Groundwater Gradients, Soil Moisture and Stream Mapping in a Boreal Landscape

Small headwater catchments are highly dynamic systems, but we often lack data and understanding of the hydrological processes taking place there. This study focused on the Krycklan catchment (67.8 km2) in Northern Sweden and the dynamics of the shallow groundwater tables, stream emergence and soil moisture over time and in space. It is generally assumed that, in humid climates, the groundwater table is a subdued copy of the surface topography but there is currently no guidance on what resolution DEM to use in hydrological analyses. Nevertheless, detailed microtopography, as can be retrieved from high-resolution Digital Elevation Models (DEMs), is unlikely to affect groundwater topography. A first step, therefore, was to look at the effects of DEM- smoothing and -aggregation on the calculated flow directions and derived catchment boundaries. For more than 40 % of the Krycklan catchment area the calculated flow directions depend strongly on the degree of smoothing or aggregation of the DEM. These are areas with local slopes in the opposite direction of the general slope, flat areas, ridges, and incised streams. We calculated the drainage area for 40 locations, outlets of catchments of different sizes, and found that the processing of the DEMs affected small catchments (i.e., first-order streams) the most. This highlights the need to carefully consider the effects of DEM smoothing or -aggregation on the calculated flow directions and drainage areas as the shifts in catchment boundaries and drainage areas can have a significant effect on the calculated water balance. To compare the results from the theoretical DEM analyses with real observations, a network of groundwater wells was installed in two areas in the Krycklan catchment. One is a small headwater catchment (3.5 ha; 54 wells) and the other a hillslope (1 ha; 21 wells). The wells were 274 cm deep on average (range of 70–581 cm). The positions of the wells were determined using a Light Detection and Ranging (LiDAR) scanner. The groundwater-level variations were recorded between 18 July 2018 and 1 November 2020 using capacitance water level loggers. During the summers of 2018 and 2019, manual water-level measurements were done to validate and re-calibrate the automatic water-level measurements. The groundwater-level data were carefully processed to determine differences in absolute groundwater levels and to calculate groundwater gradients. Additionally, all wells with sufficient water were sampled once during the summer of 2019 and analyzed for electrical conductivity, pH, absorbance (at 254 nm, 365 nm, 420 nm, and 436 nm), and anion and cation concentrations, and for the stable isotopes of hydrogen and oxygen. Groundwater gradients were calculated from the groundwater levels for the period May 2019 to October 2020. As expected, the calculated gradients changed with catchment wetness. Gradient directions calculated over short distances (5 m) changed by up to 360° and gradients calculated over larger distances (20 m) varied by up to 270°. As expected from the DEM analysis, the variation in groundwater gradient directions was largest for flatter locations and locations where the local surface slope differed from the surrounding topography. Though smoothed DEMs represented the groundwater surface better than high-resolution DEMs, the optimal degree of smoothing varied over the year. It was lowest for very wet periods, such as the snowmelt period, when groundwater tables were high. To complement the groundwater data, campaigns to map the soil moisture and stream state were carried out during the summer seasons of 2018 and 2019. These campaigns were based on qualitative citizen science approaches and showed how soil moisture decreased and increased and how stream networks contracted and were reactivated in the small subcatchment throughout the two field seasons. These qualitative measurements highlight the need for detailed spatially distributed measurements to understand the spatial variation in water storage (soil moisture, groundwater, or surface water) across the catchment and the usefulness of qualitative observations

Functional magnetic resonance imaging : an intermediary between behavior and neural activity

Blood oxygen level dependent (BOLD) functional magnetic resonance imaging is a non-invasive technique used to trace changes in neural dynamics in reaction to mental activity caused by perceptual, motor or cognitive tasks. The BOLD response is a complex signal, a consequence of a series of physiological events regulated by increased neural activity. A method to infer from the BOLD signal onto underlying neuronal activity (hemodynamic inverse problem) is proposed in Chapter 2 under the assumption of a previously proposed mathematical model on the transduction of neural activity to the BOLD signal. Also, in this chapter we clarify the meaning of the neural activity function used as the input for an intrinsic dynamic system which can be viewed as an advanced substitute for the impulse response function. Chapter 3 describes an approach for recovering neural timing information (mental chronometry) in an object interaction decision task via solving the hemodynamic inverse problem. In contrast to the hemodynamic level, at the neural level, we were able to determine statistically significant latencies in activation between functional units in the model used. In Chapter 4, two approaches for regularization parameter tuning in a regularized-regression analysis are compared in an attempt to find the optimal amount of smoothing to be imposed on fMRI data in determining an empirical hemodynamic response function. We found that the noise autocorrelation structure can be improved by tuning the regularization parameter but the whitening-based criterion provides too much smoothing when compared to cross-validation. Chapter~5 illustrates that the smoothing techniques proposed in Chapter 4 can be useful in the issue of correlating behavioral and hemodynamic characteristics. Specifically, Chapter 5, based on the smoothing techniques from Chapter 4, seeks to correlate several parameters characterizing the hemodynamic response in Broca's area to behavioral measures in a naming task. In particular, a condition for independence between two routes of converting print to speech in a dual route cognitive model was verified in terms of hemodynamic parameters

Quantifying bed roughness of ice streams using palaeo-glacial landscapes

The roughness of the bed beneath ice is an important control on ice stream location and dynamics. Deglaciated terrain provides the opportunity to explore bed roughness in greater detail and over larger areas compared to glaciated terrain. This thesis examines three differ- ent aspects of palaeo-ice streams bed roughness. Firstly, this thesis explores methods used to measure bed roughness in glaciology. The choices made by researchers on transect orien- tation, window size, detrending and roughness methods have an impact on results. The Fast Fourier Transform analysis and Standard Deviation methods are both useful for calculating bed roughness in glaciology. Secondly, this thesis directly compares the roughness of contemporary and palaeo-ice stream beds. The bed roughness of Minch Palaeo-Ice Stream (MPIS) is compared to the Institute and M ̈oller Ice Streams (IMIS). The MPIS has a rough bed along major flow paths in the onshore onset zones. The results from the MPIS demonstrate that the presence of sediment does not necessarily correspond with fast flowing ice. The spacing of Radio Echo Sounding (RES) transects (10 x 30 km) used to measure bed roughness under contemporary- ice streams was too wide to capture bed roughness of MPIS glacial landforms. Thirdly, this thesis investigates whether glacial landforms have unique bed roughness signatures. The results show that groups of glacial landforms have unique bed roughness signatures when anisotropy is taken into account. Bed roughness signatures of glacial land- forms have the potential to be compared with known and unknown areas of glacial landforms at the bed of contemporary-ice streams. Future studies should acquire RES transects where a rough bed or glacial landforms are inferred. 250 m transect spacing would be desirable but 1 km transect spacing is likely to be more practical because it would allow orthogonal and parallel transects to be acquired

Understanding uncertainty to inform conservation: tools to protect the endangered black-throated finch southern subspecies

Juan Mula Laguna used the case of the southern black-throated finch to apply tools to inform the conservation of rare and poorly-known species. Mula Laguna created a list of investment priorities for a due update of the current Recovery Plan, a spatial classification of habitat value, and refuted hypotheses about a diet-driven decline

Novel mathematical modeling approaches to assess ischemic stroke lesion evolution on medical imaging

Stroke is a major cause of disability and death worldwide. Although different clinical studies and trials used Magnetic Resonance Imaging (MRI) to examine patterns of change in different imaging modalities (eg: perfusion and diffusion), we still lack a clear and definite answer to the question: “How does an acute ischemic stroke lesion grow?” The inability to distinguish viable and dead tissue in abnormal MR regions in stroke patients weakens the evidence accumulated to answer this question, and relying on static snapshots of patient scans to fill in the spatio-temporal gaps by “thinking/guessing” make it even harder to tackle. Different opposing observations undermine our understanding of ischemic stroke evolution, especially at the acute stage: viable tissue transiting into dead tissue may be clear and intuitive, however, “visibly” dead tissue restoring to full recovery is still unclear. In this thesis, we search for potential answers to these raised questions from a novel dynamic modelling perspective that would fill in some of the missing gaps in the mechanisms of stroke evolution. We divided our thesis into five parts. In the first part, we give a clinical and imaging background on stroke and state the objectives of this thesis. In the second part, we summarize and review the literature in stroke and medical imaging. We specifically spot gaps in the literature mainly related to medical image analysis methods applied to acute-subacute ischemic stroke. We emphasize studies that progressed the field and point out what major problems remain. Noticeably, we have discovered that macroscopic (imaging-based) dynamic models that simulate how stroke lesion evolves in space and time were completely overlooked: an untapped potential that may alter and hone our understanding of stroke evolution. Progress in the dynamic simulation of stroke was absent –if not inexistent. In the third part, we answer this new call and apply a novel current-based dynamic model âpreviously applied to compare the evolution of facial characteristics between Chimpanzees and Bonobos [Durrleman 2010] – to ischemic stroke. This sets a robust numerical framework and provides us with mathematical tools to fill in the missing gaps between MR acquisition time points and estimate a four-dimensional evolution scenario of perfusion and diffusion lesion surfaces. We then detect two characteristics of patterns of abnormal tissue boundary change: spatial, describing the direction of change –outward as tissue boundary expands or inward as it contracts–; and kinetic, describing the intensity (norm) of the speed of contracting and expanding ischemic regions. Then, we compare intra- and inter-patients estimated patterns of change in diffusion and perfusion data. Nevertheless, topology change limits this approach: it cannot handle shapes with different parts that vary in number over time (eg: fragmented stroke lesions, especially in diffusion scans, which are common). In the fourth part, we suggest a new mathematical dynamic model to increase rigor in the imaging-based dynamic modeling field as a whole by overcoming the topology-change hurdle. Metamorphosis. It morphs one source image into a target one [Trouvé 2005]. In this manuscript, we extend it into dealing with more than two time-indexed images. We propose a novel extension of image-to-image metamorphosis into longitudinal metamorphosis for estimating an evolution scenario of both scattered and solitary ischemic lesions visible on serial MR. It is worth noting that the spatio-temporal metamorphosis we developed is a generic model that can be used to examine intensity and shape changes in time-series imaging and study different brain diseases or disorders. In the fifth part, we discuss our main findings and investigate future directions to explore to sharpen our understanding of ischemia evolution patterns

Understanding visual attention with RAGNAROC: A Reflexive Attention Gradient through Neural AttRactOr Competition

A quintessential challenge for any perceptual system is the need to focus on task-relevant information without being blindsided by unexpected, yet important information. The human visual system incorporates several solutions to this challenge, one of which is a reflexive covert attention system that is rapidly responsive to both the physical salience and the task-relevance of new information. This paper presents a model that simulates behavioral and neural correlates of reflexive attention as the product of brief neural attractor states that are formed across the visual hierarchy when attention is engaged. Such attractors emerge from an attentional gradient distributed over a population of topographically organized neurons and serve to focus processing at one or more locations in the visual field, while inhibiting the processing of lower priority information. The model moves towards a resolution of key debates about the nature of reflexive attention, such as whether it is parallel or serial, and whether suppression effects are distributed in a spatial surround, or selectively at the location of distractors. Most importantly, the model develops a framework for understanding the neural mechanisms of visual attention as a spatiotopic decision process within a hierarchy and links them to observable correlates such as accuracy, reaction time, and the N2pc and PD components of the EEG. This last contribution is the most crucial for repairing the disconnect that exists between our understanding of behavioral and neural correlates of attention

Pre-processing, classification and semantic querying of large-scale Earth observation spaceborne/airborne/terrestrial image databases: Process and product innovations.

By definition of Wikipedia, “big data is the term adopted for a collection of data sets so large and complex that it becomes difficult to process using on-hand database management tools or traditional data processing applications. The big data challenges typically include capture, curation, storage, search, sharing, transfer, analysis and visualization”. Proposed by the intergovernmental Group on Earth Observations (GEO), the visionary goal of the Global Earth Observation System of Systems (GEOSS) implementation plan for years 2005-2015 is systematic transformation of multisource Earth Observation (EO) “big data” into timely, comprehensive and operational EO value-adding products and services, submitted to the GEO Quality Assurance Framework for Earth Observation (QA4EO) calibration/validation (Cal/Val) requirements. To date the GEOSS mission cannot be considered fulfilled by the remote sensing (RS) community. This is tantamount to saying that past and existing EO image understanding systems (EO-IUSs) have been outpaced by the rate of collection of EO sensory big data, whose quality and quantity are ever-increasing. This true-fact is supported by several observations. For example, no European Space Agency (ESA) EO Level 2 product has ever been systematically generated at the ground segment. By definition, an ESA EO Level 2 product comprises a single-date multi-spectral (MS) image radiometrically calibrated into surface reflectance (SURF) values corrected for geometric, atmospheric, adjacency and topographic effects, stacked with its data-derived scene classification map (SCM), whose thematic legend is general-purpose, user- and application-independent and includes quality layers, such as cloud and cloud-shadow. Since no GEOSS exists to date, present EO content-based image retrieval (CBIR) systems lack EO image understanding capabilities. Hence, no semantic CBIR (SCBIR) system exists to date either, where semantic querying is synonym of semantics-enabled knowledge/information discovery in multi-source big image databases. In set theory, if set A is a strict superset of (or strictly includes) set B, then A B. This doctoral project moved from the working hypothesis that SCBIR computer vision (CV), where vision is synonym of scene-from-image reconstruction and understanding EO image understanding (EO-IU) in operating mode, synonym of GEOSS ESA EO Level 2 product human vision. Meaning that necessary not sufficient pre-condition for SCBIR is CV in operating mode, this working hypothesis has two corollaries. First, human visual perception, encompassing well-known visual illusions such as Mach bands illusion, acts as lower bound of CV within the multi-disciplinary domain of cognitive science, i.e., CV is conditioned to include a computational model of human vision. Second, a necessary not sufficient pre-condition for a yet-unfulfilled GEOSS development is systematic generation at the ground segment of ESA EO Level 2 product. Starting from this working hypothesis the overarching goal of this doctoral project was to contribute in research and technical development (R&D) toward filling an analytic and pragmatic information gap from EO big sensory data to EO value-adding information products and services. This R&D objective was conceived to be twofold. First, to develop an original EO-IUS in operating mode, synonym of GEOSS, capable of systematic ESA EO Level 2 product generation from multi-source EO imagery. EO imaging sources vary in terms of: (i) platform, either spaceborne, airborne or terrestrial, (ii) imaging sensor, either: (a) optical, encompassing radiometrically calibrated or uncalibrated images, panchromatic or color images, either true- or false color red-green-blue (RGB), multi-spectral (MS), super-spectral (SS) or hyper-spectral (HS) images, featuring spatial resolution from low (> 1km) to very high (< 1m), or (b) synthetic aperture radar (SAR), specifically, bi-temporal RGB SAR imagery. The second R&D objective was to design and develop a prototypical implementation of an integrated closed-loop EO-IU for semantic querying (EO-IU4SQ) system as a GEOSS proof-of-concept in support of SCBIR. The proposed closed-loop EO-IU4SQ system prototype consists of two subsystems for incremental learning. A primary (dominant, necessary not sufficient) hybrid (combined deductive/top-down/physical model-based and inductive/bottom-up/statistical model-based) feedback EO-IU subsystem in operating mode requires no human-machine interaction to automatically transform in linear time a single-date MS image into an ESA EO Level 2 product as initial condition. A secondary (dependent) hybrid feedback EO Semantic Querying (EO-SQ) subsystem is provided with a graphic user interface (GUI) to streamline human-machine interaction in support of spatiotemporal EO big data analytics and SCBIR operations. EO information products generated as output by the closed-loop EO-IU4SQ system monotonically increase their value-added with closed-loop iterations

Geographic Feature Mining: Framework and Fundamental Tasks for Geographic Knowledge Discovery from User-generated Data

We live in a data-rich environment where massive amounts of data such as text messages, articles, images, and search queries are continuously generated by users. In this environment, new opportunities to discover and utilize knowledge about the real-world arise, such as the extraction and description of places and events from social media records, the organization of documents by spatio-temporal topics, and the prediction of epidemics by search engine queries. Major challenges addressed in these data- and application-specific works arise from the unstructured and complex nature of the data, and the high level of uncertainty and sparsity of the attributes. Despite the evident progress in utilizing specific data sources for different applications, there remains a lack of common concepts and techniques on how to exploit the data as high-quality sensors of geographic space in a general manner. However, such a general point of view allows to address the common challenges and to define fundamental building blocks to deal with problems in fields like information retrieval, recommender systems, market research, health surveillance, and social sciences. In this thesis, we develop concepts and techniques to utilize various kinds of user-generated data as a steady source of information about geographic processes and entities (together called geographic phenomena). For this, we introduce a novel conceptual data mining framework, called geographic feature mining, that provides the foundation to discover and extract highly informative and discriminative dimensions of geographic space in a unifying and systematic fashion. This is achieved by representing the qualitative and geographic information in the records as geographic feature signals, each constituting a potential dimensions to describe geographic space. The mining process then determines highly informative features or feature combinations from the candidate sets that can be used as a steady source of auxiliary information for domain-specific applications. In developing the framework, we make contributions to several fundamental problems: (1) We introduce a novel probabilistic model to extract high-quality geographic feature signals. The signals are robust to noise and background distributions, and the model allows to exploit diverse kinds of qualitative and geographic information in the records. This flexibility is achieved by utilizing a Bayesian network model and the robustness by choosing appropriate prior distributions. (2) We address the problem of categorizing and selecting geographic features based on their spatio-temporal type, such as feature signals having landmark, regional, or global semantics. For this, we introduce representations of the signals by interaction characteristics and evaluate their performance in clustering and data summarization tasks. (3) To extract a small number of highly informative feature combinations that reflect geographic phenomena, we introduce a model that extracts latent geographic features from the candidate signals using dimensionality reduction. We show that this model outperforms document-centric topic models with respect to the informativeness of the extracted phenomena, and we exhaustively evaluate how different statistical properties of the approaches affect the characteristics of the resulting feature combinations