets landform classes. You can view the Ecological Land Units landform classes by choosing Image Display, and changing the Renderer. This layer was produced using the Improved Hammond Landform Classification Algorithm produced by Esri in 2016. This algorithm published and described by Karagulle et al. 2017: Modeling global Hammond landform regions from 250-m elevation data in Transactions in GIS.The algorithm, which is based on the most recent work in this area by Morgan, J. & Lesh, A. 2005: Developing Landform Maps Using Esri’s Model Builder., Esri converted Morgan’s model into a Python script and revised it to work on global 250-meter resolution GMTED2010 elevation data. Hammond’s landform classification characterizes regions rather than identifying individual features, thus, this layer contains sixteen classes of landforms:Nearly flat plainsSmooth plains with some local reliefIrregular plains with moderate relief Irregular plains with low hillsScattered moderate hillsScattered high hillsScattered low mountainsScattered high mountainsModerate hillsHigh hills Tablelands with moderate reliefTablelands with considerable reliefTablelands with high relief Tablelands with very high relief Low mountainsHigh mountainsTo produce these classes, Esri staff first projected the 250-meter resolution GMTED elevation data to the World Equidistant Cylindrical coordinate system. Each cell in this dataset was assigned three characteristics: slope based on 3-km neighborhood, relief based on 6 km neighborhood, and profile based on 6-km neighborhood. The last step was to overlay the combination of these three characteristics with areas that are exclusively plains. Slope is the percentage of the 3-km neighborhood occupied by gentle slope. Hammond specified 8% as the threshold for gentle slope. Slope is used to define how flat or steep the terrain is. Slope was classified into one of four classes: Percent of neighborhood over 8% of slopeSlope Classes0 - 20%40021% -50%30051% - 80%200>81% 100Local Relief is the difference between the maximum and minimum elevation within in the 6-km neighborhood. Local relief is used to define terrain how rugged or the complexity of the terrain's texture. Relief was assigned one of six classes:Change in elevationRelief Class ID0 – 30 meters1031 meter – 90 meters2091 meter – 150 meters30151 meter – 300 meters40301 meter – 900 meters50>900 meters60The combination of slope and relief begin to define terrain as mountains, hills and plains. However, the difference between mountains or hills and tablelands cannot be distinguished using only these parameters. Profile is used to determine tableland areas. Profile identifies neighborhoods with upland and lowland areas, and calculates the percent area of gently sloping terrain within those upland and lowland areas. A 6-km circular neighborhood was used to calculate the profile parameter. Upland/lowland is determined by the difference between average local relief and elevation. In the 6-km neighborhood window, if the difference between maximum elevation and cell’s elevation is smaller than half of the local relief it’s an upland. If the difference between maximum elevation and cell’s elevation is larger than half of the local relief it’s a lowland. Profile was assigned one of five classes:Percent of neighborhood over 8% slope in upland or lowland areasProfile ClassLess than 50% gentle slope is in upland or lowland0More than 75% of gentle slope is in lowland150%-75% of gentle slope is in lowland250-75% of gentle slope is in upland3More than 75% of gentle slope is in upland4Early reviewers of the resulting classes noted one confusing outcome, which was that areas were classified as "plains with low mountains", or "plains with hills" were often mostly plains, and the hills or mountains were part of an adjacent set of exclusively identified hills or mountains. To address this areas that are exclusively plains were produced, and used to override these confusing areas. The hills and mountains within those areas were converted to their respective landform class.The combination of slope, relief and profile merged with the areas of plains, can be better understood using the following diagram, which uses the colors in this layer to show which classes are present and what parameter values produced them:What can you do with this layer?This layer is suitable for both visualization and analysis. It can be used in ArcGIS Online in web maps and applications and can be used in ArcGIS Desktop. This layer is part of a larger collection of landscape layers that you can use to perform a wide variety of mapping and analysis tasks.The Living Atlas of the World provides an easy way to explore the landscape layers and many other beautiful and authoritative maps on hundreds of topics.Geonet is a good resource for learning more about landscape layers and the Living Atlas of the World. To get started see the Living Atlas Discussion Group.The Esri Insider Blog provides an introduction to the Ecophysiographic Mapping project.

The Global Imagery Browse Services (GIBS) system is a core EOSDIS component which provides a scalable, responsive, highly available, and community standards based set of imagery services. These services are designed with the goal of advancing user interactions with EOSDIS’ inter-disciplinary data through enhanced visual representation and discovery.GIBS Available Imagery ProductsThe GIBS imagery archive includes approximately 1000 imagery products representing visualized science data from the NASA Earth Observing System Data and Information System (EOSDIS). Each imagery product is generated at the native resolution of the source data to provide "full resolution" visualizations of a science parameter. GIBS works closely with the science teams to identify the appropriate data range and color mappings, where appropriate, to provide the best quality imagery to the Earth science community. Many GIBS imagery products are generated by the EOSDIS LANCE near real-time processing system resulting in imagery available in GIBS within 3.5 hours of observation. These products and others may also extend from present to the beginning of the satellite mission. In addition, GIBS makes available supporting imagery layers such as data/no-data, water masks, orbit tracks, and graticules to improve imagery usage.The GIBS team is actively engaging the NASA EOSDIS Distributed Active Archive Centers (DAACs) to add more imagery products and to extend their coverage throughout the life of the mission. The remainder of this page provides a structured view of the layers currently available within GIBS grouped by science discipline and science observation. For information regarding how to access these products, see the GIBS API section of this wiki. For information regarding how to access these products through an existing client, refer to the Map Library and GIS Client sections of this wiki. If you are aware of a science parameter that you would like to see visualized, please contact us at support@earthdata.nasa.gov. https://wiki.earthdata.nasa.gov/display/GIBS/GIBS+Available+Imagery+Products#expand-AerosolOpticalDepth29ProductsNASA GIS API for Developers https://wiki.earthdata.nasa.gov/display/GIBS/GIBS+API+for+Developers

The Federal Emergency Management Agency (FEMA) produces Flood Insurance Rate maps and identifies Special Flood Hazard Areas as part of the National Flood Insurance Program's floodplain management. Special Flood Hazard Areas have regulations that include the mandatory purchase of flood insurance.Dataset SummaryPhenomenon Mapped: Flood Hazard AreasCoordinate System: Web Mercator Auxiliary SphereExtent: 50 United States plus Puerto Rico, the US Virgin Islands, Guam, the Northern Mariana Islands and American SamoaVisible Scale: The layer is limited to scales of 1:1,000,000 and larger. Use the USA Flood Hazard Areas imagery layer for smaller scales.Source: Federal Emergency Management AgencyPublication Date: April 1, 2019This layer is derived from the April 1, 2019 version of the National Flood Hazard Layer feature class S_Fld_Haz_Ar. The data were aggregated into eight classes to produce the Esri Symbology field based on symbology provided by FEMA. All other layer attributes are derived from the National Flood Hazard Layer. The layer was projected to Web Mercator Auxiliary Sphere and the resolution set to 1 meter.To improve performance Flood Zone values "Area Not Included", "Open Water", "D", "NP", and No Data were removed from the layer. Areas with Flood Zone value "X" subtype "Area of Minimal Flood Hazard" were also removed. An imagery layer created from this dataset provides access to the full set of records in the National Flood Hazard Layer.A web map featuring this layer is available for you to use.What can you do with this Feature Layer?Feature layers work throughout the ArcGIS system. Generally your work flow with feature layers will begin in ArcGIS Online or ArcGIS Pro. Below are just a few of the things you can do with a feature service in Online and Pro.ArcGIS OnlineAdd this layer to a map in the map viewer. The layer is limited to scales of approximately 1:1,000,000 or larger but an imagery layer created from the same data can be used at smaller scales to produce a webmap that displays across the full range of scales. The layer or a map containing it can be used in an application.Change the layer’s transparency and set its visibility rangeOpen the layer’s attribute table and make selections and apply filters. Selections made in the map or table are reflected in the other. Center on selection allows you to zoom to features selected in the map or table and show selected records allows you to view the selected records in the table.Change the layer’s style and filter the data. For example, you could change the symbology field to Special Flood Hazard Area and set a filter for = “T” to create a map of only the special flood hazard areas. Add labels and set their propertiesCustomize the pop-upUse in analysis tools to discover patterns in the dataArcGIS ProAdd this layer to a 2d or 3d map. The same scale limit as Online applies in ProUse as an input to geoprocessing. For example, copy features allows you to select then export portions of the data to a new feature class. Areas up to 1,000-2,000 features can be exported successfully.Change the symbology and the attribute field used to symbolize the dataOpen table and make interactive selections with the mapModify the pop-upsApply Definition Queries to create sub-sets of the layerThis layer is part of the Living Atlas of the World that provides an easy way to explore the landscape layers and many other beautiful and authoritative maps on hundreds of topics.

Source: NASA LANCE - VNP14IMG_NRT active fire detection - World
Scale/Resolution: 375-meter
Update Frequency: Hourly using the aggregated live feed methodology
Area Covered: World

What can I do with this layer?
This layer represents the most frequently updated and most detailed global remotely sensed wildfire information. Detection attributes include time, location, and intensity. It can be used to track the location of fires from the recent past, a few hours up to seven days behind real time. This layer also shows the location of wildfire over the past 7 days as a time-enabled service so that the progress of fires over that timeframe can be reproduced as an animation.

The VIIRS thermal activity layer can be used to visualize and assess wildfires worldwide. However, it should be noted that this dataset contains many “false positives” (e.g., oil/natural gas wells or volcanoes) since the satellite will detect any large thermal signal.

Fire points in this service are generally available within 3 1/4 hours after detection by a VIIRS device. LANCE estimates availability at around 3 hours after detection, and esri livefeeds updates this feature layer every 15 minutes from LANCE.

Even though these data display as point features, each point in fact represents a pixel that is >= 375 m high and wide. A point feature means somewhere in this pixel at least one "hot" spot was detected which may be a fire.

VIIRS is a scanning radiometer device aboard the Suomi NPP and NOAA-20 satellites that collects imagery and radiometric measurements of the land, atmosphere, cryosphere, and oceans in several visible and infrared bands. The VIIRS Thermal Hotspots and Fire Activity layer is a livefeed from a subset of the overall VIIRS imagery, in particular from NASA's VNP14IMG_NRT active fire detection product. The downloads are automatically downloaded from LANCE, NASA's near real time data and imagery site, every 15 minutes.

The 375-m data complements the 1-km Moderate Resolution Imaging Spectroradiometer (MODIS) Thermal Hotspots and Fire Activity layer; they both show good agreement in hotspot detection but the improved spatial resolution of the 375 m data provides a greater response over fires of relatively small areas and provides improved mapping of large fire perimeters.

Attribute information

• Latitude and Longitude: The center point location of the 375 m (approximately) pixel flagged as containing one or more fires/hotspots.
• Satellite: Whether the detection was picked up by the Suomi NPP satellite (N) or NOAA-20 satellite (1). For best results, use the virtual field WhichSatellite, redefined by an arcade expression, that gives the complete satellite name.
• Confidence: The detection confidence is a quality flag of the individual hotspot/active fire pixel. This value is based on a collection of intermediate algorithm quantities used in the detection process. It is intended to help users gauge the quality of individual hotspot/fire pixels. Confidence values are set to low, nominal and high. Low confidence daytime fire pixels are typically associated with areas of sun glint and lower relative temperature anomaly (<15K) in the mid-infrared channel I4. Nominal confidence pixels are those free of potential sun glint contamination during the day and marked by strong (>15K) temperature anomaly in either day or nighttime data. High confidence fire pixels are associated with day or nighttime saturated pixels.
• Please note: Low confidence nighttime pixels occur only over the geographic area extending from 11 deg E to 110 deg W and 7 deg N to 55 deg S. This area describes the region of influence of the South Atlantic Magnetic Anomaly which can cause spurious brightness temperatures in the mid-infrared channel I4 leading to potential false positive alarms. These have been removed from the NRT data distributed by FIRMS.
• FRP: Fire Radiative Power. Depicts the pixel-integrated fire radiative power in MW (MegaWatts). FRP provides information on the measured radiant heat output of detected fires. The amount of radiant heat energy liberated per unit time (the Fire Radiative Power) is thought to be related to the rate at which fuel is being consumed (Wooster et. al. (2005)).
• DayNight: D = Daytime fire, N = Nighttime fire
• Hours Old: Derived field that provides age of record in hours between Acquisition date/time and latest update date/time. 0 = less than 1 hour ago, 1 = less than 2 hours ago, 2 = less than 3 hours ago, and so on.

Predator-prey interactions can be profoundly influenced by vegetation conditions, particularly when predator and prey prefer different habitats. Although such interactions have proven challenging to study for small and cryptic predators, recent methodological advances substantially improve opportunities for understanding how vegetation influences prey acquisition and strengthen conservation planning for this group. The California Spotted Owl (Strix occidentalis occidentalis) is well-known as an old-forest species of conservation concern, but whose primary prey in many regions – woodrats (Neotoma spp.) – occurs in a broad range of vegetation conditions. Here, we used high-resolution GPS tracking coupled with nest video monitoring to test the hypothesis that prey capture rates vary as a function of vegetation structure and heterogeneity, with emergent, reproductive consequences for Spotted Owls in Southern California. Foraging owls were more successful capturing prey, including woodrats, in taller multilayered forests, in areas with higher heterogeneity in vegetation types, and near forest-chaparral edges. Consistent with these findings, Spotted Owls delivered prey items more frequently to nests in territories with greater heterogeneity in vegetation types and delivered prey biomass at a higher rate in territories with more forest-chaparral edge. Spotted Owls had higher reproductive success in territories with higher mean canopy cover, taller trees, and more shrubby vegetation. Collectively, our results provide additional and compelling evidence that a mosaic of large tree forests with complex canopy and shrubby vegetation increases access to prey with potential reproductive benefits to Spotted Owls in landscapes where woodrats are a primary prey item. We suggest that forest management activities that enhance forest structure and vegetation heterogeneity could help curb declining Spotted Owl populations while promoting resilient ecosystems in some regions. Methods See README DOCUMENT Naming conventions *RSF or prey refers to prey capture analysis *delivery in a file name refers to delivery rate analysis *repro in a filename means that file is for the delivery rate analysis

Setup *files with vegetation data should work with minimal alteration(will need to specify working directory) with associated R code for each analysis *Shapefiles were made in ArcGIS pro but they can be opened with any GIS software such as QGIS.

Locational data files

NOTE LOCATIONAL DATA IS SHIFTED AND ROTATED FROM THE ORIGINAL -due to the sensitive nature of this species. The locational_data includes: * All_2021_owls_shifted * Point file showing all GPS tag locations for prey capture analysis * Attributes include: * TERRITORY ID: Numerical identifier for each bird * Year: year GPS tag was recorded * Month: month GPS tag was recorded * Day: Day GPS tag was recorded * Hour: Hour GPS tag was recorded * Minute: minute GPS tag was recorded * All_linked_polygons_shifted * Polygon file showing capture polygons for prey capture analysis * Attributes include * Territory ID: numerical identifier for each bird * Polygon id: numerical identifier for each capture polygon for each bird * Shape area: area of each polygon * SBNF_camera_nests_shifted * Point file showing spotted owl nests for prey capture analysis * Attributes include * Territory id: numerical identifier for each bird * C95_KDE_2021_socal_shifted * Polygon file of owls 95% kernel density estimate for prey delivery rate analysis * Attributes include * Id: numerical identifier for each territory(bird) * Area: area of each polygon * San_bernardino_territory_centers * Point file showing Territory centers for historical SBNF territories – shifted for repro success analysis * Attributes include * Repro Territory id: unique identifier for each territory in broader set of territories

Besides the sifted locational data we have included - For the Resource selection function vegetation data, for the delivery analysis we have included an overview of prey deliveries by territory and vegetation data used, and for the reproductive analysis we have again included vegetation data as well as an overview of reproductive success. these are labled as follows:

Files for the prey capture analysis

Socal_RSF_data.txt

*description: Text file with vegetation data paired with capture locations both buffered polygons used in prey capture analysis and the unbuffered ones which were not used.(Pair with Socal_rsf_code R script) *format: .txt *Dimensions: 2641 X 35

*Variables: *ORIG_fid: completely unique identifier for each row *unique_id: unique identifier for each capture polygon(shared between a buffered capture location and its unbuffered pair) *territory_id: unique numerical idenifier of territory *Polygon_id: within territory unique prey capture polygon id *buff: bianary buffered or unbuffered (1=buffered, 0=unbuffered) *used: bianary used=1 available=0 *prey_type: prey species associated with polygon unkn:unknown, flsq:flying squirel, wora:woodrat, umou:mouse, pogo:pocketgopher, grsq: grey squirel, ubrd: unknown bird, umol:unknown mole, uvol, unknown vole. *area_sqm: area of polygon in square meters *CanCov_2020_buff: average canopy cover in polygon *CanHeight_2020_buff: average canopy height in polygon *Canlayer_2020_buff: average number of canopy layers in polygon *Understory_density_2020_buff: average brushy vegetation density in polygon *pix_COUNT: count of pixels in polygon (not needed for analysis) *p_chaparral: percent of polygon comprised of chaparral habitat
*p_conifer: percent of polygon comprised of conifer habitat *p_hardwood: percent of polygon comprised of hardwood habitat *p_other: percent of polygon comprised of other habitat types *Calveg_cap_CHt_gt10_CC_30to70_intersect_buff: percent of polygon comprised of trees taller than 10m with 30-70percent canopy cover (used to check data) *Calveg_cap_CHt_gt10_CCgt70_intersect_buff: percent of polygon comprised of trees taller than 10m with greater than 70percent canopy cover (used to check data) *Calveg_cap_CHt_lt10_intersect_buff:percent of polygon comprised of trees less than 10m (used to check data)
*p_sm_conifer: percent of polygon comprised of conifer trees less than 10m (used to calculate diversity)
*p_lrg_conifer_sc: percent of polygon comprised of conifer forests >10m tall with sparse canopy(used to calculate diversity) *p_large_conifer_dc: percent of polygon comprised of conifer forests greater than 10m tall with dense canopy (used to calculate diversity) *p_sm_hard: percent of polygon comprised of hardwood trees less than 10m (used to calculate diversity) *p_lrg_hard_sc: percent of polygon comprised of hardwood forests greater than 10m with sparse canopy(used to calculate diversity)
*p_lrg_hard_dc: percent of polygon comprised of hardwood forests greater than 10m dense canopy (used to calculate diversity) *p_forests_gt10_verysparse_CC: percent of polygon comprised of trees less than 10m with very sparse canopies (used to calculate diversity) *primary_edge: total distance in meters of primary edge in a polygon
*normalized_by_area_primary_edge: total distance in m of primary edge in a polygon divided by the area of the polygon
*secondary_edge: total distance in meters of secondary edge in a polygon *normalized_by_area_secondary_edge:total distance in m of secondary edge in a polygon divided by the area of the polygon *coarse_diversity: shannon diversity in each polygon (see methods below) *fine_diversity: shannon diversity in each polygon (see methods below) *nest_distance: distance from polygon center to nest for each polygon in meters

For the Delivery analysis

note: For information on determining average prey biomass see methods as well as zulla et al 2022 for flying squirels and woodrat masses Zulla CJ, Jones GM, Kramer HA, Keane JJ, Roberts KN, Dotters BP, Sawyer SC, Whitmore SA, Berigan WJ, Kelly KG, Gutiérrez RJ, Peery MZ. Forest heterogeneity outweighs movement costs by enhancing hunting success and fitness in spotted owls. doi:10.21203/rs.3.rs-1370884/v1. PPR:PPR470028.

prey_deliveries_byterritory.csv *Description: overview file of prey delivered to each nest *format: .csv *dimensions:332 x 8

*Variables: *SITE: Unique numerical identifier for each territory *DATE: date prey was delivered (in UTC) *CAMERA TIME: time in UTC prey was delivered *VIDEO TIME: time on video prey was delivered - unrelated to real time just original file
*PREY ITEM: prey species delivered to nest unkn:unknown, uncr: unknown if delivery(removed from eventual analysis due to

The California State Water Resources Control Board is currently in the process of improving the functionality and accessibility of information residing in their Water Quality Control Plans (aka Basin Plans). In order to achieve this, the data (i.e. statewide water quality objectives, beneficial uses, applicable TMDLs, etc.), are being transferred to a standardized digital format and linked to applicable surface water features. This dataset is limited to the beneficial uses data, while the water quality objectives, applicable TMDLs, etc. will be released at a later date. Data formats will include GIS data layers and numerous nonspatial data tables. The GIS layers contain hydrography features derived from a 2012 snapshot of the high-resolution (1:24000 scale) National Hydrography Dataset with added attribution. Nonspatial tables will contain various textual and numeric data from the Regional Basin and State Plans. The extent of the dataset covers the state of California and the non-spatial tables reflect the information and elements from the various plans used up to 2020. The GIS layers and associated attribution will enable the future integration of the various elements of the Basin Plans to ensure that all applicable Basin Plan requirements for a particular waterbody can be determined in a quick and precise manner across different modern mediums. The data are being managed and the project implemented by State and Regional Water Board staff using ESRI's ArcGIS Server and ArcSDE technology.

Mature Support Notice: This item is in mature support as of December 2024. A new version of this item is available for your use. Esri recommends updating your maps and apps to use the new version. See blog for more information.This web map presents a vector basemap of OpenStreetMap (OSM) data hosted by Esri. It provides a detailed base layer for the world featuring a dark neutral style with minimal colors, OpenStreetMap Dark Gray Canvas Base (WGS84) and also an overlaying reference layer, OpenStreetMap Dark Gray Canvas Reference (WGS84). Created from the sunsetted Daylight map distribution, data updates supporting this layer are no longer available.OpenStreetMap is an open collaborative project to create a free editable map of the world. Volunteers gather location data using GPS, local knowledge, and other free sources of information and upload it. The resulting free map can be viewed and downloaded from the OpenStreetMap site: www.OpenStreetMap.org. Esri is a supporter of the OSM project. Precise Tile Registration: The tile layer uses the improved tiling scheme “WGS84 Geographic, Version 2” to ensure proper tile positioning at higher resolutions (neighborhood level and beyond). The new tiling scheme is much more precise than tiling schemes of the legacy basemaps Esri released years ago. We recommend that you start using this new basemap for any new web maps in WGS84 that you plan to author. Due to the number of differences between the old and new tiling schemes, some web clients will not be able to overlay tile layers in the old and new tiling schemes in one web map.

Mature Support Notice: This item is in mature support as of December 2024. A new version of this item is available for your use. Esri recommends updating your maps and apps to use the new version. See blog for more information.This tile layer presents a vector basemap of OpenStreetMap (OSM) data hosted by Esri. It provides a reference layer featuring map labels, boundary lines, and roads. This layer is designed to be overlaid on imagery. Created from the sunsetted Daylight map distribution, data updates supporting this layer are no longer available.OpenStreetMap is an open collaborative project to create a free editable map of the world. Volunteers gather location data using GPS, local knowledge, and other free sources of information and upload it. The resulting free map can be viewed and downloaded from the OpenStreetMap site: www.OpenStreetMap.org. Esri is a supporter of the OSM project. Precise Tile Registration: The tile layer uses the improved tiling scheme “WGS84 Geographic, Version 2” to ensure proper tile positioning at higher resolutions (neighborhood level and beyond). The new tiling scheme is much more precise than tiling schemes of the legacy basemaps Esri released years ago. We recommend that you start using this new basemap for any new web maps in WGS84 that you plan to author. Due to the number of differences between the old and new tiling schemes, some web clients will not be able to overlay tile layers in the old and new tiling schemes in one web map.

This vector tile layer presents the World Street Map (with Relief - WGS84) style (World Edition) and provides a basemap for the world, symbolized with a classic Esri street map style. This comprehensive street map includes highways, major roads, minor roads, railways, water features, cities, parks, landmarks, building footprints, and administrative boundaries. Additionally, this layer is designed for use with World Hillshade (WGS84). This vector tile layer provides unique capabilities for customization and high-resolution display, and use in mobile devices.This vector tile layer is built using the same data sources used for other Esri Vector Basemaps. For details on data sources contributed by the GIS community, view the map of Community Maps Basemap Contributors. Esri Vector Basemaps (WGS84) are updated quarterly.This layer is used in the Streets (with Relief - WGS84) web map included in ArcGIS Living Atlas of the World.Check out other WGS84 basemaps in the World Basemaps (WGS84) group. Customize this StyleLearn more about customizing this vector basemap style using the Vector Tile Style Editor. Additional details are available in ArcGIS Online Blogs and the Esri Vector Basemaps Reference Document.Precise Tile RegistrationThe map uses the improved tiling scheme “WGS84 Geographic, Version 2” to ensure proper tile positioning at higher resolutions (neighborhood level and beyond). The new tiling scheme is much more precise than tiling schemes of the legacy basemaps Esri released years ago. We recommend that you start using this new basemap for any new web maps in WGS84 that you plan to author. Due to the number of differences between the old and new tiling schemes, some web clients will not be able to overlay tile layers in the old and new tiling schemes in one web map.

Seafloor geomorphology is the study of physical features on the seafloor. This layer represents the characterizations of geomorphic features and the zones within the ocean where they occur. The data for this layer are from the first map of seafloor geomorphology ever published; this map was published 2014 by GRID Arendal.“The new seafloor features map provides a foundation on which to build an understanding of the living and non-living resources of the ocean and to improve decision making on a range of global issues like food security, resource use and conservation.” - Dr. Peter Harris, the project leader and Managing Director of GRID-Arendal.Dataset SummaryThe geomorphic features in this layer were created by automated and manual processes over the course of many months. The source data for the process is a modified 30-arc second (~1 km) resolution version of SRTM30_PLUS global bathymetry produced in 2009. The following features and zones are included as sub-layers:FeaturesCanyons - Submarine canyons are defined as steep-walled, sinuous valleys with V-shaped cross sections, axes sloping outward as continuously as river-cut land canyons and relief comparable to even the largest of land canyons.Seamounts - Seamounts are a single or group of peaks, greater than 1,000 meters in relief above the sea floor, characteristically of conical form.Guyots - Guyots are isolated or a group of seamount having a comparatively smooth flat top. Also called tablemounts.Troughs - Troughs are long depressions of the sea floor characteristically flat bottomed and steep sided and normally shallower than a trench. In this study we found that troughs are also commonly open at one end (i.e. not defined by closed bathymetric contours) and their broad, flat floors may exhibit a continuous gradient. Troughs may originate from glacial erosion processes or have form through tectonic processes.Glacial Troughs - Glacial troughs are the largest of the shelf valleys at high latitudes incised by glacial erosion during the Pleistocene ice ages to form elongate troughs, typically trending across the continental shelf and extending inland as fjord complexes. Glacial troughs are characterized by depths of over 100 m (often exceeding 1,000 m depth) and are distinguished from shelf valleys by an over-deepened longitudinal profile that reaches a maximum depth inboard of the shelf break, thus creating a perched basin on the shelf with an associated sill.Trenches - Trenches are long narrow very deep asymmetrical depressions of the sea floor, with relatively steep sides. Trenches are generally distinguished from troughs by their “V” shape in cross section (in contrast with flat-bottomed troughs). Bridges - Bridge features are blocks of material that partially infill Trenches forming a “bridge”across the trench.Sills - Sills are a sea floor barrier of relatively shallow depth restricting water movement between basins. Thus every basin has a sill, over which fluid would escape if the basin were filled to overflowing. Shelf Valleys - Shelf valleys are greater than 10 km in length and greater than 10 m in depth overall with an elongate shape more than 4 times greater in length than width.Rift Valleys - Rift valleys are confined to the central axis of mid-ocean spreading ridges; they are elongate, local depressions flanked generally on both sides by ridges.Ridges - Ridges are isolated or a group of elongated narrow elevations of varying complexity with steep sides, often separating basin features. Ridges have greater than 1,000 meters of relief.Spreading Ridges - Spreading ridges are mid-oceanic mountain systems of global extent.Terraces - Terraces an isolated or a group of relatively flat horizontal or gently inclined surface(s), sometimes long and narrow, which is (are) bounded by a steeper ascending slope on one side and by a steeper descending slope on the opposite side. Fans - Fans are relatively smooth, fan-like, depositional featured normally sloping away from the outer termination of a canyon or canyon system. Fans overlay and comprise part of the continental rise and are located offshore from the base of the continental slope. Fans are inter-related with submarine canyons and sediment drift deposits; in cases where canyon axes extend across the rise, the canyon-channels may be flanked by sediment drift deposits, which have been grouped with fans in this study. Fans are defined in the present study by 100 m isobaths that form a concentric series exhibiting an expanding spacing in a seaward direction away from the base of the slope, sometimes clearly associated with a canyon mouth, but also comprising low-relief ridges between canyon-channels on the abyssal plain.Rises - Continental rises are areas with sediment thickness greater than 300 meters and the occurrence of a smooth sloping seabed as indicated by evenly-spaced, slope-parallel contours. In this study, the term “Rise” was restricted to features that abut continental margins and does not include the mid-ocean ridge.Plateaus - Plateaus are flat or nearly flat elevations of considerable areal extent, dropping off abruptly on one or more sides. TerrainMountains - Greater than 1,000 meters of local relief within ~25 kilometers.Hills - Between 300 and 1,000 meters of local relief within ~25 kilometers.Plains - Less than 300 meters of local relief within ~25 kilometers.Basins - Basins are depressions in the sea floor that are more or less equi-dimensional in plan, of variable extent, and are restricted to seafloor depressions defined by closed bathymetric contours.Escarpments - Escarpments are “an elongated, characteristically linear, steep slope separating horizontal or gently sloping sectors of the sea floor in non-shelf areas. Also abbreviated to scarp” (IHO, 2008). Escarpments, like basins, overlay other features (i.e. other individual features may be partly or wholly covered by escarpments). Thus features like the continental slope, seamounts, guyots, ridges and submarine canyons (for example) may be sub-classified in terms of their area of overlain escarpment.ZonesShelf - The zone adjacent to the continents or islands. Slope - The deepening seafloor from the edge of the shelf to the top of the continental rise.Abyss - Areas below the foot of the continental rise and includes all depths up to 6,000 meters.Hadal - Depths greater than 6,000 metersNote that the above definitions are brief summarizations of the definitions contained in Geomorphology of the Oceans.Esri staff edited several of the layers: Zones, Terrain, Basins, and Glacial Troughs to improve drawing performance. All of these edits were split polygon operations; no vertexes were moved, only at cut points were vertexes introduced. If these layers are downloaded, these edits can be removed by using the Dissolve tool, with all fields, including shape, and producing no multi-part polygons in the output.For metadata info, please see Bluehabitats.org.What can you do with this layer?This layer is based on a dynamic map service, which means there are several sub-layers of vector features that can be used for visualization and analysis throughout the ArcGIS Platform. This layer is not editable.This layer is part of a larger collection of Oceans layers that you can use to perform a wide variety of mapping and analysis tasks.The Living Atlas of the World provides an easy way to explore the landscape layers and many other beautiful and authoritative maps on hundreds of topics.Geonet is a good resource for learning more about oceans layers and the Living Atlas of the World. To get started see the Living Atlas Discussion Group.