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

Temporal shoreline series analysis using GNSS

In recent decades, Boa Viagem beach located in the city of Recife-PE and Piedade in Jaboatão dos Guararapes-PE (Brazil) has seen urbanization near the coastline causing changes in social, economic and morphological aspects, where coastal erosion problems are observed. This study uses GNSS (global navigation satellite system) shoreline monitoring approach, which is quicker, and provides continuously updatable data at cm-level accuracy to analyze and determine temporal positional shifts of the shoreline as well as annual average rates through EPR (end point rate). To achieve this, kinematic GNSS survey data for the years 2007, 2009, 2010 and 2012 were used. The results show sectorial trends over the years, with the highest annual retreat rate of 8.16 m /year occurring during the period 2007-2009. Variety of different patterns over the shoreline were also observed. These findings could be essential for decision making in coastal environments

Barley grain (1,3;1,4)-β-glucan content:effects of transcript and sequence variation in genes encoding the corresponding synthase and endohydrolase enzymes

The composition of plant cell walls is important in determining cereal end uses. Unlike other widely consumed cereal grains barley is comparatively rich in (1,3;1,4)-β-glucan, a source of dietary fibre. Previous work showed Cellulose synthase-like genes synthesise (1,3;1,4)-β-glucan in several tissues. HvCslF6 encodes a grain (1,3;1,4)-β-glucan synthase, whereas the function of HvCslF9 is unknown. Here, the relationship between mRNA levels of HvCslF6, HvCslF9, HvGlbI (1,3;1,4)-β-glucan endohydrolase, and (1,3;1,4)-β-glucan content was studied in developing grains of four barley cultivars. HvCslF6 was differentially expressed during mid (8-15 DPA) and late (38 DPA) grain development stages while HvCslF9 transcript was only clearly detected at 8-10 DPA. A peak of HvGlbI expression was detected at 15 DPA. Differences in transcript abundance across the three genes could partially explain variation in grain (1,3;1,4)-β-glucan content in these genotypes. Remarkably narrow sequence variation was found within the HvCslF6 promoter and coding sequence and does not explain variation in (1,3;1,4)-β-glucan content. Our data emphasise the genotype-dependent accumulation of (1,3;1,4)-β-glucan during barley grain development and a role for the balance between hydrolysis and synthesis in determining (1,3;1,4)-β-glucan content, and suggests that other regulatory sequences or proteins are likely to be involved in this trait in developing grain.Guillermo Garcia-Gimenez, Joanne Russell, Matthew K. Aubert, Geoffrey B. Fincher, Rachel A. Burton, Robbie Waugh, Matthew R. Tucker, Kelly Housto

AXY3 encodes a α-xylosidase that impacts the structure and accessibility of the hemicellulose xyloglucan in Arabidopsis plant cell walls

Xyloglucan is the most abundant hemicellulose in the walls of dicots such as Arabidopsis. It is part of the load-bearing structure of a plant cell and its metabolism is thought to play a major role in cell elongation. However, the molecular mechanism by which xyloglucan carries out this and other functions in planta is not well understood. We performed a forward genetic screen utilizing xyloglucan oligosaccharide mass profiling on chemically mutagenized Arabidopsis seedlings to identify mutants with altered xyloglucan structures termed axy-mutants. One of the identified mutants, axy3.1, contains xyloglucan with a higher proportion of non-fucosylated xyloglucan subunits. Mapping revealed that axy3.1 contains a point mutation in XYLOSIDASE1 (XYL1) known to encode for an apoplastic glycoside hydrolase releasing xylosyl residues from xyloglucan oligosaccharides at the non-reducing end. The data support the hypothesis that AXY3/XYL1 is an essential component of the apoplastic xyloglucan degradation machinery and as a result of the lack of function in the various axy3-alleles leads not only to an altered xyloglucan structure but also a xyloglucan that is less tightly associated with other wall components. However, the plant can cope with the excess xyloglucan relatively well as the mutant does not display any visible growth or morphological phenotypes with the notable exception of shorter siliques and reduced fitness. Taken together, these results demonstrate that plant apoplastic hydrolases have a larger impact on wall polymer structure and function than previously thought

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

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

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