This data release includes aerial imagery collected during two uncrewed aerial system (UAS) imagery surveys at an edge-of-field site north of Medina River Natural Area near San Antonio, Texas, on August 14, 2019, and July 8, 2022. A total of 1,153 images were collected during the survey on August 14, 2019, and a total of 1,277 images were collected during the survey on July 8, 2022. In total, 2,430 images provided in the form of geotagged true-color aerial images in JPG format are provided.

Aerial imagery was acquired following flooding in targeted areas in Texas. The aerial photography missions were conducted by the NOAA Remote Sensing Division. The images were acquired using a Digital Sensor System (DSS) version 6.

The imagery posted on this site is of the Texas and Louisiana Gulf Coast after Hurricane Rita made landfall. The regions photographed range from San Luis Pass, Texas to Deep Lake, Louisiana. The aerial photograph missions were conducted by the NOAA Remote Sensing Division the day after Rita made landfall, September 22 and concluded September 30. The images were acquired from an altitude of 7,50...

Digitized index sheets and 43 photogrammetric scans of original aerial reel film from an aerial survey of Red River County, Texas, taken November 15 & 16, 1940. Scale is 1:20,000.

Digitized index sheets and 69 photogrammetric scans of original aerial reel film from an aerial survey of Lamar County, Texas, taken November 15,1940. Scale is 1:20,000.

The National Aeronautics and Space Administration (NASA) Aerial Photography data set is a film archive of photographs from the Lyndon B. Johnson Space Center (JSC) in Houston, Texas, and the NASA Ames Research Center in Moffett Field, California. In 1965, the JSC initiated the Earth Resources Aircraft Program and began flying photographic missions for Federal Government agencies and other entities involved in remote sensing experiments. Beginning in 1966, NASA conducted an Earth Observations Program, including Earth surveys using aircraft platforms.

 Photographs from a variety of NASA programs provide project-specific coverage
 over the United States, Grand Bahama, Jamaica, and Central America at base
 scales ranging from 1:16,000 scale to 1:450,000 scale. Film types, scales,
 acquisition schedules, flight altitudes, and end products differ, according to
 project requirements.

In 2006 and 2007 the NOAA Office for Coastal Management purchased services to reprocess existing digital multi-spectral imagery (ADS-40) and create digital benthic habitat data from this imagery for selected Texas coastal bend bays. The Center worked cooperatively with the Texas Parks and Wildlife Department (TPWD) and the Texas A and M University Center for Coastal Studies to develop benthic h...

Digitized index sheets and 26 photogrammetric scans of original aerial reel film from an aerial survey of Wichita County, Texas taken March 20, 1937. Scale is 1:63,360. These photographs overlap with photographs from Tillman County, Oklahoma and Wilbarger County, Texas.

The U.S. Geological Survey (USGS) conducts baseline and storm-response photography missions to document and understand the changes in vulnerability of the Nation's coasts to extreme storms. On September 9-10, 2008, the USGS conducted an oblique aerial photographic survey (during Field Activity Number [FAN] 08ACH05) from Calcasieu Lake, Louisiana, to Brownsville, Texas, aboard a Cessna C-210 aircraft at an altitude of 500 feet (ft) and approximately 1,000 ft offshore (Figure 2, http://pubs.usgs.gov/ds/0991/html/ds991_fig2.html). This mission was flown to collect data for assessing incremental changes in the beach and nearshore area and can be used for assessing future coastal change. The photographs provided here are Joint Photographic Experts Group (JPEG) images. The photograph locations are an estimate of the position of the aircraft and do not indicate the location of the feature in the images (See the Navigation Data page, http://pubs.usgs.gov/ds/0991/html/ds991_nav.html). These photographs document the configuration of the barrier islands and other coastal features at the time of the survey. ExifTool was used to add the following to the header of each photo: time of collection, Global Positioning System (GPS) latitude, GPS longitude, keywords, credit, artist (photographer), caption, copyright, and contact information. Photographs can be opened directly with any JPEG-compatible image viewer by clicking on a thumbnail on the contact sheet. All image times are recorded in UTC. Table 1 (http://pubs.usgs.gov/ds/0991/html/ds991_table.html) provides detailed information about the assigned location, name, date, and time the photograph was taken along with links to the photograph. In addition to the photographs, a Google Earth Keyhole Markup Language (KML) file is provided and can be used to view the images by clicking on the marker and then clicking on either the thumbnail or the link above the thumbnail. The KML files were created using the photographic navigation files. Note: A KML number was assigned to each photograph to aid navigation of the Google Earth file. These numbers correspond to the site labels in Google Earth.

Our Co-design team is from the University of Texas, working on a Department of Energy-funded project focused on the Beaumont-Port Arthur area. As part of this project, we will be developing climate-resilient design solutions for areas of the region. More on www.caee.utexas.edu. We used a DJI Mavic 2 Pro to capture aerial photos in Beaumont-Port Arthur, TX, in February 2023, including: I. Beaumont Soccer Club II. Corps’ Port Arthur Resident Office III. Halbouty Pump Station comprises its vicinity IV. Lamar University (Including Exxon Power Plants close to Lamar Univ.) V. MLK Boulevard for aerial images of the industry and the ship channel VI. Salt Water Barrier (include some aerial images about the Big Thicket) Aerial photos taken were through DroneDeploy autonomous flight, and models were processed through the DroneDeploy engine as well. All aerial photos are in .JPG format and contained in zipped files for each location. The processed data package including 3D models, geospatial data, mappings, point clouds, and the animation video of Halbouty Pump Station has various file types: - The Adobe Suite gives you great software to open .Tif files. - You can use LASUtility (Windows), ESRI ArcGIS Pro (Windows), or Blaze3D (Windows, Linux) to open a LAS file and view the data it contains. - Open an .OBJ file with a large number of free and commercial applications. Some examples include Microsoft 3D Builder, Apple Preview, Blender, and Autodesk. - You may use ArcGIS, Merkaartor, Blender (with the Google Earth Importer plug-in), Global Mapper, and Marble to open .KML files. - The .tfw world file is a text file used to georeference the GeoTIFF raster images, like the orthomosaic and the DSM. You need suitable software like ArcView to open a .TFW file. This dataset provides researchers with sufficient geometric data and the status quo of the land surface at the locations mentioned above. This dataset could streamline researchers' decision-making processes and enhance the design as well. In October 2023, we had our follow-up data collection, including: I. Beaumont Soccer Club II. Shipping and Receiving Center at Lamar University After the aerial collection, we obtained aerial photos of those two locations mentioned above, as well as processed data (such as point clouds and models).

Observations of Whooping Cranes collected during aerial and ground surveys on the Texas coast (Aransas National Wildlife Refuge) between 1950 and 2011. Data include survey date, observers, location coordinates, age class counts, band details, and environmental notes. This is a variant of the original dataset as it was reformatted to Darwin Core. This variant does not include the aircraft and pilot information. That information can be found in the original dataset.

This variant moved the attribute fields of Adult, Juvenile, and Unknown to an individualCount. Those three attributes fields were reformatted into the measurementorfact table.

Our Co-design team is from the University of Texas, working on a Department of Energy-funded project focused on the Beaumont-Port Arthur area. As part of this project, we will be developing climate-resilient design solutions for areas of the region. More on www.caee.utexas.edu. We captured aerial photos in the Port Arthur Coastal Neighborhood Community and the Golf Course on Pleasure Island, Texas, in June 2024. Aerial photos taken were through DroneDeploy autonomous flight, and models were processed through the DroneDeploy engine as well. All aerial photos are in .JPG format and contained in zipped files for each area. The processed data package includes 3D models, geospatial data, mappings, and point clouds. Please be aware that DTM, Elevation toolbox, Point cloud, and Orthomosaic use EPSG: 6588. And 3D Model uses EPSG: 3857. For using these data: - The Adobe Suite gives you great software to open .Tif files. - You can use LASUtility (Windows), ESRI ArcGIS Pro (Windows), or Blaze3D (Windows, Linux) to open a LAS file and view the data it contains. - Open an .OBJ file with a large number of free and commercial applications. Some examples include Microsoft 3D Builder, Apple Preview, Blender, and Autodesk. - You may use ArcGIS, Merkaartor, Blender (with the Google Earth Importer plug-in), Global Mapper, and Marble to open .KML files. - The .tfw world file is a text file used to georeference the GeoTIFF raster images, like the orthomosaic and the DSM. You need suitable software like ArcView to open a .TFW file. This dataset provides researchers with sufficient geometric data and the status quo of the land surface at the locations mentioned above. This dataset could streamline researchers' decision-making processes and enhance the design as well.

This Light Detection and Ranging (LiDAR) classified (ASPRS LAS classifications) dataset is a topographic survey conducted for the West Texas Aerial Survey 2009 project. This data was produced for the US Army Corps of Engineers (USACE) Joint Airborne Lidar Bathymetry Technical Center of Expertise (JALBTCX). The LiDAR point cloud was flown at a density sufficient to support a maximum final post s...

This model is a two-dimensional (2D) hydraulic model created in the Hydraulic Engineering Center’s River Analysis System (HEC-RAS). The model was created for a segment of the San Saba River between Harkeyville and San Saba, TX, USA. The model’s geometry is based on United States Geological Survey 3D Elevation Program data collected in 2018, and the channel bathymetry was burned in using cross-sectional data collected by Texas State University researchers in 2018. The model was calibrated using water surface elevation and velocity measurements taken during field data collection. Methods Available data: Researchers from Texas State University collected depth, flow velocity, and wetted width data at 200 cross-sections spaced approximately 350 ft apart using the equipment listed in Table 1. Table 1. Equipment used and their accuracy for Texas State University data collection. Table from Harris et al. (2023).

Parameter

Equipment

Unit Accuracy

Location

GPSMap 64 Handheld GPS

10-50 feet

Velocity

Hach Velocity Meter (Model FH950.1)

0.1 feet/second

Depth

An adjustable “ruler” stick with feet as units

0.1 feet

Wetted Width

Laser Technology Inc. TruPulse 360r

3 feet to nonideal (natural) target

Data was collected between June 4th and June 27th, 2018. During this time period, USGS gage 08146000 (San Saba, TX) recorded discharges ranging from 11.9 to 396 cfs, with an average discharge of 20 cfs. USGS 3DEP 1 m resolution data collected between February 14th and April 22nd, 2018, was used to create the HEC-RAS terrain (Merrick-Surdex 2018). Discharge at USGS gage 08146000 ranged from 40.5 to 966 cfs during this time period. For much of the time period, the discharge was approximately 60 cfs. Bathymetric areas: The 3DEP data was imported as a terrain in HEC-RAS v.6.2, and field-collected cross sections were burned into the channel following methods from Harris et al. (2023). The 95 most upstream sites in the segment were associated with a single depth measurement in the center of the channel, whereas the remaining 105 cross sections were associated with three depth measurements collected in the center of the channel and on the left and right, although the position of measurements were not recorded. For cross sections that had three depth measurements, if the standard deviation of the depth exceeded 0.25 ft, all three measurements were used to delineate the cross section in HEC-RAS. For all other cross sections, a single depth was used to delineate the cross section (either the single available depth measurement or the average depth based on three measurements; Harris et al., 2023). A final bathymetric/topographic surface was generated following Harris et al. (2023) using inverse distance weighted interpolation with the field-collected cross sections to estimate channel bathymetry. Landcover was delineated using aerial photography (USDA 2018) and associated Manning’s N roughness values were determined following Chow (1959) and Harris et al. (2023) (Table 2). Table 2. Selected Manning’s N roughness values based on delineated landcover. Adapted from Harris et al. (2023).

Landcover Description

Chow 1959 Description, which has minimum/normal/maximum ranges (Manning's n Values (orst.edu))

Selected Roughness

Channel

(Main channel or Mountain Streams)

Channel

Sluggish reaches, weedy, deep pools (normal)

0.07

Channel2

clean, winding, some pools and shoals, some weeds and more stones (maximum)

0.05

Channel3

Clean, straight, full stage, no rifts or deep pools (minimum)

0.025

Cobbly3

No vegetation in channel, banks usually steep, trees and brush along banks submerged at high stages, bottom: gravel, cobbles, and few boulders (minimum)

0.03

Ineffective Sec2

Sluggish reaches, weedy, deep pools (maximum)

0.08

Ineffective Sec3

Very weedy reaches, deep pools, or floodways with heavy stand of timber and underbrush (normal)

0.1

Ineffective Sec4

Very weedy reaches, deep pools, or floodways with heavy stand of timber and underbrush (maximum)

0.15

Intermediate Zone

(Floodplains)

Grassy Floodway

Scattered brush/heavy weeds (maximum) or light brush and trees in summer (between normal and maximum)

0.07

Floodplain

(Floodplains)

Dense Woody

Dense willows, summer straight (minimum) or heavy stand of timber, downed trees, little undergrowth (normal)

0.1

Dense Woody2

Dense willows, summer straight (normal)

0.2

Sparse Shrub

Light to dense brush (Various definitions, ranges from minimum to maximum)

0.08

NoData

Scattered brush, heavy weeds (between normal and maximum)

0.06

A 2-D HECRAS mesh was created following Harris et al. (2023) with a mesh size of 40 square feet and a breakline with cell size of 20 feet located in the center of the channel. A 12 cfs unsteady flow simulation was run as a “hot-start” to fill the modeled channel and subsequently used as the initial conditions for additional flows simulated for the segment. Because the discharge recorded at USGS gage 08146000 varied during the field sampling period, different sections of the segment were calibrated to different discharges to match field conditions at the time of data collection (Table 3). Table 3. Discharges used to calibrate 2-D HEC-RAS model based on discharges recorded at USGS gage 08146000 during field collection dates in 2018.

Calibration discharge (cfs)

Average field discharge (cfs)

Range of field discharges (cfs)

Dates (2018)

Cross section

12

12.5

9.7-15.6

6/25; 6/27

29370-20044; 8279-467

16

15.9

13.5-17.4

6/21; 6/26

19597-8667

20

20

16.8-22.4

6/13-6/14; 6/20

49011-29699

26

26.5

21.1-30.3

6/12

56420-49340

34

33.8

30.3-36.5

6/11

63009-56840

40

40.3

20.6-114

6/4

72209-63766

Calibration was conducted in accordance with methods from Harris et al. (2023), with an initial channel roughness of 0.07 adjusted on a case-by-case basis throughout the segment based on comparisons of field-measured and modeled depth and velocity at cross sections. In addition, modeled channel widths were compared to aerial imagery for select discharges, and floodplain roughness was adjusted as needed in an attempt to match channel width from imagery (USDA, 2004-2018; Table 4). Table 4. Average discharge recorded at USGS gage 08146000 on select dates when aerial imagery from the National Agricultural Inventory Program (NAIP) was available (USDA, 2004-2018), used for comparison of imagery channel width with modeled channel width.

Discharge (cfs)

Imagery

Imagery date

12.5

NAIP

August 16th, 2006

22

NAIP

July 12th, 2014

54

NAIP

July 31st, 2010

86

NAIP

August 3rd, 2016

241

NAIP

December 12th, 2004

1600

NAIP

October 26th, 2018

The final overall root mean-squared error of the model after calibration was 0.31 ft s-1 for velocity and 0.34 ft for depth. Error at individual cross sections was also recorded for reference purposes. Summary of assumptions: This HEC-RAS model has assumptions matching those of Harris et al. (2023). Discharge data from 2018 at USGS gage 08146000 (San Saba, TX) have been approved by USGS. Usage notes: HEC-RAS 6.2 is a free hydraulic analysis software available for download from the U.S. Army Corps of Engineers. References: Chow VT. Open-channel hydraulics: New York: McGraw-Hill; 1959. Harris A, Wiest S, Cushway KC, Mitchell ZA, Schwalb AN. Hydraulic model (HEC-RAS) of the Upper San Saba River between For McKavett and Menard, TX [Dataset]. Dryad Data Repository; 2023. https://doi.org/10.5061/dryad.pc866t1tt. Merrick-Surdex. Lidar Mapping Report. 2018. Prepared for United States Geological Survey contract G16PC0029. Mitchell ZA. The role of life history strategies and drying events in shaping mussel communities: a multiscale approach [dissertation]. San Marcos (TX): Texas State University. 2020. Mitchell ZA, Cottenie K, Schwalb AN. Trait-based and multi-scale approach provides insight on responses of freshwater mussels to environmental heterogeneity. Ecosphere. 2023; 14(7):e4533. https://doi.org/10.1002/ecs2.4533. Mitchell ZA, Schwalb AN, Cottenie K. Trait-based and multi-scale approach provides insight on responses of freshwater mussels to environmental heterogeneity [Dataset]. Dryad Data Repository; 2023. https://doi.org/10.5061/dryad.msbcc2g3d. United States Department of Agriculture (USDA). Texas NAIP Imagery, 2018. Web. 2022-03-09.

This study mapped land cover (vegetation, sand, and water) from the western portion of the Colorado River Delta through the eastern portion of Matagorda Island, TX, over fourteen imagery dates (1850s, 1930s, 1943, 1953, 1972, 1981, 1995, 2001, 2004, 2009, 2012, 2015, 2018, and 2020). Vegetation width, beach width, and total land width (vegetation width and beach width combined) were also calculated every 20 m. The data was created for the Texas Office of the Comptroller project titled “Matagorda Bay Ecosystem Assessment†. The data package contains 58 files. Land cover/habitat maps for 2012-2013 are available in related dataset HI.x833.000:0021 (https://doi.org/10.7266/ex6xqek7).

This data release includes two digital orthomosaic images produced from uncrewed aerial system (UAS) imagery surveys conducted on August 14, 2019, and July 8, 2022 at an edge-of-field site north of Medina River Natural Area near San Antonio, Texas. These images were compiled from sets of aerial imagery included in this data release. Orthomosaic images can be used for visual reference but do not contain elevation data.

The Civil Air Patrol is routinely tasked by FEMA and local public safety officials with taking aerial photographs. This collection comprises nearly 30,000 photos taken over the Hurricane Harvey study area, between August 19, 2017 and June 2, 2018. The majority of this collection were taken over southeast Texas from August 10 to September 2, 2017. These were originally uploaded to the web using the GeoPlatform.gov imageUploader capability, and hosted as a web map layer [1]. For this Harvey collection, I exported the dataset of photo location points to a local computer, subset it to the Harvey event, and created a shapefile, which is downloadable below. The photos and thumbnails were not included in this archive, but are attribute-linked to the FEMA-Civil Air Patrol image library on Amazon cloud [2].

The primary resource for these photos is the University of Texas at Austin Center for Space Research (UT CSR), hosted at the Texas Advanced Computational Center (TACC) [3]. These photos are organized by collection date, and each date folder has photo metadata in Javascript (js) and json format files. UT CSR has published a separate web app for browsing these photos [4], as well as several other flood imagery sources.

Note: The cameras used by the Civil Air Patrol do not have an electronic compass with their GPS to record the viewing direction. The easiest way to determine the general angle is to look at consecutive frame counterpoints to establish the flightpath direction at nadir and adjust for the photographer's position behind the pilot looking out the window hatch on the port (left) side of the aircraft. The altitude above ground level is typically between 1000-1500 feet, so it's easy to locate features in reference orthoimages.

Another source of aerial imagery is from the NOAA National Geodetic Survey (NGS) [5]. This imagery was acquired by the NOAA Remote Sensing Division to support NOAA homeland security and emergency response requirements.

References [1] US federal GeoPlatform.gov Image Uploader map service (ArcGIS Server) [https://imageryuploader.geoplatform.gov/arcgis/rest/services/ImageEvents/MapServer] [2] FEMA-Civil Air Patrol image library on Amazon cloud [https://fema-cap-imagery.s3.amazonaws.com] [3] UT CSR primary archive for Harvey photos on TACC [https://web.corral.tacc.utexas.edu/CSR/Public/17harvey/TxCAP/] [4] UT CSR web app for browsing CAP photos [http://magic.csr.utexas.edu/hurricaneharvey/public/] [5] NOAA NGS Hurricane Harvey Imagery [https://storms.ngs.noaa.gov/storms/harvey/index.html#7/28.400/-96.690]

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 includes two dense point clouds produced from uncrewed aerial system (UAS) imagery surveys conducted on August 14, 2019, and July 8, 2022 at an edge-of-field site north of Medina River Natural Area near San Antonio, Texas. The dense point cloud for the August 14, 2019, survey contains 166,261,373 points, and the dense point cloud for the July 8, 2022 survey contains 164,395,847 points. Points within the dense point cloud have not been classified.

Turtle detections and identification percentages from drone surveys.