This data set is part of an ongoing project to consolidate interagency fire perimeter data. Currently only certified perimeters and new perimeters captured starting in 2021 are included. A process for loading additional perimeters is being evaluated.The Wildland Fire Interagency Geospatial Services (WFIGS) Group provides authoritative geospatial data products under the interagency Wildland Fire Data Program. Hosted in the National Interagency Fire Center ArcGIS Online Organization (The NIFC Org), WFIGS provides both internal and public facing data, accessible in a variety of formats.This service includes perimeters for wildland fire incidents that meet the following criteria:Categorized in the IRWIN (Integrated Reporting of Wildland Fire Information) integration service as a Wildfire (WF) or Prescribed Fire (RX)Is Valid and not "quarantined" in IRWIN due to potential conflicts with other recordsAttribution of the source polygon is set to a Feature Access of Public, a Feature Status of Approved, and an Is Visible setting of YesPerimeters are not available for every incident. This data set is an ongoing project with the end goal of providing a national interagency fire history feature service of best-available perimeters.No "fall-off" rules are applied to this service. The date range for this service will extend from present day back indefinitely. Data prior to 2021 will be incomplete and incorporated as an ongoing project.Criteria were determined by an NWCG Geospatial Subcommittee task group. Data are refreshed every 5 minutes. Changes in the perimeter source may take up to 15 minutes to display.Perimeters are pulled from multiple sources with rules in place to ensure the most current or most authoritative shape is used.Warning: Please refrain from repeatedly querying the service using a relative date range. This includes using the “(not) in the last” operators in a Web Map filter and any reference to CURRENT_TIMESTAMP. This type of query puts undue load on the service and may render it temporarily unavailable.Attributes and their definitions can be found below. More detail about the NWCG Wildland Fire Event Polygon standard can be found here.Attributes:poly_SourceOIDThe OBJECTID value of the source record in the source dataset providing the polygon.poly_IncidentNameThe incident name as stored in the polygon source record.poly_MapMethodThe mapping method with which the polygon was derived.poly_GISAcresThe acreage of the polygon as stored in the polygon source record.poly_CreateDateSystem generated date for the date time the source polygon record was created (stored in UTC).poly_DateCurrentSystem generated date for the date time the source polygon record was last edited (stored in UTC).poly_PolygonDateTimeRepresents the date time that the polygon data was captured.poly_IRWINIDIRWIN ID stored in the polygon record.poly_FORIDFORID stored in the polygon record.poly_Acres_AutoCalcSystem calculated acreage of the polygon (geodesic WGS84 acres).poly_SourceGlobalIDThe GlobalID value of the source record in the source dataset providing the polygon.poly_SourceThe source dataset providing the polygon.attr_SourceOIDThe OBJECTID value of the source record in the source dataset providing the attribution.attr_ABCDMiscA FireCode used by USDA FS to track and compile cost information for emergency initial attack fire suppression expenditures. for A, B, C & D size class fires on FS lands.attr_ADSPermissionStateIndicates the permission hierarchy that is currently being applied when a system utilizes the UpdateIncident operation.attr_ContainmentDateTimeThe date and time a wildfire was declared contained. attr_ControlDateTimeThe date and time a wildfire was declared under control.attr_CreatedBySystemArcGIS Server Username of system that created the IRWIN Incident record.attr_IncidentSizeReported for a fire. The minimum size is 0.1.attr_DiscoveryAcresAn estimate of acres burning upon the discovery of the fire. More specifically when the fire is first reported by the first person that calls in the fire. The estimate should include number of acres within the current perimeter of a specific, individual incident, including unburned and unburnable islands.attr_DispatchCenterIDA unique identifier for a dispatch center responsible for supporting the incident.attr_EstimatedCostToDateThe total estimated cost of the incident to date.attr_FinalAcresReported final acreage of incident.attr_FFReportApprovedByTitleThe title of the person that approved the final fire report for the incident.attr_FFReportApprovedByUnitNWCG Unit ID associated with the individual who approved the final report for the incident.attr_FFReportApprovedDateThe date that the final fire report was approved for the incident.attr_FireBehaviorGeneralA general category describing the manner in which the fire is currently reacting to the influences of fuel, weather, and topography. attr_FireBehaviorGeneral1A more specific category further describing the general fire behavior (manner in which the fire is currently reacting to the influences of fuel, weather, and topography). attr_FireBehaviorGeneral2A more specific category further describing the general fire behavior (manner in which the fire is currently reacting to the influences of fuel, weather, and topography). attr_FireBehaviorGeneral3A more specific category further describing the general fire behavior (manner in which the fire is currently reacting to the influences of fuel, weather, and topography). attr_FireCauseBroad classification of the reason the fire occurred identified as human, natural or unknown. attr_FireCauseGeneralAgency or circumstance which started a fire or set the stage for its occurrence; source of a fire's ignition. For statistical purposes, fire causes are further broken into specific causes. attr_FireCauseSpecificA further categorization of each General Fire Cause to indicate more specifically the agency or circumstance which started a fire or set the stage for its occurrence; source of a fire's ignition. attr_FireCodeA code used within the interagency wildland fire community to track and compile cost information for emergency fire suppression expenditures for the incident. attr_FireDepartmentIDThe U.S. Fire Administration (USFA) has created a national database of Fire Departments. Most Fire Departments do not have an NWCG Unit ID and so it is the intent of the IRWIN team to create a new field that includes this data element to assist the National Association of State Foresters (NASF) with data collection.attr_FireDiscoveryDateTimeThe date and time a fire was reported as discovered or confirmed to exist. May also be the start date for reporting purposes.attr_FireMgmtComplexityThe highest management level utilized to manage a wildland fire event. attr_FireOutDateTimeThe date and time when a fire is declared out. attr_FireStrategyConfinePercentIndicates the percentage of the incident area where the fire suppression strategy of "Confine" is being implemented.attr_FireStrategyFullSuppPrcntIndicates the percentage of the incident area where the fire suppression strategy of "Full Suppression" is being implemented.attr_FireStrategyMonitorPercentIndicates the percentage of the incident area where the fire suppression strategy of "Monitor" is being implemented.attr_FireStrategyPointZonePrcntIndicates the percentage of the incident area where the fire suppression strategy of "Point Zone Protection" is being implemented.attr_FSJobCodeA code use to indicate the Forest Service job accounting code for the incident. This is specific to the Forest Service. Usually displayed as 2 char prefix on FireCode.attr_FSOverrideCodeA code used to indicate the Forest Service override code for the incident. This is specific to the Forest Service. Usually displayed as a 4 char suffix on FireCode. For example, if the FS is assisting DOI, an override of 1502 will be used.attr_GACCA code that identifies one of the wildland fire geographic area coordination center at the point of origin for the incident.A geographic area coordination center is a facility that is used for the coordination of agency or jurisdictional resources in support of one or more incidents within a geographic coordination area.attr_ICS209ReportDateTimeThe date and time of the latest approved ICS-209 report.attr_ICS209RptForTimePeriodFromThe date and time of the beginning of the time period for the current ICS-209 submission.attr_ICS209RptForTimePeriodToThe date and time of the end of the time period for the current ICS-209 submission. attr_ICS209ReportStatusThe version of the ICS-209 report (initial, update, or final). There should never be more than one initial report, but there can be numerous updates, and even multiple finals (as determined by business rules).attr_IncidentManagementOrgThe incident management organization for the incident, which may be a Type 1, 2, or 3 Incident Management Team (IMT), a Unified Command, a Unified Command with an IMT, National Incident Management Organization (NIMO), etc. This field is null if no team is assigned.attr_IncidentNameThe name assigned to an incident.attr_IncidentShortDescriptionGeneral descriptive location of the incident such as the number of miles from an identifiable town. attr_IncidentTypeCategoryThe Event Category is a sub-group of the Event Kind code and description. The Event Category further breaks down the Event Kind into more specific event categories.attr_IncidentTypeKindA general, high-level code and description of the types of incidents and planned events to which the interagency wildland fire community responds.attr_InitialLatitudeThe latitude location of the initial reported point of origin specified in decimal degrees.attr_InitialLongitudeThe longitude location of the initial reported point of origin specified in decimal degrees.attr_InitialResponseAcresAn estimate of acres burning at the time of initial response. More specifically when the IC arrives and performs initial size up. The

GPM_3IMERGHHE Early Precipitation Rate L3 V07 (GPM IMERG Early Precipitation L3 Half Hourly 0.1 degree x 0.1 degree V07 (GPM_3IMERGHHE 07)) is an image service derived from the Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (GPM) Early dataset. The image service shows precipitation rate (mm/hr), approximately four hours after observation. The image service provides global coverage with a temporal span from 06/01/2000 0:00 UTC to present at 30-minute intervals. The service is updated every three hours to incorporate the new granules. To access the REST endpoint for the service, input the URL into a browser or select View just above the URL.IMERG is an algorithm that estimates precipitation rate from multiple passive microwave sensors in the GPM constellation, the GPM Dual-Frequency Radar, and infrared (IR) sensors mounted on geostationary satellites. Currently, the near-real-time Early Run estimates have no concluding calibration. Briefly describing the Early Run, the input precipitation estimates computed from the various satellite passive microwave sensors are intercalibrated to the Combined Radar-Radiometer Algorithm (CORRA) product (because it is presumed to be the best snapshot Tropical Rainfall Measuring Mission (TRMM)/GPM estimate after adjustment to the monthly Global Precipitation Climatology Project Satellite-Gauge (GPCP SG)), then "forward morphed" and combined with microwave precipitation-calibrated geo-IR fields to provide half-hourly precipitation estimates on a 0.1°x0.1° (roughly 10x10 km) grid over the globe. Precipitation phase is computed using analyses of surface temperature, humidity, and pressure. Dataset at a glance Shortname: GPM_3IMERGHHEDOI: 10.5067/GPM/IMERG/3B-HH-E/07Version: 07Coverage: -180.0,-90.0,180.0,90.0Temporal Coverage: 2000-06-01 to PresentData ResolutionSpatial: 0.1 ° x 0.1 °Temporal: 30 minutes SymbologyThe default symbology in the Map Viewer may be changed to accommodate other color schemes using the settings in the Image Display panel from the layer settings menu. NoData values, and values less than 0.03 mm/hr (the current threshold value for the IMERG algorithm) have been removed. Ensure that pop-ups are enabled to view pixel values (select Modify Map first). Temporal CoverageThe source dataset is in UTC time but the service is displayed in the Map Viewer in local time. The data is available in 30-minute intervals, and the map visualization may be modified by opening the Time Slider Settings menu from the icon on the time slider bar. The total temporal coverage may be limited to the desired range and the time interval may also be changed. The options in the time interval units are based on the total time range input, so a shorter time range will enable shorter time units to be selected from the time interval drop-down menu. If the time settings are set to more than 30-minute intervals, the first time slice in the time interval is visible. Portal Options Select Modify Map to customize the layer visualization. More information about the image service capabilities may be found in the REST endpoint. In the portal, the basemap may be changed by selecting the desired option from the Basemap menu. Further instructions on using the image service may be found at [GES DISC How-To's: How to access the GES DISC IMERG ArcGIS Image Service using the ArcGIS Enterprise Map Viewer (nasa.gov)].

This service contains various Aquaculture data. This includes Shellfish Production, Optimum Sites of Aquaculture potential (AQ1), Bivalve Classification area and Areas of Future Potential for Aquaculture. ------------------------------------------------------------------------------------------------------------The Shellfish Production dataset shows shellfish farm species production data grouped by water body. Water bodies were taken from the water framework directive (WFD) coastal and transitional water bodies database, and joined with the data from CEFAS. Data contains information on species present and production values. This dataset was created by ABPmer under contract to DEFRA (Contract reference MB106). An Excel spreadsheet was supplied to ABPmer by CEFAS which contained a list of waterbodies with the species cultivated per waterbody, production per waterbody and the number of businesses operating for 2007. The production data was joined to a shapefile containing waterbodies based on name of waterbody, and all sites where no shellfish cultivation occurred were removed. The same procedure was repeated with the data of species present. A shapefile containing both number of species grown and tonnes produced per waterbody was created by merging the two datasets based on waterbody name. ------------------------------------------------------------------------------------------------------------The Optimum Sites of Aquaculture Potential (AQ1) dataset shows areas identified through GIS modelling of suitable environmental conditions in East Coast Inshore and Offshore Marine Plan Areas favourable for macroalgae culture, Bivalve Bottom Culture, Finfish Cage, Lobster Restocking, Rope Cultured Bivalve Shellfish or Trestle/Bag Culture of Bivalves. This dataset has been derived from of a wider study assessing aquaculture potential in the South and East Marine Plan Areas for the Marine Management Organisation, project MMO1040. It was created using the Natural Resource model which forms part of the MMO project 1040 Spatial Trends in Aquaculture Potential in the South and East Coast Inshore and Offshore Marine Plan Areas. The Natural Resource model is made up of three existing environmental datasets: bathymetry derived from the Department of Food and Rural Affairs (Defra) Digital Elevation Model (DEM), predicted seabed sediments and combined seabed energy, both from UKSeaMap 2010 (McBreen, et al., 2010). Suitable environmental conditions applied include - low-moderate seabed energy, any sediment type and 10-25 m water depth for current potential. The depth limitations in this instance are based on the industry current reliance on scuba-divers for maintenance and husbandry. It is anticipated that as the industry develops it will become less reliant on divers and be able to move into deeper waters. Note that although the Natural Resource model used the best environmental data available for use in the study but there are significant limitations and gaps. These are outlined below and are discussed in more detail in the final project report: The model does not contain any measure of water quality (e.g. dissolved oxygen, sediment loading or contaminants) and therefore is likely to overestimate the area deemed suitable for aquaculture developments, particularly fin fish cage culture, rope grown bivalve culture and macroalgae culture. The UKSeaMap 2010 predicted seabed sediment map (McBreen, et al., 2010) is modelled at a coarse scale which has led to inaccuracies in the identification of areas which have potential for aquaculture development. UKSeaMap 2010 is known to under-estimate rock habitats because of the type of sampling data (sediment grabs) used to underpin the model. The MMO is working with JNCC to develop these data to lead to improvements in future models. The UKSeaMap 2010 combined seabed energy map included in the model (McBreen, et al., 2010) provides an approximation of the environmental conditions that are likely to limit aquaculture development (e.g. strong currents and large waves) but more accurate results could be obtained by using more precise component datasets such as the maximum wave height and tidal current range, where these datasets are available and the precise limitations of the aquaculture activities of interest are known. The dataset shows potential based on current technologies as defined in Table 10 of the MMO1040 Aquaculture Potential Final Report which is published on the MMO website's evidence pages. ------------------------------------------------------------------------------------------------------------The Bivalve Classification dataset classifies where the production of shellfish can be commercially harvested. All areas listed are designated for species that may be harvested as well as the classification of the shellfish waters. Classification of harvesting areas is required and implemented directly in England and Wales under European Regulation 854/2004. The co-ordination of the shellfish harvesting area classification monitoring programme in England and Wales is carried out by the Centre for Environment, Fisheries and Aquaculture Science, Weymouth (Cefas) on behalf of the Food Standards Agency (FSA). Cefas will make recommendations on classification according to an agreed protocol with the FSA making all final classification decisions and setting out the overall policy. Shellfish production areas are classified according to the extent to which shellfish sampled from the area are contaminated with E. coli. The Classification Zones/Production areas delineate areas where shellfish may be commercially harvested. Coordinates for the zone boundaries are calculated during a sanitary (ground) survey of the production area and where appropriate they are based on the OS Mastermap Mean High Water Line (coordinate accuracy <10m). The maps/zones are correct at time of publication but are updated when necessary depending on hygiene testing results. The current maps (jpgs) are available from the Cefas website ( https://www.cefas.co.uk/publications-data/food-safety/classification-and-microbiological-monitoring/england-and-wales-classification-and-monitoring/classification-zone-maps ) or a listing is available from the FSA website ( http://www.food.gov.uk/enforcement/monitoring/shellfish/shellharvestareas ) ------------------------------------------------------------------------------------------------------------The Current Aquaculture Potential layer highlights areas identified through GIS modelling of suitable environmental conditions in the South and East Marine Plan Areas favourable for macroalgae culture, Bivalve Bottom Culture, Finfish Cage, Lobster Restocking, Rope Cultured Bivalve Shellfish or Trestle/Bag Culture of Bivalves in the South and East Coast Marine Plan Areas. This dataset forms part of a wider study assessing different aquaculture potential in the South and East Marine Plan Areas for the Marine Management Organisation, project MMO1040. This dataset was created using the Natural Resource model which forms part of the MMO project 1040 Spatial Trends in Aquaculture Potential in the South and East Coast Inshore and Offshore Marine Plan Areas. The Natural Resource model is made up of three existing environmental datasets: bathymetry derived from the Department of Food and Rural Affairs (Defra) Digital Elevation Model (DEM), predicted seabed sediments and combined seabed energy, both from UKSeaMap 2010 (McBreen, et al., 2010). Suitable environmental conditions applied include - low-moderate seabed energy, any sediment type, 10-25 m water depth for current potential and 25-50 m water depth for near future potential). The depth limitations in this instance are based on the industry current reliance on scuba-divers for maintenance and husbandry. It is anticipated that as the industry develops it will become less reliant on divers and be able to move into deeper waters. Note that although the Natural Resource model used the best environmental data available for use in the study, there are significant limitations and gaps. These are outlined below and are discussed in more detail in the final project report: The Natural Resource model does not contain any measure of water quality (e.g. dissolved oxygen, sediment loading or contaminants) and therefore is likely to overestimate the area deemed suitable for aquaculture developments, particularly fin fish cage culture, rope grown bivalve culture and macroalgae culture. The UKSeaMap 2010 predicted seabed sediment map (McBreen, et al., 2010) is modelled at a coarse scale which has led to inaccuracies in the identification of areas which have potential for aquaculture development. UKSeaMap 2010 is known to under-estimate rock habitats because of the type of sampling data (sediment grabs) used to underpin the model. It is recommended that this component of the model is supplemented or replaced by higher resolution sediment maps where they are available for the region of interest. The UKSeaMap 2010 combined seabed energy map included in the model (McBreen, et al., 2010) provides an approximation of the environmental conditions that are likely to limit aquaculture development (e.g. strong currents and large waves) but more accurate results could be obtained by using more precise component datasets such as the maximum wave height and tidal current range, where these datasets are available and the precise limitations of the aquaculture activities of interest are known. The potential for development for the feature is "Current" (0-5 years), "Near Future" (5-10 years) or "Future" (10-20 years), the definitions of which are presented in Table 13 within the main report.

DescriptionThis data is designed to assist preparing the SWMP for projects and supply Maintenance projects that need seed mixtures for work along Region 2 highways. Also, these seed mixtures are available in a digital format in ARC/GIS layer for CDOT’s OTIS Highway Map System. The intended users are Environmental, Engineers, and CDOT maintenance staff. However, there are limitations and assumptions that were considered when developing these seed mixtures. Assistance from the environmental staff is strongly recommended before using a seed mixture. Site specific conditions may require adjustment to the seed mixture(s). The seed mixtures contained in this dataset are designed for the site specific conditions (e.g., soil texture and chemical composition, precipitation, elevation, aspect, etc.) associated with the approximate mile marker along Region 2 highways. Native species were selected for the seed mixtures with few exceptions. The individual plant species were selected based on their adaptions to the site conditions, ability to control erosion, and compatibility to establish a diverse plant community. The species composition of the seed mixture was also based on the recommendations found in the Natural Resources Conservation Service’s (NRCS) “Major Land Resource Areas (MLRA).”(http://www.nrcs.usds.gov)

    Last Update
    2014


    Update FrequencyNot planned


    Data Owner
    Region 2


    Data Contact
    Region 2 Environmental Specialist or Water Pollution Control Manager


    Collection Method



    Projection
    NAD83 / UTM zone 13N


    Coverage Area
    Region 2


    Temporal



    Disclaimer/Limitations
    There are no restrictions and legal prerequisites for using the data set. The State of Colorado assumes no liability relating to the completeness, correctness, or fitness for use of this data.

The Bureau of Indian Affairs (BIA) Bison Project will serve a variety of purposes that are designed to uphold the best of Tribal bison herd expansion interests, including a focus on ecosystem restoration through bison conservation. The Bison Project will foster practices that are traditional and culturally attentive to the historical coevolved relationship with bison to support the Tribe’s own self-determined well-being. Furthermore, the projects will work to foster the intent of the Department of the Interior Secretary’s Order 3410, the purpose of which is to restore wild and healthy populations of American bison and the prairie grassland ecosystem through collaboration among the Department’s Bureaus and partners such as other Federal agencies, states, Tribes, and landowners using the best available science and Indigenous Knowledge. The analysis in this dashboard is based on data provided to the BIA from a multitude of resources including but not limited to the BIA, Federally Recognized Tribes, and partner organizations. All data was current as of time of collection for this project, data will be updated as determined by the BIA. Data displayed within this application can vary at different times of the year as external factors may affect herd sizes and will not reflect changes in real time. Herd numbers can also decrease between data updates due to range management practices performed on the local level, not managed by the BIA. The Bison Program Application’s data is made up of a polygon feature layer and a point feature layer hosted on the BIA online portal. The Bison Polygon layer features the geospatial extent of known Bison ranches as provided to the BIA. Tribes without any GIS data on ranch boundaries will only be featured in the Bison points feature layer. Both feature layers contain data including, name of Tribe, herd size, rangeland acres, and a link to their Bison website (if available). The Bison Program Application will focus on ecosystem restoration through bison conservation and expansion and improved management of existing herds on Tribal trust lands, individual Indian allotment lands, or in areas managed by Tribes through treaties or agreements. The Bison Project will focus on bison conservation and expansion and improved management of existing herds on Tribal trust resources and describe the role of Tribal bison on ecosystem restoration on Tribal landscapes and altered Tribal environmental conditions. This can cover bison as indicator keystone species on agricultural pasture, grassland, and rangeland settings. The Bison Program Application also features data from partner organizations who focus on promoting the restoration of Bison. These organizations include The Nature Conservancy (TNC). For more information on data contributors, follow the links below. The feature layers used in this application from partner organizations are not managed by the BIA. The Nature Conservancy (TNC): https://www.nature.org/en-us/. Disclaimer: The analysis in this dashboard is based on the analysis of available data provided to the BIA from a multitude of resources including but not limited to the BIA, Federally Recognized Tribes, and partner organizations. All data was current as of time of collection for this project, data will be updated as determined by the BIA. Data displayed within this application can vary at different times of the year as external factors may affect available foliage due to weather or other uncontrollable circumstances. The number of Bison within herds may also change throughout the year and might not be accounted for within this application. Herd numbers can also decrease between data updates due to outside factors or range management practices performed on the local level, not managed by the BIA.This application also uses data provided from other sources such as The Nature Conservancy (TNC). This data is owned and maintained by their respective owners. These data sources have been developed from the best available sources. Although efforts have been made to ensure that the data are accurate and reliable, errors and variable conditions originating from source documents and/or the translation of information from source documents to the systems of record continue to exist. Users must be aware of these conditions and bear responsibility for the appropriate use of the information with respect to possible errors, scale, resolution, rectification, positional accuracy, development methodology, time period, environmental and climatic conditions and other circumstances specific to these data. The user is responsible for understanding the accuracy limitations of the data provided herein. The burden for determining fitness for use lies entirely with the user. The user should refer to the accompanying metadata notes for a description of the data and data development procedures.

World Terrestrial Ecosystems use a combination of landform, land cover, and climate region information to objectively characterize ecosystem types. Using global climate models, land cover and climate region can be projected into the future. The latest global climate models, part of the 6th Coupled Model Intercomparison Program (CMIP6), use a variety of developmental and emissions scenarios called the Shared Socioeconomic Pathways, or SSPs. This layer shows the projected World Terrestrial Ecosystems in 2050 using SSP1-2.6, which includes deep cuts in CO2 emissions and a transition to more sustainable development. This layer can be directly compared to the 2015 World Terrestrial Ecosystems v2 and the projected World Terrestrial Ecosystems in 2050 for SSP3-7.0 and SSP5-8.5. Those layers and others can be found in the WTE 2015 to 2050 Comparison Project Layers and Maps ArcGIS Online Group. To learn more about this work, read our open access peer-reviewed journal article in Global Ecology and Conservation, Volume 57, January 2025, e03370: Potential 2050 distributions of World Terrestrial Ecosystems from projections of changes in World Climate Regions and Global Land Cover.MethodologyEcosystems are mapped by combining remotely-sensed and field methods. From remote sensing, data derived from satellites are combined with landforms derived from elevation, land cover derived from multi-sensor imagery, and climate variables modeled into annual averages and indicators. From the field, scientists find patterns and measurements which delineate regions that cannot be derived from imagery, most notably in differentiating savanna from dry tropical rain forests. The most subtle boundaries require ground truthing to identify tricky vegetation differences, especially when telling apart species of grasses in the tropics.CHELSA Climate DataGlobal climate models are quite coarse in resolution, so downscaling techniques often are applied to provide more detailed spatial resolution. CHELSA version 2.1 provides a set of downscaled (1-km) climate models from CMIP6. We obtained five different downscaled projections for 2041-2070 and three SSP scenarios (1-2.6, 3-7.0, and 5-8.5), along with a historical climatology for 1981-2010 The v2.1 data was accessed in May of 2023 from CHELSA's data download site (Karger, et. al., 2017). We classified the CHELSA models according to the climate region definitions in Sayre, et. al., 2020. This layer represents an ensemble of the five different models for SSP1-2.6.An older version of World Terrestrial Ecosystems 2015 used a different source for downscaled climate data (WorldClim version 2). CHELSA leverages more accurate downscaling techniques for both historical and projected climate information. References: Sayre, Roger, Karagülle, Deniz, Frye, Charlie, Boucher, Timothy, Wolff, Nicholas H., Breyer, Sean, Wright, Dawn, Martin, Madeline, Butler, Kevin, Van Graafeiland, Keith, Touval, Keith, Sotomayor, Leonardo , McGowan, Jennifer , Game, Edward T., Possingham, Hugh. 2020. An assessment of the representation of ecosystems in global protected areas using new maps of World Climate Regions and World Ecosystems. Global Ecology and Conservation, v21. DOI: 10.1016/j.gecco.2019.e00860.Karger, D.N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R.W., Zimmermann, N.E., Linder, P., Kessler, M. 2017): Climatologies at high resolution for the Earth land surface areas. Scientific Data. 4 170122. https://doi.org/10.1038/sdata.2017.122.Land CoverWe obtained three Plant Functional Type-based SSP land cover models for the year 2050 and one for 2015 from: https://zenodo.org/records/4584775 on Jun 2, 2023. The SSP models were for 1-2.6, 3-7.0, and 5-8.5. The land cover model for this layer was the SSP1-2.6 model.References: Chen, G., Li, X. & Liu, X. Global land projection based on plant functional types with a 1-km resolution under socio-climatic scenarios. Sci Data 9, 125 (2022). https://doi.org/10.1038/s41597-022-01208-6.Chen, G., Li, X., & Liu, X. (2021). Future global land datasets with a 1-km resolution based on the SSP-RCP scenarios [Data set]. Zenodo. https://doi.org/10.5281/zenodo.4584775.World Ecological Facets (Hammond) Landform ClassesWe used the data from the image service in ArcGIS Living Atlas of the World.Reference: Karagulle, D., Frye, C., Sayre, R., Breyer, S., Aniello, P., Vaughan, R., & Wright, D. (2017). Modeling global Hammond landform regions from 250-m elevation data. Transactions in GIS, 21(5), 1040–1060. https://doi.org/10.1111/tgis.12265.

Esri ArcGIS Online (AGOL) Feature Layer which provides access to the MDOT SHA 2015 Mean Sea Level 10% Annual Chance (10-Year Storm) - Flood Depth Grid data product.MDOT SHA 2015 Mean Sea Level 10% Annual Chance (10-Year Storm) - Flood Depth Grid consists of a depth grid image service depicting conditions of sea level change based on the 10% annual chance event (10-Year Storm) scenario for coastal areas throughout the State of Maryland in year 2015. This data product supports Maryland Department of Transportation State Highway Administration (MDOT SHA) leadership and planners as they endeavor to mitigate or prevent the impacts of sea level change resulting from land surface subsidence and rising sea levels.MDOT SHA 2015 Mean Sea Level 10% Annual Chance (10-Year Storm) - Flood Depth Grid data was produced as a result of efforts by the Maryland Department of Transportation State Highway Administration (MDOT SHA), Eastern Shore Regional GIS Cooperative (ESRGC), Salisbury University (SU), United States Corps of Engineers (USACE), National Oceanic & Atmospheric Administration (NOAA), and the United States Geological Survey (USGS). The US Army Corps of Engineers provide the sea level change estimate. Sea level change is localized using water elevations collected from a qualifying National Oceanic and Atmospheric Administration (NOAA) tidal reference station - NOAA observations are transformed from tidal datum to North American Vertical Datum of 1988. A final correction for glacial isostatic adjustment and land creates an sea level change value for the official project year, 2015.MDOT SHA 2015 Mean Sea Level 10% Annual Chance (10-Year Storm) - Flood Depth Grid data was project-based, and will only be updated on an As-Needed basis where and/or when necessary.For more information related to the data, contact MDOT SHA OPPE Innovative Planning & Performance Division (IPPD):Email: IPPD@mdot.maryland.govFor more information, contact MDOT SHA OIT Enterprise Information Services:Email: GIS@mdot.maryland.gov

This topographic contour layer was derived from LiDAR collected in spring of 2020 by Dewberry Engineers in coordination with Tallahassee - Leon County GIS. The contours were extracted at a 2 foot interval with index contours every 10 feet. This tile layer was generated as a Map Tile Package (.mtpkx) in ArcGIS Pro and published to ArcGIS online as a hosted tile layer. For web mapping compatibility, this layer has been re-projected from its original coordinate system to the web standard used by ESRI, Google, and Bing (Web Mercator Auxiliary Sphere).The feature layer used to generate this tile layer can be downloaded as a zipped geodatabase from TLCGIS' geodatahub. Download LinkLidar Acquisition Executive SummaryThe primary purpose of this project was to develop a consistent and accurate surface elevation dataset derived from high-accuracy Light Detection and Ranging (lidar) technology for the Tallahassee Leon County Project Area. The lidar data were processed and classified according to project specifications. Detailed breaklines and bare-earth Digital Elevation Models (DEMs) were produced for the project area. Data was formatted according to tiles with each tile covering an area of 5000 ft by 5000 ft. A total of 876 tiles were produced for the project encompassing an area of approximately 785.55 sq. miles.The Project TeamDewberry served as the prime contractor for the project. In addition to project management, Dewberry was responsible for LAS classification, all lidar products, breakline production, Digital Elevation Model (DEM) production, and quality assurance. Dewberry’s Frederick C. Rankin completed ground surveying for the project and delivered surveyed checkpoints. His task was to acquire surveyed checkpoints for the project to use in independent testing of the vertical accuracy of the lidar-derived surface model. He also verified the GPS base station coordinates used during lidar data acquisition to ensure that the base station coordinates were accurate. Please see Appendix A to view the separate Survey Report that was created for this portion of the project. Digital Aerial Solutions, LLC completed lidar data acquisition and data calibration for the project area.SURVEY AREAThe project area addressed by this report falls within the Florida county of Leon.DATE OF SURVEYThe lidar aerial acquisition was conducted from TBDORIGINAL COORDINATE REFERENCE SYSTEMData produced for the project were delivered in the following reference system.Horizontal Datum: The horizontal datum for the project is North American Datum of 1983 with the 2011 Adjustment (NAD 83 (2011))Vertical Datum: The Vertical datum for the project is North American Vertical Datum of 1988 (NAVD88)Coordinate System: NAD83 (2011) State Plane Florida North (US survey feet)Units: Horizontal units are in U.S. Survey Feet, Vertical units are in U.S. Survey Feet.Geiod Model: Geoid12B (Geoid 12B) was used to convert ellipsoid heights to orthometric heights).

Living England is a multi-year project which delivers a broad habitat map for the whole of England, created using satellite imagery, field data records and other geospatial data in a machine learning framework. The Living England habitat map shows the extent and distribution of broad habitats across England aligned to the UKBAP classification, providing a valuable insight into our natural capital assets and helping to inform land management decisions. Living England is a project within Natural England, funded by and supports the Defra Natural Capital and Ecosystem Assessment (NCEA) Programme and Environmental Land Management (ELM) Schemes to provide an openly available national map of broad habitats across England.This dataset includes very complex geometry with a large number of features so it has a default viewing distance set to 1:80,000 (City in the map viewer).Process Description:A number of data layers are used to develop a ground dataset of habitat reference data, which are then used to inform a machine-learning model and spatial analyses to generate a map of the likely locations and distributions of habitats across England. The main source data layers underpinning the spatial framework and models are Sentinel-2 and Sentinel-1 satellite data from the ESA Copernicus programme, Lidar from the EA's national Lidar Programme and collected data through the project's national survey programme. Additional datasets informing the approach as detailed below and outlined in the accompanying technical user guide.Datasets used:OS MasterMap® Topography Layer; Geology aka BGS Bedrock Mapping 1:50k; Long Term Monitoring Network; Uplands Inventory; Coastal Dune Geomatics Mapping Ground Truthing; Crop Map of England (RPA) CROME; Lowland Heathland Survey; National Grassland Survey; National Plant Monitoring Scheme; NE field Unit Surveys; Northumberland Border Mires Survey; Sentinel-2 multispectral imagery; Sentinel-1 backscatter imagery; Sentinel-1 single look complex (SLC) imagery; National forest inventory (NFI); Cranfield NATMAP; Agri-Environment HLS Monitoring; Living England desktop validation; Priority Habitat Inventory; Space2 Eye Lens: Ainsdale NNR, State of the Bog Bowland Survey, State of the Bog Dark Peak Condition Survey, State of the Bog Manchester Metropolitan University (MMU) Mountain Hare Habitat Survey Dark Peak, State of the Bog; Moors for the Future Dark Peak Survey; West Pennines Designation NVC Survey; Wetland Annex 1 inventory; Soils-BGS Soil Parent Material; Met Office HadUK gridded climate product; Saltmarsh Extent and Zonation; EA LiDAR DSM & DTM; New Forest Mires Wetland Survey; New Forest Mires Wetland Survey; West Cumbria Mires Survey; England Peat Map Vegetation Surveys; NE protected sites monitoring; ERA5; OS Open Built-up Areas; OS Boundaries dataset; EA IHM (Integrated height model) DTM; OS VectorMap District; EA Coastal Flood Boundary: Extreme Sea Levels; AIMS Spatial Sea Defences; LIDAR Sand Dunes 2022; EA Coastal saltmarsh species surveys; Aerial Photography GB (APGB); NASA SRT (Shuttle Radar Topography Mission) M30; Provisional Agricultural Land Classification; Renewable Energy Planning Database (REPD); Open Street Map 2024.Attribute descriptions: Column Heading Full Name Format Description

SegID SegID Character (100) Unique Living England segment identifier. Format is LEZZZZ_BGZXX_YYYYYYY where Z = release year (2223 for this version), X = BGZ and Y = Unique 7-digit number

Prmry_H Primary_Habitat Date Primary Living England Habitat

Relblty Reliability
Character (12) Reliability Metric Score

Mdl_Hbs Model_Habs Interger List of likely habitats output by the Random Forest model.

Mdl_Prb Model_Probs Double (6,2) List of probabilities for habitats listed in ‘Model_Habs’, calculated by the Random Forest model.

Mixd_Sg Mixed_Segment Character (50) Indication of the likelihood a segment contains a mixture of dominant habitats. Either Unlikely or Probable.

Source Source

Description of how the habitat classification was derived. Options are: Random Forest; Vector OSMM Urban; Vector Classified OS Water; Vector EA saltmarsh; LE saltmarsh & QA; Vector RPA Crome, ALC grades 1-4; Vector LE Bare Ground Analysis; LE QA Adjusted

SorcRsn Source_Reason

Reasoning for habitat class adjustment if ‘Source’ equals ‘LE QA Adjusted’

Shap_Ar Shape_Area

Segment area (m2) Full metadata can be viewed on data.gov.uk.

This data set is part of an ongoing project to consolidate interagency fire perimeter data. Currently only certified perimeters and new perimeters captured starting in 2021 are included. A process for loading additional perimeters is being evaluated.The Wildland Fire Interagency Geospatial Services (WFIGS) Group provides authoritative geospatial data products under the interagency Wildland Fire Data Program. Hosted in the National Interagency Fire Center ArcGIS Online Organization (The NIFC Org), WFIGS provides both internal and public facing data, accessible in a variety of formats.This service includes perimeters for wildland fire incidents that meet the following criteria:Categorized in the IRWIN (Integrated Reporting of Wildland Fire Information) integration service as a Wildfire (WF) or Prescribed Fire (RX)Is Valid and not "quarantined" in IRWIN due to potential conflicts with other recordsAttribution of the source polygon is set to a Feature Access of Public, a Feature Status of Approved, and an Is Visible setting of YesPerimeters are not available for every incident. This data set is an ongoing project with the end goal of providing a national interagency fire history feature service of best-available perimeters.No "fall-off" rules are applied to this service. The date range for this service will extend from present day back indefinitely. Data prior to 2021 will be incomplete and incorporated as an ongoing project.Criteria were determined by an NWCG Geospatial Subcommittee task group. Data are refreshed every 5 minutes. Changes in the perimeter source may take up to 15 minutes to display.Perimeters are pulled from multiple sources with rules in place to ensure the most current or most authoritative shape is used.Warning: Please refrain from repeatedly querying the service using a relative date range. This includes using the “(not) in the last” operators in a Web Map filter and any reference to CURRENT_TIMESTAMP. This type of query puts undue load on the service and may render it temporarily unavailable.Attributes and their definitions can be found below. More detail about the NWCG Wildland Fire Event Polygon standard can be found here.Attributes:poly_SourceOIDThe OBJECTID value of the source record in the source dataset providing the polygon.poly_IncidentNameThe incident name as stored in the polygon source record.poly_MapMethodThe mapping method with which the polygon was derived.poly_GISAcresThe acreage of the polygon as stored in the polygon source record.poly_CreateDateSystem generated date for the date time the source polygon record was created (stored in UTC).poly_DateCurrentSystem generated date for the date time the source polygon record was last edited (stored in UTC).poly_PolygonDateTimeRepresents the date time that the polygon data was captured.poly_IRWINIDIRWIN ID stored in the polygon record.poly_FORIDFORID stored in the polygon record.poly_Acres_AutoCalcSystem calculated acreage of the polygon (geodesic WGS84 acres).poly_SourceGlobalIDThe GlobalID value of the source record in the source dataset providing the polygon.poly_SourceThe source dataset providing the polygon.attr_SourceOIDThe OBJECTID value of the source record in the source dataset providing the attribution.attr_ABCDMiscA FireCode used by USDA FS to track and compile cost information for emergency initial attack fire suppression expenditures. for A, B, C & D size class fires on FS lands.attr_ADSPermissionStateIndicates the permission hierarchy that is currently being applied when a system utilizes the UpdateIncident operation.attr_ContainmentDateTimeThe date and time a wildfire was declared contained. attr_ControlDateTimeThe date and time a wildfire was declared under control.attr_CreatedBySystemArcGIS Server Username of system that created the IRWIN Incident record.attr_IncidentSizeReported for a fire. The minimum size is 0.1.attr_DiscoveryAcresAn estimate of acres burning upon the discovery of the fire. More specifically when the fire is first reported by the first person that calls in the fire. The estimate should include number of acres within the current perimeter of a specific, individual incident, including unburned and unburnable islands.attr_DispatchCenterIDA unique identifier for a dispatch center responsible for supporting the incident.attr_EstimatedCostToDateThe total estimated cost of the incident to date.attr_FinalAcresReported final acreage of incident.attr_FFReportApprovedByTitleThe title of the person that approved the final fire report for the incident.attr_FFReportApprovedByUnitNWCG Unit ID associated with the individual who approved the final report for the incident.attr_FFReportApprovedDateThe date that the final fire report was approved for the incident.attr_FireBehaviorGeneralA general category describing the manner in which the fire is currently reacting to the influences of fuel, weather, and topography. attr_FireBehaviorGeneral1A more specific category further describing the general fire behavior (manner in which the fire is currently reacting to the influences of fuel, weather, and topography). attr_FireBehaviorGeneral2A more specific category further describing the general fire behavior (manner in which the fire is currently reacting to the influences of fuel, weather, and topography). attr_FireBehaviorGeneral3A more specific category further describing the general fire behavior (manner in which the fire is currently reacting to the influences of fuel, weather, and topography). attr_FireCauseBroad classification of the reason the fire occurred identified as human, natural or unknown. attr_FireCauseGeneralAgency or circumstance which started a fire or set the stage for its occurrence; source of a fire's ignition. For statistical purposes, fire causes are further broken into specific causes. attr_FireCauseSpecificA further categorization of each General Fire Cause to indicate more specifically the agency or circumstance which started a fire or set the stage for its occurrence; source of a fire's ignition. attr_FireCodeA code used within the interagency wildland fire community to track and compile cost information for emergency fire suppression expenditures for the incident. attr_FireDepartmentIDThe U.S. Fire Administration (USFA) has created a national database of Fire Departments. Most Fire Departments do not have an NWCG Unit ID and so it is the intent of the IRWIN team to create a new field that includes this data element to assist the National Association of State Foresters (NASF) with data collection.attr_FireDiscoveryDateTimeThe date and time a fire was reported as discovered or confirmed to exist. May also be the start date for reporting purposes.attr_FireMgmtComplexityThe highest management level utilized to manage a wildland fire event. attr_FireOutDateTimeThe date and time when a fire is declared out. attr_FireStrategyConfinePercentIndicates the percentage of the incident area where the fire suppression strategy of "Confine" is being implemented.attr_FireStrategyFullSuppPrcntIndicates the percentage of the incident area where the fire suppression strategy of "Full Suppression" is being implemented.attr_FireStrategyMonitorPercentIndicates the percentage of the incident area where the fire suppression strategy of "Monitor" is being implemented.attr_FireStrategyPointZonePrcntIndicates the percentage of the incident area where the fire suppression strategy of "Point Zone Protection" is being implemented.attr_FSJobCodeA code use to indicate the Forest Service job accounting code for the incident. This is specific to the Forest Service. Usually displayed as 2 char prefix on FireCode.attr_FSOverrideCodeA code used to indicate the Forest Service override code for the incident. This is specific to the Forest Service. Usually displayed as a 4 char suffix on FireCode. For example, if the FS is assisting DOI, an override of 1502 will be used.attr_GACCA code that identifies one of the wildland fire geographic area coordination center at the point of origin for the incident.A geographic area coordination center is a facility that is used for the coordination of agency or jurisdictional resources in support of one or more incidents within a geographic coordination area.attr_ICS209ReportDateTimeThe date and time of the latest approved ICS-209 report.attr_ICS209RptForTimePeriodFromThe date and time of the beginning of the time period for the current ICS-209 submission.attr_ICS209RptForTimePeriodToThe date and time of the end of the time period for the current ICS-209 submission. attr_ICS209ReportStatusThe version of the ICS-209 report (initial, update, or final). There should never be more than one initial report, but there can be numerous updates, and even multiple finals (as determined by business rules).attr_IncidentManagementOrgThe incident management organization for the incident, which may be a Type 1, 2, or 3 Incident Management Team (IMT), a Unified Command, a Unified Command with an IMT, National Incident Management Organization (NIMO), etc. This field is null if no team is assigned.attr_IncidentNameThe name assigned to an incident.attr_IncidentShortDescriptionGeneral descriptive location of the incident such as the number of miles from an identifiable town. attr_IncidentTypeCategoryThe Event Category is a sub-group of the Event Kind code and description. The Event Category further breaks down the Event Kind into more specific event categories.attr_IncidentTypeKindA general, high-level code and description of the types of incidents and planned events to which the interagency wildland fire community responds.attr_InitialLatitudeThe latitude location of the initial reported point of origin specified in decimal degrees.attr_InitialLongitudeThe longitude location of the initial reported point of origin specified in decimal degrees.attr_InitialResponseAcresAn estimate of acres burning at the time of initial response. More specifically when the IC arrives and performs initial size up. The

This service contains various Aquaculture data. This includes Shellfish Production, Optimum Sites of Aquaculture potential (AQ1), Bivalve Classification area and Areas of Future Potential for Aquaculture. ------------------------------------------------------------------------------------------------------------The Shellfish Production dataset shows shellfish farm species production data grouped by water body. Water bodies were taken from the water framework directive (WFD) coastal and transitional water bodies database, and joined with the data from CEFAS. Data contains information on species present and production values. This dataset was created by ABPmer under contract to DEFRA (Contract reference MB106). An Excel spreadsheet was supplied to ABPmer by CEFAS which contained a list of waterbodies with the species cultivated per waterbody, production per waterbody and the number of businesses operating for 2007. The production data was joined to a shapefile containing waterbodies based on name of waterbody, and all sites where no shellfish cultivation occurred were removed. The same procedure was repeated with the data of species present. A shapefile containing both number of species grown and tonnes produced per waterbody was created by merging the two datasets based on waterbody name. ------------------------------------------------------------------------------------------------------------The Optimum Sites of Aquaculture Potential (AQ1) dataset shows areas identified through GIS modelling of suitable environmental conditions in East Coast Inshore and Offshore Marine Plan Areas favourable for macroalgae culture, Bivalve Bottom Culture, Finfish Cage, Lobster Restocking, Rope Cultured Bivalve Shellfish or Trestle/Bag Culture of Bivalves. This dataset has been derived from of a wider study assessing aquaculture potential in the South and East Marine Plan Areas for the Marine Management Organisation, project MMO1040. It was created using the Natural Resource model which forms part of the MMO project 1040 Spatial Trends in Aquaculture Potential in the South and East Coast Inshore and Offshore Marine Plan Areas. The Natural Resource model is made up of three existing environmental datasets: bathymetry derived from the Department of Food and Rural Affairs (Defra) Digital Elevation Model (DEM), predicted seabed sediments and combined seabed energy, both from UKSeaMap 2010 (McBreen, et al., 2010). Suitable environmental conditions applied include - low-moderate seabed energy, any sediment type and 10-25 m water depth for current potential. The depth limitations in this instance are based on the industry current reliance on scuba-divers for maintenance and husbandry. It is anticipated that as the industry develops it will become less reliant on divers and be able to move into deeper waters. Note that although the Natural Resource model used the best environmental data available for use in the study but there are significant limitations and gaps. These are outlined below and are discussed in more detail in the final project report: The model does not contain any measure of water quality (e.g. dissolved oxygen, sediment loading or contaminants) and therefore is likely to overestimate the area deemed suitable for aquaculture developments, particularly fin fish cage culture, rope grown bivalve culture and macroalgae culture. The UKSeaMap 2010 predicted seabed sediment map (McBreen, et al., 2010) is modelled at a coarse scale which has led to inaccuracies in the identification of areas which have potential for aquaculture development. UKSeaMap 2010 is known to under-estimate rock habitats because of the type of sampling data (sediment grabs) used to underpin the model. The MMO is working with JNCC to develop these data to lead to improvements in future models. The UKSeaMap 2010 combined seabed energy map included in the model (McBreen, et al., 2010) provides an approximation of the environmental conditions that are likely to limit aquaculture development (e.g. strong currents and large waves) but more accurate results could be obtained by using more precise component datasets such as the maximum wave height and tidal current range, where these datasets are available and the precise limitations of the aquaculture activities of interest are known. The dataset shows potential based on current technologies as defined in Table 10 of the MMO1040 Aquaculture Potential Final Report which is published on the MMO website's evidence pages. ------------------------------------------------------------------------------------------------------------The Bivalve Classification dataset classifies where the production of shellfish can be commercially harvested. All areas listed are designated for species that may be harvested as well as the classification of the shellfish waters. Classification of harvesting areas is required and implemented directly in England and Wales under European Regulation 854/2004. The co-ordination of the shellfish harvesting area classification monitoring programme in England and Wales is carried out by the Centre for Environment, Fisheries and Aquaculture Science, Weymouth (Cefas) on behalf of the Food Standards Agency (FSA). Cefas will make recommendations on classification according to an agreed protocol with the FSA making all final classification decisions and setting out the overall policy. Shellfish production areas are classified according to the extent to which shellfish sampled from the area are contaminated with E. coli. The Classification Zones/Production areas delineate areas where shellfish may be commercially harvested. Coordinates for the zone boundaries are calculated during a sanitary (ground) survey of the production area and where appropriate they are based on the OS Mastermap Mean High Water Line (coordinate accuracy <10m). The maps/zones are correct at time of publication but are updated when necessary depending on hygiene testing results. The current maps (jpgs) are available from the Cefas website ( https://www.cefas.co.uk/publications-data/food-safety/classification-and-microbiological-monitoring/england-and-wales-classification-and-monitoring/classification-zone-maps ) or a listing is available from the FSA website ( http://www.food.gov.uk/enforcement/monitoring/shellfish/shellharvestareas ) ------------------------------------------------------------------------------------------------------------The Current Aquaculture Potential layer highlights areas identified through GIS modelling of suitable environmental conditions in the South and East Marine Plan Areas favourable for macroalgae culture, Bivalve Bottom Culture, Finfish Cage, Lobster Restocking, Rope Cultured Bivalve Shellfish or Trestle/Bag Culture of Bivalves in the South and East Coast Marine Plan Areas. This dataset forms part of a wider study assessing different aquaculture potential in the South and East Marine Plan Areas for the Marine Management Organisation, project MMO1040. This dataset was created using the Natural Resource model which forms part of the MMO project 1040 Spatial Trends in Aquaculture Potential in the South and East Coast Inshore and Offshore Marine Plan Areas. The Natural Resource model is made up of three existing environmental datasets: bathymetry derived from the Department of Food and Rural Affairs (Defra) Digital Elevation Model (DEM), predicted seabed sediments and combined seabed energy, both from UKSeaMap 2010 (McBreen, et al., 2010). Suitable environmental conditions applied include - low-moderate seabed energy, any sediment type, 10-25 m water depth for current potential and 25-50 m water depth for near future potential). The depth limitations in this instance are based on the industry current reliance on scuba-divers for maintenance and husbandry. It is anticipated that as the industry develops it will become less reliant on divers and be able to move into deeper waters. Note that although the Natural Resource model used the best environmental data available for use in the study, there are significant limitations and gaps. These are outlined below and are discussed in more detail in the final project report: The Natural Resource model does not contain any measure of water quality (e.g. dissolved oxygen, sediment loading or contaminants) and therefore is likely to overestimate the area deemed suitable for aquaculture developments, particularly fin fish cage culture, rope grown bivalve culture and macroalgae culture. The UKSeaMap 2010 predicted seabed sediment map (McBreen, et al., 2010) is modelled at a coarse scale which has led to inaccuracies in the identification of areas which have potential for aquaculture development. UKSeaMap 2010 is known to under-estimate rock habitats because of the type of sampling data (sediment grabs) used to underpin the model. It is recommended that this component of the model is supplemented or replaced by higher resolution sediment maps where they are available for the region of interest. The UKSeaMap 2010 combined seabed energy map included in the model (McBreen, et al., 2010) provides an approximation of the environmental conditions that are likely to limit aquaculture development (e.g. strong currents and large waves) but more accurate results could be obtained by using more precise component datasets such as the maximum wave height and tidal current range, where these datasets are available and the precise limitations of the aquaculture activities of interest are known. The potential for development for the feature is "Current" (0-5 years), "Near Future" (5-10 years) or "Future" (10-20 years), the definitions of which are presented in Table 13 within the main report.

This Beta layer contains the latest collection of research-grade species observations contributed by iNaturalist users through the iNaturalist social network app and website. These Open Data observations can be used by the GIS community to better understand biodiversity, sustainability, migration patterns, invasive and threatened species distributions, and climate change adaptations, among many other use cases. Consumption Best PracticesDue to the high volume of observations, the service limits individual point visibility to only draw at the largest scales, using multi-scale H3 hexbins to summarize predominant observations at smaller scales.Small subsets of iNaturalist observations (128,000) can be copied from the service for use in analysis, data enrichment, or other visualizations. For larger iNaturalist archive requests or for access to iNaturalist Project datasets, use the iNaturalist website, or the iNaturalist AWS S3 Open Data extract, from which this service was derived. Source: iNaturalist AWS S3 Open DataUpdate Frequency: Monthly, end of the month Spatial Reference: WGS 1984 (WKID 4326)Area Covered: World Attribute InformationTaxonomy: Each observation contains its taxonomic hierarchy (Kingdom, Phylum, Class, Order, Family, Genus, Species), as well as its Scientific Name and Common Name (where available).iNaturalist Taxon Category: Observations are symbolized according to 12 unique taxonomic groups used by the iNaturalist community. User Information: All observations are credited to the iNaturalist User ID, User Login, and User Name (where provided)Media and Licenses: Direct URL links are provided to one original-resolution image from the iNaturalist observation. Creative Commons licensing also indicates the sharing and attribution of any photographic media associated with a user observation.Dates: Observations include an Observed on Date and a Modified on Date provided by iNaturalist. In addition, these date fields were added to simplify the filtering and visualization of observations by year or month:Observed on Month (integer)Observed on Year (integer)Note about Research Grade Observations Only Verifiable and Research Grade observations are included in this service. An observation is Verifiable if it meets these requirements: Has a dateIs georeferenced (has lat/lon coordinates)Has photographs or soundsIsn’t of a captive or cultivated organismIn addition, a Verifiable observation moves from "Needs ID" to "Research Grade" in iNaturalist when at least 2 species-level identifications (and 2/3 of all suggested identifications) are in agreement. See here for more information on how iNaturalist assesses data quality. Note about Location Privacy To protect the livelihood of endangered or threatened species, the X/Y locations of some iNaturalist observations are automatically obscured to a random location in a 400 square-kilometer grid cell. Similarly, users can choose to obscure the location of their observations in the iNaturalist app settings for personal privacy reasons. The result is that you may see dense, blocky aggregations of observations as you navigate around the map – or observations that appear in unusual places (e.g., an endangered coastal plant that has been relocated out in the ocean.) Additional iNaturalist ResourcesiNaturalist GuidesiNaturalist statistics and observationsiNaturalist ForumiNaturalist within the pressSpatial Filtering options and examplesRevisionsJuly 10, 2024: Beta release of the iNaturalist Observations Live Feed service.This layer is provided for informational purposes and is not monitored 24/7 for accuracy and currency. Any changes or deletions made to user observations through the iNaturalist app or website will not be reflected in this service until the next monthly update.

This layer contains the latest collection of research-grade species observations contributed by iNaturalist users through the iNaturalist social network app and website. These Open Data observations can be used by the GIS community to better understand biodiversity, sustainability, migration patterns, invasive and threatened species distributions, and climate change adaptations, among many other use cases.Consumption Best PracticesDue to the high volume of observations, the service limits individual point visibility to only draw at the largest scales, using multi-scale H3 hexbins to summarize predominant observations at smaller scales.Small subsets of iNaturalist observations (128,000) can be copied from the service for use in analysis, data enrichment, or other visualizations. For larger iNaturalist archive requests or for access to iNaturalist Project datasets, use the iNaturalist website, or the iNaturalist AWS S3 Open Data extract, from which this service was derived.Source: iNaturalist AWS S3 Open DataUpdate Frequency: Monthly, end of the monthSpatial Reference: WGS 1984 (WKID 4326)Area Covered: WorldAttribute InformationTaxonomy: Each observation contains its taxonomic hierarchy (Kingdom, Phylum, Class, Order, Family, Genus, Species), as well as its Scientific Name and Common Name (where available). iNaturalist Taxon Category: Observations are symbolized according to 12 unique taxonomic groups used by the iNaturalist community. User Information: All observations are credited to the iNaturalist User ID, User Login, and User Name (where provided)Media and Licenses: Direct URL links are provided to one original-resolution image from the iNaturalist observation. Creative Commons licensing also indicates the sharing and attribution of any photographic media associated with a user observation.Dates: Observations include an Observed on Date and a Modified on Date provided by iNaturalist. In addition, these date fields were added to simplify the filtering and visualization of observations by year or month:Observed on Month (integer)Observed on Year (integer)Note about Research Grade ObservationsOnly Verifiable and Research Grade observations are included in this service. An observation is Verifiable if it meets these requirements:Has a dateIs georeferenced (has lat/lon coordinates)Has photographs or soundsIsn’t of a captive or cultivated organismIn addition, a Verifiable observation moves from "Needs ID" to "Research Grade" in iNaturalist when at least 2 species-level identifications (and 2/3 of all suggested identifications) are in agreement. See here for more information on how iNaturalist assesses data quality.Note about Location PrivacyTo protect the livelihood of endangered or threatened species, the X/Y locations of some iNaturalist observations are automatically obscured to a random location in a 400 square-kilometer grid cell. Similarly, users can choose to obscure the location of their observations in the iNaturalist app settings for personal privacy reasons. The result is that you may see dense, blocky aggregations of observations as you navigate around the map – or observations that appear in unusual places (e.g., an endangered coastal plant that has been relocated out in the ocean.)Additional iNaturalist ResourcesiNaturalist Guides iNaturalist statistics and observations iNaturalist Forum iNaturalist within the press Spatial Filtering options and examplesRevisionsJuly 10, 2024: Beta release of the iNaturalist Observations Live Feed service.This layer is provided for informational purposes and is not monitored 24/7 for accuracy and currency. Any changes or deletions made to user observations through the iNaturalist app or website will not be reflected in this service until the next monthly update.

This Beta layer contains the latest collection of research-grade species observations contributed by iNaturalist users through the iNaturalist social network app and website. These Open Data observations can be used by the GIS community to better understand biodiversity, sustainability, migration patterns, invasive and threatened species distributions, and climate change adaptations, among many other use cases. Consumption Best PracticesDue to the high volume of observations, the service limits individual point visibility to only draw at the largest scales, using multi-scale H3 hexbins to summarize predominant observations at smaller scales.Small subsets of iNaturalist observations (128,000) can be copied from the service for use in analysis, data enrichment, or other visualizations. For larger iNaturalist archive requests or for access to iNaturalist Project datasets, use the iNaturalist website, or the iNaturalist AWS S3 Open Data extract, from which this service was derived. Source: iNaturalist AWS S3 Open DataUpdate Frequency: Monthly, end of the month Spatial Reference: WGS 1984 (WKID 4326)Area Covered: World Attribute InformationTaxonomy: Each observation contains its taxonomic hierarchy (Kingdom, Phylum, Class, Order, Family, Genus, Species), as well as its Scientific Name and Common Name (where available).iNaturalist Taxon Category: Observations are symbolized according to 12 unique taxonomic groups used by the iNaturalist community. User Information: All observations are credited to the iNaturalist User ID, User Login, and User Name (where provided)Media and Licenses: Direct URL links are provided to one original-resolution image from the iNaturalist observation. Creative Commons licensing also indicates the sharing and attribution of any photographic media associated with a user observation.Dates: Observations include an Observed on Date and a Modified on Date provided by iNaturalist. In addition, these date fields were added to simplify the filtering and visualization of observations by year or month:Observed on Month (integer)Observed on Year (integer)Note about Research Grade Observations Only Verifiable and Research Grade observations are included in this service. An observation is Verifiable if it meets these requirements: Has a dateIs georeferenced (has lat/lon coordinates)Has photographs or soundsIsn’t of a captive or cultivated organismIn addition, a Verifiable observation moves from "Needs ID" to "Research Grade" in iNaturalist when at least 2 species-level identifications (and 2/3 of all suggested identifications) are in agreement. See here for more information on how iNaturalist assesses data quality. Note about Location Privacy To protect the livelihood of endangered or threatened species, the X/Y locations of some iNaturalist observations are automatically obscured to a random location in a 400 square-kilometer grid cell. Similarly, users can choose to obscure the location of their observations in the iNaturalist app settings for personal privacy reasons. The result is that you may see dense, blocky aggregations of observations as you navigate around the map – or observations that appear in unusual places (e.g., an endangered coastal plant that has been relocated out in the ocean.) Additional iNaturalist ResourcesiNaturalist GuidesiNaturalist statistics and observationsiNaturalist ForumiNaturalist within the pressSpatial Filtering options and examplesRevisionsJuly 10, 2024: Beta release of the iNaturalist Observations Live Feed service.This layer is provided for informational purposes and is not monitored 24/7 for accuracy and currency. Any changes or deletions made to user observations through the iNaturalist app or website will not be reflected in this service until the next monthly update.

Esri ArcGIS Online (AGOL) Feature Layer which provides access to the MDOT SHA 2015 Mean Sea Level 0% Annual Chance (No Storm) - Flood Depth Grid data productMDOT SHA 2015 Mean Sea Level 0% Annual Chance (No Storm) - Flood Depth Grid consists of a depth grid image service depicting mean sea level with still-water conditions based on the 0% annual chance (No Storm) event for coastal areas throughout the State of Maryland in year 2015. This data product supports Maryland Department of Transportation State Highway Administration (MDOT SHA) leadership and planners as they endeavor to mitigate or prevent the impacts of sea level change resulting from land surface subsidence and rising sea levels.MDOT SHA 2015 Mean Sea Level 0% Annual Chance (No Storm) - Depth Grid data was produced as a result of efforts by the Maryland Department of Transportation State Highway Administration (MDOT SHA), Eastern Shore Regional GIS Cooperative (ESRGC), Salisbury University (SU), United States Corps of Engineers (USACE), National Oceanic & Atmospheric Administration (NOAA), and the United States Geological Survey (USGS). The US Army Corps of Engineers provide the sea level change estimate. Sea level change is localized using water elevations collected from a qualifying National Oceanic and Atmospheric Administration (NOAA) tidal reference station - NOAA observations are transformed from tidal datum to North American Vertical Datum of 1988. A final correction for glacial isostatic adjustment and land creates an sea level change value for the official project year, 2015.MDOT SHA 2015 Mean Sea Level 0% Annual Chance (No Storm) - Flood Depth Grid data was project-based, and will only be updated on an As-Needed basis where and/or when necessary.For more information related to the data, contact MDOT SHA OPPE Innovative Planning & Performance Division (IPPD):Email: IPPD@mdot.maryland.govFor more information, contact MDOT SHA OIT Enterprise Information Services:Email: GIS@mdot.maryland.gov

This layer contains the latest collection of research-grade species observations contributed by iNaturalist users through the iNaturalist social network app and website. These Open Data observations can be used by the GIS community to better understand biodiversity, sustainability, migration patterns, invasive and threatened species distributions, and climate change adaptations, among many other use cases.Consumption Best PracticesDue to the high volume of observations, the service limits individual point visibility to only draw at the largest scales, using multi-scale H3 hexbins to summarize predominant observations at smaller scales.Small subsets of iNaturalist observations (128,000) can be copied from the service for use in analysis, data enrichment, or other visualizations. For larger iNaturalist archive requests or for access to iNaturalist Project datasets, use the iNaturalist website, or the iNaturalist AWS S3 Open Data extract, from which this service was derived.Source: iNaturalist AWS S3 Open DataUpdate Frequency: Monthly, end of the monthSpatial Reference: WGS 1984 (WKID 4326)Area Covered: WorldAttribute InformationTaxonomy: Each observation contains its taxonomic hierarchy (Kingdom, Phylum, Class, Order, Family, Genus, Species), as well as its Scientific Name and Common Name (where available). iNaturalist Taxon Category: Observations are symbolized according to 12 unique taxonomic groups used by the iNaturalist community. User Information: All observations are credited to the iNaturalist User ID, User Login, and User Name (where provided)Media and Licenses: Direct URL links are provided to one original-resolution image from the iNaturalist observation. Creative Commons licensing also indicates the sharing and attribution of any photographic media associated with a user observation.Dates: Observations include an Observed on Date and a Modified on Date provided by iNaturalist. In addition, these date fields were added to simplify the filtering and visualization of observations by year or month:Observed on Month (integer)Observed on Year (integer)Note about Research Grade ObservationsOnly Verifiable and Research Grade observations are included in this service. An observation is Verifiable if it meets these requirements:Has a dateIs georeferenced (has lat/lon coordinates)Has photographs or soundsIsn’t of a captive or cultivated organismIn addition, a Verifiable observation moves from "Needs ID" to "Research Grade" in iNaturalist when at least 2 species-level identifications (and 2/3 of all suggested identifications) are in agreement. See here for more information on how iNaturalist assesses data quality.Note about Location PrivacyTo protect the livelihood of endangered or threatened species, the X/Y locations of some iNaturalist observations are automatically obscured to a random location in a 400 square-kilometer grid cell. Similarly, users can choose to obscure the location of their observations in the iNaturalist app settings for personal privacy reasons. The result is that you may see dense, blocky aggregations of observations as you navigate around the map – or observations that appear in unusual places (e.g., an endangered coastal plant that has been relocated out in the ocean.)Additional iNaturalist ResourcesiNaturalist Guides iNaturalist statistics and observations iNaturalist Forum iNaturalist within the press Spatial Filtering options and examplesRevisionsJuly 10, 2024: Beta release of the iNaturalist Observations Live Feed service.This layer is provided for informational purposes and is not monitored 24/7 for accuracy and currency. Any changes or deletions made to user observations through the iNaturalist app or website will not be reflected in this service until the next monthly update.

The Passage Assessment Database (PAD) is an ongoing inventory of known and potential barriers to anadromous fish in California. It compiles currently available fish passage information from more than two hundred data sources, and allows past and future barrier assessments to be standardized and stored in one place. The inventory is to be used to identify barriers suitable for removal or modification to restore spawning and riparian habitat for salmon and steelhead, and to enhance aquatic and riparian habitat. The PAD is intended to be compatible with a variety of other data sets related to anadromous fish issues. PAD records are saved with geographic location information. Each barrier record is indexed to the 24k high-resolution NHD allowing the user to combine the PAD with other fisheries data tied to the same hydrography. The Passage Assessment Database (PAD) geospatial file contains locations of known and potential barriers to salmonid migration in California streams with additional information about each record. The PAD is an ongoing map-based inventory of known and potential barriers to anadromous fish in California, compiled and maintained through a cooperative interagency agreement. The PAD compiles currently available fish passage information from many different sources, allows past and future barrier assessments to be standardized and stored in one place, and enables the analysis of cumulative effects of passage barriers in the context of overall watershed health. The database is set up to capture basic information about each potential barrier. It is designed to be flexible. As the database grows, other modules may be added to increase data detail and complexity. For the PAD to be useful as a restoration tool, the data within the PAD need to accurately depict the on-the ground reality of fish passage constraints. This requires the PAD to retrieve new barrier data and updates to existing sites and to have verified and vetted the information it receives. In 2013, new PAD data standards were designed to standardize this process, and refine the data in PAD making the data more robust. The standard is available online at: https://nrmsecure.dfg.ca.gov/FileHandler.ashx?DocumentID=78802. The new standards have been implemented for all new records since 2013. In the future, the new standards will be implemented for all existing records. If after reading the metadata, additional details about the PAD project are needed, please visit the CalFish website at www.calfish.org/PAD or refer to the PAD Methodology document at https://nrm.dfg.ca.gov/FileHandler.ashx?DocumentID=19037. To send comments about data issues, corrections, edits or to map a new barrier location not yet reported in the PAD, please use the PAD Online Data Review Application: https://map.dfg.ca.gov/pad/ or send an email to: Anne.Elston@wildlife.ca.gov. Preferred citation: California Department of Fish and Wildlife, Passage Assessment Database, September 2020 Version. New as of 2020: This feature classes identifies species and life stages that may be blocked or otherwise not blocked by structures and sites. It identifies if it blocks upstream or downstream migration or both. Since one structure/site can be a barrier to more than one species or block a species and not another species there may be multiple records at each site. Please note that these are not duplicates and each site/structure has a unique PAD ID and Passage ID.Preprocessing methods: Merged fish passage barriers (0047) and fish passage priority barriers (0168) into one dataset.