Using ground-penetrating radar, topography and classification of vegetation to model the sediment and active layer thickness in a periglacial lake catchment, Western Greenland, link to shapefiles

The geometries of a catchment constitute the basis for distributed physically based numerical modeling of different geoscientific disciplines. In this paper results from ground-penetrating radar (GPR) measurements, in terms of a 3D model of total sediment thickness and active layer thickness in a periglacial catchment in western Greenland, is presented. Using the topography, thickness and distribution of sediments is calculated. Vegetation classification and GPR measurements are used to scale active layer thickness from local measurements to catchment scale models. Annual maximum active layer thickness varies from 0.3 m in wetlands to 2.0 m in barren areas and areas of exposed bedrock. Maximum sediment thickness is estimated to be 12.3 m in the major valleys of the catchment. A method to correlate surface vegetation with active layer thickness is also presented. By using relatively simple methods, such as probing and vegetation classification, it is possible to upscale local point measurements to catchment scale models, in areas where the upper subsurface is relatively homogenous. The resulting spatial model of active layer thickness can be used in combination with the sediment model as a geometrical input to further studies of subsurface mass-transport and hydrological flow paths in the periglacial catchment through numerical modelling.

Species name, classification (WORMS), abundance and biomass of the megabenthic communities of the Algarve

The present study analysed the megabenthic diversity in subtidal soft bottoms and assessed the main environmental drivers of megabenthic community organisation along the Algarve coast (southern Portugal). We tested the hypothesis that megabenthic communities respond to the same environmental drivers than macrofauna. We found that similar to macrofauna, megafaunal communities were organised in relation to the depth of closure, light reaching the bottom, and the hydrodynamic conditions related with exposure within the shallower areas. The influence of the main river outflow prevailed over other drivers, but only up to 9 m depth. We found that seven different spatial units should be considered, each characterised by different indicator species. Additionally, among a total of 412 taxa collected between 4 and 50 m depth, we provide the characteristics of the 64 commonest species in terms of occurrence, frequency, distribution, abundance, bathymetric and sedimentary preferences, which constitutes most valuable information for ecosystem modelling. Megabenthic alpha diversity decreased with depth, contrary to evenness and was higher in the proximity of the river Guadiana and in highly exposed shores. We conclude that the megafauna, which is significantly quicker to collect and analyse, can provide an accurate alternative to macrofauna sampling, as their communities are shaped by the same drivers.

Caracterización multiescala de objetos como herramienta para la clasificación de imágenes de alta resolución espacial

This paper presents a new methodology, simple and affordable, for the definition and characterization of objects at different scales in high spatial resolution images. The objects have been generated by integrating texturally and spectrally homogeneous segments. The former have been obtained from the segmentation of Wavelet coefficients of the panchromatic image. The multi-scale character of this transform has yielded texturally homogeneous segments of different sizes for each of the scales. The spectrally homogeneous segments have been obtained by segmenting the classified corresponding multispectral image. In this way, it has been defined a set of objects characterized by different attributes, which give to the objects a semantic meaning, allowing to determine the similarities and differences between them. To demonstrate the capabilities of the methodology proposed, different experiments of unsupervised classification of a Quickbird image have been carried out, using different subsets of attributes and 1-D ascendant hierarchical classifier. Obtained results have shown the capability of the proposed methodology for separating semantic objects at different scales, as well as, its advantages against pixel-based image interpretation.

Lacunarity of the Spatial Distributions of Soil Types in Europe.

Lacunarity as a means of quantifying textural properties of spatial distributions suggests a classification into three main classes of the most abundant soils that cover 92% of Europe. Soils with a well-defined self-similar structure of the linear class are related to widespread spatial patterns that are nondominant but ubiquitous at continental scale. Fractal techniques have been increasingly and successfully applied to identify and describe spatial patterns in natural sciences. However, objects with the same fractal dimension can show very different optical properties because of their spatial arrangement. This work focuses primary attention on the geometrical structure of the geographical patterns of soils in Europe. We made use of the European Soil Database to estimate lacunarity indexes of the most abundant soils that cover 92% of the surface of Europe and investigated textural properties of their spatial distribution. We observed three main classes corresponding to three different patterns that displayed the graphs of lacunarity functions, that is, linear, convex, and mixed. They correspond respectively to homogeneous or self-similar, heterogeneous or clustered and those in which behavior can change at different ranges of scales. Finally, we discuss the pedological implications of that classification.

Sea Bottom Classification by Means of Bathymetric LIDAR Data

Light Detection and Ranging (LIDAR) provides high horizontal and vertical resolution of spatial data located in point cloud images, and is increasingly being used in a number of applications and disciplines, which have concentrated on the exploit and manipulation of the data using mainly its three dimensional nature. Bathymetric LIDAR systems and data are mainly focused to map depths in shallow and clear waters with a high degree of accuracy. Additionally, the backscattering produced by the different materials distributed over the bottom surface causes that the returned intensity signal contains important information about the reflection properties of these materials. Processing conveniently these values using a Simplified Radiative Transfer Model, allows the identification of different sea bottom types. This paper presents an original method for the classification of sea bottom by means of information processing extracted from the images generated through LIDAR data. The results are validated using a vector database containing benthic information derived by marine surveys.

Plant β-diversity i in human-altered forest ecosystems: the importance of the structural, spatial, and topographical characteristics of stands in patterning plant species assemblages

An understanding of spatial patterns of plant species diversity and the factors that drive those patterns is critical for the development of appropriate biodiversity management in forest ecosystems. We studied the spatial organization of plants species in human- modified and managed oak forests (primarily, Quercus faginea) in the Central Pre- Pyrenees, Spain. To test whether plant community assemblages varied non-randomly across the spatial scales, we used multiplicative diversity partitioning based on a nested hierarchical design of three increasingly coarser spatial scales (transect, stand, region). To quantify the importance of the structural, spatial, and topographical characteristics of stands in patterning plant species assemblages and identify the determinants of plant diversity patterns, we used canonical ordination. We observed a high contribution of ˟-diversity to total -diversity and found ˟-diversity to be higher and ˞-diversity to be lower than expected by random distributions of individuals at different spatial scales. Results, however, partly depended on the weighting of rare and abundant species. Variables expressing the historical management intensities of the stand such as mean stand age, the abundance of the dominant tree species (Q. faginea), age structure of the stand, and stand size were the main factors that explained the compositional variation in plant communities. The results indicate that (1) the structural, spatial, and topographical characteristics of the forest stands have the greatest effect on diversity patterns, (2) forests in landscapes that have different land use histories are environmentally heterogeneous and, therefore, can experience high levels of compositional differentiation, even at local scales (e.g., within the same stand). Maintaining habitat heterogeneity at multiple spatial scales should be considered in the development of management plans for enhancing plant diversity and related functions in human-altered forests

Automated detection and classification of brain injury lesions on structural magnetic resonance imaging

El daño cerebral adquirido (DCA) es un problema social y sanitario grave, de magnitud creciente y de una gran complejidad diagnóstica y terapéutica. Su elevada incidencia, junto con el aumento de la supervivencia de los pacientes, una vez superada la fase aguda, lo convierten también en un problema de alta prevalencia. En concreto, según la Organización Mundial de la Salud (OMS) el DCA estará entre las 10 causas más comunes de discapacidad en el año 2020. La neurorrehabilitación permite mejorar el déficit tanto cognitivo como funcional y aumentar la autonomía de las personas con DCA. Con la incorporación de nuevas soluciones tecnológicas al proceso de neurorrehabilitación se pretende alcanzar un nuevo paradigma donde se puedan diseñar tratamientos que sean intensivos, personalizados, monitorizados y basados en la evidencia. Ya que son estas cuatro características las que aseguran que los tratamientos son eficaces. A diferencia de la mayor parte de las disciplinas médicas, no existen asociaciones de síntomas y signos de la alteración cognitiva que faciliten la orientación terapéutica. Actualmente, los tratamientos de neurorrehabilitación se diseñan en base a los resultados obtenidos en una batería de evaluación neuropsicológica que evalúa el nivel de afectación de cada una de las funciones cognitivas (memoria, atención, funciones ejecutivas, etc.). La línea de investigación en la que se enmarca este trabajo de investigación pretende diseñar y desarrollar un perfil cognitivo basado no sólo en el resultado obtenido en esa batería de test, sino también en información teórica que engloba tanto estructuras anatómicas como relaciones funcionales e información anatómica obtenida de los estudios de imagen. De esta forma, el perfil cognitivo utilizado para diseñar los tratamientos integra información personalizada y basada en la evidencia. Las técnicas de neuroimagen representan una herramienta fundamental en la identificación de lesiones para la generación de estos perfiles cognitivos. La aproximación clásica utilizada en la identificación de lesiones consiste en delinear manualmente regiones anatómicas cerebrales. Esta aproximación presenta diversos problemas relacionados con inconsistencias de criterio entre distintos clínicos, reproducibilidad y tiempo. Por tanto, la automatización de este procedimiento es fundamental para asegurar una extracción objetiva de información. La delineación automática de regiones anatómicas se realiza mediante el registro tanto contra atlas como contra otros estudios de imagen de distintos sujetos. Sin embargo, los cambios patológicos asociados al DCA están siempre asociados a anormalidades de intensidad y/o cambios en la localización de las estructuras. Este hecho provoca que los algoritmos de registro tradicionales basados en intensidad no funcionen correctamente y requieran la intervención del clínico para seleccionar ciertos puntos (que en esta tesis hemos denominado puntos singulares). Además estos algoritmos tampoco permiten que se produzcan deformaciones grandes deslocalizadas. Hecho que también puede ocurrir ante la presencia de lesiones provocadas por un accidente cerebrovascular (ACV) o un traumatismo craneoencefálico (TCE). Esta tesis se centra en el diseño, desarrollo e implementación de una metodología para la detección automática de estructuras lesionadas que integra algoritmos cuyo objetivo principal es generar resultados que puedan ser reproducibles y objetivos. Esta metodología se divide en cuatro etapas: pre-procesado, identificación de puntos singulares, registro y detección de lesiones. Los trabajos y resultados alcanzados en esta tesis son los siguientes: Pre-procesado. En esta primera etapa el objetivo es homogeneizar todos los datos de entrada con el objetivo de poder extraer conclusiones válidas de los resultados obtenidos. Esta etapa, por tanto, tiene un gran impacto en los resultados finales. Se compone de tres operaciones: eliminación del cráneo, normalización en intensidad y normalización espacial. Identificación de puntos singulares. El objetivo de esta etapa es automatizar la identificación de puntos anatómicos (puntos singulares). Esta etapa equivale a la identificación manual de puntos anatómicos por parte del clínico, permitiendo: identificar un mayor número de puntos lo que se traduce en mayor información; eliminar el factor asociado a la variabilidad inter-sujeto, por tanto, los resultados son reproducibles y objetivos; y elimina el tiempo invertido en el marcado manual de puntos. Este trabajo de investigación propone un algoritmo de identificación de puntos singulares (descriptor) basado en una solución multi-detector y que contiene información multi-paramétrica: espacial y asociada a la intensidad. Este algoritmo ha sido contrastado con otros algoritmos similares encontrados en el estado del arte. Registro. En esta etapa se pretenden poner en concordancia espacial dos estudios de imagen de sujetos/pacientes distintos. El algoritmo propuesto en este trabajo de investigación está basado en descriptores y su principal objetivo es el cálculo de un campo vectorial que permita introducir deformaciones deslocalizadas en la imagen (en distintas regiones de la imagen) y tan grandes como indique el vector de deformación asociado. El algoritmo propuesto ha sido comparado con otros algoritmos de registro utilizados en aplicaciones de neuroimagen que se utilizan con estudios de sujetos control. Los resultados obtenidos son prometedores y representan un nuevo contexto para la identificación automática de estructuras. Identificación de lesiones. En esta última etapa se identifican aquellas estructuras cuyas características asociadas a la localización espacial y al área o volumen han sido modificadas con respecto a una situación de normalidad. Para ello se realiza un estudio estadístico del atlas que se vaya a utilizar y se establecen los parámetros estadísticos de normalidad asociados a la localización y al área. En función de las estructuras delineadas en el atlas, se podrán identificar más o menos estructuras anatómicas, siendo nuestra metodología independiente del atlas seleccionado. En general, esta tesis doctoral corrobora las hipótesis de investigación postuladas relativas a la identificación automática de lesiones utilizando estudios de imagen médica estructural, concretamente estudios de resonancia magnética. Basándose en estos cimientos, se han abrir nuevos campos de investigación que contribuyan a la mejora en la detección de lesiones. ABSTRACT Brain injury constitutes a serious social and health problem of increasing magnitude and of great diagnostic and therapeutic complexity. Its high incidence and survival rate, after the initial critical phases, makes it a prevalent problem that needs to be addressed. In particular, according to the World Health Organization (WHO), brain injury will be among the 10 most common causes of disability by 2020. Neurorehabilitation improves both cognitive and functional deficits and increases the autonomy of brain injury patients. The incorporation of new technologies to the neurorehabilitation tries to reach a new paradigm focused on designing intensive, personalized, monitored and evidence-based treatments. Since these four characteristics ensure the effectivity of treatments. Contrary to most medical disciplines, it is not possible to link symptoms and cognitive disorder syndromes, to assist the therapist. Currently, neurorehabilitation treatments are planned considering the results obtained from a neuropsychological assessment battery, which evaluates the functional impairment of each cognitive function (memory, attention, executive functions, etc.). The research line, on which this PhD falls under, aims to design and develop a cognitive profile based not only on the results obtained in the assessment battery, but also on theoretical information that includes both anatomical structures and functional relationships and anatomical information obtained from medical imaging studies, such as magnetic resonance. Therefore, the cognitive profile used to design these treatments integrates information personalized and evidence-based. Neuroimaging techniques represent an essential tool to identify lesions and generate this type of cognitive dysfunctional profiles. Manual delineation of brain anatomical regions is the classical approach to identify brain anatomical regions. Manual approaches present several problems related to inconsistencies across different clinicians, time and repeatability. Automated delineation is done by registering brains to one another or to a template. However, when imaging studies contain lesions, there are several intensity abnormalities and location alterations that reduce the performance of most of the registration algorithms based on intensity parameters. Thus, specialists may have to manually interact with imaging studies to select landmarks (called singular points in this PhD) or identify regions of interest. These two solutions have the same inconvenient than manual approaches, mentioned before. Moreover, these registration algorithms do not allow large and distributed deformations. This type of deformations may also appear when a stroke or a traumatic brain injury (TBI) occur. This PhD is focused on the design, development and implementation of a new methodology to automatically identify lesions in anatomical structures. This methodology integrates algorithms whose main objective is to generate objective and reproducible results. It is divided into four stages: pre-processing, singular points identification, registration and lesion detection. Pre-processing stage. In this first stage, the aim is to standardize all input data in order to be able to draw valid conclusions from the results. Therefore, this stage has a direct impact on the final results. It consists of three steps: skull-stripping, spatial and intensity normalization. Singular points identification. This stage aims to automatize the identification of anatomical points (singular points). It involves the manual identification of anatomical points by the clinician. This automatic identification allows to identify a greater number of points which results in more information; to remove the factor associated to inter-subject variability and thus, the results are reproducible and objective; and to eliminate the time spent on manual marking. This PhD proposed an algorithm to automatically identify singular points (descriptor) based on a multi-detector approach. This algorithm contains multi-parametric (spatial and intensity) information. This algorithm has been compared with other similar algorithms found on the state of the art. Registration. The goal of this stage is to put in spatial correspondence two imaging studies of different subjects/patients. The algorithm proposed in this PhD is based on descriptors. Its main objective is to compute a vector field to introduce distributed deformations (changes in different imaging regions), as large as the deformation vector indicates. The proposed algorithm has been compared with other registration algorithms used on different neuroimaging applications which are used with control subjects. The obtained results are promising and they represent a new context for the automatic identification of anatomical structures. Lesion identification. This final stage aims to identify those anatomical structures whose characteristics associated to spatial location and area or volume has been modified with respect to a normal state. A statistical study of the atlas to be used is performed to establish which are the statistical parameters associated to the normal state. The anatomical structures that may be identified depend on the selected anatomical structures identified on the atlas. The proposed methodology is independent from the selected atlas. Overall, this PhD corroborates the investigated research hypotheses regarding the automatic identification of lesions based on structural medical imaging studies (resonance magnetic studies). Based on these foundations, new research fields to improve the automatic identification of lesions in brain injury can be proposed.

A fully automatic evolutionary classification of protein folds: Dali Domain Dictionary version 3

The Dali Domain Dictionary (http://www.ebi.ac.uk/dali/domain) is a numerical taxonomy of all known structures in the Protein Data Bank (PDB). The taxonomy is derived fully automatically from measurements of structural, functional and sequence similarities. Here, we report the extension of the classification to match the traditional four hierarchical levels corresponding to: (i) supersecondary structural motifs (attractors in fold space), (ii) the topology of globular domains (fold types), (iii) remote homologues (functional families) and (iv) homologues with sequence identity above 25% (sequence families). The computational definitions of attractors and functional families are new. In September 2000, the Dali classification contained 10 531 PDB entries comprising 17 101 chains, which were partitioned into five attractor regions, 1375 fold types, 2582 functional families and 3724 domain sequence families. Sequence families were further associated with 99 582 unique homologous sequences in the HSSP database, which increases the number of effectively known structures several-fold. The resulting database contains the description of protein domain architecture, the definition of structural neighbours around each known structure, the definition of structurally conserved cores and a comprehensive library of explicit multiple alignments of distantly related protein families.

The episcopal complex of Eio-El Tolmo de Minateda (Hellín, Albacete, Spain). Architecture and spatial organization. 7th to 8th centuries AD

The episcopal complex of Eio, located in El Tolmo de Minateda, was built between the end of the 6th century and the beginning of the 7th century, possibly as a political decision taken by the ecclesiastical authority in the capital of the Visigothic kingdom (Toletum). With the comprehensive study of the whole complex presented below (construction cycles, furniture, decoration and location of spaces), we can interpret the function of each space in the basilica and the domus episcopi, the liturgical and general movement routes, the existence of some hierarchical environments, and specify the chronological development of the buildings. After the Arab-Berber conquest of Hispania in the early 8th century, the whole complex will experience a series of transformations that will convert the religious and monumental public area into a private, residential and industrial Islamic quarter.

Feature selection by multi-objective optimisation: Application to network anomaly detection by hierarchical self-organising maps

Feature selection is an important and active issue in clustering and classification problems. By choosing an adequate feature subset, a dataset dimensionality reduction is allowed, thus contributing to decreasing the classification computational complexity, and to improving the classifier performance by avoiding redundant or irrelevant features. Although feature selection can be formally defined as an optimisation problem with only one objective, that is, the classification accuracy obtained by using the selected feature subset, in recent years, some multi-objective approaches to this problem have been proposed. These either select features that not only improve the classification accuracy, but also the generalisation capability in case of supervised classifiers, or counterbalance the bias toward lower or higher numbers of features that present some methods used to validate the clustering/classification in case of unsupervised classifiers. The main contribution of this paper is a multi-objective approach for feature selection and its application to an unsupervised clustering procedure based on Growing Hierarchical Self-Organising Maps (GHSOMs) that includes a new method for unit labelling and efficient determination of the winning unit. In the network anomaly detection problem here considered, this multi-objective approach makes it possible not only to differentiate between normal and anomalous traffic but also among different anomalies. The efficiency of our proposals has been evaluated by using the well-known DARPA/NSL-KDD datasets that contain extracted features and labelled attacks from around 2 million connections. The selected feature sets computed in our experiments provide detection rates up to 99.8% with normal traffic and up to 99.6% with anomalous traffic, as well as accuracy values up to 99.12%.

Spatial distribution patterns of ascidians (Ascidiacea: Tunicata) in combination with information on bathymetry, oceanography, chlorophyll-a, and sea-ice on the continental shelves off the northern Antarctic Peninsula during POLARSTERN cruise ANT-XXIX/3

Ascidians (Ascidiacea: Tunicata) are sessile suspension feeders that represent dominant epifaunal components of the Southern Ocean shelf benthos and play a significant role in the pelagic-benthic coupling. Here, we report the results of a first study on the relationship between the distribution patterns of eight common and/or abundant (putative) ascidian species, and environmental drivers in the waters off the northern Antarctic Peninsula. During RV Polarstern cruise XXIX/3 (PS81) in January-March 2013, we used seabed imaging surveys along 28 photographic transects of 2 km length each at water depths from 70 to 770 m in three regions (northwestern Weddell Sea, southern Bransfield Strait and southern Drake Passage), differing in their general environmental setting, primarily oceanographic characteristics and sea-ice dynamics, to comparatively analyze the spatial patterns in the abundance of the selected ascidians, reliably to be identified in the photographs, at three nested spatial scales. At a regional (100-km) scale, the ascidian assemblages of the Weddell Sea differed significantly from those of the other two regions, whereas at an intermediate 10-km scale no such differences were detected among habitat types (bank, upper slope, slope, deep/canyon) on the shelf and at the shelf break within each region. These spatial patterns were superimposed by a marked small-scale (10-m) patchiness of ascidian distribution within the 2-km-long transects. Among the environmental variables considered in our study, a combination of water-mass characteristics, sea-ice dynamics (approximated by 5-year averages in sea-ice cover in the region of or surrounding the photographic stations), as well as the seabed ruggedness, was identified as explaining best the distribution patterns of the ascidians.

Spatial distribution and morphometric analysis of thermokarst lakes and other water bodies : Case study from the sporadic permafrost region, Tavvavuoma, Sweden

Projected air and ground temperatures are expected to be higher in Arctic and sub-Arcticlatitudes and with temperatures already close to the limit where permafrost can exist,resistance against degradation is low. With thawing permafrost, the landscape is modifiedwith depression in which thermokarst lakes emerge. In permafrost soils a considerableamount of soil organic carbon is stored, with the potential of altering climate even furtherif expansion and formation of new thermokarst lakes emerge, as decay releasesgreenhouse gases (C02 and CH4) to the atmosphere. Analyzing the spatial distribution andmorphometry over time of thermokarst lakes and other water bodies, is of importance inaccurately predict carbon budget and feedback mechanisms, as well as to assess futurelandscape layout and these features interaction. Different types of high-spatial resolutionaerial and satellite imageries from 1963, 1975, 2003, 2010 and 2015, were used in bothpre- and post-classification change detection analyses. Using object oriented segmentationin eCognition combined with manual adjustments, resulted in digitalized water bodies>28m2 from which direction of change and morphometric values were extracted. Thequantity of thermokarst lakes and other water bodies was in 1963 n=92, with succeedingyears as a trend decreased in numbers, until 2010-2015 when eleven water bodies wereadded in 2015 (n=74 to n=85). In 1963-2003, area of these water bodies decreased with50 651m2 (189 446-138 795m2) and continued to decrease in 2003-2015 ending at 129337m2. Limnicity decreased from 19.9% in 1963 to 14.6% in 2003 (-5.3%). In 2010 and2015 13.7-13.6%. The late increase in water bodies differs from an earlier hypothesis thatsporadic permafrost regions experience decrease in both area and quantity of thermokarstlakes and water bodies. During 1963-2015, land gain has been in dominance of the ratiobetween the two competing processes of expansion and drainage. In 1963-1975, 55/45%,followed by 90/10% in 1975-2003. After major drainage events, land loss increased to62/38% in 2010-2015. Drainage and infilling rates, calculated for 15 shorelines werevaried across both landscape and parts of shorelines, with in average 0.17/0.15/0.14m/yr.Except for 1963-1975 when rate of change in average was in opposite direction (-0.09m/yr.), likely due to evident expansion of a large thermokarst lake. Using a squaregrid, distribution of water bodies was determined, with an indistinct cluster located in NEand central parts. Especially for water bodies <250m2, which is the dominant area classthroughout 1963-2015 ranging from n=39-51. With a heterogeneous composition of bothsmall and large thermokarst lakes, and with both expansion and drainage altering thelandscape in Tavvavuoma, both positive and negative climate feedback mechanisms are inplay - given that sporadic permafrost still exist.

Determining Spatial Distribution and Physical Properties of the Vashon Advance Outwash near Mountlake Terrace, Washington

This study is aimed at determining the spatial distribution, physical properties, and groundwater conditions of the Vashon advance outwash (Qva) in the Mountlake Terrace, WA area. The Qva is correlative with the Esperance Sand, as defined at its type section; however, local variations in the Qva are not well-characterized (Mullineaux, 1965). While the Qva is a dense glacial unit with low compressibility and high frictional shear strength (Gurtowski and Boirum, 1989), the strength of this unit can be reduced when it becomes saturated (Tubbs, 1974). This can lead to caving or flowing in excavations, and on a larger scale, can lead to slope failures and mass-wasting when intersected by steep slopes. By studying the Qva, we can better predict how it will behave under certain conditions, which will be beneficial to geologists, hydrogeologists, engineers, and environmental scientists during site assessments and early phases of project planning. In this study, I use data from 27 geotechnical borings from previous field investigations and C-Tech Corporation’s EnterVol software to create three-dimensional models of the subsurface geology in the study area. These models made it possible to visualize the spatial distribution of the Qva in relation to other geologic units. I also conducted a comparative study between data from the borings and generalized published data on the spatial distribution, relative density, soil classification, grain-size distribution, moisture content, groundwater conditions, and aquifer properties of the Qva. I found that the elevation of the top of the Qva ranges from 247 to 477 ft. I found that the Qva is thickest where the modern topography is high, and is thinnest where the topography is low. The thickness of the Qva ranges from absent to 242 ft. Along the northern, east-west trending transect, the Qva thins to the east as it rises above a ridge composed of Pre- Vashon glacial deposits. Along the southern, east-west trending transect, the Qva pinches out against a ridge composed of pre-Vashon interglacial deposits. Two plausible explanations for this ridge are paleotopography and active faulting associated with the Southern Whidbey Fault Zone. Further investigations should be done using geophysical methods and the modeling methods described in this study to determine the nature of this ridge. The relative density of the Qva in the study area ranges from loose to very dense, with the loose end of the spectrum probably relating to heave in saturated sands. I found subtle correlations between density and depth. Volumetric analysis of the soil groups listed in the boring logs indicate that the Qva in the study area is composed of approximately 9.5% gravel, 89.3% sand, and 1.2% silt and clay. The natural moisture content ranges from 3.0 to 35.4% in select samples from the Qva. The moisture content appears to increase with depth and fines content. The water table in the study area ranges in elevation from 231.9 to 458 ft, based on observations and measurements recorded in the boring logs. The results from rising-head and falling-head slug tests done at a single well in the study area indicate that the geometric mean of hydraulic conductivity is 15.93 ft/d (5.62 x 10-03 cm/s), the storativity is 3.28x10-03, and the estimated transmissivity is 738.58 ft2/d in the vicinity of this observation well. At this location, there was 1.73 ft of seasonal variation in groundwater elevation between August 2014 and March 2015.