The National Hydrography Dataset Plus (NHDplus) maps the lakes, ponds, streams, rivers and other surface waters of the United States. Created by the US EPA Office of Water and the US Geological Survey, the NHDPlus provides mean annual and monthly flow estimates for rivers and streams. Additional attributes provide connections between features facilitating complicated analyses. For more information on the NHDPlus dataset see the NHDPlus v2 User Guide.Dataset SummaryPhenomenon Mapped: Surface waters and related features of the United States and associated territories not including Alaska.Geographic Extent: The United States not including Alaska, Puerto Rico, Guam, US Virgin Islands, Marshall Islands, Northern Marianas Islands, Palau, Federated States of Micronesia, and American SamoaProjection: Web Mercator Auxiliary Sphere Visible Scale: Visible at all scales but layer draws best at scales larger than 1:1,000,000Source: EPA and USGSUpdate Frequency: There is new new data since this 2019 version, so no updates planned in the futurePublication Date: March 13, 2019Prior to publication, the NHDPlus network and non-network flowline feature classes were combined into a single flowline layer. Similarly, the NHDPlus Area and Waterbody feature classes were merged under a single schema.Attribute fields were added to the flowline and waterbody layers to simplify symbology and enhance the layer's pop-ups. Fields added include Pop-up Title, Pop-up Subtitle, On or Off Network (flowlines only), Esri Symbology (waterbodies only), and Feature Code Description. All other attributes are from the original NHDPlus dataset. No data values -9999 and -9998 were converted to Null values for many of the flowline fields.What can you do with this 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 a vector tile 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. 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.Apply filters. For example you can set a filter to show larger streams and rivers using the mean annual flow attribute or the stream order attribute. Change the layer’s style and symbologyAdd labels and set their propertiesCustomize the pop-upUse as an input to the ArcGIS Online analysis tools. This layer works well as a reference layer with the trace downstream and watershed tools. The buffer tool can be used to draw protective boundaries around streams and the extract data tool can be used to create copies of portions of the data.ArcGIS ProAdd this layer to a 2d or 3d map. Use as an input to geoprocessing. For example, copy features allows you to select then export portions of the data to a new feature class. 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 ArcGIS 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.Questions?Please leave a comment below if you have a question about this layer, and we will get back to you as soon as possible.

*This dataset is authored by ESRI and is being shared as a direct link to the feature service by Pend Oreille County. NHD is a primary hydrologic reference used by our organization.The National Hydrography Dataset Plus High Resolution (NHDplus High Resolution) maps the lakes, ponds, streams, rivers and other surface waters of the United States. Created by the US Geological Survey, NHDPlus High Resolution provides mean annual flow and velocity estimates for rivers and streams. Additional attributes provide connections between features facilitating complicated analyses.For more information on the NHDPlus High Resolution dataset see the User’s Guide for the National Hydrography Dataset Plus (NHDPlus) High Resolution.Dataset SummaryPhenomenon Mapped: Surface waters and related features of the United States and associated territoriesCoordinate System: Web Mercator Auxiliary Sphere Extent: The Contiguous United States, Hawaii, portions of Alaska, Puerto Rico, Guam, US Virgin Islands, Northern Marianas Islands, and American Samoa Visible Scale: Visible at all scales but layer draws best at scales larger than 1:1,000,000Source: USGSPublication Date: July 2022This layer was symbolized in the ArcGIS Map Viewer and while the features will draw in the Classic Map Viewer the advanced symbology will not.Prior to publication, the network and non-network flowline feature classes were combined into a single flowline layer. Similarly, the Area and Waterbody feature classes were merged under a single schema.Attribute fields were added to the flowline and waterbody layers to simplify symbology and enhance the layer's pop-ups. Fields added include Pop-up Title, Pop-up Subtitle, Esri Symbology (waterbodies only), and Feature Code Description. All other attributes are from the original dataset. No data values -9999 and -9998 were converted to Null values.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 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. 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.Apply filters. For example you can set a filter to show larger streams and rivers using the mean annual flow attribute or the stream order attribute.Change the layer’s style and symbologyAdd labels and set their propertiesCustomize the pop-upUse as an input to the ArcGIS Online analysis tools. This layer works well as a reference layer with the trace downstream and watershed tools. The buffer tool can be used to draw protective boundaries around streams and the extract data tool can be used to create copies of portions of the data.ArcGIS ProAdd this layer to a 2d or 3d map.Use as an input to geoprocessing. For example, copy features allows you to select then export portions of the data to a new feature class.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 ArcGIS 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.

The National Hydrography Dataset Plus High Resolution (NHDplus High Resolution) maps the lakes, ponds, streams, rivers and other surface waters of the United States. Created by the US Geological Survey, NHDPlus High Resolution provides mean annual flow and velocity estimates for rivers and streams. Additional attributes provide connections between features facilitating complicated analyses.For more information on the NHDPlus High Resolution dataset see the User’s Guide for the National Hydrography Dataset Plus (NHDPlus) High Resolution.Dataset SummaryPhenomenon Mapped: Surface waters and related features of the United States and associated territoriesGeographic Extent: The Contiguous United States, Hawaii, portions of Alaska, Puerto Rico, Guam, US Virgin Islands, Northern Marianas Islands, and American SamoaProjection: Web Mercator Auxiliary Sphere Visible Scale: Visible at all scales but layer draws best at scales larger than 1:1,000,000Source: USGSUpdate Frequency: AnnualPublication Date: July 2022This layer was symbolized in the ArcGIS Map Viewer and while the features will draw in the Classic Map Viewer the advanced symbology will not. Prior to publication, the network and non-network flowline feature classes were combined into a single flowline layer. Similarly, the Area and Waterbody feature classes were merged under a single schema.Attribute fields were added to the flowline and waterbody layers to simplify symbology and enhance the layer's pop-ups. Fields added include Pop-up Title, Pop-up Subtitle, Esri Symbology (waterbodies only), and Feature Code Description. All other attributes are from the original dataset. No data values -9999 and -9998 were converted to Null values.What can you do with this 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 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. 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.Apply filters. For example you can set a filter to show larger streams and rivers using the mean annual flow attribute or the stream order attribute.Change the layer’s style and symbologyAdd labels and set their propertiesCustomize the pop-upUse as an input to the ArcGIS Online analysis tools. This layer works well as a reference layer with the trace downstream and watershed tools. The buffer tool can be used to draw protective boundaries around streams and the extract data tool can be used to create copies of portions of the data.ArcGIS ProAdd this layer to a 2d or 3d map.Use as an input to geoprocessing. For example, copy features allows you to select then export portions of the data to a new feature class.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 ArcGIS 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.Questions?Please leave a comment below if you have a question about this layer, and we will get back to you as soon as possible.

Soil map units are the basic geographic unit of the Soil Survey Geographic Database (SSURGO). The SSURGO dataset is a compilation of soils information collected over the last century by the Natural Resources Conservation Service (NRCS). Map units delineate the extent of different soils. Data for each map unit contains descriptions of the soil’s components, productivity, unique properties, and suitability interpretations.Each soil type has a unique combination of physical, chemical, nutrient and moisture properties. Soil type has ramifications for engineering and construction activities, natural hazards such as landslides, agricultural productivity, the distribution of native plant and animal life and hydrologic and other physical processes. Soil types in the context of climate and terrain can be used as a general indicator of engineering constraints, agriculture suitability, biological productivity and the natural distribution of plants and animals. Data from the gSSURGO databasewas used to create this layer. To download ready-to-use project packages of useful soil data derived from the SSURGO dataset, please visit the USA SSURGO Downloader app. Dataset SummaryPhenomenon Mapped: Soils of the United States and associated territoriesGeographic Extent: The 50 United States, Puerto Rico, Guam, US Virgin Islands, Marshall Islands, Northern Marianas Islands, Palau, Federated States of Micronesia, and American SamoaCoordinate System: Web Mercator Auxiliary SphereVisible Scale: 1:144,000 to 1:1,000Source: USDA Natural Resources Conservation ServiceUpdate Frequency: AnnualPublication Date: December 2024 What can you do with this layer?ArcGIS OnlineFeature 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.Add this layer to a map in the map viewer. The layer is limited to scales of approximately 1:144,000 or larger but avector tile layercreated from the same data can be used at smaller scales to produce awebmapthat displays across the full scale range. 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 set a filter forFarmland Class= "All areas are prime farmland" to create a map of only prime farmland.Add labels and set their propertiesCustomize the pop-up ArcGIS 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.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 theLiving Atlas of the Worldthat provides an easy way to explore the landscape layers and many other beautiful and authoritative maps on hundreds of topics. Data DictionaryAttributesKey fields from nine commonly used SSURGO tables were compiled to create the 173 attribute fields in this layer. Some fields were joined directly to the SSURGO Map Unit polygon feature class while others required summarization and other processing to create a 1:1 relationship between the attributes and polygons prior to joining the tables. Attributes of this layer are listed below in their order of occurrence in the attribute table and are organized by the SSURGO table they originated from and the processing methods used on them. Map Unit Polygon Feature Class Attribute TableThe fields in this table are from the attribute table of the Map Unit polygon feature class which provides the geographic extent of the map units.Area SymbolSpatial VersionMap Unit Symbol Map Unit TableThe fields in this table have a 1:1 relationship with the map unit polygons and were joined to the table using the Map Unit Key field.Map Unit NameMap Unit KindFarmland ClassInterpretive FocusIntensity of MappingIowa Corn Suitability Rating Legend TableThis table has 1:1 relationship with the Map Unit table and was joined using the Legend Key field.Project Scale Survey Area Catalog TableThe fields in this table have a 1:1 relationship with the polygons and were joined to the Map Unit table using the Survey Area Catalog Key and Legend Key fields.Survey Area VersionTabular Version Map Unit Aggregated Attribute TableThe fields in this table have a 1:1 relationship with the map unit polygons and were joined to the Map Unit attribute table using the Map Unit Key field. Slope Gradient - Dominant ComponentSlope Gradient - Weighted AverageBedrock Depth - MinimumWater Table Depth - Annual MinimumWater Table Depth - April to June MinimumFlooding Frequency - Dominant ConditionFlooding Frequency - MaximumPonding Frequency - PresenceAvailable Water Storage 0-25 cm - Weighted AverageAvailable Water Storage 0-50 cm - Weighted AverageAvailable Water Storage 0-100 cm - Weighted AverageAvailable Water Storage 0-150 cm - Weighted AverageDrainage Class - Dominant ConditionDrainage Class - WettestHydrologic Group - Dominant ConditionIrrigated Capability Class - Dominant ConditionIrrigated Capability Class - Proportion of Mapunit with Dominant ConditionNon-Irrigated Capability Class - Dominant ConditionNon-Irrigated Capability Class - Proportion of Mapunit with Dominant ConditionRating for Buildings without Basements - Dominant ConditionRating for Buildings with Basements - Dominant ConditionRating for Buildings with Basements - Least LimitingRating for Buildings with Basements - Most LimitingRating for Septic Tank Absorption Fields - Dominant ConditionRating for Septic Tank Absorption Fields - Least LimitingRating for Septic Tank Absorption Fields - Most LimitingRating for Sewage Lagoons - Dominant ConditionRating for Sewage Lagoons - Dominant ComponentRating for Roads and Streets - Dominant ConditionRating for Sand Source - Dominant ConditionRating for Sand Source - Most ProbableRating for Paths and Trails - Dominant ConditionRating for Paths and Trails - Weighted AverageErosion Hazard of Forest Roads and Trails - Dominant ComponentHydric Classification - PresenceRating for Manure and Food Processing Waste - Weighted Average Component Table – Dominant ComponentMap units have one or more components. To create a 1:1 join component data must be summarized by map unit. For these fields a custom script was used to select the component with the highest value for the Component Percentage Representative Value field (comppct_r). Ties were broken with the Slope Representative Value field (slope_r). Components with lower average slope were selected as dominant. If both soil order and slope were tied, the first value in the table was selected. Component Percentage - Low ValueComponent Percentage - Representative ValueComponent Percentage - High ValueComponent NameComponent KindOther Criteria Used to Identify ComponentsCriteria Used to Identify Components at the Local LevelRunoff ClassSoil loss tolerance factorWind Erodibility IndexWind Erodibility GroupErosion ClassEarth Cover 1Earth Cover 2Hydric ConditionHydric RatingAspect Range - Counter Clockwise LimitAspect - Representative ValueAspect Range - Clockwise LimitGeomorphic DescriptionNon-Irrigated Capability SubclassNon-Irrigated Unit Capability ClassIrrigated Capability SubclassIrrigated Unit Capability ClassConservation Tree Shrub GroupGrain Wildlife HabitatGrass Wildlife HabitatHerbaceous Wildlife HabitatShrub Wildlife HabitatConifer Wildlife HabitatHardwood Wildlife HabitatWetland Wildlife HabitatShallow Water Wildlife HabitatRangeland Wildlife HabitatOpenland Wildlife HabitatWoodland Wildlife HabitatWetland Wildlife HabitatSoil Slip PotentialSusceptibility to Frost HeavingConcrete CorrosionSteel CorrosionTaxonomic ClassTaxonomic OrderTaxonomic SuborderGreat GroupSubgroupParticle SizeParticle Size ModCation Exchange Activity ClassCarbonate ReactionTemperature ClassMoist SubclassSoil Temperature RegimeEdition of Keys to Soil Taxonomy Used to Classify SoilCalifornia Storie IndexComponent Key Component Table – Weighted AverageMap units may have one or more soil components. To create a 1:1 join, data from the Component table must be summarized by map unit. For these fields a custom script was used to calculate an average value for each map unit weighted by the Component Percentage Representative Value field (comppct_r).Slope Gradient - Low ValueSlope Gradient - Representative ValueSlope Gradient - High ValueSlope Length USLE - Low ValueSlope Length USLE - Representative ValueSlope Length USLE - High ValueElevation - Low ValueElevation - Representative ValueElevation - High ValueAlbedo - Low ValueAlbedo - Representative ValueAlbedo - High ValueMean Annual Air Temperature - Low ValueMean Annual Air Temperature - Representative ValueMean Annual Air Temperature - High ValueMean Annual Precipitation - Low ValueMean Annual Precipitation - Representative ValueMean Annual Precipitation - High ValueRelative Effective Annual Precipitation - Low ValueRelative Effective Annual Precipitation - Representative ValueRelative Effective Annual Precipitation - High ValueDays between Last and First Frost - Low ValueDays between Last and First Frost - Representative ValueDays between Last and First Frost - High ValueRange Forage Annual Potential Production - Low ValueRange Forage Annual Potential Production - Representative ValueRange Forage Annual Potential Production - High ValueInitial Subsidence - Low ValueInitial Subsidence - Representative ValueInitial Subsidence -

Calculating the total volume of water stored in a landscape can be challenging. In addition to lakes and reservoirs, water can be stored in soil, snowpack, or even inside plants and animals, and tracking the all these different mediums is not generally possible. However, calculating the change in storage is easy - just subtract the water output from the water input. Using the GLDAS layers we can do this calculation for every month from January 2000 to the present day. The precipitation layer tells us the input to each cell and runoff plus evapotranspiration is the output. When the input is higher than the output during a given month, it means water was stored. When output is higher than input, storage is being depleted. Generally the change in storage should be close to the change in soil moisture content plus the change in snowpack, but it will not match up exactly because of the other storage mediums discussed above.Dataset SummaryThe GLDAS Change in Storage layer is a time-enabled image service that shows net monthly change in storage from 2000 to the present, measured in millimeters of water. It is calculated by NASA using the Noah land surface model, run at 0.25 degree spatial resolution using satellite and ground-based observational data from the Global Land Data Assimilation System (GLDAS-2.1). The model is run with 3-hourly time steps and aggregated into monthly averages. Review the complete list of model inputs, explore the output data (in GRIB format), and see the full Hydrology Catalog for all related data and information!Phenomenon Mapped: Change in Water StorageUnits: MillimetersTime Interval: MonthlyTime Extent: 2000/01/01 to presentCell Size: 28 kmSource Type: ScientificPixel Type: Signed IntegerData Projection: GCS WGS84Mosaic Projection: Web Mercator Auxiliary SphereExtent: Global Land SurfaceSource: NASAUpdate Cycle: SporadicWhat 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 for Desktop. It is useful for scientific modeling, but only at global scales.In ArcGIS Pro you can use the built-in raster functions or create your own to create custom extracts of the data. Imagery layers provide fast, powerful inputs to geoprocessing tools, models, or Python scripts in Pro.Online you can filter the layer to show subsets of the data using the filter button and the layer's built-in raster functions.By applying the "Calculate Anomaly" raster function, it is possible to view these data in terms of deviation from the mean, instead of total change in storage. Mean change in storage for a given month is calculated over the entire period of record - 2000 to present.Time: This is a time-enabled layer. By default, it will show the first month from the map's time extent. Or, if time animation is disabled, a time range can be set using the layer's multidimensional settings. If you wish to calculate the average, sum, or min/max change in storage over the time extent, change the mosaic operator used to resolve overlapping pixels. In ArcGIS Online, you do this in the "Image Display Order" tab. In ArcGIS Pro, use the "Data" ribbon. In ArcMap, it is in the 'Mosaic' tab of the layer properties window. The minimum time extent is one month, and the maximum is 8 years. Important: You must switch from the cartographic renderer to the analytic renderer in the processing template tab in the layer properties window before using this layer as an input to geoprocessing tools.

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.

The US National Park Service manages 84.4 million acres that include the United States" 63 national parks, many national monuments, and other conservation and historical properties. These lands range from the 13 million acre Wrangell-St. Elias National Park and Preserve in Alaska to the 0.02 acre Thaddeus Kosciuszko National Memorial in Pennsylvania.Dataset SummaryPhenomenon Mapped: Administrative boundaries of U.S. National Park Service landsGeographic Extent: 50 United States, District of Columbia, Puerto Rico, US Virgin Islands, Guam, American Samoa, and Northern Mariana IslandsData Coordinate System: WGS 1984Visible Scale: The data is visible at all scalesSource: NPS Administrative Boundaries of National Park System Units layerPublication Date: April, 2025This layer is a view of the USA Federal Lands layer. A filter has been used on this layer to eliminate non-Park Service lands. For more information on layers for other agencies see the USA Federal Lands layer.What can you do with this Layer?This layer is suitable for both visualization and analysis across the ArcGIS system. This layer can be combined with your data and other layers from the ArcGIS Living Atlas of the World in ArcGIS Online and ArcGIS Pro to create powerful web maps that can be used alone or in a story map or other application.Because this layer is part of the ArcGIS Living Atlas of the World it is easy to add to your map:In ArcGIS Online you can add this layer to a map by selecting Add then Browse Living Atlas Layers. A window will open. Type "national park service" in the search box and browse to the layer. Select the layer then click Add to Map.In ArcGIS Pro open a map and select Add Data from the Map Tab. Select Data at the top of the drop down menu. The Add Data dialog box will open on the left side of the box expand Portal if necessary then select Living Atlas. Type "national park service" in the search box, browse to the layer then click OK.In both ArcGIS Online and Pro you can change the layer's symbology and view its attribute table. You can filter the layer to show subsets of the data using the filter button in Online or a definition query in Pro.The data can be exported to a file geodatabase, a shape file or other format and downloaded using the Export Data button on the top right of this webpage.This layer can be used as an analytic input in both Online and Pro through the Perform Analysis window Online or as an input to a geoprocessing tool, model, or Python script in Pro.The ArcGIS Living Atlas of the World provides an easy way to explore many other beautiful and authoritative maps on hundreds of topics like this one.Questions?Please leave a comment below if you have a question about this layer, and we will get back to you as soon as possible.

The U.S. Defense Department oversees the USA"s armed forces and manages over 30 million acres of land. With over 2.8 million service members and civilian employees the department is the world"s largest employer.Dataset SummaryPhenomenon Mapped: Lands managed by the U.S. Department of DefenseGeographic Extent: United States, Guam, Puerto RicoData Coordinate System: WGS 1984Visible Scale: The data is visible at all scalesSource: DOD Military Installations Ranges and Training Areas layer. Publication Date: May 2025This layer is a view of the USA Federal Lands layer. A filter has been used on this layer to eliminate non-Department of Defense lands. For more information on layers for other agencies see the USA Federal Lands layer.What can you do with this layer?This layer is suitable for both visualization and analysis across the ArcGIS system. This layer can be combined with your data and other layers from the ArcGIS Living Atlas of the World in ArcGIS Online and ArcGIS Pro to create powerful web maps that can be used alone or in a story map or other application.Because this layer is part of the ArcGIS Living Atlas of the World it is easy to add to your map:In ArcGIS Online you can add this layer to a map by selecting Add then Browse Living Atlas Layers. A window will open. Type "department of defense" in the search box and browse to the layer. Select the layer then click Add to Map.In ArcGIS Pro open a map and select Add Data from the Map Tab. Select Data at the top of the drop down menu. The Add Data dialog box will open on the left side of the box expand Portal if necessary then select Living Atlas. Type "department of defense" in the search box, browse to the layer then click OK.In both ArcGIS Online and Pro you can change the layer's symbology and view its attribute table. You can filter the layer to show subsets of the data using the filter button in Online or a definition query in Pro.The data can be exported to a file geodatabase, a shape file or other format and downloaded using the Export Data button on the top right of this webpage.This layer can be used as an analytic input in both Online and Pro through the Perform Analysis window Online or as an input to a geoprocessing tool, model, or Python script in Pro.The ArcGIS Living Atlas of the World provides an easy way to explore many other beautiful and authoritative maps on hundreds of topics like this one.Questions?Please leave a comment below if you have a question about this layer, and we will get back to you as soon as possible.

Abstract Gloucester Zone of Potential Hydrological change including input and derived layers. The final Zone of Potential Hydrological Change (ZPHC) is a union of the groundwater ZPHC and suface …Show full descriptionAbstract Gloucester Zone of Potential Hydrological change including input and derived layers. The final Zone of Potential Hydrological Change (ZPHC) is a union of the groundwater ZPHC and suface water ZPHC, which in turn were derived from groundwater and surface water impact modelling. The groundwater component of the ZPHC is where the the probability 5% or greater of equalling or exceeding 0.2m drawdown and is derived from the 95th Quantile layer.. The surface water component of the zone was derived by selecting assessment units within 150m or sharing an alluvial floodplain with an impacted river reach. Some manual post processing tweaks were undertaken to remove anomalous AU cells. Dataset History A selection buffer of 300m was created from the potentially impacted reaches of the surface water modelling interpolated reaches polylines. This was unioned with polygons in the alluvium layer that intersected with the potentially impacted reaches to create the composite selection layer (included in component layer folder). The composite selection layer was manually edited to constrain the extent to that reasonably potentially influenced by changes in river. The composite selection layer was used to select, by intersection, AUs that represented the surface water Dataset Citation Bioregional Assessment Programme (2017) GLO ZoPHC and component layers 20170321. Bioregional Assessment Derived Dataset. Viewed 18 July 2018, http://data.bioregionalassessments.gov.au/dataset/7f343d58-ed28-48a8-9321-df92aab9cbbc. Dataset Ancestors Derived From Standard Instrument Local Environmental Plan (LEP) - Heritage (HER) (NSW) Derived From NSW Office of Water GW licence extract linked to spatial locations - GLO v5 UID elements 27032014 Derived From GLO Assessment units 500m 20160705 Derived From Gloucester digitised coal mine boundaries Derived From GLO Surface water model RiverStyle ghost nodes 20160829 v01 Derived From Groundwater Dependent Ecosystems supplied by the NSW Office of Water on 13/05/2014 Derived From Greater Hunter Native Vegetation Mapping with Classification for Mapping Derived From Australian Coal Basins Derived From GLO DEM 1sec SRTM MGA56 Derived From Natural Resource Management (NRM) Regions 2010 Derived From NSW Office of Water GW licence extract linked to spatial locations GLOv4 UID 14032014 Derived From Communities of National Environmental Significance Database - RESTRICTED - Metadata only Derived From National Groundwater Dependent Ecosystems (GDE) Atlas Derived From Asset database for the Gloucester subregion on 12 September 2014 Derived From GEODATA 9 second DEM and D8: Digital Elevation Model Version 3 and Flow Direction Grid 2008 Derived From National Groundwater Information System (NGIS) v1.1 Derived From GLO SW Modelling Reaches and HRV lookup 20170328 v08 Derived From Groundwater Entitlement Data GLO NSW Office of Water 20150320 PersRemoved Derived From GLO Receptors 20150518 Derived From GLO Landscape Classification v01 Derived From Bioregional Assessment areas v02 Derived From R-scripts for uncertainty analysis v01 Derived From River Styles Spatial Layer for New South Wales Derived From New South Wales 2 kilometers Residential Exclusions Zone Derived From Geofabric Surface Cartography - V2.1 Derived From Groundwater Entitlement Data Gloucester - NSW Office of Water 20150320 Derived From Collaborative Australian Protected Areas Database (CAPAD) 2010 - External Restricted Derived From Mean Annual Climate Data of Australia 1981 to 2012 Derived From National Groundwater Dependent Ecosystems (GDE) Atlas (including WA) Derived From EIS Gloucester Coal 2010 Derived From GLO Assessment units 500m 20160815 v02 Derived From Report for Director Generals Requirement Rocky Hill Project 2012 Derived From Species Profile and Threats Database (SPRAT) - Australia - Species of National Environmental Significance Database (BA subset - RESTRICTED - Metadata only) Derived From GEODATA TOPO 250K Series 3, File Geodatabase format (.gdb) Derived From Geological Maps Combined for NSW Derived From Bioregional_Assessment_Programme_Catchment Scale Land Use of Australia - 2014 Derived From GEODATA TOPO 250K Series 3 Derived From Asset database for the Gloucester subregion on 28 May 2015 Derived From NSW Catchment Management Authority Boundaries 20130917 Derived From Geological Provinces - Full Extent Derived From GLO Preliminary Assessment Extent Derived From BILO Gridded Climate Data: Daily Climate Data for each year from 1900 to 2012 Derived From NSW Office of Water GW licence extract linked to spatial locations GLOv3 12032014 Derived From GLO Surface water model RiverStyle ghost nodes 20161004 v02 Derived From GLO Geological Model Extracted Horizons Final Grid XYZ V01 Derived From EIS for Rocky Hill Coal Project 2013 Derived From Gloucester River Types v01 Derived From GLO AEM dmax v01 Derived From BA ALL mean annual flow for NSW - Choudhury implementation of Budyko runoff v01 Derived From National Heritage List Spatial Database (NHL) (v2.1) Derived From Gloucester river types V02 Derived From Asset database for the Gloucester subregion on 8 April 2015 Derived From Gloucester - Additional assets from local councils Derived From NSW Office of Water combined geodatabase of regulated rivers and water sharing plan regions Derived From Gloucester Deep Wells Completion Reports - Geology Derived From Asset database for the Gloucester subregion on 29 August 2014 Derived From Gloucester Coal Basin Derived From GLO Landscape Classes split by 500m Assessment Units v01 Derived From New South Wales NSW Regional CMA Water Asset Information WAIT tool databases, RESTRICTED Includes ALL Reports Derived From Greater Hunter Native Vegetation Mapping Derived From Groundwater Modelling Report for Stratford Coal Mine Derived From Subcatchment boundaries within and nearby the Gloucester subregion Derived From AGL Gloucester Gas Project AECOM report location map features Derived From GLO RMS Model Depth Structure Eroded v01 Derived From Groundwater Economic Assets GLO 20150326 Derived From NSW Office of Water Groundwater Licence Extract Gloucester - Oct 2013 Derived From New South Wales NSW - Regional - CMA - Water Asset Information Tool - WAIT - databases Derived From GLO Deep Well Locations and Depths of Formations V01 Derived From Freshwater Fish Biodiversity Hotspots Derived From NSW Office of Water Groundwater licence extract linked to spatial locations GLOv2 19022014 Derived From GLO AEM Model v02 Derived From Australia - Species of National Environmental Significance Database Derived From Bioregional Assessment areas v01 Derived From Geofabric Hydrology Reporting Catchments - V2.1 Derived From Australia, Register of the National Estate (RNE) - Spatial Database (RNESDB) Internal Derived From GLO AEM interpolated exceedance probabilities v01 Derived From NSW Office of Water Groundwater Entitlements Spatial Locations Derived From GLO Receptors 20150828 Derived From Directory of Important Wetlands in Australia (DIWA) Spatial Database (Public) Derived From Geoscience Australia, 1 second SRTM Digital Elevation Model (DEM) Derived From Collaborative Australian Protected Areas Database (CAPAD) 2010 (Not current release) Derived From GLO Surface Water Receptors Landscape Types 20150611 Derived From Australian Geological Provinces, v02

This dataset represents a unique compiled environmental data set for the circumpolar Arctic ocean region 45N to 90N region. It consists of 170 layers (mostly marine, some terrestrial) in ArcGIS 10 format to be used with a Geographic Information System (GIS) and which are listed below in detail. Most layers are long-term average raster GRIDs for the summer season, often by ocean depth, and represent value-added products easy to use. The sources of the data are manifold such as the World Ocean Atlas 2009 (WOA09), International Bathimetric Chart of the Arctic Ocean (IBCAO), Canadian Earth System Model 2 (CanESM2) data (the newest generation of models available) and data sources such as plankton databases and OBIS. Ocean layers were modeled and predicted into the future and zooplankton species were modeled based on future data: Calanus hyperboreus (AphiaID104467), Metridia longa (AphiaID 104632), M. pacifica (AphiaID 196784) and Thysanoessa raschii (AphiaID 110711). Some layers are derived within ArcGIS. Layers have pixel sizes between 1215.819573 meters and 25257.72929 meters for the best pooled model, and between 224881.2644 and 672240.4095 meters for future climate data. Data was then reprojected into North Pole Stereographic projection in meters (WGS84 as the geographic datum). Also, future layers are included as a selected subset of proposed future climate layers from the Canadian CanESM2 for the next 100 years (scenario runs rcp26 and rcp85). The following layer groups are available: bathymetry (depth, derived slope and aspect); proximity layers (to,glaciers,sea ice, protected areas, wetlands, shelf edge); dissolved oxygen, apparent oxygen, percent oxygen, nitrogen, phosphate, salinity, silicate (all for August and for 9 depth classes); runoff (proximity, annual and August); sea surface temperature; waterbody temperature (12 depth classes); modeled ocean boundary layers (H1, H2, H3 and Wx).This dataset is used for a M.Sc. thesis by the author, and freely available upon request. For questions and details we suggest contacting the authors. Process_Description: Please contact Moritz Schmid for the thesis and detailed explanations. Short version: We model predicted here for the first time ocean layers in the Arctic Ocean based on a unique dataset of physical oceanography. Moreover, we developed presence/random absence models that indicate where the studied zooplankton species are most likely to be present in the Arctic Ocean. Apart from that, we develop the first spatially explicit models known to science that describe the depth in which the studied zooplankton species are most likely to be at, as well as their distribution of life stages. We do not only do this for one present day scenario. We modeled five different scenarios and for future climate data. First, we model predicted ocean layers using the most up to date data from various open access sources, referred here as best-pooled model data. We decided to model this set of stratification layers after discussions and input of expert knowledge by Professor Igor Polyakov from the International Arctic Research Center at the University of Alaska Fairbanks. We predicted those stratification layers because those are the boundaries and layers that the plankton has to cross for diel vertical migration and a change in those would most likely affect the migration. I assigned 4 variables to the stratification layers. H1, H2, H3 and Wx. H1 is the lower boundary of the mixed layer depth. Above this layer a lot of atmospheric disturbance is causing mixing of the water, giving the mixed layer its name. H2, the middle of the halocline is important because in this part of the ocean a strong gradient in salinity and temperature separates water layers. H3, the isotherm is important, because beneath it flows denser and colder Atlantic water. Wx summarizes the overall width of the described water column. Ocean layers were predicted using machine learning algorithms (TreeNet, Salford Systems). Second, ocean layers were included as predictors and used to predict the presence/random absence, most likely depth and life stage layers for the zooplankton species: Calanus hyperboreus, Metridia longa, Metridia pacifica and Thysanoessa raschii, This process was repeated for future predictions based on the CanESM2 data (see in the data section). For zooplankton species the following layers were developed and for the future. C. hyperboreus: Best-pooled model as well as future predictions (rcp26 including ocean layer(also excluding), rcp85 including oocean layers (also excluding) for 2010 and 2100.For parameters: Presence/random absence, most likely depth and life stage layers M. longa: Best-pooled model as well as future predictions (rcp26 including ocean layer(also excluding), rcp85 including oocean layers (also excluding) for 2010 and 2100. For parameters: Presence/rand... Visit https://dataone.org/datasets/f63d0f6c-7d53-46ce-b755-42a368007601 for complete metadata about this dataset.

One-eighth of the United States (247.3 million acres) is managed by the Bureau of Land Management. As part of the Department of the Interior, the agency oversees the 30 million acre National Landscape Conservation System, a collection of lands that includes 221 wilderness areas, 23 national monuments and 636 other protected areas. Bureau of Land Management Lands contain over 63,000 oil and gas wells and provide forage for over 18,000 grazing permit holders on 155 million acres of land. Dataset SummaryPhenomenon Mapped: United States lands managed by the Bureau of Land ManagementGeographic Extent: Contiguous United States and AlaskaData Coordinate System: WGS 1984Visible Scale: The data is visible at all scales but draws best at scales larger than 1:2,000,000.Source: BLM Surface Management Agency layer, Rasterized by Esri from features May 2025.Publication Date: December 2024This layer is a view of the USA Federal Lands layer. A filter has been used on this layer to eliminate non-Bureau of Land Management lands. For more information on layers for other agencies see the USA Federal Lands layer.What can you do with this layer?This layer is suitable for both visualization and analysis across the ArcGIS system. This layer can be combined with your data and other layers from the ArcGIS Living Atlas of the World in ArcGIS Online and ArcGIS Pro to create powerful web maps that can be used alone or in a story map or other application.Because this layer is part of the ArcGIS Living Atlas of the World it is easy to add to your map:In ArcGIS Online you can add this layer to a map by selecting Add then Browse Living Atlas Layers. A window will open. Type "bureau of land management" in the search box and browse to the layer. Select the layer then click Add to Map.In ArcGIS Pro open a map and select Add Data from the Map Tab. Select Data at the top of the drop down menu. The Add Data dialog box will open on the left side of the box expand Portal if necessary then select Living Atlas. Type "bureau of land management" in the search box, browse to the layer then click OK.In both ArcGIS Online and Pro you can change the layer's symbology and view its attribute table. You can filter the layer to show subsets of the data using the filter button in Online or a definition query in Pro.The data can be exported to a file geodatabase, a shape file or other format and downloaded using the Export Data button on the top right of this webpage.This layer can be used as an analytic input in both Online and Pro through the Perform Analysis window Online or as an input to a geoprocessing tool, model, or Python script in Pro.The ArcGIS Living Atlas of the World provides an easy way to explore many other beautiful and authoritative maps on hundreds of topics like this one.Questions?Please leave a comment below if you have a question about this layer, and we will get back to you as soon as possible.

Urban Habitat Connectivity Project (UHCP) Short description: A package of data containing potential habitat and fragmentation for seven species groups in the urban ACT. Each species group has two layer files. Connected habitat layers show potential core and corridor habitat for the species group, and connectivity/fragmentation between these habitat patches. Remnant patches layers contain areas which are predicted to be fragmented and inaccessible for the species group, but may be important for restoration activities. These layers are outputs of ecological connectivity modelling and have been developed using spatial data representing habitat and connectivity requirements specific to the species group. The following attributes are available in the data table for Connected Habitat layers:

Species Group* - indicates the species group of interestPatch ID – a unique identifier for each ‘patch’ of connected habitat, an ID that is given to group all habitat areas which are predicted to be connected to each other.Habitat Type* – identifies if the polygon meets core or corridor habitat requirements, or if it is a remnant patch.Habitat Number – a numeric value linked to Habitat Type to support statistics and symbology. Core habitat has a value of 0 and corridor habitat has a value of 1.Patch Area (Ha)* – the area of the individual polygon in hectares.Connected Habitat Area (Ha) – the total area of potential habitat in the connected patch, determined by summing the Patch Area for all polygons with the same Patch ID.Shape area – the polygon’s area, calculated by default in meters squared.Shape length – the length of the line enclosing the polygon, calculated by default in meters squared.

• Is also available in the data table for Remnant Patches layers. Spatial resolution: 1:10,000 Coordinate system: GDA2020 MGA zone 55 METHODS Data collection / creation: Spatial layers for habitat and barriers were created and input into a habitat connectivity/fragmentation model specifically designed for the species group. The model was developed using metrics derived from expert elicitation. These metrics quantified essential habitat and connectivity requirements for the species group, for example the preferred spacing of trees, the maximum crossable width of a road, the typical dispersal distance, etc. The model identified habitat and barriers to connectivity, based on the metrics which could be mapped. Habitat was delineated by patch size to determine core and corridor habitat, and to remove areas which are too small to be functional. The habitat type is visible in the attribute table of the data. Connectivity between habitat patches is dependent on the species group’s dispersal capacity and the availability of core habitat, suitable corridors and a path without barriers. To assess this core habitat areas were buffered by the species group’s dispersal distance. This identified how far an individual will move to find a new core habitat patch. Movement to this distance is dependent on a suitable path. All habitat was buffered by the distance the species can move outside habitat (through non-habitat areas). This identified how far an individual will move outside any habitat (core or corridor) before they require another habitat patch (i.e. how far they can travel between stepping stones).Connectivity is further complicated by impassable barriers. Barriers were used to slice up the dispersal buffers and identify ‘dispersal patches’, areas which an individual can move within. Fragmentation is seen when a barrier is present, patches are too far from core habitat, or corridor habitat is too far apart. A unique ID was applied to each patch and represents connectivity/fragmentation. The patches were intersected with habitat to apply the new ID to the habitat areas. The final model outputs identify areas of potential core, corridor or remnant (inaccessible) habitat. Core and corridor habitat are viewable in the connected habitat dataset, whilst remnant patches are available separately. The data was simplified using the Douglas-Peuker algorithm, a tolerance of 0.5-2m, minimum size of 2-5m2 for retention, and holes filled in if less than 20m2. Small adjoining slithers <20m2 were dissolved into neighbouring polygons to optimise drawing speeds. Please contact the project team for the model script or further details on the methodology. NOTES ON USE Quality: The habitat connectivity modelling used to produce the data was informed by work by the City of Melbourne (Kirk et al., 2018). The original methods were expanded on, with habitat and connectivity requirements (metrics) specific to the species group determined from expert elicitation and further analysis to consider patch size for core or corridor patches. The expert elicitation process provided the best and most relevant quantitative description of habitat and barriers available (for a species group rather than a specific species). The input datasets were then tailored to the metrics for this project. Existing datasets were refined to be relevant and reflect the metrics identified through expert elicitation. New datasets were created where data was missing. All data was derived from existing authoritative sources and/or remotely sensed data. This data curation process ensured the input datasets, and resulting output, were relevant and fit for purpose. Limitations: This data should be considered indicative only as there are limitations to the modelling process. It considers all habitat and barriers equally and as discrete objects (i.e. it applies a discrete boundary around a patch and does not account for gradients or flexible boundaries).The model predicts habitat and connectivity based on the data available. It does not assess whether a species is present or consider temporal variability. Some habitat requirements are not mapped (e.g. native vegetation, lack of predators) due to the lack of an accurate or complete dataset. Some of these requirements are critical to the success of the species group. These habitat requirements are available and have been derived from expert elicitation. They should be considered at an area of interest. The model assumes the input data is up to date and accurate. Many of the habitat and barrier datasets used as inputs into the models are in some way informed by remote sensing data. Remote sensing data has limitations, such as potential for misclassification (e.g. bare ground and pavement could be confused). Additionally, remotely sensed data captures a point in time and will become outdated. Manual checks and improvements using supplementary data for specific sites have been completed to reduce as much error as possible. Data refinement: Unmapped habitat and connectivity requirements should be considered when using the data. The full list of known habitat and connectivity requirements for each species group, including those considered by the model and those unaccounted for, is available by request. Other data may also be used to track changes post-LiDAR capture. For example, new development footprints may be used to remove non-habitat areas and can be done so at a faster rate than waiting for new LiDAR captures and re-running the model.

SHARING Licenses/restrictions on use: Creative Commons By Attribution 4.0 (Australian Capital Territory) How to cite this data: ACT Government, 2023. Potential Habitat and Fragmentation in Urban ACT dataset, version 3. Polygon layer developed by the Office of Nature Conservation, Environment, Planning and Sustainable Development Directorate, Canberra. CONTACT For accessibility issues or data enquiries please contact the Connecting Nature, Connecting People team cncp@act.gov.au.

Urban Habitat Connectivity Project (UHCP) Short description: A package of data containing potential habitat and fragmentation for seven species groups in the urban ACT. Each species group has two layer files. Connected habitat layers show potential core and corridor habitat for the species group, and connectivity/fragmentation between these habitat patches. Remnant patches layers contain areas which are predicted to be fragmented and inaccessible for the species group, but may be important for restoration activities. These layers are outputs of ecological connectivity modelling and have been developed using spatial data representing habitat and connectivity requirements specific to the species group. The following attributes are available in the data table for Connected Habitat layers: Species Group* - indicates the species group of interestPatch ID – a unique identifier for each ‘patch’ of connected habitat, an ID that is given to group all habitat areas which are predicted to be connected to each other.Habitat Type* – identifies if the polygon meets core or corridor habitat requirements, or if it is a remnant patch.Habitat Number – a numeric value linked to Habitat Type to support statistics and symbology. Core habitat has a value of 0 and corridor habitat has a value of 1.Patch Area (Ha)* – the area of the individual polygon in hectares.Connected Habitat Area (Ha) – the total area of potential habitat in the connected patch, determined by summing the Patch Area for all polygons with the same Patch ID.Shape area – the polygon’s area, calculated by default in meters squared.Shape length – the length of the line enclosing the polygon, calculated by default in meters squared. * Is also available in the data table for Remnant Patches layers. Spatial resolution: 1:10,000 Coordinate system: GDA2020 MGA zone 55 METHODS Data collection / creation: Spatial layers for habitat and barriers were created and input into a habitat connectivity/fragmentation model specifically designed for the species group. The model was developed using metrics derived from expert elicitation. These metrics quantified essential habitat and connectivity requirements for the species group, for example the preferred spacing of trees, the maximum crossable width of a road, the typical dispersal distance, etc. The model identified habitat and barriers to connectivity, based on the metrics which could be mapped. Habitat was delineated by patch size to determine core and corridor habitat, and to remove areas which are too small to be functional. The habitat type is visible in the attribute table of the data. Connectivity between habitat patches is dependent on the species group’s dispersal capacity and the availability of core habitat, suitable corridors and a path without barriers. To assess this core habitat areas were buffered by the species group’s dispersal distance. This identified how far an individual will move to find a new core habitat patch. Movement to this distance is dependent on a suitable path. All habitat was buffered by the distance the species can move outside habitat (through non-habitat areas). This identified how far an individual will move outside any habitat (core or corridor) before they require another habitat patch (i.e. how far they can travel between stepping stones).Connectivity is further complicated by impassable barriers. Barriers were used to slice up the dispersal buffers and identify ‘dispersal patches’, areas which an individual can move within. Fragmentation is seen when a barrier is present, patches are too far from core habitat, or corridor habitat is too far apart. A unique ID was applied to each patch and represents connectivity/fragmentation. The patches were intersected with habitat to apply the new ID to the habitat areas. The final model outputs identify areas of potential core, corridor or remnant (inaccessible) habitat. Core and corridor habitat are viewable in the connected habitat dataset, whilst remnant patches are available separately. The data was simplified using the Douglas-Peuker algorithm, a tolerance of 0.5-2m, minimum size of 2-5m2 for retention, and holes filled in if less than 20m2. Small adjoining slithers <20m2 were dissolved into neighbouring polygons to optimise drawing speeds. Please contact the project team for the model script or further details on the methodology. NOTES ON USE Quality: The habitat connectivity modelling used to produce the data was informed by work by the City of Melbourne (Kirk et al., 2018). The original methods were expanded on, with habitat and connectivity requirements (metrics) specific to the species group determined from expert elicitation and further analysis to consider patch size for core or corridor patches. The expert elicitation process provided the best and most relevant quantitative description of habitat and barriers available (for a species group rather than a specific species). The input datasets were then tailored to the metrics for this project. Existing datasets were refined to be relevant and reflect the metrics identified through expert elicitation. New datasets were created where data was missing. All data was derived from existing authoritative sources and/or remotely sensed data. This data curation process ensured the input datasets, and resulting output, were relevant and fit for purpose. Limitations: This data should be considered indicative only as there are limitations to the modelling process. It considers all habitat and barriers equally and as discrete objects (i.e. it applies a discrete boundary around a patch and does not account for gradients or flexible boundaries).The model predicts habitat and connectivity based on the data available. It does not assess whether a species is present or consider temporal variability. Some habitat requirements are not mapped (e.g. native vegetation, lack of predators) due to the lack of an accurate or complete dataset. Some of these requirements are critical to the success of the species group. These habitat requirements are available and have been derived from expert elicitation. They should be considered at an area of interest. The model assumes the input data is up to date and accurate. Many of the habitat and barrier datasets used as inputs into the models are in some way informed by remote sensing data. Remote sensing data has limitations, such as potential for misclassification (e.g. bare ground and pavement could be confused). Additionally, remotely sensed data captures a point in time and will become outdated. Manual checks and improvements using supplementary data for specific sites have been completed to reduce as much error as possible. Data refinement: Unmapped habitat and connectivity requirements should be considered when using the data. The full list of known habitat and connectivity requirements for each species group, including those considered by the model and those unaccounted for, is available by request. Other data may also be used to track changes post-LiDAR capture. For example, new development footprints may be used to remove non-habitat areas and can be done so at a faster rate than waiting for new LiDAR captures and re-running the model. SHARING Licenses/restrictions on use: Creative Commons By Attribution 4.0 (Australian Capital Territory) How to cite this data: ACT Government, 2023. Potential Habitat and Fragmentation in Urban ACT dataset, version 3. Polygon layer developed by the Office of Nature Conservation, Environment, Planning and Sustainable Development Directorate, Canberra. CONTACT For accessibility issues or data enquiries please contact the Connecting Nature, Connecting People team cncp@act.gov.au.

The tree canopy layer displays the proportion of the land surface covered by trees for the years 2011 to 2021 from the National Land Cover Database. Source: https://www.mrlc.govPhenomenon Mapped: Proportion of the landscape covered by trees.Time Extent: 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021Units: Percent (of each pixel that is covered by tree canopy)Cell Size: 30 metersSource Type: DiscretePixel Type: Unsigned integerData Coordinate Systems: North America Albers Equal Area ConicMosaic Projection: WGS 1984 Web Mercator Auxiliary SphereExtent: CONUS, Southeastern Alaska, Hawaii, Puerto Rico and the US Virgin IslandsSource: Multi-Resolution Land Characteristics ConsortiumPublication Date: April 1, 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 changing appearance every year in the lower 48 states from 2011 to 2021. (In Alaska, Hawaii, Puerto Rico and the US Virgin Islands, the animation will only show a change between 2011 and 2016.) 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.Alaska, Hawaii, Puerto Rico, and the US Virgin IslandsAt this time Alaska, Hawaii, Puerto Rico, and the US Virgin Islands do not have tree canopy cover for every year in the series like MRLC produced for the Lower 48 states. Furthermore, only a portion of coastal Southeastern Alaska from Kodiak to the Panhandle is available, but not the entire state. Alaska, Hawaii, Puerto Rico, and the US Virgin Islands have data in the series only from 2011 and 2016. 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.

Urban Habitat Connectivity Project (UHCP) Short description: A package of data containing potential habitat and fragmentation for seven species groups in the urban ACT. Each species group has two layer files. Connected habitat layers show potential core and corridor habitat for the species group, and connectivity/fragmentation between these habitat patches. Remnant patches layers contain areas which are predicted to be fragmented and inaccessible for the species group, but may be important for restoration activities. These layers are outputs of ecological connectivity modelling and have been developed using spatial data representing habitat and connectivity requirements specific to the species group. The following attributes are available in the data table for Connected Habitat layers:

Species Group* - indicates the species group of interestPatch ID – a unique identifier for each ‘patch’ of connected habitat, an ID that is given to group all habitat areas which are predicted to be connected to each other.Habitat Type* – identifies if the polygon meets core or corridor habitat requirements, or if it is a remnant patch.Habitat Number – a numeric value linked to Habitat Type to support statistics and symbology. Core habitat has a value of 0 and corridor habitat has a value of 1.Patch Area (Ha)* – the area of the individual polygon in hectares.Connected Habitat Area (Ha) – the total area of potential habitat in the connected patch, determined by summing the Patch Area for all polygons with the same Patch ID.Shape area – the polygon’s area, calculated by default in meters squared.Shape length – the length of the line enclosing the polygon, calculated by default in meters squared.

• Is also available in the data table for Remnant Patches layers. Spatial resolution: 1:10,000 Coordinate system: GDA2020 MGA zone 55 METHODS Data collection / creation: Spatial layers for habitat and barriers were created and input into a habitat connectivity/fragmentation model specifically designed for the species group. The model was developed using metrics derived from expert elicitation. These metrics quantified essential habitat and connectivity requirements for the species group, for example the preferred spacing of trees, the maximum crossable width of a road, the typical dispersal distance, etc. The model identified habitat and barriers to connectivity, based on the metrics which could be mapped. Habitat was delineated by patch size to determine core and corridor habitat, and to remove areas which are too small to be functional. The habitat type is visible in the attribute table of the data. Connectivity between habitat patches is dependent on the species group’s dispersal capacity and the availability of core habitat, suitable corridors and a path without barriers. To assess this core habitat areas were buffered by the species group’s dispersal distance. This identified how far an individual will move to find a new core habitat patch. Movement to this distance is dependent on a suitable path. All habitat was buffered by the distance the species can move outside habitat (through non-habitat areas). This identified how far an individual will move outside any habitat (core or corridor) before they require another habitat patch (i.e. how far they can travel between stepping stones).Connectivity is further complicated by impassable barriers. Barriers were used to slice up the dispersal buffers and identify ‘dispersal patches’, areas which an individual can move within. Fragmentation is seen when a barrier is present, patches are too far from core habitat, or corridor habitat is too far apart. A unique ID was applied to each patch and represents connectivity/fragmentation. The patches were intersected with habitat to apply the new ID to the habitat areas. The final model outputs identify areas of potential core, corridor or remnant (inaccessible) habitat. Core and corridor habitat are viewable in the connected habitat dataset, whilst remnant patches are available separately. The data was simplified using the Douglas-Peuker algorithm, a tolerance of 0.5-2m, minimum size of 2-5m2 for retention, and holes filled in if less than 20m2. Small adjoining slithers <20m2 were dissolved into neighbouring polygons to optimise drawing speeds. Please contact the project team for the model script or further details on the methodology. NOTES ON USE Quality: The habitat connectivity modelling used to produce the data was informed by work by the City of Melbourne (Kirk et al., 2018). The original methods were expanded on, with habitat and connectivity requirements (metrics) specific to the species group determined from expert elicitation and further analysis to consider patch size for core or corridor patches. The expert elicitation process provided the best and most relevant quantitative description of habitat and barriers available (for a species group rather than a specific species). The input datasets were then tailored to the metrics for this project. Existing datasets were refined to be relevant and reflect the metrics identified through expert elicitation. New datasets were created where data was missing. All data was derived from existing authoritative sources and/or remotely sensed data. This data curation process ensured the input datasets, and resulting output, were relevant and fit for purpose. Limitations: This data should be considered indicative only as there are limitations to the modelling process. It considers all habitat and barriers equally and as discrete objects (i.e. it applies a discrete boundary around a patch and does not account for gradients or flexible boundaries).The model predicts habitat and connectivity based on the data available. It does not assess whether a species is present or consider temporal variability. Some habitat requirements are not mapped (e.g. native vegetation, lack of predators) due to the lack of an accurate or complete dataset. Some of these requirements are critical to the success of the species group. These habitat requirements are available and have been derived from expert elicitation. They should be considered at an area of interest. The model assumes the input data is up to date and accurate. Many of the habitat and barrier datasets used as inputs into the models are in some way informed by remote sensing data. Remote sensing data has limitations, such as potential for misclassification (e.g. bare ground and pavement could be confused). Additionally, remotely sensed data captures a point in time and will become outdated. Manual checks and improvements using supplementary data for specific sites have been completed to reduce as much error as possible. Data refinement: Unmapped habitat and connectivity requirements should be considered when using the data. The full list of known habitat and connectivity requirements for each species group, including those considered by the model and those unaccounted for, is available by request. Other data may also be used to track changes post-LiDAR capture. For example, new development footprints may be used to remove non-habitat areas and can be done so at a faster rate than waiting for new LiDAR captures and re-running the model.

SHARING Licenses/restrictions on use: Creative Commons By Attribution 4.0 (Australian Capital Territory) How to cite this data: ACT Government, 2023. Potential Habitat and Fragmentation in Urban ACT dataset, version 3. Polygon layer developed by the Office of Nature Conservation, Environment, Planning and Sustainable Development Directorate, Canberra. CONTACT For accessibility issues or data enquiries please contact the Connecting Nature, Connecting People team cncp@act.gov.au.

Abstract The dataset was derived by the Bioregional Assessment Programme from multiple source datasets. The source datasets are identified in the Lineage field in this metadata statement. The processes undertaken to produce this derived dataset are described in the History field in this metadata statement. This resource contains raster datasets created using ArcGIS to analyse groundwater levels in the Namoi subregion. Purpose These data layers were created in ArcGIS as part of the analysis to …Show full descriptionAbstract The dataset was derived by the Bioregional Assessment Programme from multiple source datasets. The source datasets are identified in the Lineage field in this metadata statement. The processes undertaken to produce this derived dataset are described in the History field in this metadata statement. This resource contains raster datasets created using ArcGIS to analyse groundwater levels in the Namoi subregion. Purpose These data layers were created in ArcGIS as part of the analysis to investigate surface water - groundwater connectivity in the Namoi subregion. The data layers provide several of the figures presented in the Namoi 2.1.5 Surface water - groundwater interactions report. Dataset History Extracted points inside Namoi subregion boundary. Converted bore and pipe values to Hydrocode format, changed heading of 'Value' column to 'Waterlevel' and removed unnecessary columns then joined to Updated_NSW_GroundWaterLevel_data_analysis_v01\NGIS_NSW_Bore_Join_Hydmeas_unique_bores.shp clipped to only include those bores within the Namoi subregion. Selected only those bores with sample dates between >=26/4/2012 and <31/7/2012. Then removed 4 gauges due to anomalous ref_pt_height values or WaterElev values higher than Land_Elev values. Then added new columns of calculations: WaterElev = TsRefElev - Water_Leve DepthWater = WaterElev - Ref_pt_height Ref_pt_height = TsRefElev - LandElev Alternatively - Selected only those bores with sample dates between >=1/5/2006 and <1/7/2006 2012_Wat_Elev - This raster was created by interpolating Water_Elev field points from HydmeasJune2012_only.shp, using Spatial Analyst - Topo to Raster tool. And using the alluvium boundary (NAM_113_Aquifer1_NamoiAlluviums.shp) as a boundary input source. 12_dw_olp_enf - Select out only those bores that are in both source files. Then using depthwater in Topo to Raster, with alluvium as the boundary, ENFORCE field chosen, and using only those bores present in 2012 and 2006 dataset. 2012dw1km_alu - Clipped the 'watercourselines' layer to the Namoi Subregion, then selected 'Major' water courses only. Then used the Geoprocessing 'Buffer' tool to create a polygon delineating an area 1km around all the major streams in the Namoi subregion. selected points from HydmeasJune2012_only.shp that were within 1km of features the WatercourseLines then used the selected points and the 1km buffer around the major water courses and the Topo to Raster tool in Spatial analyst to create the raster. Then used the alluvium boundary to truncate the raster, to limit to the area of interest. 12_minus_06 - Select out bores from the 2006 dataset that are also in the 2012 dataset. Then create a raster using depth_water in topo to raster, with ENFORCE field chosen to remove sinks, and alluvium as boundary. Then, using Map Algebra - Raster Calculator, subtract the raster just created from 12_dw_olp_enf Dataset Citation Bioregional Assessment Programme (2017) Namoi bore analysis rasters. Bioregional Assessment Derived Dataset. Viewed 10 December 2018, http://data.bioregionalassessments.gov.au/dataset/7604087e-859c-4a92-8548-0aa274e8a226. Dataset Ancestors Derived From Bioregional Assessment areas v02 Derived From Gippsland Project boundary Derived From Bioregional Assessment areas v04 Derived From Upper Namoi groundwater management zones Derived From Natural Resource Management (NRM) Regions 2010 Derived From Bioregional Assessment areas v03 Derived From Victoria - Seamless Geology 2014 Derived From GIS analysis of HYDMEAS - Hydstra Groundwater Measurement Update: NSW Office of Water - Nov2013 Derived From Bioregional Assessment areas v01 Derived From GEODATA TOPO 250K Series 3, File Geodatabase format (.gdb) Derived From GEODATA TOPO 250K Series 3 Derived From NSW Catchment Management Authority Boundaries 20130917 Derived From Geological Provinces - Full Extent Derived From Hydstra Groundwater Measurement Update - NSW Office of Water, Nov2013

Urban Habitat Connectivity Project (UHCP) Short description: A package of data containing potential habitat and fragmentation for seven species groups in the urban ACT. Each species group has two layer files. Connected habitat layers show potential core and corridor habitat for the species group, and connectivity/fragmentation between these habitat patches. Remnant patches layers contain areas which are predicted to be fragmented and inaccessible for the species group, but may be important for restoration activities. These layers are outputs of ecological connectivity modelling and have been developed using spatial data representing habitat and connectivity requirements specific to the species group. The following attributes are available in the data table for Connected Habitat layers:

Species Group* - indicates the species group of interestPatch ID – a unique identifier for each ‘patch’ of connected habitat, an ID that is given to group all habitat areas which are predicted to be connected to each other.Habitat Type* – identifies if the polygon meets core or corridor habitat requirements, or if it is a remnant patch.Habitat Number – a numeric value linked to Habitat Type to support statistics and symbology. Core habitat has a value of 0 and corridor habitat has a value of 1.Patch Area (Ha)* – the area of the individual polygon in hectares.Connected Habitat Area (Ha) – the total area of potential habitat in the connected patch, determined by summing the Patch Area for all polygons with the same Patch ID.Shape area – the polygon’s area, calculated by default in meters squared.Shape length – the length of the line enclosing the polygon, calculated by default in meters squared.

• Is also available in the data table for Remnant Patches layers. Spatial resolution: 1:10,000 Coordinate system: GDA2020 MGA zone 55 METHODS Data collection / creation: Spatial layers for habitat and barriers were created and input into a habitat connectivity/fragmentation model specifically designed for the species group. The model was developed using metrics derived from expert elicitation. These metrics quantified essential habitat and connectivity requirements for the species group, for example the preferred spacing of trees, the maximum crossable width of a road, the typical dispersal distance, etc. The model identified habitat and barriers to connectivity, based on the metrics which could be mapped. Habitat was delineated by patch size to determine core and corridor habitat, and to remove areas which are too small to be functional. The habitat type is visible in the attribute table of the data. Connectivity between habitat patches is dependent on the species group’s dispersal capacity and the availability of core habitat, suitable corridors and a path without barriers. To assess this core habitat areas were buffered by the species group’s dispersal distance. This identified how far an individual will move to find a new core habitat patch. Movement to this distance is dependent on a suitable path. All habitat was buffered by the distance the species can move outside habitat (through non-habitat areas). This identified how far an individual will move outside any habitat (core or corridor) before they require another habitat patch (i.e. how far they can travel between stepping stones).Connectivity is further complicated by impassable barriers. Barriers were used to slice up the dispersal buffers and identify ‘dispersal patches’, areas which an individual can move within. Fragmentation is seen when a barrier is present, patches are too far from core habitat, or corridor habitat is too far apart. A unique ID was applied to each patch and represents connectivity/fragmentation. The patches were intersected with habitat to apply the new ID to the habitat areas. The final model outputs identify areas of potential core, corridor or remnant (inaccessible) habitat. Core and corridor habitat are viewable in the connected habitat dataset, whilst remnant patches are available separately. The data was simplified using the Douglas-Peuker algorithm, a tolerance of 0.5-2m, minimum size of 2-5m2 for retention, and holes filled in if less than 20m2. Small adjoining slithers <20m2 were dissolved into neighbouring polygons to optimise drawing speeds. Please contact the project team for the model script or further details on the methodology. NOTES ON USE Quality: The habitat connectivity modelling used to produce the data was informed by work by the City of Melbourne (Kirk et al., 2018). The original methods were expanded on, with habitat and connectivity requirements (metrics) specific to the species group determined from expert elicitation and further analysis to consider patch size for core or corridor patches. The expert elicitation process provided the best and most relevant quantitative description of habitat and barriers available (for a species group rather than a specific species). The input datasets were then tailored to the metrics for this project. Existing datasets were refined to be relevant and reflect the metrics identified through expert elicitation. New datasets were created where data was missing. All data was derived from existing authoritative sources and/or remotely sensed data. This data curation process ensured the input datasets, and resulting output, were relevant and fit for purpose. Limitations: This data should be considered indicative only as there are limitations to the modelling process. It considers all habitat and barriers equally and as discrete objects (i.e. it applies a discrete boundary around a patch and does not account for gradients or flexible boundaries).The model predicts habitat and connectivity based on the data available. It does not assess whether a species is present or consider temporal variability. Some habitat requirements are not mapped (e.g. native vegetation, lack of predators) due to the lack of an accurate or complete dataset. Some of these requirements are critical to the success of the species group. These habitat requirements are available and have been derived from expert elicitation. They should be considered at an area of interest. The model assumes the input data is up to date and accurate. Many of the habitat and barrier datasets used as inputs into the models are in some way informed by remote sensing data. Remote sensing data has limitations, such as potential for misclassification (e.g. bare ground and pavement could be confused). Additionally, remotely sensed data captures a point in time and will become outdated. Manual checks and improvements using supplementary data for specific sites have been completed to reduce as much error as possible. Data refinement: Unmapped habitat and connectivity requirements should be considered when using the data. The full list of known habitat and connectivity requirements for each species group, including those considered by the model and those unaccounted for, is available by request. Other data may also be used to track changes post-LiDAR capture. For example, new development footprints may be used to remove non-habitat areas and can be done so at a faster rate than waiting for new LiDAR captures and re-running the model.

SHARING Licenses/restrictions on use: Creative Commons By Attribution 4.0 (Australian Capital Territory) How to cite this data: ACT Government, 2023. Potential Habitat and Fragmentation in Urban ACT dataset, version 3. Polygon layer developed by the Office of Nature Conservation, Environment, Planning and Sustainable Development Directorate, Canberra. CONTACT For accessibility issues or data enquiries please contact the Connecting Nature, Connecting People team cncp@act.gov.au.

Native Vegetation Management Benefits (NVMB) mapping

NVMB mapping is a way of identifying the relative benefits to NSW biodiversity of protecting or restoring native vegetation. NVMB mapping is used for cross-tenure, whole-of-landscape conservation planning, decision support, prioritisation and scenario planning.

The NVMB method employs well-developed ecological theory to combine vascular plant records, bioclimatic data, vegetation condition mapping and connectivity analysis.

NVMB Series 2

Series 2 is a fully complementary set of NVMB layers with consistent units (with a range from zero to one), such that for any location, the set of benefit values across the set of NVMB layers sum to a single maximum level of overall potential benefit for that location, referred to as the 'Maximum Biodiversity Benefit' (MBB). MBB reflects each location's capacity to support species and communities which have been depleted across NSW. The schema describing the nesting of the set of layers is provided in the attached resource: NVMB Series2 chart. Two 'delta' (change) layers are included to represent what additional benefits can be achieved in 15 years of fostering regeneration (delta improve benefits) and through full restoration action (delta restore benefits). A 'manage and improve' layer quantifies the combined benefits conserved by managing existing vegetation, and the additional benefits that can accrue through fostering regeneration (over a nominal 15-year period).

All layers are derived from a common set of inputs. The various NVMB layers become differentiated through the application of variants of the ecological condition layer, at the final stage of developing the layers (current condition is used for manage benefits, partially restored condition for improve benefits, and fully restored condition for the restoration benefits).

Series 2 represents a slight but significant departure from previous NVMB versions. Previous versions were provided in 4 separate SEED records: Manage benefits; Improve benefits; Restore benefits; and Landscape benefits. The landscape value benefits from the previous version are now integrated into Manage, Improve and Restoration benefits. A new layer of Maximum Biodiversity Benefit is added.

End users will notice significant differences between previous versions and these Series 2 layers. Stage 2 puts greater emphasis on cross-scale ecological connectivity across the benefit layers rather than treating landscape connectivity separately. For example, cleared areas of highly diminished communities such as box-woodlands in the wheat-sheep belt, are only given the highest restore benefit value in areas that are also well connected to areas of existing native vegetation.

Versioning

Series 2 is an update on the previous NVMB series (Series 1). Users may wish to employ Series 1 in cases where connectivity considerations are less (e.g., for large scale conservation actions - which produce their own 'critical mass'). In most cases Series 2 is the preferred source for conservation planning.

Due to the Series 2 layers forming an integrated set, they are provided together in a single SEED record. Because of the step change from previous version, Series 2 is reset as Series 2 v1.0.

Series 2 v1.0 is relevant to 2017. It does not consider the 2019-20 megafires or the degree of subsequent recovery. However, 2017 and 2020 NVMB surfaces have been produced in Series 1 (see below for more information).

More technical detail

The probabilistic method used for accumulating values draws on the 'equitable' approach (Drielsma and Love 2021) which applies 'diminishing returns' to connectivity, rather than the previous 'any additional unit of connectivity always provides proportionally more benefit'.

This series also incorporates the following advances:

• use of continuous values in GDM/environmental space (i.e., no loss of information by unnecessarily reducing to discrete classes)

• by incorporating an improved connectivity links approach (Drielsma et al. 2022), the new layers better consider how different locations can contribute to maintaining or restoring habitat linkages that allow species to move and migrate across landscapes

• incorporation of generic REMP approach (Drielsma and Love 2021) for spatial context component

More information

For more detail on the NVMB Series 2 method view this presentation.

Series 1

The previous series of LVMB mapping can be found at the following SEED records: Manage benefits; Improve benefits; Restore benefits; Landscape benefits.

Post-megafires

2017 and 2020 (post-megafire) NVMB surfaces have been produced in Series 1, for manage and restore. If comparing between 2017 and 2020 be sure to use outputs from the same series (i.e., series 1).

Climate-informed NVMB

Climate-informed versions of the manage benefits and restore benefits (Series 1 v.1) can be found here. These are being updated in 2024-24.

References

Drielsma MJ, Love J, & Thapa R 2023, Ecological models for reporting and conservation prioritisation - meeting the rising challenges, with examples from NSW, Australia. Presentation to the 6th International Ecosummit, Gold Coast 13-17 June 2023.

Drielsma MJ, Love J, Thapa R, Taylor S, & Williams KJ 2022, General Landscape Connectivity Model (GLCM): a new way to map whole of landscape biodiversity functional connectivity for operational planning and reporting. Ecological Modelling, Vol.465, pp.109858, doi: https://doi.org/10.1016/j.ecolmodel.2021.109858.

Drielsma M, & Love J 2021, An equitable method for evaluating habitat amount and potential occupancy. Ecological Modelling, 440:109388, doi: https://doi.org/10.1016/j.ecolmodel.2020.109388.

Drielsma MJ, Ferrier S, Howling G, Manion G, Taylor S, Love J (2014) The Biodiversity Forecasting Toolkit: Answering the 'how much', 'what' and 'where' of planning for biodiversity persistence, Ecological Modelling, 274:80-91. https://www.sciencedirect.com/science/article/pii/S0304380013005760?via%3Dihub.

The U.S. Geological Survey has been forecasting sea-level rise impacts on the landscape to evaluate where coastal land will be available for future use. The purpose of this project is to develop a spatially explicit, probabilistic model of coastal response for the Northeastern U.S. to a variety of sea-level scenarios that take into account the variable nature of the coast and provides outputs at spatial and temporal scales suitable for decision support. Model results provide predictions of adjusted land elevation ranges (AE) with respect to forecast sea-levels, a likelihood estimate of this outcome (PAE), and a probability of coastal response (CR) characterized as either static or dynamic. The predictions span the coastal zone vertically from -12 meters (m) to 10 m above mean high water (MHW). Results are produced at a horizontal resolution of 30 meters for four decades (the 2020s, 2030s, 2050s and 2080s). Adjusted elevations and their respective probabilities are generated using regional geospatial datasets of current sea-level forecasts, vertical land movement rates, and current elevation data. Coastal response type predictions incorporate adjusted elevation predictions with land cover data and expert knowledge to determine the likelihood that an area will be able to accommodate or adapt to water level increases and maintain its initial land class state or transition to a new non-submerged state (dynamic) or become submerged (static). Intended users of these data include scientific researchers, coastal planners, and natural resource management communities.

These GIS layers provide the probability of observing the forecast of adjusted land elevation (PAE) with respect to predicted sea-level rise or the Northeastern U.S. for the 2020s, 2030s, 2050s and 2080s. These data are based on the following inputs: sea-level rise, vertical land movement rates due to glacial isostatic adjustment and elevation data. The output displays the highest probability among the five adjusted elevation ranges (-12 to -1, -1 to 0, 0 to 1, 1 to 5, and 5 to 10 m) to be observed for the forecast year as defined by a probabilistic framework (a Bayesian network), and should be used concurrently with the adjusted land elevation layer (AE), also available from http://woodshole.er.usgs.gov/project-pages/coastal_response/, which provides users with the forecast elevation range occurring when compared with the four other elevation ranges. These data layers primarily show the distribution of adjusted elevation range probabilities over a large spatial scale and should therefore be used qualitatively.

Calculating the total volume of water stored in a landscape can be challenging. In addition to lakes and reservoirs, water can be stored in soil, snowpack, or even inside plants and animals, and tracking the all these different mediums is not generally possible. However, calculating the change in storage is easy - just subtract the water output from the water input. Using the GLDAS layers we can do this calculation for every month from January 2000 to the present day. The precipitation layer tells us the input to each cell and runoff plus evapotranspiration is the output. When the input is higher than the output during a given month, it means water was stored. When output is higher than input, storage is being depleted. Generally the change in storage should be close to the change in soil moisture content plus the change in snowpack, but it will not match up exactly because of the other storage mediums discussed above.Dataset SummaryThe GLDAS Change in Storage layer is a time-enabled image service that shows net monthly change in storage from 2000 to the present, measured in millimeters of water. It is calculated by NASA using the Noah land surface model, run at 0.25 degree spatial resolution using satellite and ground-based observational data from the Global Land Data Assimilation System (GLDAS-2.1). The model is run with 3-hourly time steps and aggregated into monthly averages. Review the complete list of model inputs, explore the output data (in GRIB format), and see the full Hydrology Catalog for all related data and information!Phenomenon Mapped: Change in Water StorageUnits: MillimetersTime Interval: MonthlyTime Extent: 2000/01/01 to presentCell Size: 28 kmSource Type: ScientificPixel Type: Signed IntegerData Projection: GCS WGS84Mosaic Projection: Web Mercator Auxiliary SphereExtent: Global Land SurfaceSource: NASAUpdate Cycle: SporadicWhat 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 for Desktop. It is useful for scientific modeling, but only at global scales.In ArcGIS Pro you can use the built-in raster functions or create your own to create custom extracts of the data. Imagery layers provide fast, powerful inputs to geoprocessing tools, models, or Python scripts in Pro.Online you can filter the layer to show subsets of the data using the filter button and the layer's built-in raster functions.By applying the "Calculate Anomaly" raster function, it is possible to view these data in terms of deviation from the mean, instead of total change in storage. Mean change in storage for a given month is calculated over the entire period of record - 2000 to present.Time: This is a time-enabled layer. By default, it will show the first month from the map's time extent. Or, if time animation is disabled, a time range can be set using the layer's multidimensional settings. If you wish to calculate the average, sum, or min/max change in storage over the time extent, change the mosaic operator used to resolve overlapping pixels. In ArcGIS Online, you do this in the "Image Display Order" tab. In ArcGIS Pro, use the "Data" ribbon. In ArcMap, it is in the 'Mosaic' tab of the layer properties window. The minimum time extent is one month, and the maximum is 8 years. Important: You must switch from the cartographic renderer to the analytic renderer in the processing template tab in the layer properties window before using this layer as an input to geoprocessing tools.