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.

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).

Climate change is transforming coral reefs by increasing the frequency and intensity of marine heatwaves, often leading to coral bleaching and mortality. Coral communities have demonstrated modest increases in thermal tolerance following repeated exposure to moderate heat stress, but it is unclear whether these shifts represent acclimatization of individual colonies or mortality of thermally susceptible individuals. For corals that survive repeated bleaching events, it is important to understand how past bleaching responses impact future growth potential. Here, we track the bleaching responses of 1,832 corals in leeward Maui through multiple marine heatwaves and document patterns of coral growth and survivorship over a seven-year period. While we find limited evidence of acclimatization at population scales, we document reduced bleaching over time in specific individuals, primarily in the stress-tolerant taxa Porites lobata, indicative of acclimatization. For corals that survived both bleaching events, we find no relationship between bleaching response and coral growth in three of four taxa studied. This decoupling between bleaching and growth suggests that coral survivorship is a better indicator of future growth than is a coral’s bleaching history. Based on these results, we recommend restoration practitioners in Hawaiʻi obtain outplants from Porites and Montipora colonies with a proven track-record of growth and survivorship, rather than devote resources toward identifying and cultivating bleaching-resistant phenotypes. Survivorship followed a latitudinal thermal stress gradient, but because this gradient was small, it is likely that local environmental factors also drove differences in coral performance between sites. Efforts to reduce human impacts at low performing sites would likely improve coral survivorship in the future. Methods We used large-area imaging (photogrammetry) to capture a 3D snapshot of coral reef condition at our six sites in leeward Maui in 2014, 2015, 2017, 2019, and 2021. Large-area imaging is the process of generating a composite visual reconstruction (i.e., 3D model, orthoprojection, DEM, etc.) via the overlap of many component images. Large-area imaging has been used with increased frequency to archive coral reef structure and condition, and provides a basis for in silico field work to answer a variety of ecological questions. Details of our large-area imaging workflow are available elsewhere and will be described in minimal detail here. At each fixed site, divers entered the water with two D7000 SLR Nikon cameras (focal lengths of 18mm and 55mm) in Ikelite underwater housings. Divers marked the boundaries of each 10 x 10 m long term monitoring plot with six calibration tiles, and placed four 0.5 m long scale bars within the plot. One diver swam approximately 1.5 m over the reef in a gridded pattern with the cameras, which were programmed to take a picture every second. This produced approximately 5,000 pictures per site. The camera diver imaged a core area of 10x10 m, and swam several meters beyond the calibration tiles to ensure that a buffer region (> 1 m wide) around the core area was also imaged. Once imagery was collected, we used the software Agisoft Metashape (St. Petersburg, Russia) to build a dense point cloud, which we refer to here as a 3D model. After building the 3D models in Metashape, we loaded them into the custom software Viscore for postprocessing. These postprocessing steps included 1) scaling the 3D model using the 0.5 m scale bars, 2) entering depth measurements collected at each calibration tile so that the 3D model could be oriented with respect to the sea surface, 3) manually aligning 3D models of the same site collected in different years, and 4) exporting a high-resolution top-down view of the model known as an orthoprojection. Each orthoprojection was 12 x 12 m and had a resolution of 1 mm per pixel. We used these orthoprojections rather than the 3D models for all subsequent data collection steps.
Researchers used TagLab and ArcGIS Pro to trace patches of live coral tissue following the approach of Rodrguez et al. 2021. We found no effect of software on traced planar area, and a single annotator QCed all tracings in TagLab to control for any potential effect of annotator or software. We used the high-resolution imagery that underlies the 3D model (raw images) as a reference to assist with tracing and species ID. For this study, we chose to identify Pocillopora to the genus level because morphology is not a good indicator of species ID for Pocillopora in Hawaiʻi, whereas the three other taxa could be confidently identified to species. For P. lobata, we focused on massive and submassive growth forms to avoid confusion with the related branching coral Porites compressa. After corals were traced, we used TagLab to “match” patches of live tissue that represented the same individual through time. This created a network of temporally linked coral patches. This enabled us to more accurately identify individual genets of corals such as Porites and Montipora, which readily exhibit fusion and fission over time due to partial morality. Hereafter we use “patch” to denote a single contiguous region of live coral tissue in one timepoint, and “genet” to denote a network of patches interconnected through time. While we cannot definitively say that linked corals represent a single genetic individual (since we did no genetic testing), the precise tracking capabilities afforded by overlapping orthoprojections and associated raw images enable us to identify individual genets with a reasonable degree of confidence. Additionally, we visually inspected orthoprojections to identify potential instances of pseudoreplication arising from colony fission prior to our timeseries, and found minimal evidence to support this concern. We performed all subsequent analyses at the genet level, and considered a genet to have survived the full timeseries if at least one patch of that genet existed in both 2014 (our first sampling year) and 2021 (our final sampling year). For each genet, we calculated the total planar area (cm2) in each timestep by summing the planar area of individual associated patches. To achieve an appropriate sample size of M. capitata, M. patula, and P. lobata, we traced corals within 10 randomly placed non-overlapping 0.5 m2 quadrats within the orthoprojection. We identified and traced all coral patches with a diameter > 5cm whose centroid fell within the boundaries of a quadrat. To account for fission and fusion dynamics, we also traced patches outside of a quadrat or < 5cm in diameter if they were temporally linked to a genet inside a quadrat. At sites where < 40 genets were traced in our first timestep, we placed additional quadrats (up to 25 total) and continued tracing taxa until we reached 40 genets or until 25 quadrats had been placed. For Pocillopora, which was less abundant than other taxa, we identified and traced all patches within each 12 x 12 m orthoprojection. To ensure a balanced design for statistical analyses, we randomly selected 100 P. lobata genets for n = 2 sites where > 100 genets had been traced. We assessed the extent and severity of bleaching for every patch of coral tissue > 1cm2 in each year of our timeseries. First, we visually estimated bleaching extent as the percent of a patch’s area (0-100%) with some degree of paling unrelated to coral growth or disease. Focusing on this bleached tissue only, we then scored bleaching severity (from 0 to 3) based on the overall degree of paling observed (0 for practically no pigmentation loss; 1 for slight paling, 2 for significant loss of pigmentation, and 3 for almost or completely stark white). When estimating both bleaching extent and severity, we used Viscore’s Virtual Point Intercept interface to reference the original imagery. This allowed us to view multiple angles of each coral patch to assess bleaching, enabling us to account for changes in tissue color due to lighting or image quality. Once we assessed bleaching extent and severity for all patches in all years, we calculated genet-level bleaching extent and severity in each timepoint by summing bleaching extent and severity across patches, weighted by patch area. Then, we incorporated both bleaching extent and severity into a single bleaching metric for each genet in each timepoint. According to this metric, genets fell into one of five categories: no bleaching or very minor paling, minor bleaching, moderate bleaching, severe bleaching, or extreme bleaching. If a genet changed by more than one step between 2015 and 2019, the genet was considered to have an increased or decreased bleaching response over time. We considered genets that changed one bleaching step or less between 2015 and 2019 to have a stable bleaching response over time. Among these stable genets, those that exhibited moderate bleaching or higher were considered to have “high bleaching susceptibility”, and all other genets were considered to be “thermally tolerant”. These bleaching responses (thermally tolerant, decreased bleaching response, increased bleaching response, and high bleaching susceptibility) were used as fixed factors to compare genets over time. To test for signs of acclimatization among surviving genets in each population (corals of the same taxa within a given site), we employed a bootstrapping approach using only genets that were present in both bleaching events. We calculated the difference between each genet’s bleaching score in 2015 and 2019, which produced an integer response variable ranging from -4 to 4, where 0 indicated no change in bleaching, -4 indicated extreme bleaching in 2015 and no bleaching in 2019, and 4 indicated no bleaching in 2015 and extreme bleaching in 2019. Then, we generated a null distribution of the median change in bleaching scores for each coral population via 100,000 resamples. P values were

Presented here are point clouds derived from aerial photography collected by the U.S. Geological Survey (USGS) using an oblique plane-mounted camera system, covering approximately 13 km of the coastline near Big Sur, California. These point clouds are referenced to previously published lidar data and contain RGB information as well as XYZ. Point cloud coordinates are in NAD83 UTM Zone 10 meters. Imagery was collected with a Nikon D800 camera in RAW format and processed using structure-from-motion photogrammetry with Agisoft PhotoScan version 1.7 through 2.0. Point clouds were clipped to an area of interest (AOI) using LASTools. The AOI was created in ArcGIS Pro 3.3.1.

Historical imagery was obtained from University of Florida’s historical Imagery site, “Aerial Photography: Florida”, the Florida Department of Transportation (FDOT) Aerial Photo Lookup System, or from the FDEP district offices. Images downloaded from UF were saved locally and georeferenced by GIS team members, whereas the imagery received from the district offices were georeferenced by District staff. It is understood that these "pre-georeferenced" tiles were georeferenced within ArcMap by various staff from the District offices. The following applies to the imagery georeferenced in-office by the Division of Water Resource Management (DWRM):The georeferencing was completed in either ArcMap 10.3.1 or ArcGIS Pro. The following standards were held for the georeferencing process: the minimum number of control points was 10 points. The RMS value was kept at or below 5.0 for all tiles georeferenced in 1st Order Polynomial, and 2.0 for those georeferenced in 2nd Order Polynomial (where 1st Order was not possible). The maximum individual residual was at or under twice the RMS. Again, these were the standards, but the accuracy is not guaranteed. To QC for human error, once all counties for the given decade were georeferenced a comparison task was completed. This QC emphasized that this data is only a visual aid in that distances can be off 50 meters or more in some areas. These are mostly areas where there were limited reference features to georectify the original images. The smallest distance found was under 10 meters. To attain more information on this QC please contact FDEP WRM GIS. As stated in the use limitation, but emphasized here, information contained herein is provided for informational purposes only. The State of Florida, Department of Environmental Protection provides geographic information systems (GIS) data and metadata with no claim as to the completeness, usefulness, or accuracy of its content, positional or otherwise. The State and its officials and employees make no warranty, express or implied, and assume no legal liability or responsibility for the ability of users to fulfill their intended purposes in accessing or using GIS data or metadata or for omissions in content regarding such data. The data could include technical inaccuracies and typographical errors. The data is presented "as is," without warranty of any kind, including, but not limited to, the implied warranties of merchantability, fitness for a particular purpose, or non-infringement. Your use of the information provided is at your own risk. In providing this data or access to it, the State assumes no obligation to assist the user in the use of such data or in the development, use, or maintenance of any applications applied to or associated with the data or metadata.Please contact GIS.Librarian@FloridaDEP.gov for more information.