Detecting Small-Scale Topographic Changes and Relict Geomorphic Features on Barrier Islands Using SAR

The shapes and elevations of barrier islands may change dramatically over a short period of time during a storm. Coastal scientists and engineers, however, are currently unable to measure these changes occurring over an entire barrier island at once. This three-year project, which is funded by NASA and jointly conducted by the Bureau of Economic Geology and the Center for Space Research at The University of Texas at Austin, is designed to overcome this problem by developing the use of interferometry from airborne synthetic aperture radar (AIRSAR) to measure coastal topography and to detect storm-induced changes in topography. Surrogate measures of topography observed in multiband, fully polarimetric AIRSAR (This type of data are now referred to as POLSAR data.) are also being investigated. Digital elevation models (DEM) of Galveston Island and Bolivar Peninsula, Texas obtained with Topographic SAR (TOPSAR) are compared with measurements by Global Positioning System (GPS) ground surveys and electronic total station surveys. In addition to topographic mapping, this project is evaluating the use of POLSAR to detect old features such as storm scarps, storm channels, former tidal inlets, and beach ridges that have been obscured by vegetation, erosion, deposition, and artificial filling. We have also expanded the work from the original proposal to include the mapping of coastal wetland vegetation and depositional environments. Methods developed during this project will provide coastal geologists with an unprecedented tool for monitoring and understanding barrier island systems. This understanding will improve overall coastal management policies and will help reduce the effects of natural and man-induced coastal hazards. This report summarizes our accomplishments during the second year of the study. Also included is a discussion of our planned activities for year 3 and a revised budget

Summary Report for the 2005-2006 STATEMAP Project: Geologic Mapping to Support Improved Database Development and Understanding of Urban Corridors, Critical Acquifers. And Special Areas of Environmental Concern in Texas

Eleven geologic maps, 1:24,000 scale, have been constructed for Galveston and Mustang Barrier Islands and for part of the Brazos River Valley and its aquifers. The maps are intended to be used by professionals and laypersons as a source of general geologic information that relates to land and resource use and management. The geologic maps of the barrier islands include (a) Northern Mustang Island (Port Aransas quadrangle), (b) Southern Mustang Island (Crane Islands NW quadrangle), (c) Northeastern Galveston Island (Galveston quadrangle), (d) Central Galveston Island (Lake Como quadrangle), and (e) Southeastern Galveston Island (Sea Isle and San Luis Pass quadrangles). These maps display island wetland and upland geologic environments. Geologic maps of the Brazos River Valley study area include six quadrangles: Baileyville, Hammond, Maysfield, Calvert, Gause, and Hearne South. These maps and cross sections show the geologic framework of the Brazos alluvial aquifer in an area where it intersects three other Texas aquifers: the Carrizo-Wilcox, Queen City, and Sparta.Bureau of Economic Geolog

An inter-sensor calibration and atmospheric correction system for long-term time series of AVHRR imagery for coastal waters.

The Advanced Very High Resolution Radiometer (AVHRR) data series has more than 30 years of unique and valuable Earth observation imagery available. To use the longterm remote-sensor data, this article presents a system that produces consistent intersensor calibration and atmospheric correction for coastal waters. The system can process all five High Resolution Picture Transmission (HRPT) file formats from the twelve (12) AVHRR sensors that operated from the 1980s to the present. The system has been used to process AVHRR data of three Texas estuaries from 1985 to 2010 to document changes in suspended sediment pattern

Estuarine Suspended Sediment Dynamics: Observations Derived from over a Decade of Satellite Data

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

Unsupervised Clustering of Multi-Perspective 3D Point Cloud Data in Marshes: A Case Study

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

Ensemble Neural Networks for Modeling DEM Error

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

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

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