This dataset was created by the Transportation Planning and Programming (TPP) Division of the Texas Department of Transportation (TxDOT) for planning and asset inventory purposes, as well as for visualization and general mapping. County boundaries were digitized by TxDOT using USGS quad maps, and converted to line features using the Feature to Line tool. This dataset depicts a generalized coastline.Update Frequency: As NeededSource: Texas General Land OfficeSecurity Level: PublicOwned by TxDOT: FalseRelated LinksData Dictionary PDF [Generated 2025/03/14]

This project updates the geothermal resources beneath our oil and gas fields, as part of the research for the Texas GEO project. This report "Analysis of Geothermal Resources in Three Texas Counties" (October 2020) improves on previous mapping of the Texas resources for the counties of Crockett (West Texas), Jackson (central Gulf Coast) and Webb (South Texas). Through additional bottom-hole temperatures (BHT), the number of well sites increased from 532 to 5,410 in total for these counties. The improved methodology to calculate formation temperatures from 3.5 km (11,500 ft) to 10 km (32,800 ft) includes thermal conductivity values more closely related to the actual county geological formations, incorporated radiogenic heat production of formations, and the related mapped depth to basement. The results show deep temperatures as hotter than previously calculated, with temperatures of 150 degrees Celcius possible for Webb County between depths of 2.6 - 5.1 km, Jackson County between depths 3.0 - 5.4 km, and Crockett County between depths of 2.7 - 8.0 km.

We mapped 66 Ecological Mapping Systems (EMS) for eight coastal counties in south Texas, from Refugio and Aransas County south to the Mexican border. Land cover (LC), geophysical setting information, and woody vegetation height were all attributed to image objects derived from 10 m Sentinel-2 satellite imagery to model EMS type. A supervised process with training data collected from aerial photographs, aided by quantitative, species-specific, ground-collected virtual plot data, was used to classify LC in a RandomForest framework. Out of bag (OOB) error for LC was 15.24%. Recently collected LiDAR point cloud information was used to map height for woody vegetation, and the height was, in turn, used to distinguish between herbaceous, shrubland, and woodland/forest types via modification of LC results, and to define several canopy >10 m versions of forested EMS types. Geophysical settings were mapped based primarily on the distribution of soil Map Units (MUs) from the national digital soil survey (gSSURGO). Elevation and potential ponding information were derived from analysis of LiDAR-derived digital elevation models (DEMs) as an aid in mapping several EMS types. Heads-up modification of both LC and EMS modeling results using aerial photograph interpretation improved results. The agreement between EMS mapped type and field-collected data (most 10 years old or more) was >75%. The most abundant EMS types included Coastal and Sandsheet: Deep Sand Grassland (10.7% of the region), Native Invasive: Mesquite/Mixed Shrubland (5.0%), Gulf Coast: Coastal Prairie (4.6%), and South Texas: Sandy Mesquite Savanna Grassland (4.4%). The improved land cover, geophysical settings data, vegetation height data, and the use of finer-resolution image objects for modeling enabled mapping of all EMS types more accurately than previous datasets. The new EMS dataset will facilitate analysis and conservation of important habitats and modeling of species of concern that are tied to those habitats.

This data release supports the U.S. Geological Survey Scientific Investigation Map (SIM) by Clark and others (2020) by documenting the data used to create the geologic maps and describe geologic framework and hydrostratigraphy of the Edwards and Trinity aquifers for a 442 square-mile area in northern Medina County in south Texas. The karstic Edwards and Trinity aquifers that are the subject of the SIM by Clark and others (2020) are classified as major sources of water in south-central Texas by the Texas Water Development Board (George and others, 2011). The geologic framework and hydrostratigraphy of the Edwards and Trinity aquifers largely control groundwater-flow paths and storage in northern Medina County (Kuniasky and Ardis, 2004). The data provided in this data release and the detailed maps and descriptions of the geologic framework and hydrostratigraphy in Clark and others (2020) are intended to help provide water managers information that is useful for effectively managing available groundwater resources in the study area. These digital data accompany Clark, A.K., Morris, R.E., and Pedraza, D.E., 2020, Geologic framework and hydrostratigraphy of the Edwards and Trinity aquifers within northern Medina County, Texas: U.S. Geological Survey Scientific Investigations Map 3461, 13 p. pamphlet, 1 pl., scale 1:24,000, https://doi.org/10.3133/sim3461.

The Texas Water Development Board classifies the karstic Edwards and Trinity aquifers as major sources of water in south-central Texas. To effectively manage the water resources in the area, detailed maps and descriptions of the geologic framework and hydrostratigraphic units of the aquifers outcropping in Hays County, Tex. are needed. In 2016 and 2018, the U.S. Geological Survey, in cooperation with the Edwards Aquifer Authority, mapped the geologic framework and hydrostratigraphy of the Edwards and Trinity aquifers within Hays County, Tex. at 1:24,000 scale. These digital data accompany Clark, A.K., Pedraza, D.E., and Morris, R.R., 2018, Geologic framework and hydrostratigraphy of the Edwards and Trinity aquifers within Hays County, Texas: U.S. Geological Survey Scientific Investigations Map 3418, pamphlet XX p., 1 sheet, scale 1:24,000, https://doi.org/10.3133/sim3418.

The Sea Level Affecting Marshes Model (SLAMM) simulates the dominant processes involved in wetland conversions and shoreline modifications during long-term sea level rise. Map distributions of wetlands are predicted under conditions of accelerated sea level rise.

Tidal marshes are among the most susceptible ecosystems to climate change, especially accelerated sea-level rise (SLR). The Intergovernmental Panel on Climate Change (IPCC) Special Report on Emissions Scenarios (SRES) suggested that global sea level will increase by approximately 30 cm to 100 cm by 2100 (IPCC 2001). Rahmstorf (2007) suggests that this range may be too conservative and that the feasible range by 2100 is 50 to 140 cm. Rising sea levels may result in tidal marsh submergence (Moorhead and Brinson 1995) and habitat migration as salt marshes transgress landward and replace tidal freshwater and irregularly-flooded marsh (R. A. Park et al. 1991).

The model used the 1/1.5/2 meter of sea-level rise by 2100 scenario and was produced for the Nature Conservancy by Warren Pinnacle Consulting, Inc. The purpose of this series of maps was to show how marshes are predicted to migrate inland due to increases in sea level by 2100. The SLAMM model produced landcover maps for 5 points in time for this specific sea level rise scenario, which included actual landcover maps from either 2004 or 2009 and predicted landcover maps for 2025, 2050, 2075 and 2100 for each project site.

Impacts of Sea-level Rise, Habitat Conservation & Spatial Data Platform Project in Northern Gulf of Mexico

Contact detail for the project: The Nature Conservancy

Jorge Brenner, Ph.D. Associate Director of Marine Science The Nature Conservancy of Texas 205 N. Carrizo St. Corpus Christi, Texas 78401 Phone: (361) 882-3584; ext: 104 Email: jbrenner@tnc.org

This dataset combines the work of several different projects to create a seamless data set for the contiguous United States. Data from four regional Gap Analysis Projects and the LANDFIRE project were combined to make this dataset. In the northwestern United States (Idaho, Oregon, Montana, Washington and Wyoming) data in this map came from the Northwest Gap Analysis Project. In the southwestern United States (Colorado, Arizona, Nevada, New Mexico, and Utah) data used in this map came from the Southwest Gap Analysis Project. The data for Alabama, Florida, Georgia, Kentucky, North Carolina, South Carolina, Mississippi, Tennessee, and Virginia came from the Southeast Gap Analysis Project and the California data was generated by the updated California Gap land cover project. The Hawaii Gap Analysis project provided the data for Hawaii. In areas of the county (central U.S., Northeast, Alaska) that have not yet been covered by a regional Gap Analysis Project, data from the Landfire project was used. Similarities in the methods used by these projects made possible the combining of the data they derived into one seamless coverage. They all used multi-season satellite imagery (Landsat ETM+) from 1999-2001 in conjunction with digital elevation model (DEM) derived datasets (e.g. elevation, landform) to model natural and semi-natural vegetation. Vegetation classes were drawn from NatureServe's Ecological System Classification (Comer et al. 2003) or classes developed by the Hawaii Gap project. Additionally, all of the projects included land use classes that were employed to describe areas where natural vegetation has been altered. In many areas of the country these classes were derived from the National Land Cover Dataset (NLCD). For the majority of classes and, in most areas of the country, a decision tree classifier was used to discriminate ecological system types. In some areas of the country, more manual techniques were used to discriminate small patch systems and systems not distinguishable through topography. The data contains multiple levels of thematic detail. At the most detailed level natural vegetation is represented by NatureServe's Ecological System classification (or in Hawaii the Hawaii GAP classification). These most detailed classifications have been crosswalked to the five highest levels of the National Vegetation Classification (NVC), Class, Subclass, Formation, Division and Macrogroup. This crosswalk allows users to display and analyze the data at different levels of thematic resolution. Developed areas, or areas dominated by introduced species, timber harvest, or water are represented by other classes, collectively refered to as land use classes; these land use classes occur at each of the thematic levels. Raster data in both ArcGIS Grid and ERDAS Imagine format is available for download at http://gis1.usgs.gov/csas/gap/viewer/land_cover/Map.aspx Six layer files are included in the download packages to assist the user in displaying the data at each of the Thematic levels in ArcGIS. In adition to the raster datasets the data is available in Web Mapping Services (WMS) format for each of the six NVC classification levels (Class, Subclass, Formation, Division, Macrogroup, Ecological System) at the following links. http://gis1.usgs.gov/arcgis/rest/services/gap/GAP_Land_Cover_NVC_Class_Landuse/MapServer http://gis1.usgs.gov/arcgis/rest/services/gap/GAP_Land_Cover_NVC_Subclass_Landuse/MapServer http://gis1.usgs.gov/arcgis/rest/services/gap/GAP_Land_Cover_NVC_Formation_Landuse/MapServer http://gis1.usgs.gov/arcgis/rest/services/gap/GAP_Land_Cover_NVC_Division_Landuse/MapServer http://gis1.usgs.gov/arcgis/rest/services/gap/GAP_Land_Cover_NVC_Macrogroup_Landuse/MapServer http://gis1.usgs.gov/arcgis/rest/services/gap/GAP_Land_Cover_Ecological_Systems_Landuse/MapServer

The karstic Edwards and Trinity aquifers are classified as major sources of water in south-central Texas by the Texas Water Development Board, and both are classified as major aquifers by the State of Texas. The Edwards and Trinity aquifers developed because of the original depositional history of the carbonate limestone and dolomite rocks that contain them, and the primary and secondary porosity, diagenesis, fracturing, and faulting that modified the porosity, permeability, and transmissivity of each aquifer and of the geologic units separating the aquifers. Previous studies such as those by the U.S. Geological Survey (USGS) and the Edwards Aquifer Authority (EAA) have mapped the geology, hydrostratigraphy, and structure in these areas at various scales. The purpose of this data release is to present the data that were collected and compiled to describe the geologic framework and hydrostratigraphy of northern Medina county, Texas in order to help water managers, water purveyors, and local residents better understand and manage water resources. The scope of the larger work and this accompanying data release is focused on the geologic framework and hydrostratigraphy of the outcrops and hydrostratigraphy of the rocks that contain the Edwards and Trinity aquifers within northern Medina county, Texas. These digital data accompany Clark and others (2024), which supersedes Scientific Investigations Map 3461.

Reason for Selection Native grasslands and savannas are important for many endemic species, provide critical habitat and food for pollinators, and are often hotspots for biodiversity. Once a predominant ecosystem type, grasslands and savannas have significantly declined from their historical extent. In part because of the regular disturbance (e.g., mowing, fire) typically required to maintain high-quality grasslands, they are difficult to detect through remote sensing and are not well-captured by other indicators. In addition, grassland and savanna birds are experiencing significant declines and are currently off-track for meeting the SECAS 10% goal, so it is important that the Blueprint capture known and potential habitat. Input Data

Texas Ecological Mapping Systems: statewide raster, accessed 12-2023
Oklahoma Ecological Systems Map: download the raster, accessed 12-2023
Protected Areas Database of the United States (PAD-US): PAD-US 3.0 national geodatabase - Combined Proclamation Marine Fee Designation Easement; PAD-US 4.0 national geodatabase - Combined Proclamation Marine Fee Designation Easement
National Land Cover Database (NLCD): 2021 Land Cover, 2021 U.S. Forest Service (USFS) Tree Canopy Cover, 2013 Land Cover, and 2013 USFS Tree Canopy Cover
2020 LANDFIRE Biophysical Settings (BPS) [LF 2.2.0]
Southeast Blueprint 2024 landscape condition indicator
Southeast Blueprint 2024 extent

Known grasslands

Known grassland prairies dataset for the Middle Southeast subregion, provided by Toby Gray with Mississippi State University in Oct 2020 (available on request by emailing rua_mordecai@fws.gov); this is an improved version of the Known Prairie Patches in the Gulf Coastal Plains and Ozarks (GCPO) layer
Known Piedmont prairie locations in the South Atlantic subregion: We identified known prairie locations by requesting spatial data on known prairies from the 74 members of the Piedmont Prairie Partnership mailing list and other prairie managers (Wake County Open Space program and Prairie Ridge Ecostation in NC). We combined that information with known locations in Virginia aggregated by the Virginia Natural Heritage Program (available on request by emailing rua_mordecai@fws.gov).
Grassland polygons from the Catawba Indian Nation, provided by Aaron Baumgardner, Natural Resources Director, in July 2023 (for more information email rua_mordecai@fws.gov)
Grassland polygons from two iNaturalist projects in Texas: erwin-park-prairie-restoration-area, stella-rowan-prairie
Southeastern Grasslands Institute polygons from selected iNaturalist projects. We used only projects with polygons digitized at a fine resolution and did not include projects with more coarse polygons covering a large area. Specific projects used were: 
    allegheny-mountains-riverscour-barrens, big-south-fork-riverscour-barrens-1, big-south-fork-riverscour-barrens-2, big-south-fork-riverscour-barrens-4-us, big-south-fork-riverscour-barrens-6, biodiversity-of-piedmont-granite-glades-outcrops, bluff-mountain-fen, caney-fork-sandstone-riverscour-barrens-and-glades, clear-creek-sandstone-riverscour-barrens, clear-fork-river-riverscour-barrens, craggy-mountains-mafic-outcrops-and-barrens, cumberland-plateau-escarpment-limestone-barrens, cumberland-river-limestone-riverscour-glades, daddy-s-creek-riverscour-barrens, dunbar-cave-prairie-restoration, eastern-highland-rim-limestone-riverscour-glade, emory-river-sandstone-riverscour-barrens, falls-of-the-ohio-river-limestone-riverscour-glade, flat-rock-cedar-glades-and-barrens-state-natural-area, grasshopper-hollow-fen, gunstocker-glade, hiwassee-river-phyllite-riverscour-glade, ketona-dolomite-barrens, laurel-river-riverscour-barrens-and-glades, lime-hills-limestone-barrens, limestone-barrens-of-the-western-valley-of-the-tennessee-river, little-mountains-limestone-barrens, little-river-canyon-riverscour-barrens-and-glades, moulton-valley-limestone-glades, mulberry-fork-of-black-warrior-river-riverscour-barrens-and-glades, muldraugh-s-hill-limestone-barrens, nashville- basin-limestone-glades, new-river-riverscour-barrens, obed-river-sandstone-riverscour-barrens, outer-bluegrass-dolomite-barrens, ridge-and-valley-sandstone-outcrops, rock-creek-sandstone-riverscour-barrens, rockcastle-river-sandstone-riverscour-barrens, shawnee-hills-sandstone-glades-and-outcrops, southern-blue-ridge-mountains-grass-balds, southern-blue-ridge-mountains-serpentine-barrens, southern-blue-ridge-phyllite-outcrops, southern-ridge-and-valley-limestone-glades, southern-ridge-and-valley-shale-barrens, southern-ridge-and-valley-siltstone-barrens, tennessee-ridge-and-valley-dolomite-barrens-and-woodlands-tn-us, the-farm-prairie-and-oak-savanna, tin-top-road-savanna, western-allegheny-escarpment-limestone-barrens, western-highland-rim-limestone-glade-and-barrens, western-valley-limestone-barrens-decatur-co-north-us, western-valley-limestone-barrens-hardin-wayne-cos, western-valley-limestone-barrens-perry-co, western-valley-silurian-limestone-barrens, white-s-creek-sandstone-riverscour-barrens-and-glades, folder-six-glades

Mapping Steps

Combine all known grasslands polygons and convert to raster, assigning them a value of 7.
From the 2021 and 2013 NLCD landcover, create rasters that only include classes likely to have grasslands and savannas. The classes included are based on NLCD classes that overlap known grassland and savanna polygons. Any class that covered >1% of known grasslands and savannas is included: 31 Barren Land, 41 Deciduous Forest, 42 Evergreen Forest, 43 Mixed Forest, 52 Scrub/Shrub, 71 Grassland/Herbaceous, 81 Pasture/Hay.
For those 2021 and 2013 selected landcover rasters, remove forest with ≥ 60% canopy cover using NLCD USFS Tree Canopy Cover for the corresponding year. This results in potential grassland and savanna rasters for 2021 and 2013.
Make a single potential grassland and savanna raster that only includes pixels that are potential grasslands and savannas in both 2013 and 2021. This removes temporary grasslands and savannas that result from clearcuts.
From the Texas and Oklahoma ecological systems maps, extract classes that predict areas invaded by mesquite, a non-native tree that spreads aggressively in the grasslands and savannas of the Southwest and disrupts natural ecosystems through its heavy water consumption. For Oklahoma, this is VegName = 'Ruderal Mesquite Shrubland'. For Texas, this is CommonName = 'Native Invasive: Mesquite Shrubland'. Combine these and use them to remove areas that are no longer grassland and savanna due to mesquite invasion. The resulting layer represents potential grasslands.
To identify potential grasslands and savannas in natural landscapes, use values 5 and 6 from the landscape condition indicator. Assign a value of 3 to any potential grassland pixel that receives a landscape condition score of 5 or 6. Assign all other potential grassland pixels a value of 2.
To identify likely grasslands and savannas, overlay the potential grasslands and savannas raster with select polygons from PAD-US 4.0. To pull out types of protected lands that commonly manage grasslands and savannas, we used GAP status, designation type, manager name, and easement holder. We also identified a number of protected areas directly by name that had important areas of grassland and savanna but weren’t captured by the other rules.
    GAP status (GAP_sts) 1 or 2: Gap status 1 and 2 refer to areas managed for biodiversity that are not subject to extractive uses like logging and mining. GAP status 2 is technically intended to encompass areas where disturbance events are suppressed, but in practice, most protected areas in the Southeast that are actively managing grasslands and savannas are classified as GAP status 2.
    Designation type (Des_Tp) of ‘NWR’, ‘MIL’, ‘NF’, or ‘NG’ (i.e. National Wildlife Refuge, military installation, National Forest, or National Grassland)
    Manager name (Mang_Name) of ‘RWD’ (i.e. Regional Water District)
    Local manager name (Loc_Mang) of 'Ducks Unlimited (Wetlands America Trust)'
    Easement holder (EsmtHldr) of 'Tall Timbers Research Station & Land Conservancy'
    Unit name (Unit_Nm) of ‘Point Washington State Forest’, ‘Pine Log State Forest’, ‘M. C. Davis - Seven Runs Creek Conservation Easement’, ‘Nokuse Plantation Conservation Easements’, ‘Tate's Hell State Forest’, ‘Box-R Wildlife Management Area’, ‘Aucilla Wildlife Management Area’, ‘Snipe Island Unit’, ‘Big Bend Wildlife Management Area’, ‘Goethe State Forest’, ‘Amelia Wildlife Management Area’, ‘Powhatan Wildlife Management Area’, ‘Cumberland State Forest’, ‘Appomattox-Buckingham State Forest’, ‘Haw River State Park’, ‘R. Wayne Bailey - Caswell Game Land’, ‘Medoc Mountain State Park’, ‘Embro Game Land’, ‘Dupont State Forest’, ‘Hanging Rock State Park’, ‘Bladen Lakes State Forest’, ‘Whitehall Plantation Game Land’, ‘Suggs Mill Pond Game Land’, ‘Bushy Lake State Natural Area’, ‘Pondberry Bay Plant Conservation Preserve’, ‘Green Swamp Game Land’, ‘Holly Shelter Game Land’, ‘Chowan Swamp Game Land’, ‘Brookgreen Gardens’, ‘Cary State Forest’, ‘Suwannee Ridge Mitigation Park Wildlife and Environmental Area’, ‘Adams-Alapha Ag & Conservation Easement’, ‘Twin Rivers State Forest’, ‘Chattahoochee Fall Line Wildlife Management Area’, ‘Enon Plantation’, or ‘Georgia-Alabama Land Trust Easement #214’, ‘Covington Wildlife Management Area’, ’ Magnolia Branch Wildlife Reserve’, ‘Little River State Forest’, or ‘Susan Turner Plantation’, or have the local name (Loc_Nm) 'Sandhills Game Land', 'Blackwater River State Forest', 'Three Lakes Wildlife Management Area', 'Herky Huffman/Bull Creek Wildlife Management

The High Plains aquifer extends from south of 32 degrees to almost 44 degrees north latitude and from 96 degrees 30 minutes to 104 degrees west longitude. The aquifer underlies about 175,000 square miles in parts of Colorado, Kansas, Nebraska, New Mexico, Oklahoma, South Dakota, Texas, and Wyoming. The Republican River Basin is about 25,000 square miles and is located in northeast Colorado, northern Kansas, and southwest Nebraska. The Republican River Basin overlies the High Plains aquifer for 87 percent of the basin area. This dataset consists of a raster of water-level changes for the High Plains aquifer, in the Republican River Basin, 2002 to 2015. This digital dataset was created using water-level measurements from (1) 977 wells, which are located in the Republican River Basin, and (2) 546 wells, which are located within 20 miles outside the boundary of the Republican River Basin. These 1,523 wells were measured in both 2002 and in 2015. The map was reviewed for consistency with the relevant data at a scale of 1:1,000,000.