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    Vulnerability of Wetlands due to Projected Sea-Level Rise in the Coastal Plains of the South and Southeast United States
    (Remote Sensing for Wetland Restoration, 2024-06-07) Lizcano-Sandoval, Luis; Gibeaut, James; McCarthy, Matthew; Murray, Tylar; Rueda-Roa, Digna; Muller-Karger, Frank
    Coastal wetlands are vulnerable to accelerated sea-level rise, yet knowledge about their extent and distribution is often limited. We developed a land cover classification of wetlands in the coastal plains of the southern United States along the Gulf of Mexico (Texas, Louisiana, Mississippi, Alabama, and Florida) using 6161 very-high (2 m per pixel) resolution WorldView-2 and WorldView-3 satellite images from 2012 to 2015. Area extent estimations were obtained for the following vegetated classes: marsh, scrub, grass, forested upland, and forested wetland, located in elevation brackets between 0 and 10 m above sea level at 0.1 m intervals. Sea-level trends were estimated for each coastal state using tide gauge data collected over the period 1983–2021 and projected for 2100 using the trend estimated over that period. These trends were considered conservative, as sea level rise in the region accelerated between 2010 and 2021. Estimated losses in vegetation area due to sea level rise by 2100 are projected to be at least 12,587 km2, of which 3224 km2 would be coastal wetlands. Louisiana is expected to suffer the largest losses in vegetation (80%) and coastal wetlands (75%) by 2100. Such high-resolution coastal mapping products help to guide adaptation plans in the region, including planning for wetland conservation and coastal development.
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    Growing pains of a data repository: GRIIDC's evolution from environmental disaster rapid response to promoting FAIR data
    (Frontiers in Climate, 2022-08-25) Rossi, Rosalie; LeBel, Deborah; Gibeaut, James
    GRIIDC is a multidisciplinary data repository created in the aftermath of the Deepwater Horizon oil spill. Development of the repository occurred even as researchers collected post-spill data, and as a result, the data management system initially focused on the ingestion of data and metadata. Data sharing was not as prevalent as it is currently, and many researchers were not familiar with data sharing and data organization best practices. Implementation of data management planning, submission, citation, and distribution features required many iterations and occurred while GRIIDC was assisting researchers with managing their rapid response data. From this challenging beginning, over the decade since the Deepwater Horizon oil spill, GRIIDC has improved the data management system and the training of researchers, which has enhanced the ease of submission and quality of data submitted. The GRIIDC system has also evolved to prioritize the implementation of FAIR data principles to ensure the data are findable, accessible, interoperable, and reusable. All data are issued digital object identifiers (DOIs) through DataCite and are findable via GRIIDC's data search page, DataONE, and Google Dataset Search. Each dataset has a landing page where the data and metadata can be accessed. GRIIDC is continuously striving to add FAIR principles to the system. Although there are still many challenges including quality of data and metadata received, funding limitations, and program priorities, GRIIDC must always continue to improve its ability to meet user needs while implementing FAIR data principles.
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    Extending Marine Species Distribution Maps Using Non-Traditional Sources
    (Pensoft, 2015-04-17) Wood, John Stephen; Moretzsohn, Fabio; Gibeaut, James C.
    Background Traditional sources of species occurrence data such as peer-reviewed journal articles and museum-curated collections are included in species databases after rigorous review by species experts and evaluators. The distribution maps created in this process are an important component of species survival evaluations, and are used to adapt, extend and sometimes contract polygons used in the distribution mapping process. New Information During an IUCN Red List Gulf of Mexico Fishes Assessment Workshop held at The Harte Research Institute for Gulf of Mexico Studies, a session included an open discussion on the topic of including other sources of species occurrence data. During the last decade, advances in portable electronic devices and applications enable 'citizen scientists' to record images, location and data about species sightings, and submit that data to larger species databases. These applications typically generate point data. Attendees of the workshop expressed an interest in how that data could be incorporated into existing datasets, how best to ascertain the quality and value of that data, and what other alternate data sources are available. This paper addresses those issues, and provides recommendations to ensure quality data use.
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    Using UAS Hyperspatial RGB Imagery for Identifying Beach Zones along the South Texas Coast
    (Multidisciplinary Digital Publishing Institute, 2017-02-15) Su, Lihong; Gibeaut, James C.
    Shoreline information is fundamental for understanding coastal dynamics and for implementing environmental policy. The analysis of shoreline variability usually uses a group of shoreline indicators visibly discernible in coastal imagery, such as the seaward vegetation line, wet beach/dry beach line, and instantaneous water line. These indicators partition a beach into four zones: vegetated land, dry sand or debris, wet sand, and water. Unmanned aircraft system (UAS) remote sensing that can acquire imagery with sub-decimeter pixel size provides opportunities to map these four beach zones. This paper attempts to delineate four beach zones based on UAS hyperspatial RGB (Red, Green, and Blue) imagery, namely imagery of sub-decimeter pixel size, and feature textures. Besides the RGB images, this paper also uses USGS (the United States Geological Survey) Munsell HSV (Hue, Saturation, and Value) and CIELUV (the CIE 1976 (L*, u*, v*) color space) images transformed from an RGB image. The four beach zones are identified based on the Gray Level Co-Occurrence Matrix (GLCM) and Local Binary Pattern (LBP) textures. Experiments were conducted with South Padre Island photos acquired by a Nikon D80 camera mounted on the US-16 UAS during March 2014. The results show that USGS Munsell hue can separate land and water reliably. GLCM and LBP textures can slightly improve classification accuracies by both unsupervised and supervised classification techniques. The experiments also indicate that we could reach acceptable results on different photos while using training data from another photo for site-specific UAS remote sensing. The findings imply that parallel processing of classification is feasible.
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    Estuarine Suspended Sediment Dynamics: Observations Derived from over a Decade of Satellite Data
    (Frontiers in Marine Science, 2017-12-20) Reisinger, Anthony; Gibeaut, James C.; Tissot, Philippe E.
    Suspended sediment dynamics of Corpus Christi Bay, Texas, USA, a shallow-water wind-driven estuary, were investigated by combining field and satellite measurements of total suspended solids (TSS). An algorithm was developed to transform 500-m Moderate Resolution Imaging Spectroradiometer (MODIS) Aqua satellite reflectance data into estimated TSS values. The algorithm was developed using a reflectance ratio regression of MODIS Band 1 (red) and Band 3 (green) with TSS measurements (n = 54) collected by the Texas Commission on Environmental Quality for Corpus Christi Bay and other Texas estuaries. The algorithm was validated by independently collected TSS measurements during the period of 2011–2014 with an uncertainty estimate of 13%. The algorithm was applied to the period of 2002–2014 to create a synoptic time series of TSS for Corpus Christi Bay. Potential drivers of long-term variability in suspended sediment were investigated. Median and IQR composites of suspended sediments were generated for seasonal wind regimes. From this analysis it was determined that long-term, spatial patterns of suspended sediment in the estuary are related to wind-wave resuspension during the predominant northerly and prevalent southeasterly seasonal wind regimes. The impact of dredging is also apparent in long-term patterns of Corpus Christi Bay as concentrations of suspended sediments over dredge spoil disposal sites are higher and more variable than surrounding areas, which is most likely due to their less consolidated sediments and shallower depths requiring less wave energy for sediment resuspension. This study highlights the advantage of how long-synoptic time series of TSS can be used to elucidate the major drivers of suspended sediments in estuaries.
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    Unsupervised Clustering Method for Complexity Reduction of Terrestrial Lidar Data in Marshes
    (Multidisciplinary Digital Publishing Institute, 2018-01-18) Nguyen, Chuyen; Starek, Michael J.; Tissot, Philippe E.; Gibeaut, James C.
    Accurate characterization of marsh elevation and landcover evolution is important for coastal management and conservation. This research proposes a novel unsupervised clustering method specifically developed for segmenting dense terrestrial laser scanning (TLS) data in coastal marsh environments. The framework implements unsupervised clustering with the well-known K-means algorithm by applying an optimization to determine the “k” clusters. The fundamental idea behind this novel framework is the application of multi-scale voxel representation of 3D space to create a set of features that characterizes the local complexity and geometry of the terrain. A combination of point- and voxel-generated features are utilized to segment 3D point clouds into homogenous groups in order to study surface changes and vegetation cover. Results suggest that the combination of point and voxel features represent the dataset well. The developed method compresses millions of 3D points representing the complex marsh environment into eight distinct clusters representing different landcover: tidal flat, mangrove, low marsh to high marsh, upland, and power lines. A quantitative assessment of the automated delineation of the tidal flat areas shows acceptable results considering the proposed method is unsupervised with no training data. Clustering results based on K-means are also compared to results based on the Self Organizing Map (SOM) clustering algorithm. Results demonstrate that the developed multi-scale voxelization approach and representative feature set are transferrable to other clustering algorithms, thereby providing an unsupervised framework for intelligent scene segmentation of TLS point cloud data in marshes.
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    Ensemble Neural Networks for Modeling DEM Error
    (Multidisciplinary Digital Publishing Institute, 2019-10-09) Nguyen, Chuyen; Starek, Michael J.; Tissot, Philippe E.; Cai, Xiaopeng; Gibeaut, James C.
    Digital elevation models (DEMs) have become ubiquitous and remarkably effective in the field of earth sciences as a tool to characterize surface topography. All DEMs have a degree of inherent error and uncertainty that is propagated to subsequent models and analyses, which can lead to misinterpretation and inaccurate estimates. A new method was developed to estimate local DEM errors and implement corrections while quantifying the uncertainties of the implemented corrections. The method is based on the flexibility and ability to model complex problems with ensemble neural networks (ENNs). The method was developed to be applied to any DEM created from a corresponding set of elevation points (point cloud) and a set of ground truth measurements. The method was developed and tested using hyperspatial resolution terrestrial laser scanning (TLS) data (sub-centimeter point spacing) collected from a marsh site located along the southern portion of the Texas Gulf Coast, USA. ENNs improve the overall DEM accuracy in the study area by 68% for six model inputs and by 75% for 12 model inputs corresponding to root mean square errors (RMSEs) of 0.056 and 0.045 m, respectively. The 12-input model provides more accurate tolerance interval estimates, particularly for vegetated areas. The accuracy of the method is confirmed based on an independent data set. Although the method still underestimates the 95% tolerance interval, 8% below the 95% target, results show that it is able to quantify the spatial variability in uncertainties due to a relationship between vegetation/land cover and accuracy of the DEM for the study area. There are still opportunities and challenges in improving and confirming the applicability of this method for different study sites and data sets.
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    Unsupervised Clustering of Multi-Perspective 3D Point Cloud Data in Marshes: A Case Study
    (Multidisciplinary Digital Publishing Institute, 2019-11-19) Nguyen, Chuyen; Starek, Michael J.; Tissot, Philippe E.; Gibeaut, James C.
    Dense three-dimensional (3D) point cloud data sets generated by Terrestrial Laser Scanning (TLS) and Unmanned Aircraft System based Structure-from-Motion (UAS-SfM) photogrammetry have different characteristics and provide different representations of the underlying land cover. While there are differences, a common challenge associated with these technologies is how to best take advantage of these large data sets, often several hundred million points, to efficiently extract relevant information. Given their size and complexity, the data sets cannot be efficiently and consistently separated into homogeneous features without the use of automated segmentation algorithms. This research aims to evaluate the performance and generalizability of an unsupervised clustering method, originally developed for segmentation of TLS point cloud data in marshes, by extending it to UAS-SfM point clouds. The combination of two sets of features are extracted from both datasets: “core” features that can be extracted from any 3D point cloud and “sensor specific” features unique to the imaging modality. Comparisons of segmented results based on producer’s and user’s accuracies allow for identifying the advantages and limitations of each dataset and determining the generalization of the clustering method. The producer’s accuracies suggest that UAS-SfM (94.7%) better represents tidal flats, while TLS (99.5%) is slightly more suitable for vegetated areas. The users’ accuracies suggest that UAS-SfM outperforms TLS in vegetated areas with 98.6% of those points identified as vegetation actually falling in vegetated areas whereas TLS outperforms UAS-SfM in tidal flat areas with 99.2% user accuracy. Results demonstrate that the clustering method initially developed for TLS point cloud data transfers well to UAS-SfM point cloud data to enable consistent and accurate segmentation of marsh land cover via an unsupervised method.
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    Meeting Regional, Coastal and Ocean User Needs With Tailored Data Products: A Stakeholder-Driven Process
    (Frontiers in Marine Science, 2019-06-07) Iwamoto, Melissa M.; Dorton, Jennifer; Newton, Jan; Yerta, Moirah; Gibeaut, James C.; Shyka, Tom; Kirkpatrick, Barbara; Currier, Robert
    New coastal and ocean observing stations and instruments deployed across the globe are providing increasing amounts of meteorological, biological, and oceanographic data. While these developments are essential for the development of various data products to inform decision-making among coastal communities, more data does not automatically translate into more benefits to society. Rather, decision-makers and other potential end-users must be included in an ongoing stakeholder-driven process to determine what information to collect and how to best streamline access to information. We present a three-step approach to develop effective tailored data products: (1) tailor stakeholder engagement to identify specific user needs; (2) design and refine data products to meet specific requirements and styles of interaction; and (3) iterate engagement with users to ensure data products remain relevant. Any of the three steps could be implemented alone or with more emphasis than others, but in order to successfully address stakeholders’ needs, they should be viewed as a continuum—as steps in a process to arrive at effective translation of coastal and ocean data to those who need it. Examples from the Regional Associations of the U.S. Integrated Ocean Observing System (IOOS®), the Texas General Land Office, and the Vanuatu Meteorology and Geo-hazards Department (VMGD) are woven throughout the discussion. These vignettes illustrate the value of this stakeholder-driven approach and provide a sample of the breadth of flexibility and customizability it affords. We hope this community white paper inspires others to evaluate how they connect their stakeholders to coastal and ocean observing data and provides managers of observing systems with a guide on how to evolve in a manner that addresses societal needs.
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    Using Lidar Data to Assess the Relationship Between Beach Geomorphology and Kemp’s Ridley (Lepidochelys kempii) Nest Site Selection Along Padre Island, TX, United States
    (Frontiers in Marine Science, 2020-04-08) Culver, Michelle; Gibeaut, James C.; Shaver, Donna J.; Tissot, Philippe E.; Starek, Michael J.
    The Kemp’s ridley sea turtle (Lepidochelys kempii) is the most endangered sea turtle species in the world, largely due to the limited geographic range of its nesting habitat. There has been limited research regarding the connection between beach geomorphology and Kemp’s ridley nesting patterns, but studies concerning other sea turtle species suggest that certain beach geomorphology variables, such as beach slope and width, influence nest site selection. This research attempts to address the literature gap by quantifying the terrestrial habitat variability of the Kemp’s ridley and investigating the connection between beach geomorphology characteristics and Kemp’s ridley nesting preferences on Padre Island, TX, United States. Geomorphology characteristics, such as beach width and slope, were extracted from lidar-derived digital elevation models and associated with Kemp’s nest coordinates and pseudo-absence points randomly created within the study area. Generalized linear models and random forest models were used to assess the significance of variables for nesting preferences. Kemp’s ridley nest presence was successfully modeled using beach geomorphology characteristics, and elevation, distance from shoreline, maximum dune slope, and average beach slope were the most important variables in the models. Kemp’s ridleys exhibit a preference for a limited range of the study area and avoid nesting on beaches with beach characteristics of extreme values. The results of this study include new information regarding Kemp’s ridley terrestrial habitat and nesting preferences that have many applications for species conservation and management.