Introduction and Rationale: Due to our increasing understanding of the role the surrounding landscape plays in ecological processes, a detailed characterization of land cover, including both agricultural and natural habitats, is ever more important for both researchers and conservation practitioners. Unfortunately, in the United States, different types of land cover data are split across thematic datasets that emphasize agricultural or natural vegetation, but not both. To address this data gap and reduce duplicative efforts in geospatial processing, we merged two major datasets, the LANDFIRE National Vegetation Classification (NVC) and USDA-NASS Cropland Data Layer (CDL), to produce an integrated land cover map. Our workflow leveraged strengths of the NVC and the CDL to produce detailed rasters comprising both agricultural and natural land-cover classes. We generated these maps for each year from 2012-2021 for the conterminous United States, quantified agreement between input layers and accuracy of our merged product, and published the complete workflow necessary to update these data. In our validation analyses, we found that approximately 5.5% of NVC agricultural pixels conflicted with the CDL, but we resolved a majority of these conflicts based on surrounding agricultural land, leaving only 0.6% of agricultural pixels unresolved in our merged product. Contents: Spatial data

Attribute table for merged rasters

Technical validation data

Number and proportion of mismatched pixels Number and proportion of unresolved pixels Producer's and User's accuracy values and coverage of reference data Resources in this dataset:Resource Title: Attribute table for merged rasters. File Name: CombinedRasterAttributeTable_CDLNVC.csvResource Description: Raster attribute table for merged raster product. Class names and recommended color map were taken from USDA-NASS Cropland Data Layer and LANDFIRE National Vegetation Classification. Class values are also identical to source data, except classes from the CDL are now negative values to avoid overlapping NVC values. Resource Title: Number and proportion of mismatched pixels. File Name: pixel_mismatch_byyear_bycounty.csvResource Description: Number and proportion of pixels that were mismatched between the Cropland Data Layer and National Vegetation Classification, per year from 2012-2021, per county in the conterminous United States.Resource Title: Number and proportion of unresolved pixels. File Name: unresolved_conflict_byyear_bycounty.csvResource Description: Number and proportion of unresolved pixels in the final merged rasters, per year from 2012-2021, per county in the conterminous United States. Unresolved pixels are a result of mismatched pixels that we could not resolve based on surrounding agricultural land (no agriculture with 90m radius).Resource Title: Producer's and User's accuracy values and coverage of reference data. File Name: accuracy_datacoverage_byyear_bycounty.csvResource Description: Producer's and User's accuracy values and coverage of reference data, per year from 2012-2021, per county in the conterminous United States. We defined coverage of reference data as the proportional area of land cover classes that were included in the reference data published by USDA-NASS and LANDFIRE for the Cropland Data Layer and National Vegetation Classification, respectively. CDL and NVC classes with reference data also had published accuracy statistics. Resource Title: Data Dictionary. File Name: Data_Dictionary_RasterMerge.csv

This dataset is called the Gridded SSURGO (gSSURGO) Database and is derived from the Soil Survey Geographic (SSURGO) Database. SSURGO is generally the most detailed level of soil geographic data developed by the National Cooperative Soil Survey (NCSS) in accordance with NCSS mapping standards. The tabular data represent the soil attributes, and are derived from properties and characteristics stored in the National Soil Information System (NASIS). The gSSURGO data were prepared by merging traditional SSURGO digital vector map and tabular data into a Conterminous US-wide extent, and adding a Conterminous US-wide gridded map layer derived from the vector, plus a new value added look up (valu) table containing "ready to map" attributes. The gridded map layer is offered in an ArcGIS file geodatabase raster format.

The raster and vector map data have a Conterminous US-wide extent. The raster map data have a 10 meter cell size that approximates the vector polygons in an Albers Equal Area projection. Each cell (and polygon) is linked to a map unit identifier called the map unit key. A unique map unit key is used to link to raster cells and polygons to attribute tables, including the new value added look up (valu) table that contains additional derived data.

The value added look up (valu) table contains attribute data summarized to the map unit level using best practice generalization methods intended to meet the needs of most users. The generalization methods include map unit component weighted averages and percent of the map unit meeting a given criteria.

The Gridded SSURGO dataset was created for use in national, regional, and state-wide resource planning and analysis of soils data. The raster map layer data can be readily combined with other national, regional, and local raster layers, e.g., National Land Cover Database (NLCD), the National Agricultural Statistics Service (NASS) Crop Data Layer, or the National Elevation Dataset (NED).

This is a collection of Digital Surface Models and Highest Hit rasters covering selected U.S. Forest Service and adjoining lands in the Southwest Region, encompassing Arizona and New Mexico. The data are presented in a time-enabled format, allowing the end-user to view available data year-by-year, or all available years at once, within a GIS system. The data encompass varying years, varying resolutions, and varying geographic extents, dependent upon available data as provided by the region. DSM and Highest Hit rasters represent elevation of Earth's surface, including its natural and human-made features, such as vegetation and buildings.The data contains an attribute table. Notable attributes that may be of interest to an end-user are:lowps: the pixel size of the source raster, given in meters.highps: the pixel size of the top-most pyramid for the raster, given in meters.beginyear: the first year of data acquisition for an individual dataset.endyear: the final year of data acquisition for an individual dataset.dataset_name: the name of the individual dataset within the collection.metadata: A URL link to a file on IIPP's Portal containing metadata pertaining to an individual dataset within the image service.resolution: The pixel size of the source raster, given in meters.Terrain-related imagery are primarily derived from Lidar, stereoscopic aerial imagery, or Interferometric Synthetic Aperture Radar datasets. Consequently, these derivatives inherit the limitations and uncertainties of the parent sensor and platform and the processing techniques used to produce the imagery. The terrain images are orthographic; they have been georeferenced and displacement due to sensor orientation and topography have been removed, producing data that combines the characteristics of an image with the geometric qualities of a map. The orthographic images show ground features in their proper positions, without the distortion characteristic of unrectified aerial or satellite imagery. Digital orthoimages produced and used within the Forest Service are developed from imagery acquired through various national and regional image acquisition programs. The resulting orthoimages can be directly applied in remote sensing, GIS and mapping applications. They serve a variety of purposes, from interim maps to references for Earth science investigations and analysis. Because of the orthographic property, an orthoimage can be used like a map for measurement of distances, angles, and areas with scale being constant everywhere. Also, they can be used as map layers in GIS or other computer-based manipulation, overlaying, and analysis. An orthoimage differs from a map in a manner of depiction of detail; on a map only selected detail is shown by conventional symbols whereas on an orthoimage all details appear just as in original aerial or satellite imagery.Tribal lands have been masked from this public service in accordance with Tribal agreements.

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 for holders of federally regulated mortgages. In addition, this layer can help planners and firms avoid areas of flood risk and also avoid additional cost to carry insurance for certain planned activities. Dataset SummaryPhenomenon Mapped: Flood Hazard AreasGeographic Extent: Contiguous United States, Alaska, Hawaii, Puerto Rico, Guam, US Virgin Islands, Northern Mariana Islands and American Samoa.Projection: Web Mercator Auxiliary SphereData Coordinate System: USA Contiguous Albers Equal Area Conic USGS version (contiguous US, Puerto Rico, US Virgin Islands), WGS 1984 Albers (Alaska), Hawaii Albers Equal Area Conic (Hawaii), Western Pacific Albers Equal Area Conic (Guam, Northern Mariana Islands, and American Samoa)Cell Sizes: 10 meters (default), 30 meters, and 90 metersUnits: NoneSource Type: ThematicPixel Type: Unsigned integerSource: Federal Emergency Management Agency (FEMA)Update Frequency: AnnualPublication Date: May 7, 2025 This layer is derived from the May 7, 2025 version Flood Insurance Rate Map feature class S_FLD_HAZ_AR. The vector data were then flagged with an index of 94 classes, representing a unique combination of values displayed by three renderers. (In three resolutions the three renderers make nine processing templates.) Repair Geometry was run on the set of features, then the features were rasterized using the 94 class index at a resolutions of 10, 30, and 90 meters, using the Polygon to Raster tool and the "MAXIMUM_COMBINED_AREA" option. Not every part of the United States is covered by flood rate maps. This layer compiles all the flood insurance maps available at the time of publication. To make analysis easier, areas that were NOT mapped by FEMA for flood insurance rates no longer are served as NODATA but are filled in with a value of 250, representing any unmapped areas which appear in the US Census boundary of the USA states and territories. The attribute table corresponding to value 250 will indicate that the area was not mapped.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 "flood hazard areas" 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 "flood hazard areas" in the search box, browse to the layer then click OK. In ArcGIS Pro you can use the built-in raster functions to create custom extracts of the data. Imagery layers provide fast, powerful inputs to geoprocessing tools, models, or Python scripts 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. Processing TemplatesCartographic Renderer - The default. These are meaningful classes grouped by FEMA which group its own Flood Zone Type and Subtype fields. This renderer uses FEMA's own cartographic interpretations of its flood zone and zone subtype fields to help you identify and assess risk. Flood Zone Type Renderer - Specifically renders FEMA FLD_ZONE (flood zone) attribute, which distinguishes the original, broadest categories of flood zones. This renderer displays high level categories of flood zones, and is less nuanced than the Cartographic Renderer. For example, a fld_zone value of X can either have moderate or low risk depending on location. This renderer will simply render fld_zone X as its own color without identifying "500 year" flood zones within that category.Flood Insurance Requirement Renderer - Shows Special Flood Hazard Area (SFHA) true-false status. This may be helpful if you want to show just the places where flood insurance is required. A value of True means flood insurance is mandatory in a majority of the area covered by each 10m pixel. Each of these three renderers have templates at three different raster resolutions depending on your analysis needs. To include the layer in web maps to serve maps and queries, the 10 meter renderers are the preferred option. These are served with overviews and render at all resolutions. However, when doing analysis of larger areas, we now offer two coarser resolutions of 30 and 90 meters in processing templates for added convenience and time savings.Data DictionaryMaking a copy of your area of interest using copyraster in arcgis pro will copy the layer's attribute table to your network alongside the local output raster. The raster attribute table in the copied raster will contain the flood zone, zone subtype, and special flood hazard area true/false flag which corresponds to each value in the layer for your area of interest. For your convienence, we also included a table in CSV format in the box below as a data dictionary you can use as an index to every value in the layer. Value,FLD_ZONE,ZONE_SUBTY,SFHA_TF 2,A,, 3,A,,F 4,A,,T 5,A,,T 6,A,,T 7,A,1 PCT ANNUAL CHANCE FLOOD HAZARD CONTAINED IN CHANNEL,T 8,A,1 PCT ANNUAL CHANCE FLOOD HAZARD CONTAINED IN STRUCTURE,T 9,A,ADMINISTRATIVE FLOODWAY,T 10,A,COASTAL FLOODPLAIN,T 11,A,FLOWAGE EASEMENT AREA,T 12,A99,,T 13,A99,AREA WITH REDUCED FLOOD RISK DUE TO LEVEE,T 14,AE,,F 15,AE,,T 16,AE,,T 17,AE,,T 18,AE,1 PCT ANNUAL CHANCE FLOOD HAZARD CONTAINED IN CHANNEL,T 19,AE,1 PCT ANNUAL CHANCE FLOOD HAZARD CONTAINED IN STRUCTURE,T 20,AE,"1 PCT CONTAINED IN STRUCTURE, COMMUNITY ENCROACHMENT",T 21,AE,"1 PCT CONTAINED IN STRUCTURE, FLOODWAY",T 22,AE,ADMINISTRATIVE FLOODWAY,T 23,AE,AREA OF SPECIAL CONSIDERATION,T 24,AE,COASTAL FLOODPLAIN,T 25,AE,COLORADO RIVER FLOODWAY,T 26,AE,COMBINED RIVERINE AND COASTAL FLOODPLAIN,T 27,AE,COMMUNITY ENCROACHMENT,T 28,AE,COMMUNITY ENCROACHMENT AREA,T 29,AE,DENSITY FRINGE AREA,T 30,AE,FLOODWAY,T 31,AE,FLOODWAY CONTAINED IN CHANNEL,T 32,AE,FLOODWAY CONTAINED IN STRUCTURE,T 33,AE,FLOWAGE EASEMENT AREA,T 34,AE,RIVERINE FLOODWAY IN COMBINED RIVERINE AND COASTAL ZONE,T 35,AE,RIVERINE FLOODWAY SHOWN IN COASTAL ZONE,T 36,AE,STATE ENCROACHMENT AREA,T 37,AH,,T 38,AH,,T 39,AH,FLOODWAY,T 40,AO,,T 41,AO,COASTAL FLOODPLAIN,T 42,AO,FLOODWAY,T 43,AREA NOT INCLUDED,,F 44,AREA NOT INCLUDED,,T 45,AREA NOT INCLUDED,,U 46,D,,F 47,D,,T 48,D,AREA WITH FLOOD RISK DUE TO LEVEE,F 49,OPEN WATER,,F 50,OPEN WATER,,T 51,OPEN WATER,,U 52,V,,T 53,V,COASTAL FLOODPLAIN,T 54,VE,,T 55,VE,,T 56,VE,COASTAL FLOODPLAIN,T 57,VE,RIVERINE FLOODWAY SHOWN IN COASTAL ZONE,T 58,X,,F 59,X,0.2 PCT ANNUAL CHANCE FLOOD HAZARD,F 60,X,0.2 PCT ANNUAL CHANCE FLOOD HAZARD,T 61,X,0.2 PCT ANNUAL CHANCE FLOOD HAZARD,U 62,X,0.2 PCT ANNUAL CHANCE FLOOD HAZARD CONTAINED IN CHANNEL,F 63,X,0.2 PCT ANNUAL CHANCE FLOOD HAZARD CONTAINED IN STRUCTURE,F 64,X,0.2 PCT ANNUAL CHANCE FLOOD HAZARD IN COASTAL ZONE,F 65,X,0.2 PCT ANNUAL CHANCE FLOOD HAZARD IN COMBINED RIVERINE AND COASTAL ZONE,F 66,X,"1 PCT CONTAINED IN STRUCTURE, COMMUNITY ENCROACHMENT",F 67,X,"1 PCT CONTAINED IN STRUCTURE, FLOODWAY",F 68,X,1 PCT DEPTH LESS THAN 1 FOOT,F 69,X,1 PCT DRAINAGE AREA LESS THAN 1 SQUARE MILE,F 70,X,1 PCT FUTURE CONDITIONS,F 71,X,1 PCT FUTURE CONDITIONS CONTAINED IN STRUCTURE,F 72,X,"1 PCT FUTURE CONDITIONS, COMMUNITY ENCROACHMENT",F 73,X,"1 PCT FUTURE CONDITIONS, FLOODWAY",F 74,X,"1 PCT FUTURE IN STRUCTURE, COMMUNITY ENCROACHMENT",F 75,X,"1 PCT FUTURE IN STRUCTURE, FLOODWAY",F 76,X,AREA OF MINIMAL FLOOD HAZARD, 77,X,AREA OF MINIMAL FLOOD HAZARD,F 78,X,AREA OF MINIMAL FLOOD HAZARD,T 79,X,AREA OF MINIMAL FLOOD HAZARD,U 80,X,AREA OF SPECIAL CONSIDERATION,F 81,X,AREA WITH REDUCED FLOOD RISK DUE TO LEVEE,F 82,X,AREA WITH REDUCED FLOOD RISK DUE TO LEVEE,T 83,X,FLOWAGE EASEMENT AREA,F 84,X,1 PCT FUTURE CONDITIONS,T 85,AH,COASTAL FLOODPLAIN,T 86,AE,,U 87,AE,FLOODWAY,F 88,X,AREA WITH REDUCED FLOOD HAZARD DUE TO ACCREDITED LEVEE SYSTEM,F 89,X,530,F 90,VE,100,T 91,AE,100,T 92,A99,AREA WITH REDUCED FLOOD HAZARD DUE TO LEVEE SYSTEM,T 93,A99,AREA WITH REDUCED FLOOD HAZARD DUE TO NON-ACCREDITED LEVEE SYSTEM,T 94,A,COMBINED RIVERINE AND COASTAL FLOODPLAIN,T 250,AREA NOT INCLUDED,Not Mapped by FEMA, 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 called the Gridded SSURGO (gSSURGO) Database and is derived from the Soil Survey Geographic (SSURGO) Database. SSURGO is generally the most detailed level of soil geographic data developed by the National Cooperative Soil Survey (NCSS) in accordance with NCSS mapping standards. The tabular data represent the soil attributes, and are derived from properties and characteristics stored in the National Soil Information System (NASIS). The gSSURGO data were prepared by merging traditional SSURGO digital vector map and tabular data into State-wide extents, and adding a State-wide gridded map layer derived from the vector, plus a new value added look up (valu) table containing "ready to map" attributes. The gridded map layer is offered in an ArcGIS file geodatabase raster format. The raster and vector map data have a State-wide extent. The raster map data have a 10 meter cell size that approximates the vector polygons in an Albers Equal Area projection. Each cell (and polygon) is linked to a map unit identifier called the map unit key. A unique map unit key is used to link to raster cells and polygons to attribute tables, including the new value added look up (valu) table that contains additional derived data. The value added look up (valu) table contains attribute data summarized to the map unit level using best practice generalization methods intended to meet the needs of most users. The generalization methods include map unit component weighted averages and percent of the map unit meeting a given criteria.

This dataset contains the common Map Unit attributes for each polygon within the gSSURGO database plus NRCS derived attributes from a data summary table called the National Valu Added Look Up (valu) Table #1. It is comprised of 57 pre-summarized or "ready to map" derived soil survey geographic database attributes including soil organic carbon, available water storage, crop productivity indices, crop root zone depths, available water storage within crop root zone depths, drought vulnerable soil landscapes, and potential wetland soil landscapes. Related metadata values for themes are included. These attribute data are pre-summarized to the map unit level using best practice generalization methods intended to meet the needs of most users. The generalization methods include map unit component weighted averages and percent of the map unit meeting a given criteria. These themes were prepared to better meet the mapping needs of users of soil survey information and can be used with both SSURGO and Gridded SSURGO (gSSURGO) datasets. Gridded SSURGO (gSSURGO) Database is derived from the official Soil Survey Geographic (SSURGO) Database. SSURGO is generally the most detailed level of soil geographic data developed by the National Cooperative Soil Survey (NCSS) in accordance with NCSS mapping standards. The tabular data represent the soil attributes, and are derived from properties and characteristics stored in the National Soil Information System (NASIS). The gSSURGO data were prepared by merging traditional SSURGO digital vector map and tabular data into State-wide extents, and adding a State-wide gridded map layer derived from the vector, plus a new value added look up (valu) table containing "ready to map" attributes. The gridded map layer is offered in an ArcGIS file geodatabase raster format. The raster and vector map data have a State-wide extent. The raster map data have a 10 meter cell size that approximates the vector polygons in an Albers Equal Area projection. Each cell (and polygon) is linked to a map unit identifier called the map unit key. A unique map unit key is used to link to raster cells and polygons to attribute tables, including the new value added look up (valu) table that contains additional derived data.VALU Table Content:The map unit average Soil Organic Carbon (SOC) values are given in units of g C per square meter for eleven standard layer or zone depths. The average thickness of soil map unit component horizons used in these layer/zone calcuations is also included. The standard layers include: 0-5cm, 5-20cm, 20-50cm, 50-100cm, 100-150cm, and 150-150+cm (maximum reported soil depth). The standard zones include: 0-5cm (also a standard layer), o-20cm, 0-30cm, 0-100cm, and 0-150+cm (full reported soil depth). Zero cm represents the soil surface.The map unit average Available Water Storage (AWS) values are given in units of millimeters for eleven standard layer or zone depths. The average thickness of soil map unit component horizons used in these layer/zone calcuations is also included. The standard layers include: 0-5cm, 5-20cm, 20-50cm, 50-100cm, 100-150cm, and 150-150+cm (maximum reported soil depth). The standard zones include: 0-5cm (also a standard layer), 0-20cm, 0-30cm, 0-100cm, and 0-150+cm (full reported soil depth). Zero cm represents the soil surface.The map unit average National Commodity Crop Productivity Index (NCCPI) values (low index values indicate low productivity and high index values indicate high productivity) are provided for major earthy components. NCCPI values are included for corn/soybeans, small grains, and cotton crops. Of these crops, the highest overall NCCPI value is also identified. Earthy components are those soil series or higher level taxa components that can support crop growth. Major components are those soil components where the majorcompflag = 'Yes' in the SSURGO component table. A map unit percent composition for earthy major components is provided. See Dobos, R. R., H. R. Sinclair, Jr, and M. P. Robotham. 2012. National Commodity Crop Productivity Index (NCCPI) User Guide, Version 2. USDA-NRCS. Available at: ftp://ftp-fc.sc.egov.usda.gov/NSSC/NCCPI/NCCPI_user_guide.pdfThe map unit average root zone depth values for commodity crops are given in centimeters for major earthy components. Criteria for root-limiting soil depth include: presence of hard bedrock, soft bedrock, a fragipan, a duripan, sulfuric material, a dense layer, a layer having a pH of less than 3.5, or a layer having an electrical conductivity of more than 12 within the component soil profile. If no root-restricting zone is identified, a depth of 150 cm is used to approximate the root zone depth (Dobos et al., 2012). The map unit average available water storage within the root zone depth for major earthy components value is given in millimeters.Drought vulnerable soil landscapes comprise those map units that have available water storage within the root zone for commodity crops that is less than or equal to 6 inches (152 mm) expressed as "1" for a drought vulnerable soil landscape map unit or "0" for a nondroughty soil landscape map unit or NULL for miscellaneous areas (includes water bodies).The potential wetland soil landscapes (PWSL version 1) information is given as the percentage of the map unit (all components) that meet the criteria for a potential wetland soil landscape. See table column (field) description for criteria details. If water was determined to account for 80 or greater percent of a map unit, a value of 999 was used to indicate a water body. This is not a perfect solution, but is helpful to identifying a general water body class for mapping.The map unit sum of the component percentage representative values is also provided as useful metadata. For all valu table columns, NULL values are presented where data are incomplete or not available. How NoData or NULL values and incomplete data were handled during VALU table SOC and AWS calculations:The gSSURGO calculations for SOC and AWS as reported in the VALU table use the following data checking and summarization rules. The guiding principle was to only use the official data in the SSURGO database, and not to make assumptions in case there were some data entry errors. However, there were a few exceptions to this principle if there was a good reason for a Null value in a critical variable, or to accommodate the data coding conventions used in some soil surveys.Horizon depths considerations:If the depth to the top of the surface horizon was missing, but otherwise the horizon depths were all okay, then the depth to the top of the surface horizon (hzdept_r) was set to zero.If the depth to the bottom of the last horizon was missing, and the horizon represented bedrock or had missing bulk density, the depth to the bottom was set to equal to the depth to the top of the same horizon (hzdepb_r = hzdept_r), effectively giving the horizon zero thickness (and thus zero SOC or AWS), but not blocking calculation of other horizons in the profile due to horizon depth errors.Other types of horizon depth errors were considered uncorrectable, and led to all horizon depths for the component being set to a NoData value, effectively eliminating the component from the analysis. The errors included gaps or overlaps in the horizon depths of the soil profile, other cases of missing data for horizon depths, including missing data for the bottom depth of the last horizon if the soil texture information did not indicate bedrock and a bulk density value was coded. The SOC or AWS values were effectively set to zero for components eliminated in this way, so the values at the map unit level could be an underestimate for some soils.Horizon rock fragment considerations:Part of the algorithm for calculating the SOC requires finding the volume of soil that is not rock. This requires three SSURGO variables that indicate rock fragments (fraggt10_r, frag3to10_r, and sieveno10_r). If the soil is not organic, and any of these are missing, then the ratio of the volume of soil fines to the total soil volume was set to “NoData†, and the SOC results were coded as “NoData†and effectively set to zero for the horizon. If the soil is organic, then it may be logical that no measurement of rock fragments was made, and default values for the “zero rock†situation was assumed for these variables (i.e., fraggt10_r = 0, frag3to10_r = 0, sieveno10_r = 100). Organic soils were identified by an “O†in the horizon designator or the texture code represented “Peat†, “Muck†or “Decomposed Plant Material†. If all three of the fragment variables were present, but indicated more than 100% rock, then 100% rock was assumed (zero volume of soil and thus zero for SOC). The rock fragment variables do not influence the AWS calculation because rock content is already accounted for in the available water capacity (awc_r) variable at the horizon level.Horizon to component summary:To summarize data from the horizon level to the component level, the evaluation proceeded downward from the surface. If a valid value for AWS could not be calculated for any horizon, then the result for that horizon and all deeper horizons was set to NoData. The same rule was separately applied to the SOC calculation, so it was possible to have results for SOC but not AWS, or vice versa.Component to mapunit summary:To summarize data from the component level to the map unit level, the component percentages must be valid. There are tests both of the individual component percentage (comppct_r) data, and also of the sum of the component percentages at the map unit level (mu_sum_comppct_r). For the gSSURGO VALU table, the following rules were applied for the individual components: 1) The comppct_r must be in the range from 0 to 100, inclusive. 2) Individual components with a comppct_r that was Null (nothing coded) were ignored. A zero comppct_r value excludes

Description: LiDAR derived canopy structure and topography across SAFE, Maliau Conservation Area and Danum Valley in Malaysian Borneo. These maps were produced following a survey by the Natural Environment Research Council Airborne Research Facility in 2014. Georeferenced point clouds were tiled, noise points were removed and ground points classified using Lastools. Digital terrain models (DTM) were produced from classified ground points at 10 m resolution. Point clouds were normalised (through ground subtraction) to produce 1 m resolution pitfree canopy height model (CHM) rasters. Normalised CHMs were also used to produce 20 m resolution plant area density (PAD) profile and plant area index (PAI) rasters as well as a number of statistics calculated for 20 m resolution vertical profiles. Point density is reported as a means to assess data quality, higher values indicate more data and are likely to be more reliable, particularly for dense tall forests, which depend on high point densities for accurate ground detection. Above-ground carbon density was calculated at 1 ha resolution from top of canopy height and gap fraction (derived from canopy height models) also at 1 ha resolution. Project: This dataset was collected as part of the following SAFE research project: Influences of disturbance and environmental variation on biomass change in Malaysian Borneo Funding: These data were collected as part of research funded by:

Natural Environmental Research Council (Human Modified Tropical Forests Consortium Grant, NE/K016377/1) This dataset is released under the CC-BY 4.0 licence, requiring that you cite the dataset in any outputs, but has the additional condition that you acknowledge the contribution of these funders in any outputs.

Permits: These data were collected under permit from the following authorities:

Sabah Biodiversity Council (Research licence Unknown)

XML metadata: GEMINI compliant metadata for this dataset is available here Files: This dataset consists of 64 files: SAFE_archive_LiDAR_Swinfield.xlsx, Danum_acd.tif, Danum_chm.tif, Danum_dtm.tif, Danum_pad_canopy_height.tif, Danum_pad_kurt.tif, Danum_pad_mean.tif, Danum_pad_n_layers.tif, Danum_pad_shannon.tif, Danum_pad_shape.tif, Danum_pad_skew.tif, Danum_pad_std.tif, Danum_pai.tif, Danum_pai_02_10m.tif, Danum_pai_10_20m.tif, Danum_pai_20_30m.tif, Danum_pai_30_40m.tif, Danum_pai_40_50m.tif, Danum_pai_50_60m.tif, Danum_pai_60_70m.tif, Danum_pai_70_80m.tif, Danum_point_density.tif, Maliau_acd.tif, Maliau_chm.tif, Maliau_dtm.tif, Maliau_pad_n_layers.tif, Maliau_pad_canopy_height.tif, Maliau_pad_kurt.tif, Maliau_pad_mean.tif, Maliau_pad_shannon.tif, Maliau_pad_shape.tif, Maliau_pad_skew.tif, Maliau_pad_std.tif, Maliau_pai.tif, Maliau_pai_02_10m.tif, Maliau_pai_10_20m.tif, Maliau_pai_20_30m.tif, Maliau_pai_30_40m.tif, Maliau_pai_40_50m.tif, Maliau_pai_50_60m.tif, Maliau_pai_60_70m.tif, Maliau_pai_70_80m.tif, Maliau_point_density.tif, SAFE_acd.tif, SAFE_pad_canopy_height.tif, SAFE_chm.tif, SAFE_dtm.tif, SAFE_pad_kurt.tif, SAFE_pad_mean.tif, SAFE_pad_n_layers.tif, SAFE_pad_shannon.tif, SAFE_pad_shape.tif, SAFE_pad_skew.tif, SAFE_pad_std.tif, SAFE_pai.tif, SAFE_pai_02_10m.tif, SAFE_pai_10_20m.tif, SAFE_pai_20_30m.tif, SAFE_pai_30_40m.tif, SAFE_pai_40_50m.tif, SAFE_pai_50_60m.tif, SAFE_pai_60_70m.tif, SAFE_pai_70_80m.tif, SAFE_point_density.tif SAFE_archive_LiDAR_Swinfield.xlsx This file only contains metadata for the files below Danum_acd.tif Description: Danum Valley above-ground carbon density This file contains 1 data tables:

Attribute table for raster (described in worksheet Danum_acd) Description: Danum Valley above-ground carbon density Number of fields: 1 Number of data rows: Unavailable (table metadata description only). Fields:

Danum_acd: Above-ground carbon density (ACD) (Field type: numeric)

Danum_chm.tif Description: Danum Valley canopy height model This file contains 1 data tables:

Attribute table for raster (described in worksheet Danum_chm) Description: Danum Valley canopy height model Number of fields: 1 Number of data rows: Unavailable (table metadata description only). Fields:

Danum_chm: Canopy height model (CHM) (Field type: numeric)

Danum_dtm.tif Description: Danum Valley digital terrain model This file contains 1 data tables:

Attribute table for raster (described in worksheet Danum_dtm) Description: Danum Valley digital terrain model Number of fields: 1 Number of data rows: Unavailable (table metadata description only). Fields:

Danum_dtm: Digital terrain model (DTM) (Field type: numeric)

Danum_pad_canopy_height.tif Description: Danum Valley maximum canopy height This file contains 1 data tables:

Attribute table for raster (described in worksheet Danum_pad_canopy_height) Description: Danum Valley maximum canopy height Number of fields: 1 Number of data rows: Unavailable (table metadata description only). Fields:

Danum_pad_canopy_height: Maximum canopy height (Field type: numeric)

Danum_pad_kurt.tif Description: Danum Valley plant area density kurtosis This file contains 1 data tables:

Attribute table for raster (described in worksheet Danum_pad_kurt) Description: Danum Valley plant area density kurtosis Number of fields: 1 Number of data rows: Unavailable (table metadata description only). Fields:

Danum_pad_kurt: Plant area density kurtosis (Field type: numeric)

Danum_pad_mean.tif Description: Danum Valley plant area density mean This file contains 1 data tables:

Attribute table for raster (described in worksheet Danum_pad_mean) Description: Danum Valley plant area density mean Number of fields: 1 Number of data rows: Unavailable (table metadata description only). Fields:

Danum_pad_mean: Plant area density central height (Field type: numeric)

Danum_pad_n_layers.tif Description: Danum Valley number of discrete plant area density layers This file contains 1 data tables:

Attribute table for raster (described in worksheet Danum_pad_n_layers) Description: Danum Valley number of discrete plant area density layers Number of fields: 1 Number of data rows: Unavailable (table metadata description only). Fields:

Danum_pad_n_layers: Plant area density number of layers (Field type: numeric)

Danum_pad_shannon.tif Description: Danum Valley plant area density shannon index This file contains 1 data tables:

Attribute table for raster (described in worksheet Danum_pad_shannon) Description: Danum Valley plant area density shannon index Number of fields: 1 Number of data rows: Unavailable (table metadata description only). Fields:

Danum_pad_shannon: Plant area density Shannon index (Field type: numeric)

Danum_pad_shape.tif Description: Danum Valley plant area density shape This file contains 1 data tables:

Attribute table for raster (described in worksheet Danum_pad_shape) Description: Danum Valley plant area density shape Number of fields: 1 Number of data rows: Unavailable (table metadata description only). Fields:

Danum_pad_shape: Plant area density shape (Field type: numeric)

Danum_pad_skew.tif Description: Danum Valley plant area density skew This file contains 1 data tables:

Attribute table for raster (described in worksheet Danum_pad_skew) Description: Danum Valley plant area density skew Number of fields: 1 Number of data rows: Unavailable (table metadata description only). Fields:

Danum_pad_skew: Plant area density skew (Field type: numeric)

Danum_pad_std.tif Description: Danum Valley plant area density standard deviation This file contains 1 data tables:

Attribute table for raster (described in worksheet Danum_pad_std) Description: Danum Valley plant area density standard deviation Number of fields: 1 Number of data rows: Unavailable (table metadata description only). Fields:

Danum_pad_std: Plant area density standard deviation (Field type: numeric)

Danum_pai.tif Description: Danum Valley plant area index This file contains 1 data tables:

Attribute table for raster (described in worksheet Danum_pai) Description: Danum Valley plant area index Number of fields: 1 Number of data rows: Unavailable (table metadata description only). Fields:

Danum_pai: Plant area index (Field type: numeric)

Danum_pai_02_10m.tif Description: Danum Valley plant area index between 2 m and 10 m above ground This file contains 1 data tables:

Attribute table for raster (described in worksheet Danum_pai_02_10m) Description: Danum Valley plant area index between 2 m and 10 m above ground Number of fields: 1 Number of data rows: Unavailable (table metadata description only). Fields:

Danum_pai_02_10m: Plant area index between 2 m and 10 m above ground (Field type: numeric)

Danum_pai_10_20m.tif Description: Danum Valley plant area index between 10 m and 20 m above ground This file contains 1 data tables:

Attribute table for raster (described in worksheet Danum_pai_10_20m) Description: Danum Valley plant area index between 10 m and 20 m above ground Number of fields: 1 Number of data rows: Unavailable (table metadata description only). Fields:

Danum_pai_10_20m: Plant area index between 10 m and 20 m above ground (Field type: numeric)

Danum_pai_20_30m.tif Description: Danum Valley plant area index between 20 m and 30 m above ground This file contains 1 data tables:

Attribute table for raster (described in worksheet Danum_pai_20_30m) Description: Danum Valley plant area index between 20 m and 30 m above ground Number of fields: 1 Number of data rows: Unavailable (table metadata description only). Fields:

Danum_pai_20_30m: Plant area index between 20 m and 30 m above ground (Field type: numeric)

Danum_pai_30_40m.tif Description: Danum Valley plant area index between 30 m and 40 m above ground This file contains 1 data tables:

Attribute table for raster (described in worksheet Danum_pai_30_40m) Description: Danum Valley plant area index between 30 m and 40 m above ground Number of fields: 1 Number of data rows: Unavailable (table metadata description only). Fields:

Danum_pai_30_40m: Plant

This is a collection of Canopy Height rasters covering selected U.S. Forest Service and adjoining lands in the Southwest Region, encompassing Arizona and New Mexico. The data are presented in a time-enabled format, allowing the end-user to view available data year-by-year, or all available years at once, within a GIS system. The data encompass varying years, varying resolutions, and varying geographic extents, dependent upon available data as provided by the region. The data represents the height of vegetation above ground, measured in meters.The data contains an attribute table. Notable attributes that may be of interest to an end-user are:lowps: the pixel size of the source raster, given in meters.highps: the pixel size of the top-most pyramid for the raster, given in meters.beginyear: the first year of data acquisition for an individual dataset.endyear: the final year of data acquisition for an individual dataset.dataset_name: the name of the individual dataset within the collection.metadata: A URL link to a file on IIPP's Portal containing metadata pertaining to an individual dataset within the image service.resolution: The pixel size of the source raster, given in meters.Canopy Height data are primarily derived from Lidar datasets. Consequently, these derivatives inherit the limitations and uncertainties of the parent sensor and platform and the processing techniques used to produce the imagery. The images are orthographic; they have been georeferenced and displacement due to sensor orientation and topography have been removed, producing data that combines the characteristics of an image with the geometric qualities of a map. The orthographic images show ground features in their proper positions, without the distortion characteristic of unrectified aerial or satellite imagery. Digital orthoimages produced and used within the Forest Service are developed from imagery acquired through various national and regional image acquisition programs. The resulting orthoimages can be directly applied in remote sensing, GIS and mapping applications. They serve a variety of purposes, from interim maps to references for Earth science investigations and analysis. Because of the orthographic property, an orthoimage can be used like a map for measurement of distances, angles, and areas with scale being constant everywhere. Also, they can be used as map layers in GIS or other computer-based manipulation, overlaying, and analysis. An orthoimage differs from a map in a manner of depiction of detail; on a map only selected detail is shown by conventional symbols whereas on an orthoimage all details appear just as in original aerial or satellite imagery.Tribal lands have been masked from this public service in accordance with Tribal agreements.

The data contains an attribute table. Notable attributes that may be of interest to an end-user are:lowps: the pixel size of the source raster, given in meters.highps: the pixel size of the top-most pyramid for the raster, given in meters.beginyear: the first year of data acquisition for an individual dataset.endyear: the final year of data acquisition for an individual dataset.dataset_name: the name of the individual dataset within the collection.metadata: A URL link to a file on IIPP's Portal containing metadata pertaining to an individual dataset within the image service.resolution: The pixel size of the source raster, given in meters.Terrain-related imagery are primarily derived from Lidar, stereoscopic aerial imagery, or Interferometric Synthetic Aperture Radar datasets. Consequently, these derivatives inherit the limitations and uncertainties of the parent sensor and platform and the processing techniques used to produce the imagery. The terrain images are orthographic; they have been georeferenced and displacement due to sensor orientation and topography have been removed, producing data that combines the characteristics of an image with the geometric qualities of a map. The orthographic images show ground features in their proper positions, without the distortion characteristic of unrectified aerial or satellite imagery. Digital orthoimages produced and used within the Forest Service are developed from imagery acquired through various national and regional image acquisition programs. The resulting orthoimages can be directly applied in remote sensing, GIS and mapping applications. They serve a variety of purposes, from interim maps to references for Earth science investigations and analysis. Because of the orthographic property, an orthoimage can be used like a map for measurement of distances, angles, and areas with scale being constant everywhere. Also, they can be used as map layers in GIS or other computer-based manipulation, overlaying, and analysis. An orthoimage differs from a map in a manner of depiction of detail; on a map only selected detail is shown by conventional symbols whereas on an orthoimage all details appear just as in original aerial or satellite imagery.Tribal lands have been masked from this public service in accordance with Tribal agreements.

This digital data release contains gridded elevation surfaces for twenty-six (26) subsurface horizons, a grid of the estimated thickness of strata eroded during the Cenozoic, and fault traces at the level of the Precambrian surface from a previously published 3D geologic model of the Anadarko Basin Province (Higley and others, 2014). In the original release of the 3D model, elevation surfaces were exported to a Zmap interchange file format, potentially limiting access to the data for users without access to specialized software. In this digital data release, elevation surfaces are provided in more readily accessible formats and modeled horizons are given more thorough stratigraphic descriptions than provided in the original model documentation. Within the AnadarkoBasin_Higley geodatabase, the GeologicMap feature dataset contains a line feature class (ContactsAndFaults) containing fault traces at the level of the Precambrian surface, a polyline representing the approximate Anadarko Basin boundary, and model area boundary digitized from the original publication; a polygon feature dataset (MapUnitPolys) with the approximate Anadarko Basin boundary and the model area boundary; and raster datasets for the 26 subsurface horizons and a single thickness grid representing the estimated eroded thickness of strata. Nonspatial tables define the data sources used (DataSources), define terms used in the dataset (Glossary), and provide a description of the modeled surfaces (DescriptionOfMapUnits) that provides the user with far greater stratigraphic detail than the original publication. Separate file folders contain the vector data in shapefile format, the raster data in ASCII and GeoTiff file formats, and the tables as comma-separated values file format. In addition, a tabular data dictionary describes the entity and attribute information for all attributes of the geospatial data and the accompanying nonspatial tables (EntityAndAttributes). Elevation surfaces exported from the 3D model in Zmap interchange file format and additional datasets are available through the original publication (Higley and others, 2014: https://pubs.usgs.gov/dds/dds-069/dds-069-ee/).

Flood Hatch ShapefilesIn addition to the three sets of rasters (Maximum Wave Heights, Water Surface Elevations, and DFEs) provided, separate shapefiles were also created to overlap and highlight special areas within the raster datasets produced for calculating DFEs. A flood hatch shapefile is not provided for every ACFEP level or for every region, but when it is provided, it encompasses the special areas for that level and region. The same hatch shapefile is applicable for all datatypes for the particular level and region. Flood hatch shapefiles encompass all areas of special values within the data rasters (including areas of 9999, 9998, and 9997 values). All regions have a 0.1% ACFEP level flood hatch shapefile because all 0.1% ACFEP rasters contain 9999 values.The flood hatch shapefiles contain individual polygons that describe the type of special area underlying that polygon’s spatial extent. For 9999 and 9998 values in the value rasters (water surface elevations, waves, and DFEs), the special hatched polygons will have the same extent of those values within those rasters. For 9997 values in the value rasters, the hatch polygon will always encompass the 9997 values, but may be larger in extent than just the location of those value cells. For these areas, water surface elevation, wave heights, and DFEs values may be provided, but they still represent a special zone.The Hatch polygons have 5 fields (Column headers) that describe each polygon within the shapefile. These fields include FID, Shape, Hatch_Type, Zones_txt, Hatch, and Hatch_Txt. The FID field contains an ID number for each polygon within that shapefile, while the Shape fieldlists the type of shapefile contained (polygon in all cases). The Hatch_Type field contains the numerical value that can be found within the value rasters (wave height, water surface, and DFE) underlying that polygon. Zones_txt and Hatch_txt are string type fields that contain descriptors of the polygon type, while the Hatch Field contains a numerical value for the type of hatching (1 for 0.1% Edge Zone, 2 for Wave Overtopping Zones, 3 for Dynamic Zone). The following table is an example of what a flood hatch file’s attribute table might look like.FIDShapeHatch_TypeZones_TxtHatchHatch_Txt0Polygon9999Shallow water flooding during extreme storms10.1% Edge Zone1Polygon9997Influenced by wave overtopping (incl. 9997 areas)2Wave Overtopping Zone2Polygon9998Dynamic Landform Areas3Dynamic ZoneSpecifically, the various hatch shapefiles can be defined as follows:• FID 0 Hatch Type – These represent areas of shallow water flooding during extreme storms. These are locations where flooding can only be expected during the most extreme events (> 1000-year return period) or where there are only minor flood depths (shallow flooding) during 1000-year return period AEP. These values only appear in 0.1% ACFEP level since they only occur at the very upper extent of extreme flooding. Water surface elevation values in these regions can be set to 0.1 foot above the site-specific land elevation to provide an estimate of the water surface elevation. Site-specific survey information may be needed to determine the land elevation. These hatch areas directly match areas with 9999 values within the rasters.• FID 1 Hatch Type – These represent wave overtopping zones. These hatch layers encompass the 9997 areas, but also include areas that have known values. Hatched areas of this type covering 9997 values would be expected to experience flooding caused by intermittent wave spray and overtopping only. Hatched areas of this type covering locations with values indicate that the flooding is caused by both direct sheet flow and wave overtopping. These hatched zones are provided for informational purposes by identifying zones that may require special design considerations for wave overtopping. Site-specific coastal processes analysis may also be required in these areas.• FID 2 Hatch Type – These represent areas where geomorphology is extremely dynamic and as such expected flooding may vary drastically. These values can appear in any ACFEP level. There are minimal locations of this type. These hatch areas directly match areas with 9998 values within the rasters.

From gridded National Soil Survey Geographic Database (gNATSGO). Used Soil Data Development Toolbox > gSSURGO Mapping Toolset > Create Soil Map Tool, Exported Data Layer to TIFF, and Used Spatial Analyst > Reclass > Lookup Tool to create this data layer and display the HYDROLGRP_. Follow instructions in "How to Create an On-Demand Soil Property or Interpretation Grid from gNATSGO". Shows sSSURGO data for California. A - sand, loamy sand, sandy loam B - loam, silt, loam or silt C - sandy clay loam D - clay loam, silty clay loam, sandy clay, silty clay, or clay The gridded National Soil Survey Geographic Database (gNATSGO) is a USDA-NRCS Soil & Plant Science Division (SPSD) composite database that provides complete coverage of the best available soils information for all areas of the United States and Island Territories. It was created by combining data from the Soil Survey Geographic Database (SSURGO), State Soil Geographic Database (STATSGO2), and Raster Soil Survey Databases (RSS) into a single seamless ESRI file geodatabase. The state-wide gNATSGO databases contain a 10-meter raster of the soil map units and 70 related tables of soil properties and interpretations. It is designed to work with the SPSD gSSURGO ArcTools. Users can create full coverage thematic maps and grids of soil properties and interpretations for large geographic areas, such as the extent of a State or the conterminous United States. Please note that for the CONUS database, only a 30 meter raster is included. SSURGO is the SPSD flagship soils database that has over 100 years of field-validated detailed soil mapping data. SSURGO contains soils information for more than 90 percent of the United States and island territories, but unmapped land remains. Click here for the current completion status of SSURGO mapping. STATSGO2 is a general soil map that has soils data for all of the United States and island territories, but the data is not as detailed as the SSURGO data. The Raster Soil Surveys (RSSs) are the next generation soil survey databases developed using advanced digital soil mapping methods. https://www.nrcs.usda.gov/wps/portal/nrcs/detail/soils/survey/geo/?cid=nrcseprd1464625) Use the Create A Soil Map ArcTool from the gSSURGO Mapping Toolset in the Soil Data Development Toolbox to make a TIFF data layer (Instructions: https://www.nrcs.usda.gov/wps/portal/nrcs/detail/soils/survey/geo/?cid=nrcseprd1464625#grid). Make a Hydrological Soils Group Map, and display it using the Hydrolgrp_ attribute. NotesThe SPSD refreshes all published soil databases annually. gNATSGO will be included in the refresh cycle, which will provide a new up-to-date version of the database each year. gNATSGO is an ESRI file geodatabase. The soil map units are delivered only as a 10-meter raster version and are uniquely identified by the mukey, which is included in the attribute table. No vectorized version of the soil map units is included in gNATSGO. The database has 70 tables that contain soil attributes, and relationship classes are built into the database to define relationships among tables. The raster can be joined to the Mapunit and Muaggatt tables in the MUKEY field. The database contains a feature class called SAPOLYGON. The “source” field in this feature class indicates whether the data was derived from SSURGO, STATSGO2, or an RSS. A gNATSGO database was created for the conterminous United States and for each state or island territory that does not have complete coverage in SSURGO or has a published RSS. If you encounter an ArcMap error when working with a gNATSGO dataset that reads “The number of unique values exceeds the limit” try increasing the maximum number of unique values to render in your Raster ArcMap Options. Specific instructions can be obtained here: https://support.esri.com/en/technical-article/000010117

The data contains an attribute table. Notable attributes that may be of interest to an end-user are:lowps: the pixel size of the source raster, given in meters.highps: the pixel size of the top-most pyramid for the raster, given in meters.beginyear: the first year of data acquisition for an individual dataset.endyear: the final year of data acquisition for an individual dataset.dataset_name: the name of the individual dataset within the collection.metadata: A URL link to a file on IIPP's Portal containing metadata pertaining to an individual dataset within the image service.resolution: The pixel size of the source raster, given in meters.Terrain-related imagery are primarily derived from Lidar, stereoscopic aerial imagery, or Interferometric Synthetic Aperture Radar datasets. Consequently, these derivatives inherit the limitations and uncertainties of the parent sensor and platform and the processing techniques used to produce the imagery. The terrain images are orthographic; they have been georeferenced and displacement due to sensor orientation and topography have been removed, producing data that combines the characteristics of an image with the geometric qualities of a map. The orthographic images show ground features in their proper positions, without the distortion characteristic of unrectified aerial or satellite imagery. Digital orthoimages produced and used within the Forest Service are developed from imagery acquired through various national and regional image acquisition programs. The resulting orthoimages can be directly applied in remote sensing, GIS and mapping applications. They serve a variety of purposes, from interim maps to references for Earth science investigations and analysis. Because of the orthographic property, an orthoimage can be used like a map for measurement of distances, angles, and areas with scale being constant everywhere. Also, they can be used as map layers in GIS or other computer-based manipulation, overlaying, and analysis. An orthoimage differs from a map in a manner of depiction of detail; on a map only selected detail is shown by conventional symbols whereas on an orthoimage all details appear just as in original aerial or satellite imagery.Tribal lands have been masked from this public service in accordance with Tribal agreements.

Data about the top and bottom altitude, depth from land surface and/or the thickness of three geologic units in Tennessee were converted into geospatial format for this USGS data release from previously published paper maps and converted into digital formats for use by the public. The three geologic units were the Chattanooga Shale of Mississippian-Devonian age (Moore and Horton, 1999), the Wells Creek Dolomite of middle Ordovician age (Smith, 1959), and the Knox Group of lower Ordovician age (Newcome, 1954). These geologic units represent important geologic horizons across Tennessee. Geologic structure maps provide important information and, in digital format, support investigative and modeling efforts pertaining to water and mineral resources. Prior to this work, the paper source maps used for this data release existed in limited quantities, mainly restricted to the Nashville, TN offices of the Tennessee Department of Environment and Conservation (TDEC) and United States Geological Survey (USGS). The work for this project included (1) scanning and georeferencing original paper maps to create georeferenced images (GRI), (2) digitizing well _location points and contour lines, (3) populating well and contour attribute tables with data from maps and associated reports, and (4) when possible, interpolating raster surfaces for the three geologic units of top and bottom altitude, depth from land surface to the top and bottom surface, and thickness. All raster surfaces were aligned to a modified version of the National Hydrogeologic Grid (Clark and others, 2018) to support USGS Lower Mississippi Gulf Water Science Center efforts to create a statewide hydrogeologic framework. All horizontal coordinated data are projected to NAD 1983 USGS Contiguous USA Albers. The raster vertical coordinate information was referenced to the North American Vertical Datum of 1988 (NAVD 88). This data release includes GRIs, vector data of the wells and mapped contours of top, bottom, or thickness, raster data, and related metadata files for each three geologic units under the associated child item tab. Dataset types can be identified by the following naming convention: i_ = georeferenced map images (GRI) po_ = points c_ = contours and closed depressions f_= faults and other structural features p_ = extent polygon ra_ = altitude raster rd_ = depth from land surface raster rt_ = thickness raster The datasets included on this main landing page are as follows: project_metadata.xml – metadata file for general project information studyarea_ext.zip: p_chttshl_ext.shp - mapped extent of the Chattanooga Shale in Tennessee p_wllscr_ext.shp - mapped extent of the Wells Creek Dolomite in Tennessee p_knx_ext.shp - mapped extent of the Knox Group in Tennessee The datasets included on the child item pages are as follows: Chattanooga Shale: geospatial geologic structural datasets in Tennessee: chttshl_metadata.xml - metadata file chttshl_alldata.zip: GRI/ i_chttshl_btm.tif - structure contour map of the bottom of the Chattanooga Shale (Moore and Horton, 1999) i_chttshl_data.tif - map of data used to create structure and isopach maps (Moore and Horton, 1999) i_chttshl_thk.tif - thickness contour map for the Chattanooga Shale (Moore and Horton, 1999) polygons/ p_knx_ext.shp - study area extent for the Chattanooga Shale p_hohenwald.shp - polygon for extend of the Hohenwald Platform (Moore and Horton, 1999) - supplemental data rasters/ ra_chttshl_btm.tif - altitude raster for the bottom of the Chattanooga Shale ra_chttshl_tp.tif - altitude raster for the top of the Chattanooga Shale rd_chttshl_btm.tif - depth from land surface raster of the bottom of the Chattanooga Shale rd_chttshl_tp.tif - depth from land surface raster of the top of the Chattanooga Shale rt_chttshl.tif - thickness raster for the Chattanooga Shale vectors/ c_chttshl_btm.shp - structure contours for the bottom of the Chattanooga Shale c_chttshl_btm_modified.shp - modified structure contours for the bottom of the Chattanooga Shale (hachures removed from closed basins). This vector used to interpolated raster for the bottom of the Chattanooga Shale c_chttshl_thk.shp - thickness contours for the Chattanooga Shale c_chttshl_thk_modified.shp - modified thickness contours for the Chattanooga Shale (hachures removed from closed basins). This vector used to interpolated raster for the thickness of the Chattanooga Shale po_chttshl.shp - point data of altitude and thickness for the Chattanooga Shale Knox Group: geospatial geologic structural datasets in Middle Tennessee: knx_metadata.xml - metadata file knx_alldata.zip: GRI/ i_knx_tp.tif - structure contour map on the top of the Knox Group (Newcome, 1954) i_knx_outcrop.tif - map of the Wells Creek Disturbance (Wilson and Stearns, 1968) polygons/ p_chttshl_ext.shp - study area extent for the Knox Group p_hohenwald.shp - extent of the Hohenwald Platform - supplemental data rasters/ ra_knx_tp.tif - altitude raster for the top of the Knox Group rd_knx_tp.tif - depth from land surface raster of the top of Knox Group vectors/ c_knx_tp.shp - structure contours for the top of the Knox Group c_knx_tp_modified.shp - modified structure contours for the top of the Knox Group (hachures removed from closed basins). This vector used to interpolated raster for the top of the Knox Group po_knx_tp.shp - point data for the altitude of top of the Knox Group Wells Creek Dolomite: geospatial geologic structural datasets in Tennessee: wllscr_metadata.xml - metadata file wllscr_alldata.zip: GRI/ i_wllscr.tif - thickness contour map for the Wells Creek Dolomite (Smith, 1959) polygons/ p_wllscr_ext.shp - study area extent for the Wells Creek Dolomite rasters/ ra_wllscr_btm.tif - altitude raster for the bottom of the Wells Creek Dolomite (same dataset as ra_knx_tp.tif [Newcome, 1954; Smith, 1959]) ra_wllscr_tp.tif - altitude raster for the top of the Wells Creek Dolomite rd_wllscr_btm.tif - depth from land surface raster of the bottom of the Wells Creek Dolomite (same dataset as ra_knx_tp.tif [Newcome, 1954; Smith, 1959]) rd_wllscr_tp.tif - depth from land surface raster of the top of the Wells Creek Dolomite rt_wllscr.tif - thickness raster for the Wells Creek Dolomite vectors/ c_wllscr.shp - thickness contours for the Wells Creek Dolomite po_wllscr.shp - point data for the thickness of Wells Creek Dolomite References: Moore, J.L., and Horton, A.B., 1999, Structure and Isopach Maps of the Chattanooga Shale in Tennessee, Tennessee Dept. of Conservation, Division of Geology, Report of Investigations 48, 3 plates. Newcome, R. Jr., 1954, Structure contour map on top of the Knox Dolomite in Middle Tennessee, Tennessee Division of Geology, Ground-Water Investigations Preliminary Chart 5, 1 sheet. Smith, O. Jr., 1959, Isopach map of the Wells Creek Dolomite in Middle Tennessee: Tennessee Division of Water Resources, one sheet. Wilson, C.W. and Stearns, R.G., 1968 Geology of the Wells Creek Structure, Tennessee: Tennessee Division of Geology, Bulletin 68, 248 p.

TreeMap 2016 provides a tree-level model of the forests of the conterminous United States. We matched forest plot data from Forest Inventory and Analysis (FIA) to a 30x30 meter (m) grid. TreeMap 2016 is being used in both the private and public sectors for projects including fuel treatment planning, snag hazard mapping, and estimation of terrestrial carbon resources. We used a random forests machine-learning algorithm to impute the forest plot data to a set of target rasters provided by Landscape Fire and Resource Management Planning Tools (LANDFIRE: https://landfire.gov). Predictor variables consisted of percent forest cover, height, and vegetation type, as well as topography (slope, elevation, and aspect), location (latitude and longitude), biophysical variables (photosynthetically active radiation, precipitation, maximum temperature, minimum temperature, relative humidity, and vapour pressure deficit), and disturbance history (time since disturbance and disturbance type) for the landscape circa 2016.

The main output of this project (the GeoTIFF included in this data publication) is a raster map of imputed plot identifiers at 30×30 m spatial resolution for the conterminous U.S. for landscape conditions circa 2016. In the attribute table of this raster, we also present a set of attributes drawn from the FIA databases, including forest type and live basal area. The raster map of plot identifiers can be linked to the FIA databases available through the FIA DataMart (https://doi.org/10.2737/RDS-2001-FIADB) or to the text and SQL files included in this data publication to produce tree-level maps or to map other plot attributes. The accompanying database files included in this publication also contain attributes regarding the FIA plot CN (or control number, a unique identifier for each time a plot is measured), the subplot number, the tree record number, and for each tree: the status (live or dead), species, diameter, height, actual height (where broken), crown ratio, number of trees per acre, and a code for cause of death where applicable. The dataset has been validated for applications including percent live tree cover, height of the dominant trees, forest type, species of trees with most basal area, aboveground biomass, fuel treatment planning, and snag hazard. Because falling snags cause hazard to firefighting personnel and other forest users, in response to requests from the field, we provide a separate map that provides a rating of the severity of snag hazard based on the density and height of snags. Application of the dataset to research questions other than those for which it has been validated should be investigated by the researcher before proceeding. The dataset may be suitable for other applications and for use across various scales (stand, landscape, and region), however, the researcher should test the dataset's applicability to a particular research question before proceeding.Geospatial data describing tree species or forest structure are required for many analyses and models of forest landscape dynamics. Forest data must have resolution and continuity sufficient to reflect site gradients in mountainous terrain and stand boundaries imposed by historical events, such as wildland fire and timber harvest. The TreeMap 2014 dataset (Riley et al. 2019) was the first of its kind to provide such detailed forest structure data across the forests of the conterminous United States. The TreeMap 2016 dataset updates the TreeMap 2014 dataset to landscape conditions c2016. Prior to this imputed forest data, assessments relied largely on forest inventory at fixed plot locations at sparse densities.See the Entity and Attributes section for details regarding the relationship between the data files included in this publication and the FIA DataMart (https://doi.org/10.2737/RDS-2001-FIADB).

These data were published on 08/26/2021. On 02/01/2024, the metadata was updated to include reference to a recently published article and update URLs for Forest Service websites.

For more information about these data, see Riley et al. (2022).

This is a collection of bare-Earth digital elevation models covering selected U.S. Forest Service and adjoining lands in the Southwest Region, encompassing Arizona and New Mexico. The data are presented in a time-enabled format, allowing the end-user to view available data year-by-year, or all available years at once, within a GIS system. The data encompass varying years, varying resolutions, and varying geographic extents, dependent upon available data as provided by the region. Bare-Earth DEMs, also commonly called Digital Terrain Models (DTM), represent the ground topography after removal of persistent objects such as vegetation and buildings, and therefore show the natural terrain.The data contains an attribute table. Notable attributes that may be of interest to an end-user are:lowps: the pixel size of the source raster, given in meters.highps: the pixel size of the top-most pyramid for the raster, given in meters.beginyear: the first year of data acquisition for an individual dataset.endyear: the final year of data acquisition for an individual dataset.dataset_name: the name of the individual dataset within the collection.metadata: A URL link to a file on IIPP's Portal containing metadata pertaining to an individual dataset within the image service.resolution: The pixel size of the source raster, given in meters.Terrain-related imagery are primarily derived from Lidar, stereoscopic aerial imagery, or Interferometric Synthetic Aperture Radar datasets. Consequently, these derivatives inherit the limitations and uncertainties of the parent sensor and platform and the processing techniques used to produce the imagery. The terrain images are orthographic; they have been georeferenced and displacement due to sensor orientation and topography have been removed, producing data that combines the characteristics of an image with the geometric qualities of a map. The orthographic images show ground features in their proper positions, without the distortion characteristic of unrectified aerial or satellite imagery. Digital orthoimages produced and used within the Forest Service are developed from imagery acquired through various national and regional image acquisition programs. The resulting orthoimages can be directly applied in remote sensing, GIS and mapping applications. They serve a variety of purposes, from interim maps to references for Earth science investigations and analysis. Because of the orthographic property, an orthoimage can be used like a map for measurement of distances, angles, and areas with scale being constant everywhere. Also, they can be used as map layers in GIS or other computer-based manipulation, overlaying, and analysis. An orthoimage differs from a map in a manner of depiction of detail; on a map only selected detail is shown by conventional symbols whereas on an orthoimage all details appear just as in original aerial or satellite imagery.Tribal lands have been masked from this public service in accordance with Tribal agreements.This record was taken from the USDA Enterprise Data Inventory that feeds into the https://data.gov catalog. Data for this record includes the following resources: ISO-19139 metadata ArcGIS Hub Dataset ArcGIS GeoService For complete information, please visit https://data.gov.

The data contains an attribute table. Notable attributes that may be of interest to an end-user are:lowps: the pixel size of the source raster, given in meters.highps: the pixel size of the top-most pyramid for the raster, given in meters.beginyear: the first year of data acquisition for an individual dataset.endyear: the final year of data acquisition for an individual dataset.dataset_name: the name of the individual dataset within the collection.metadata: A URL link to a file on IIPP's Portal containing metadata pertaining to an individual dataset within the image service.resolution: The pixel size of the source raster, given in meters.A digital orthophoto is a georeferenced image prepared from aerial imagery, or other remotely-sensed data in which the displacement within the image due to sensor orientation and terrain relief has been removed. Orthophotos combine the characteristics of an image with the geometric qualities of a map. Orthoimages show ground features such as roads, buildings, and streams in their proper positions, without the distortion characteristic of unrectified aerial imagery. Digital orthoimages produced and used within the Forest Service are developed from imagery acquired through various national and regional image acquisition programs. The resulting orthoimages, also known as orthomaps, can be directly applied in remote sensing, GIS and mapping applications. They serve a variety of purposes, from interim maps to references for earth science investigations and analysis. Because of the orthographic property, an orthoimage can be used like a map for measurement of distances, angles, and areas with scale being constant everywhere. Also, they can be used as map layers in GIS or other computer-based manipulation, overlaying, and analysis. An orthoimage differs from a map in a manner of depiction of detail; on a map only selected detail is shown by conventional symbols, whereas on an orthoimage all details appear just as in original aerial or satellite imagery.

Abstract

This dataset and its metadata statement were supplied to the Bioregional Assessment Programme by a third party and are presented here as originally supplied.

Resource contains an ArcGIS file geodatabase raster for the National Vegetation Information System (NVIS) Major Vegetation Groups - Australia-wide, present extent (FGDB_NVIS4_1_AUST_MVG_EXT).

Related datasets are also included: FGDB_NVIS4_1_KEY_LAYERS_EXT - ArcGIS File Geodatabase Feature Class of the Key Datasets that make up NVIS Version 4.1 - Australia wide; and FGDB_NVIS4_1_LUT_KEY_LAYERS - Lookup table for Dataset Key Layers.

This raster dataset provides the latest summary information (November 2012) on Australia's present (extant) native vegetation. It is in Albers Equal Area projection with a 100 m x 100 m (1 Ha) cell size. A comparable Estimated Pre-1750 (pre-european, pre-clearing) raster dataset is available: - NVIS4_1_AUST_MVG_PRE_ALB. State and Territory vegetation mapping agencies supplied a new version of the National Vegetation Information System (NVIS) in 2009-2011. Some agencies did not supply new data for this version but approved re-use of Version 3.1 data. Summaries were derived from the best available data in the NVIS extant theme as at June 2012. This product is derived from a compilation of data collected at different scales on different dates by different organisations. Please refer to the separate key map showing scales of the input datasets. Gaps in the NVIS database were filled by non-NVIS data, notably parts of South Australia and small areas of New South Wales such as the Curlewis area. The data represent on-ground dates of up to 2006 in Queensland, 2001 to 2005 in South Australia (depending on the region) and 2004/5 in other jurisdictions, except NSW. NVIS data was partially updated in NSW with 2001-09 data, with extensive areas of 1997 data remaining from the earlier version of NVIS. Major Vegetation Groups were identified to summarise the type and distribution of Australia's native vegetation. The classification contains different mixes of plant species within the canopy, shrub or ground layers, but are structurally similar and are often dominated by a single genus. In a mapping sense, the groups reflect the dominant vegetation occurring in a map unit where there are a mix of several vegetation types. Subdominant vegetation groups which may also be present in the map unit are not shown. For example, the dominant vegetation in an area may be mapped as dominated by eucalypt open forest, although it contains pockets of rainforest, shrubland and grassland vegetation as subdominants. The (related) Major Vegetation Subgroups represent more detail about the understorey and floristics of the Major Vegetation Groups and are available as separate raster datasets: - NVIS4_1_AUST_MVS_EXT_ALB - NVIS4_1_AUST_MVS_PRE_ALB A number of other non-vegetation and non-native vegetation land cover types are also represented as Major Vegetation Groups. These are provided for cartographic purposes, but should not be used for analyses. For further background and other NVIS products, please see the links on http://www.environment.gov.au/erin/nvis/index.html.

The current NVIS data products are available from http://www.environment.gov.au/land/native-vegetation/national-vegetation-information-system.

Purpose

For use in Bioregional Assessment land classification analyses

Dataset History

NVIS Version 4.1

The input vegetation data were provided from over 100 individual projects representing the majority of Australia's regional vegetation mapping over the last 50 years. State and Territory custodians translated the vegetation descriptions from these datasets into a common attribute framework, the National Vegetation Information System (ESCAVI, 2003). Scales of input mapping ranged from 1:25,000 to 1:5,000,000. These were combined into an Australia-wide set of vector data. Non-terrestrial areas were mostly removed by the State and Territory custodians before supplying the data to the Environmental Resources Information Network (ERIN), Department of Sustainability Environment Water Population and Communities (DSEWPaC).

Each NVIS vegetation description was written to the NVIS XML format file by the custodian, transferred to ERIN and loaded into the NVIS database at ERIN. A considerable number of quality checks were performed automatically by this system to ensure conformity to the NVIS attribute standards (ESCAVI, 2003) and consistency between levels of the NVIS Information Hierarchy within each description. Descriptions for non-vegetation and non-native vegetation mapping codes were transferred via CSV files.

The NVIS vector (polygon) data for Australia comprised a series of jig-saw pieces, eachup to approx 500,000 polygons - the maximum tractable size for routine geoprocesssing. The spatial data was processed to conform to the NVIS spatial format (ESCAVI, 2003; other papers). Spatial processing and attribute additions were done mostly in ESRI File Geodatabases. Topology and minor geometric corrections were also performed at this stage. These datasets were then loaded into ESRI Spatial Database Engine as per the ERIN standard. NVIS attributes were then populated using Oracle database tables provided by custodians, mostly using PL/SQL Developer or in ArcGIS using the field calculator (where simple).

Each spatial dataset was joined to and checked against a lookup table for the relevant State/Territory to ensure that all mapping codes in the dominant vegetation type of each polygon (NVISDSC1) had a valid lookup description, including an allocated MVG. Minor vegetation components of each map unit (NVISDSC2-6) were not checked, but could be considered mostly complete.

Each NVIS vegetation description was allocated to a Major Vegetation Group (MVG) by manual interpretation at ERIN. The Australian Natural Resources Atlas (http://www.anra.gov.au/topics/vegetation/pubs/native_vegetation/vegfsheet.html) provides detailed descriptions of most Major Vegetation Groups. Three new MVGs were created for version 4.1 to better represent open woodland formations and forests (in the NT) with no further data available. NVIS vegetation descriptions were reallocated into these classes, if appropriate:

• Unclassified Forest

• Other Open Woodlands

• Mallee Open Woodlands and Sparse Mallee Shublands

(Thus there are a total of 33 MVGs existing as at June 2012). Data values defined as cleared or non-native by data custodians were attributed specific MVG values such as 25 - Cleared or non native, 27 - naturally bare, 28 - seas & estuaries, and 99 - Unknown.

As part of the process to fill gaps in NVIS, the descriptive data from non-NVIS sources was also referenced in the NVIS database, but with blank vegetation descriptions. In general. the gap-fill data comprised (a) fine scale (1:250K or better) State/Territory vegetation maps for which NVIS descriptions were unavailable and (b) coarse-scale (1:1M) maps from Commonwealth and other sources. MVGs were then allocated to each description from the available desciptions in accompanying publications and other sources.

Parts of New South Wales, South Australia, QLD and the ACT have extensive areas of vector "NoData", thus appearing as an inland sea. The No Data areas were dealt with differently by state. In the ACT and SA, the vector data was 'gap-filled' and attributed using satellite imagery as a guide prior to rasterising. Most of these areas comprised a mixture of MVG 24 (inland water) and 25 (cleared), and in some case 99 (Unknown). The NSW & QLD 'No Data' areas were filled using a raster mask to fill the 'holes'. These areas were attributed with MVG 24, 26 (water & unclassified veg), MVG 25 (cleared); or MVG 99 Unknown/no data, where these areas were a mixture of unknown proportions.

Each spatial dataset with joined lookup table (including MVG_NUMBER linked to NVISDSC1) was exported to a File Geodatabase as a feature class. These were reprojected into Albers Equal Area projection (Central_Meridian: 132.000000, Standard_Parallel_1: -18.000000, Standard_Parallel_2: -36.000000, Linear Unit: Meter (1.000000), Datum GDA94, other parameters 0).

Each feature class was then rasterised to a 100m raster with extents to a multiple of 1000 m, to ensure alignment. In some instances, areas of 'NoData' had to be modelled in raster. For example, in NSW where non-native areas (cleared, water bodies etc) have not been mapped. The rasters were then merged into a 'state wide' raster. State rasters were then merged into this 'Australia wide' raster dataset.

November 2012 Corrections

Closer inspection of the original 4.1 MVG Extant raster dataset highlighted some issues with the raster creation process which meant that raster pixels in some areas did not align as intended. These were corrected, and the new properly aligned rasters released in November 2012.

Dataset Citation

Department of the Environment (2012) Australia - Present Major Vegetation Groups - NVIS Version 4.1 (Albers 100m analysis product). Bioregional Assessment Source Dataset. Viewed 10 July 2017, http://data.bioregionalassessments.gov.au/dataset/57c8ee5c-43e5-4e9c-9e41-fd5012536374.

This collection of files are part of a larger dataset uploaded in support of Low Temperature Geothermal Play Fairway Analysis for the Appalachian Basin (GPFA-AB, DOE Project DE-EE0006726). Phase 1 of the GPFA-AB project identified potential Geothermal Play Fairways within the Appalachian basin of Pennsylvania, West Virginia and New York. This was accomplished through analysis of 4 key criteria: thermal quality, natural reservoir productivity, risk of seismicity, and heat utilization. Each of these analyses represent a distinct project task, with the fifth task encompassing combination of the 4 risks factors. Supporting data for all five tasks has been uploaded into the Geothermal Data Repository node of the National Geothermal Data System (NGDS).

This submission comprises the data for Thermal Quality Analysis (project task 1) and includes all of the necessary shapefiles, rasters, datasets, code, and references to code repositories that were used to create the thermal resource and risk factor maps as part of the GPFA-AB project. The identified Geothermal Play Fairways are also provided with the larger dataset. Figures (.png) are provided as examples of the shapefiles and rasters. The regional standardized 1 square km grid used in the project is also provided as points (cell centers), polygons, and as a raster. Two ArcGIS toolboxes are available: 1) RegionalGridModels.tbx for creating resource and risk factor maps on the standardized grid, and 2) ThermalRiskFactorModels.tbx for use in making the thermal resource maps and cross sections. These toolboxes contain item description documentation for each model within the toolbox, and for the toolbox itself. This submission also contains three R scripts: 1) AddNewSeisFields.R to add seismic risk data to attribute tables of seismic risk, 2) StratifiedKrigingInterpolation.R for the interpolations used in the thermal resource analysis, and 3) LeaveOneOutCrossValidation.R for the cross validations used in the thermal interpolations.

Some file descriptions make reference to various 'memos'. These are contained within the final report submitted October 16, 2015.

Each zipped file in the submission contains an 'about' document describing the full Thermal Quality Analysis content available, along with key sources, authors, citation, use guidelines, and assumptions, with the specific file(s) contained within the .zip file highlighted.

UPDATE: Newer version of the Thermal Quality Analysis has been added here: https://gdr.openei.org/submissions/879 (Also linked below) Newer version of the Combined Risk Factor Analysis has been added here: https://gdr.openei.org/submissions/880 (Also linked below) This is one of sixteen associated .zip files relating to thermal resource interpolation results within the Thermal Quality Analysis task of the Low Temperature Geothermal Play Fairway Analysis for the Appalachian Basin. This file contains the binary grid (raster) for the predicted depth to 100 degrees C.

The sixteen files contain the results of the thermal resource interpolation as binary grid (raster) files, images (.png) of the rasters, and toolbox of ArcGIS Models used. Note that raster files ending in “pred” are the predicted mean for that resource, and files ending in “err” are the standard error of the predicted mean for that resource. Leave one out cross validation results are provided for each thermal resource.

Several models were built in order to process the well database with outliers removed. ArcGIS toolbox ThermalRiskFactorModels contains the ArcGIS processing tools used. First, the WellClipsToWormSections model was used to clip the wells to the worm sections (interpolation regions). Then, the 1 square km gridded regions (see series of 14 Worm Based Interpolation Boundaries .zip files) along with the wells in those regions were loaded into R using the rgdal package. Then, a stratified kriging algorithm implemented in the R gstat package was used to create rasters of the predicted mean and the standard error of the predicted mean. The code used to make these rasters is called StratifiedKrigingInterpolation.R Details about the interpolation, and exploratory data analysis on the well data is provided in 9_GPFA-AB_InterpolationThermalFieldEstimation.pdf (Smith, 2015), contained within the final report.

The output rasters from R are brought into ArcGIS for further spatial processing. First, the BufferedRasterToClippedRaster tool is used to clip the interpolations back to the Worm Sections. Then, the Mosaic tool in ArcGIS is used to merge all predicted mean rasters into a single raster, and all error rasters into a single raster for each thermal resource.

A leave one out cross validation was performed on each of the thermal resources. The code used to implement the cross validation is provided in the R script LeaveOneOutCrossValidation.R. The results of the cross validation are given for each thermal resource.

Other tools provided in this toolbox are useful for creating cross sections of the thermal resource. ExtractThermalPropertiesToCrossSection model extracts the predicted mean and the standard error of predicted mean to the attribute table of a line of cross section. The AddExtraInfoToCrossSection model is then used to add any other desired information, such as state and county boundaries, to the cross section attribute table. These two functions can be combined as a single function, as provided by the CrossSectionExtraction model.

This data publication contains the results of four models of cheatgrass presence or dominance within the sagebrush biome from plot level data collected from 2004 through 2019 projected onto simulated predictions of climate and soil water availability based on the norms from 1980 through 2019 for the sagebrush biome of the western United States and two associated maps that combine the conditional models with fire risk assessment of 2020 fuelscape to better understand the impact of fire risk on cheatgrass presence or dominance risk. These data include: 1) predictive maps for the probability of the presence or dominance (>15% relative cover) for cheatgrass, Bromus tectorum, under burned and unburned conditions (4 raster files); 2) predictive maps for the total risk of cheatgrass presence or dominance given the determined fire risk (2 raster files); and 3) predictive maps of susceptibility of cheatgrass presence or dominance categories (susceptible to presence or dominance regardless of fire, resistant to presence or dominance regardless of fire, fire-induced dominance, and fire-reduced dominance) (2 raster files). Also included are the plot level data used to create the models, which includes plot level climate and soil water availability predictions based on SOILWAT2 ecohydrological model and cheatgrass cover as recorded on site and cheatgrass relative cover (cheatgrass cover / sum of all species covers recorded on site) used to build the models (1 tabular file). Additionally, the raster of ecohydrologic conditions that the model was projected on to which includes predictive maps of climate and soil water availability long-term normals and interannual variability (1980-2019) that allowed the mapping of the models across the sagebrush biome (1 categorical raster file and 1 raster attribute table).The purpose of this work is to define: 1) the environmental niche (presence/absence models) and potential risk of cheatgrass dominance (>15% relative cover) of cheatgrass burned and unburned conditions; 2) the total risk of cheatgrass presence/absence and dominance (risk under both burned conditions) given current fire risk; 3) the cheatgrass susceptibility types, categorization of the impact of fire on cheatgrass presence or dominance (high risk regardless of fire, low risk regardless of fire, increase of risk under burned conditions, decreased risk under unburned conditions); and 4) provide access to the underlying data that produced the maps.For complete details regarding these data, see Urza et al. (2024).