Continental-scale high-resolution river geometry and real-time inundation mapping

Flooding is the most threatening natural disaster worldwide considering the fatalities and property damage it causes. Recent flood disasters have raised concerns for accurate and responsive inundation forecast due to the rapid spread and astonishing destructive power of these events. Although recent development in large scale hydrologic simulation has enabled the real-time streamflow simulation operating on millions of river reaches, a framework for converting the forecast discharge into corresponding water surface elevation and inundation maps at a continental-scale is absent to better support local flood response. To accurately map flood inundation extent, a comprehensive description of the geometry of the channel is indispensable. As such, this dissertation presents an innovative approach for estimating river geometry and conducting inundation mapping at a continental-scale with a high spatial resolution. This approach is based on the concept of Height Above Nearest Drainage (HAND). Advanced hydrologic terrain analysis workflows have been designed to derive channel hydraulic properties, stage-discharge rating curves, and inundation extents using HAND. After the mechanism being presented, the implementation of this approach across the contiguous United States has been demonstrated using the 10-meter National Elevation Dataset. The integrity of the outputs has been validated through the comparison with best available references at multiple test sites. Considering the increasingly availability of high-resolution topographic data derived from lidar technology, the dissertation further presents how advanced geomorphic feature extraction tools are integrated into the proposed approach to overcome the challenges associated with the enrichment of terrain details. At last, this dissertation presents how banklines, an essential piece of river geometry characteristic as the boundary differentiates channel zone from floodplain, is detected with enhanced geomorphic feature extraction tools for improving large-scale hydrologic simulation and inundation mapping accuracy.Civil, Architectural, and Environmental Engineerin

A Quantitative Graph-Based Approach to Monitoring Ice-Wedge Trough Dynamics in Polygonal Permafrost Landscapes

In response to increasing Arctic temperatures, ice-rich permafrost landscapes are undergoing rapid changes. In permafrost lowlands, polygonal ice wedges are especially prone to degradation. Melting of ice wedges results in deepening troughs and the transition from low-centered to high-centered ice-wedge polygons. This process has important implications for surface hydrology, as the connectivity of such troughs determines the rate of drainage for these lowland landscapes. In this study, we present a comprehensive, modular, and highly automated workflow to extract, to represent, and to analyze remotely sensed ice-wedge polygonal trough networks as a graph (i.e., network structure). With computer vision methods, we efficiently extract the trough locations as well as their geomorphometric information on trough depth and width from high-resolution digital elevation models and link these data within the graph. Further, we present and discuss the benefits of graph analysis algorithms for characterizing the erosional development of such thaw-affected landscapes. Based on our graph analysis, we show how thaw subsidence has progressed between 2009 and 2019 following burning at the Anaktuvuk River fire scar in northern Alaska, USA. We observed a considerable increase in the number of discernible troughs within the study area, while simultaneously the number of disconnected networks decreased from 54 small networks in 2009 to only six considerably larger disconnected networks in 2019. On average, the width of the troughs has increased by 13.86%, while the average depth has slightly decreased by 10.31%. Overall, our new automated approach allows for monitoring ice-wedge dynamics in unprecedented spatial detail, while simultaneously reducing the data to quantifiable geometric measures and spatial relationships.BMBF PermaRiskNational Science FoundationPeer Reviewe

Interaction Templates for Multi-Robot Systems

This work describes a framework for multi-robot problems that require or utilize interactions between robots. Solutions consider interactions on a motion planning level to determine the feasibility and cost of the multi-robot team solution. Modeling these problems with current integrated task and motion planning (TMP) approaches typically requires reasoning about the possible interactions and checking many of the possible robot combinations when searching for a solution. We present a multi-robot planning method called Interaction Templates (ITs) which moves certain types of robot interactions from the task planner to the motion planner. ITs model interactions between a set of robots with a small roadmap. This roadmap is then tiled into the environment and connected to the robots’ individual roadmaps. The resulting combined roadmap allows interactions to be considered by the motion planner. We apply ITs to homogeneous and heterogeneous robot teams under both required and optional cooperation scenarios which previously required a task planning method. We show improved performance over a current TMP planning approach

The Role of Canids in Ritual and Domestic Contexts: New Ancient DNA Insights from Complex Hunter-Gatherer Sites in Prehistoric Central California

This study explores the interrelationship between the genus Canis and hunter–gatherers through a case study of prehistoric Native Americans in the San Francisco Bay-Sacramento Delta area. A distinctive aspect of the region\u27s prehistoric record is the interment of canids, variously classified as coyotes, dogs, and wolves. Since these species are difficult to distinguish based solely on morphology, ancient DNA analysis was employed to distinguish species. The DNA study results, the first on canids from archaeological sites in California, are entirely represented by domesticated dogs (including both interments and disarticulated samples from midden deposits). These results, buttressed by stable isotope analyses, provide new insight into the complex interrelationship between humans and canids in both ritual and prosaic contexts, and reveal a more prominent role for dogs than previously envisioned

A hybrid algorithm for Bayesian network structure learning with application to multi-label learning

We present a novel hybrid algorithm for Bayesian network structure learning, called H2PC. It first reconstructs the skeleton of a Bayesian network and then performs a Bayesian-scoring greedy hill-climbing search to orient the edges. The algorithm is based on divide-and-conquer constraint-based subroutines to learn the local structure around a target variable. We conduct two series of experimental comparisons of H2PC against Max-Min Hill-Climbing (MMHC), which is currently the most powerful state-of-the-art algorithm for Bayesian network structure learning. First, we use eight well-known Bayesian network benchmarks with various data sizes to assess the quality of the learned structure returned by the algorithms. Our extensive experiments show that H2PC outperforms MMHC in terms of goodness of fit to new data and quality of the network structure with respect to the true dependence structure of the data. Second, we investigate H2PC's ability to solve the multi-label learning problem. We provide theoretical results to characterize and identify graphically the so-called minimal label powersets that appear as irreducible factors in the joint distribution under the faithfulness condition. The multi-label learning problem is then decomposed into a series of multi-class classification problems, where each multi-class variable encodes a label powerset. H2PC is shown to compare favorably to MMHC in terms of global classification accuracy over ten multi-label data sets covering different application domains. Overall, our experiments support the conclusions that local structural learning with H2PC in the form of local neighborhood induction is a theoretically well-motivated and empirically effective learning framework that is well suited to multi-label learning. The source code (in R) of H2PC as well as all data sets used for the empirical tests are publicly available.Comment: arXiv admin note: text overlap with arXiv:1101.5184 by other author

Fast extraction of neuron morphologies from large-scale SBFSEM image stacks

Neuron morphology is frequently used to classify cell-types in the mammalian cortex. Apart from the shape of the soma and the axonal projections, morphological classification is largely defined by the dendrites of a neuron and their subcellular compartments, referred to as dendritic spines. The dimensions of a neuron’s dendritic compartment, including its spines, is also a major determinant of the passive and active electrical excitability of dendrites. Furthermore, the dimensions of dendritic branches and spines change during postnatal development and, possibly, following some types of neuronal activity patterns, changes depending on the activity of a neuron. Due to their small size, accurate quantitation of spine number and structure is difficult to achieve (Larkman, J Comp Neurol 306:332, 1991). Here we follow an analysis approach using high-resolution EM techniques. Serial block-face scanning electron microscopy (SBFSEM) enables automated imaging of large specimen volumes at high resolution. The large data sets generated by this technique make manual reconstruction of neuronal structure laborious. Here we present NeuroStruct, a reconstruction environment developed for fast and automated analysis of large SBFSEM data sets containing individual stained neurons using optimized algorithms for CPU and GPU hardware. NeuroStruct is based on 3D operators and integrates image information from image stacks of individual neurons filled with biocytin and stained with osmium tetroxide. The focus of the presented work is the reconstruction of dendritic branches with detailed representation of spines. NeuroStruct delivers both a 3D surface model of the reconstructed structures and a 1D geometrical model corresponding to the skeleton of the reconstructed structures. Both representations are a prerequisite for analysis of morphological characteristics and simulation signalling within a neuron that capture the influence of spines

Disease and De Soto: A Bioarchaeological Approach to the Introduction of Malaria to the Southeast US

It is well known through documentation in historical accounts that numerous diseases were introduced to the Americas during the time of Spanish and French exploration. Diseases such as smallpox, measles and yellow fever have been credited in playing a role in the Spanish conquest of the New World through drastic Native American population decline. Many researchers have studied the biological consequences of European contact, some using direct skeletal analyses to study changes in Native American health and disease. However, one major population disease that has not been part of these discussions is malaria. This is mostly due to the current paradigm in epidemiological history that malaria was first introduced by African slaves during the colonial period slave trades. In addition, the skeletal markers of this disease have not been known until recently. However, recent advances in bioarchaeology have developed a method to identify and diagnose malaria in skeletal remains. This study investigates the possible introduction of malaria to the Central Mississippi Valley (CMV) and Trans-Mississippi South (TMS) regions by the Hernando de Soto expedition. Numerous factors hint at the possibility that the members of the expedition were the first to introduce malaria to the region during their explorations in AD 1541-1542. These factors include endemic malaria in Spain during the age of exploration, the inclusion of numerous African slaves in the crew, the ability of the Plasmodium parasite to lay dormant within a hosts’ liver for an extended period of time, and various ecological characteristics of the protohistoric CMV/TMS environment. To investigate this possibility, the newly developed bioarchaeological method for identifying malaria in the skeletal record was employed on Native American skeletal data from 113 CMV/TMS sites spanning temporally from 8,000 BC to AD 1920. Results of this study confirm the presence and increase of malaria indicators in the region during the protohistoric period, which strongly suggests that members of the de Soto expedition could have been the first to introduce malaria to the region. These results will enhance our understanding of the spread of malaria to the New World, and contribute to studies on European contact with indigenous populations

Hydrologic controls on the natural drainage networks extracted from high-resolution topographic data

Drainage networks are important geomorphologic and hydrologic features which significantly control runoff generation. Drainage networks are composed of unchannelized valleys and channels. At valley heads, flow changes from unconfined sheet flow on the hillslope to confined flow in valley. Localized confined flow dominates in valleys as a result of convergent topography with positive curvature. Channels initiate at some distance down from the valley head, and the transition from unchannelized valley to channel is referred to as the channel head. Channel heads occur at a point where fluvial transport dominates over diffusive transport. From the hydrologic perspective, channels are categorized as perennial, intermittent, and ephemeral streams based on the flow durations. Perennial streams flow for the most of the time during normal years and are maintained by groundwater discharge. Intermittent (i.e. seasonal) streams flow during certain times of the year receiving water from surface sources such as melting snow or from groundwater. Lastly, ephemeral streams flow only in direct response to precipitation without continuous surface flow. In this dissertation, the hydrologic controls on the drainage networks extracted from high resolution Digital Elevation Models (DEMs) based on Light Detection and Ranging (LiDAR) are investigated. A method for automatic extraction of valley and channel networks from high-resolution DEMs is presented. This method utilizes both positive (i.e., convergent topography) and negative (i.e., divergent topography) curvature to delineate the valley network. The valley and ridge skeletons are extracted using the pixels\u27 curvature and the local terrain conditions. The valley network is generated by checking the terrain for the existence of at least one ridge between two intersecting valleys. The transition from unchannelized to channelized sections (i.e., channel head) in each 1st-order valley tributary is identified independently by categorizing the corresponding contours using an unsupervised approach based on K-means clustering. The method does not require a spatially constant channel initiation threshold (e.g., curvature or contributing area). Moreover, instead of a point attribute (e.g., curvature), the proposed clustering method utilizes the shape of contours, which reflects the entire cross-sectional profile including possible banks. The method was applied to three catchments: Indian Creek and Mid Bailey Run in Ohio, and Feather River in California. The accuracy of channel head extraction from the proposed method is comparable to state-of-the-art channel extraction methods. Valleys extracted from DEMs may be wet (flowing) or dry at any given time depending on the hydrologic conditions. The temporal dynamics of flowing streams are vitally important for understanding hydrologic processes including surface water and groundwater interaction and hydrograph recession. However, observations of wet channel networks are limited, especially in headwater catchments. Near infrared LiDAR data provide an opportunity to map wet channel networks owing to the fine spatial resolution and strong absorption of light energy by water surfaces. A systematic method is developed to map wet channel networks by integrating elevation and signal intensity of ground returns. The signal intensity thresholds for identifying wet pixels are extracted from frequency distributions of intensity return within the convergent topography extent using a Gaussian mixture model. Moreover, the concept of edge in digital image processing, defined based on the intensity gradient, is utilized to enhance detection of small wet channels. The developed method was applied to the Lake Tahoe area based on eight LiDAR acquisitions during recession periods in five watersheds. A power-law relationship between streamflow and wetted channel length during recession periods was derived, and the scaling exponent (LαQ^0.38) is within the range of reported values from fieldwork in other regions. Several studies in the past focused on the relationship between drainage density (i.e., drainage length divided by drainage area) and long-term climate and reported a U-shape pattern. In this dissertation, this relationship was re-visited and the effect of drainage area on drainage density was investigated. Long-term climate was quantified by climate aridity indices which is the ratio between long-term potential evaporation and precipitation. 120 study sites across the United States with minimal human disturbance and a wide range of climate aridity index were selected based on the availability of LiDAR data. The drainage networks were delineated from LiDAR-based 1 m DEMs using the proposed curvature-based method. Despite the U-shaped relationship in the literature, our result shows a significant decreasing trend in the drainage density versus climate aridity index in arid regions; whereas no trend is observed in humid watersheds. This observation and its discrepancy with the reported pattern in the literature are justified considering the dynamics of the runoff erosive force and the resistance of vegetation and the climate controls on them. Our findings suggest that natural drainage networks in arid regions are more sensitive to the change in long-term climate conditions compared with drainage networks in humid climate. It was also found that drainage density has a decreasing trend with drainage area in arid regions; however, no trend was observed in humid regions. In a broader sense, the findings influence our understanding of the formation of drainage networks and the response of hydrologic systems to climate change. The formation and growth of river channels and their network evolution are governed by the erosional and depositional processes operating on the landscape due to movement of water. The branching angles, i.e., the angle between two adjoining channels, in drainage networks are important features related to the network topology and contain valuable information about the forming mechanisms of the landscape. Based on channel networks extracted from 1 m Digital Elevation Models of 120 catchments with minimal human impacts across the United States, we showed that the junction angles have two distinct modes with α1 ≈ 49.5° and α2 ≈ 75.0°. The observed angles are physically explained as the optimal angles that result in minimum energy dissipation and are linked to the exponent characterizing slope-area curve. Our findings suggest that the flow regimes, debris-flow dominated or fluvial, have distinct characteristic angles which are functions of the scaling exponent of the slope-area curve. These findings enable us to understand the geomorphologic signature of hydrologic processes on drainage networks and develop more refined landscape evolution models

From Images to Hydrologic Networks - Understanding the Arctic Landscape with Graphs

Remote sensing-based Earth Observation plays an important role in assessing environmental changes throughout our planet. As an image-heavy domain, the evaluation of the data strongly focuses on statistical and pixel-based spatial analysis methods. However, considering the complexity of our Earth system, there are some environmental structures and dependencies that are not possible to accurately describe with these traditional image analysis approaches. One example for such a limitation is the representation of (spatial) networks and their characteristics. In this study, we thus propose a computer vision approach that enables the representation of semantic information gained from images as graphs. As an example, we investigate digital terrain models of Arctic permafrost landscapes with its very characteristic polygonal patterned ground. These regular patterns, which are clearly visible in high-resolution image and elevation data, are formed by subsurface ice bodies that are very vulnerable to rising temperatures in a warming Arctic. Observing these networks’ topologies and metrics in space and time with graph analysis thus allows insights into the landscape’s complex geomorphology, hydrology, and ecology and therefore helps to quantify how they interact with climate change. We show that results extracted with this analytical and highly automated approach are in line with those gathered from other manual studies or from manual validation. Thus, with this approach, we introduce a method that, for the first time, enables upscaling of such terrain and network analysis to potentially pan-Arctic scales where collecting in-situ field data is strongly limited

Understanding Hydroclimatic Controls on Stream Network Dynamics using LiDAR Data

This dissertation investigates the hydroclimatic controls on drainage network dynamics and characterizes the variation of drainage density in various climate regions. The methods were developed to extract the valley and wet channel networks based on Light Detection and Ranging (LiDAR) data including the elevation and intensity of laser returns. The study watersheds were selected based on the availability of streamflow observations and LiDAR data. Climate aridity index was used as a quantitative indicator for climate. The climate controls on drainage density were re-visited using watersheds with minimal anthropogenic interferences and compared with the U-shape relationship reported in the previous studies. A curvature-based method was developed to extract a valley network from 1-m LiDAR-based Digital Elevation Models. The relationship between drainage density and climate aridity index showed a monotonic increasing trend and the discrepancy was explained by human interventions and underestimated drainage density due to the coarse spatial resolution (30-meter) of the topographic maps used in previous research. Observations of wet channel networks are limited, especially in headwater catchments in comparison with the importance of stream network expansion and contraction. A systematic method was developed to extract wet channel networks based on the signal intensities of LiDAR ground returns, which are lower on water surfaces than on dry surfaces. The frequency distributions of intensities associated with wet surface and dry surface returns were constructed. With the aid of LiDAR-based ground elevations, signal intensity thresholds were identified for extracting wet channels. The developed method was applied to Lake Tahoe area during recession periods in five watersheds. A power-law relationship between streamflow and wet channel length was obtained and the scaling exponent was consistent with the reported findings from field work in other regions. Perennial streams flow for the most of the time during normal years and are usually defined based on a flow duration threshold. The streamflow characteristics of perennial streams in this research were assessed using the relationship between streamflow exceedance probability and wet channel ratio based on wet channel networks extracted from LiDAR data. Non-dimensional analysis based on the relationship between streamflow exceedance probability and wet channel ratio showed that results were consistent with previous research about perennial stream definition, and provided the possibility to use wet channel ratio to define perennial streams. Wetlands are important natural resources and need to be monitored regularly in order to understand their inundation dynamics, function and health. Wetland mapping is a key part of monitoring programs. A framework for detecting wetland was developed based on LiDAR elevation and intensity information. After masking out densely vegetated areas, wet areas were identified based on signal intensity of ground returns for barrier islands in East-Central Florida. The intensity threshold of wet surface was identified by decomposing composite probability distribution functions using a Gamma mixture model and the Expectation-Maximization algorithm. This method showed good potential for wetland mapping. The methodology developed in this dissertation demonstrated that incorporating LiDAR data into the drainage networks, stream network dynamics and wetlands results in enhanced understanding of hydroclimatic controls on stream network dynamics. LiDAR data provide a rich information source including elevation and intensity, and are of great benefit to hydrologic research community