Looking over a tidal wetland, the tidal network characterised by its intricate system of bifurcating, blind-ended tidal courses clearly stands out from the overall landscape. This tidal landform exerts a fundamental control on the morphology and ecology within the tidal environment. With today’s recognition of the ecological, economical and societal values provided by tidal wetlands, which has been notably reflected in the development of restoration management strategies across Europe and USA, there is a need to fully understand the nature and development of tidal networks as well as their relationships with associated landforms and biotic components (e.g. vegetation), to eventually guarantee the success of current and future restoration practices. Accordingly, this research aims to bring further insights into the bio-physical controls on the geomorphology of tidal networks. To this end, a combination of remote sensing, modelling and field activities was employed. A geo-spatial analysis was performed at Queen Mary, University of London (UK), to address the variability of tidal network patterns. A series of network scale morphometric variables was extracted using airborne LiDAR data among selected tidal networks across the UK depicting different planview morphologies, and supplemented with the collection of corresponding marsh scale environmental variables from published sources. Multivariate statistics were then performed to characterise the variability of tidal network patterns and identify the inherent environmental controls. The analysis has revealed that every network type can be characterised based upon measures of network size and complexity, with each network pattern depicting proper morphometric aspects. Particularly, the stream Strahler order and the median depth of the network main channel have the highest discriminating weight on the patterns investigated. High correlation between the latter variable and network main channel width has revealed that linear, linear-dendritic and dendritic networks followed a transitional gradient in their aspect ratio approximated by a power law and thus are seen to depict similar erosional processes. To the contrary, meandering networks clearly depart from this relationship, and show particular segregation in their aspect ratios with respect to dendritic networks. Globally, differentiation on network morphometric properties has been linked to environmental conditions specific to the marsh physiographic setting within which a tidal network develops. Conceptually, tidal networks seem to adapt to marsh environmental conditions by adopting suitable morphologies to drain their tidal basin effectively.An eco-geomorphic modelling framework was developed at University of Trento (Italy), to address tidal network morphological development. In line with current theories as well as modelling advances and challenges in the field of tidal network ontogeny, emphasis was thus placed on the investigation of tidal channel formation and evolution in progressive marsh accretional context. Under these environmental conditions, tidal network development can be ascribed to the combination of two channel-forming processes: channel initiation results from bottom incisions in regions where topographic depressions occur; channel elaboration results from differential deposition, contributing to the deepening of the tidal channels relative to the adjacent marsh platform. Further evolutionary stages including channel reduction proceed from the horizontal progradation of the marsh platform which may lead eventually to channel infilling. Moreover, both qualitative and quantitative results allude to an acceleration of the morphological development of the synthetic tidal networks with increasing sediment supply. These different observations thus emphasise the prevalence of depositional processes in shaping tidal channels. In a second stage, the investigation was extended to the role of the initial tidal flat morphology as an inherent control on tidal network development, by considering different scenarios of topographic perturbations, which has revealed its legacy on tidal network morphological features. Modelling experiments have also acknowledged salt marsh macrophytes as a potential control on network evolution depending on their biomass distribution within the tidal frame. However, tidal channel morphodynamcis appears to be sensitive to the way biomass growth is mathematically parameterised in the model. In view of the current challenges in transcribing mathematically such a dynamic process and the relevance of bio-physical interactions in driving salt marsh and tidal network evolution, a field survey was conducted in a temperate salt marsh in the Netherlands, as part of the mobility to UNESCO-IHE (Netherlands) in partnership with University of Antwerp (Belgium), to assess vegetation distribution and productivity in the tidal frame. Particularly, emphasis was placed on extending investigations on the possible presence of relationships involving vegetation properties in different climatic and ecological conditions from those characterising these previously documented relationships. Regression analysis has revealed that biomass growth can be expressed as a linear function of marsh relative elevation, providing therefore direct empirical validation for corresponding assumptions reported in the literature and used in the present modelling framework; surprisingly, that increase did not correlate with an increase in species richness and diversity. Analysis of likely associations between vegetation morphometrics and total standing biomass yielded only a single linear relationship linking the latter variable to stem height. In truth, these observations may bear reconsiderations on the global validity of the assumptions used in the formulation of some eco-geomorphic processes which are applied in the study and prediction of wetland resiliency facing climate change.

Braided rivers are complex, fascinating fluvial pattern, which represent the natural state of many gravel and sand bed rivers. Both natural and human causes may force a change in the boundary conditions, and consequently impact the river functionality. Detailed knowledge on the consequent morphological response is important in order to define management strategies which combine different needs, from protection of human activities and infrastructures to preservation of the ecological and biological richness. During the last decades, research has made significant advance to the description of this complex system, thanks to flume investigations, development of new survey techniques and, to a lesser extent, numerical and analytical solutions of mathematical models (e.g. Ashmore_2013). Despite that, many relevant questions, concerning the braided morphodynamics at different spatial and temporal scales (from the unit process scale, to the reach scale, and eventually to the catchment scale) remain unanswered. For example, quantitative analysis of the morphological response to varying external controls still requires investigation and needs the definition of suitable, stage-independent braiding indicators. In addition, the morphodynamics of the fundamental processes, such as bifurcations, also needs further analysis of the driving mechanisms. General aim of the present study is to develop new methods to exploit, in an integrated way, the potential of the new possibilities offered by advanced monitoring techniques, laboratory models, numerical schemes and analytical solutions. The final goal is to fill some gaps in the present knowledge, which could ultimately provide scientific support to river management policies. We adopted analytical perturbation approaches to solve the two-dimensional shallow water model; we performed laboratory simulations on a large, mobile-bed flume; we analysed existing topographic measurements from LiDAR and Terrestrial Laser scanning Devices; and we simulated numerically the river hydrodynamics. Within each of the six, independent, research chapters, we interconnected results from the different approaches and methodologies, in order to take advantage of their potential. Summarising, the more relevant and novel outcomes of the present work can be listed as follows: 1) We explored the morphological changes during a sequence of flood events in a natural braided river (Rees River, NZ)and we proposed a morphological method to assess the sediment transport rate. In particular we propose a semi-automatic method for estimating the particles path-length (Ashmore and Church, 1998) on the basis of the size of the deposition patches, which can be identified on the basis of DEM of differences. Comparison with results of numerical simulation confirmed that such an approach can reproduce the response of the bedload rate to floods of different duration and magnitude. 2) We developed a new indicator of the reach-scale morphology and, on the basis of existing laboratory experiments, we explored its dependence, under regime conditions, to the controlling factors: slope, discharge, confinement width, grain size. In spite of its synthetic nature, this simple indicator embeds the information needed to estimate the variability of the Shield stress throughout the braided network, and consequently enables to assess the transport-rate and its variation with the driving discharge. 3) We investigated, through flume experiments, the effect of the flow unsteadiness on the sediment transport in a braided river. This is possible only by following a statistical approach based on multiple repetitions of the same flow hydrograph. Results revealed that for confined network an hysteresis of the bedload response occurs, which leads to higher sediment transport during increasing flow, whereas relatively unconfined networks always show quasi-equilibrium transport rates. 4) A second set of laboratory experiments provided information on the morphodynamics of a braided network subject to variations of the sediment supply. We proposed a simple diffusive model to quantify the evolution of the one-dimensional bed elevation profile. Such simple approach, albeit having a limited range of practical applications, represents the first attempt to quantify this process and enables to study the relevant temporal and spatial scales of the phenomenon. 5) We solved analytically the two-dimensional morphodynamic model for a gravel-bed river bifurcation. This furnishes a rigorous proof to the idea proposed by Bertoldi and Tubino (2007) to interpret the morphological response of bifurcation in light of the theory of the morphodynamic influence. The analytical approach enables to investigate the fundamental mechanics which leads to balance, and unbalance, configurations and, from a more practical point of view, allows for a better prediction of the instability point than the existing 1D models (e.g. Bolla Pittaluga et al., 2003).

Interactions between fluvial processes and vegetation along the natural channel margins have been shown to be fundamental in determining meandering rivers development. By colonizing exposed sediments, riparian trees increase erosion resistance and stabilize fluvial sediment transport through their root systems, while during a flood event the above-ground biomass interacts with the water flow inducing sediment deposition and altering scour patterns. In turn river dynamics and hydrology influence vegetative biomass growth, affecting the spatial distribution of vegetation. These bio-morphological dynamics have been observed to direct control accretion and degradation rates of the meander bend. In particular, vegetation encroachments within the point bar (i.e. colonizing species and strand wood), initiate pioneeristic landforms that, when evolving, determine the lateral shifting of the margin that separates active channel from river floodplain and thus inner bank aggradation (bar push). This diminishes the portion of the morphologically active channel cross-section, influencing the erosion of the cutting bank and promoting channel widen- ing (bank pull ). As a result of the cyclical occurrence of these erosional and depositional processes, meandering rivers floodplain show a typical ridge and swale pattern characterized by the presence of complex morphological structures, namely, benches, scrolls and chutes within the new-created floodplain. Moreover, difference in migration rate between the two banks have been observed to induce local temporal variations in channel width that affect river channel morphodynamics and its overall planform through their influence on the local flow field and channel bed morphology. Despite enormous advances in field and laboratory techniques and modelling development of the last decades, little is known about the relation between floodplain patterns and their controlling bio-morphological interactions that determine the bank accretion process. This knowledge gap has so far limited the development of physically-based models for the evolution of meandering rivers able to describe the lateral migration of banklines separately. Most existing meander migration models are indeed based on the hypothesis of constant channel width. Starting from this knowledge gap, the present doctoral research has aimed to provide more insight in the mutual interactions among flow, sediment transport and riparian vegetation dynamics in advancing banks of meandering rivers. In order to achieve its aims, the research has been designed as an integration of remote sensing and in-situ field observations with a mathematical modelling approach to i) provide a quantitative description of vegetation and floodplain channel topography patterns in advancing meanders bend and to ii) explore the key control factors and their role in generating the observed patterns. The structure of the present PhD work is based on four main elements. First, two types of airborne historical data (air photographs and Lidar survey) have been investigated, in order to quantify the effects of spatial-temporal evolution of vegetation pattern on meander morphology and to provide evidence for the influence of vegetation within the topography of the present floodplain. Such remote sensing analysis has highlighted a strong correspondence between riparian canopy structure and geomorphological patterns within the floodplain area: this has clearly shown the need to interpret the final river morphology as the result of a two-way interaction between riparian vegetation dynamics and river processes. Second, field measurments have been conducted on a dynamic meander bend of the lower reach of the Tagliamento River, Italy, with the initial aim of checking the outcomes of the remote sensing analysis through ground data. The outcomes of the field measurements have further supported the results, providing ground evidence on the relations between vegetation and topographic patterns within the transition zone that is intermediate between the active channel bed and the vegetated portion of the accreting floodplain. The influence of vegetation on inner bank morphology has also been interpreted in the light of the expected time scales of inundation and geomorphic dynamics that characterize the advancing process of the inner bank. The combined analysis of both remotely sensed data and field measurements associated with the historical hydrological dataset have allowed to quantitatively characterize the biophysical characteristics of the buffer zone, close to the river edge, where the accretion processes take place. The third research element has foreseen the development of a biophysically-based, simplified bio-morphodynamic model for the lateral migration of a meander bend that took advantage of the empirical knowledge gained in the analysis of field data. The model links a minimalist approach that includes biophysically-based relationships to describe the interaction between riparian vegetation and river hydromorphodynamic processes, and employs a non linear mathematical model to describe the morphodynamics of meander channel bed. Model application has allowed to reproduce the spatial oscillations of vegetation biomass density and ground morphology observed in the previous analyses. Overall, the model allows to understand the role of the main controlling factors for the ground and vegetation patterns that characterize the advancing river bank and to investigate the temporal dynamics of the morphologically active channel width, providing insights into the bank pull and bar push phenomena. The fourth and concluding element of the present PhD research is a analytical investigation of the fundamental role of unsteadiness on the morphodynamic response of the river channel. Results obtained in the previous elements have clearly showed the tendency of a meander bend to develop temporal oscillations of the active channel width during its evolution, but no predictive analytical tool was previously available to investigate the channel bed response to such non-stationary planform dynamics. A non linear model has therefore been proposed to investigate the effect of active channel width unsteadiness on channel bed morphology. The basic case of free bar instability in a straight channel has been used in this first investigation, which has shown the tendency of channel widening to increase river bed instability compared to the steady case, in qualitative agreement with experimental observations. Overall, the research conducted within the present Doctoral Thesis represents a step forward in understanding the bio-morphodynamics of meandering rivers that can help the development of a complete bio-morphodynamic model for meandering rivers evolution, able to provide support for sustainable river management.

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The Paintings of Jeff Koons: 1994 - 2008" is an in depth look at the painting of an artist who is still primarily known for his sculptural work of the 1980's. This thesis examines Koons' paintings in light of his previous work and looks at his studio practices, sources, connection to Photorealism, Surrealism, and Duchamp, etc. The thesis contends that a greater understanding and appreciation for Koons' paintings is necessary in order to grasp the importance of his entire oeuvre. / Art History

Heterogeneous geospatial big data, from multi-modal Earth Observation (EO) data to geo-social media data, has become more and more accessible in recent years. This provides a potential data source for automatically extracting and mapping key geographical characteristics, hence mitigating the global mapping problem using current data mining methods. These automated geographic feature mapping techniques, especially for man-made infrastructure, are crucial to a lot of our socio-economic existence. Machine learning techniques, among many other data mining methodologies, have demonstrated better performance across a wide range of academic domains, most notably natural language processing and computer vision. In recent times, there has been a growing interest in research on ML-based Geospatial Artificial Intelligence (GeoAI), particularly in its ability to support autonomous mapping with heterogeneous geographical data. Though the potential is high and obvious, it remains a major challenge to handle inherent heterogeneity and empower data synergy when building robust and scalable GeoAI models for large-scale automated mapping purposes. For geospatial analysis, citizen science initiative is seen to be the most effective. This is a result of the fast development of Web 2.0 and crowdsourcing/Volunteered Geographic Information (VGI) technologies. These technologies enable even regular users or volunteer mappers to develop, gather, and distribute geospatial data using a variety of digital devices (such as desktop computers, mobile tablets, and smartphones). The technological obstacles to digital mapping have been significantly reduced by ongoing crowdsourcing and VGI efforts. In the real world, though, problems with global mapping have persisted for a considerable amount of time even in higher-income nations. Intelligent automated mapping techniques for geospatial analysis are desperately needed in this situation since they may effectively and efficiently close significant data gaps across nations. The research effort reported in this dissertation explores the possibilities of using citizen science or VGI to conduct geospatial analysis of different man-made infrastructures using ML from diverse geospatial data sources (e.g., multi-modal EO data, OSM, and GIS data). Three main research questions (RQs), derived from data-driven, method-driven, and application-driven research perspectives, are established to better address the issue of geospatial analysis with remote sensing and citizen science. The thesis especially goes in this direction by i) investigating the data-driven issue that combines ML for segmentation tasks; ii) creating strategies to deal with VGI data noises; and iii) using the created strategies in various mapping tasks. This creates even more intriguing possibilities for related works in the future.

Natural disasters exert a profound impact on the world population. In 2012, natural disasters affected 106 million people, forcing over 31.7 million people to leave their homes. Climate change has intensified natural disasters, resulting in more catastrophic events and making extreme weather more difficult to predict. Understanding and predicting human movements plays a critical role in disaster evacuation, response and relief. Researchers have developed different methodologies and applied several models to study human mobility patterns, including random walks, Lévy flight, and Brownian walks. However, the extent to which these models may apply to perturbed human mobility patterns during disasters and the associated implications for improving disaster evacuation, response and relief efforts is lacking. My PhD research aims to address the limitation in human mobility research and gain a ground truth understanding of human mobility patterns under the influence of natural disasters. The research contains three interdependent projects. In the first project, I developed a novel data collecting system. The system can be used to collect large scale data of human mobility from large online social networking platforms. By analyzing both the general characteristics of the collected data and conducting a case study in NYC, I confirmed that the data collecting system is a viable venue to collect empirical data for human mobility research. My second project examined human mobility patterns in NYC under the influence of Hurricane Sandy. Using the data collecting system developed in the first project, I collected 12 days of human mobility data from NYC. The data set contains movements during and several days after the strike of Hurricane Sandy. The results showed that human mobility was strongly perturbed by Hurricane Sandy, but meanwhile inherent resilience was observed in human movements. In the third project, I extended my research to fifteen additional natural disasters from five categories. Using over 3.5 million data entries of human movement, I found that while human mobility still followed the Lévy flight model during these disaster events, extremely powerful natural disasters could break the correlation between human mobility in steady states and perturbation states and thus destroy the inherent resilience in human mobility. The overall findings have significant implications in improving understanding and predicting human mobility under the influence of natural disasters and extreme events. / Ph. D.

Yes / Social media is a platform to express one’s view in real time. This real time nature of social media makes it an attractive tool for disaster management, as both victims and officials can put their problems and solutions at the same place in real time. We investigate the Twitter post in a flood related disaster and propose an algorithm to identify victims asking for help. The developed system takes tweets as inputs and categorizes them into high or low priority tweets. User location of high priority tweets with no location information is predicted based on historical locations of the users using the Markov model. The system is working well, with its classification accuracy of 81%, and location prediction accuracy of 87%. The present system can be extended for use in other natural disaster situations, such as earthquake, tsunami, etc., as well as man-made disasters such as riots, terrorist attacks etc. The present system is first of its kind, aimed at helping victims during disasters based on their tweets.

During the past decades, the systematic investigation of the morphodynamics of meandering rivers mostly involved the theoretical-analytical methodology. The development of analytical models enabled the definition of equilibrium conditions, stability and evolution of river meanders and to investigate the interaction between planform and bedform processes and mechanisms. In recent years the new branch of remote sensing applied to river morphodynamics has been constantly developing simultaneously to the rapid increase of computational and satellite resources. The remote sensing analysis is nowadays employed in a wide range fields in geophysics; for this reason, the past years have seen the prolific development of numerous algorithms for remote sensing analysis. However, remote sensing of meandering river morphodynamics has not been consistently integrated with morphodynamic modelling so far. There is a lack of sophisticated algorithms for the extraction of extensive morphodynamic information from the available remotely sensed data; this gap prevented researchers from seeking systematic validation of analytical models to define their range of applicability, and to exploit their potential for improved insight on observations in real world meandering rivers. The evolutionary dynamics of the channel width, at local and bend scale, as well as the dynamics of bars in meandering rivers represent two major unsettled issues in our present understanding of river meandering dynamics. In this thesis I first provide a systematic methodology for the automated extraction of meandering river morphodynamic information from multitemporal, multispectral remotely sensed data, coded in the PyRIS software. Moreover, I develop an analytical model to investigate the long-term planform evolution of periodic sequences of meander bends incorporating spatio-temporal variations of channel curvature, width and slope. A first model component predicts the temporal evolution of the channel width and slope based on a novel treatment of the sediment continuity at the reach scale. A second model component is a fully analytical, evolutionary model of periodic meanders with spatially and temporally oscillating width accounting for nonlinear feedbacks in flow and sediment transport by means of a two-parameters perturbation approach. Application of the PyRIS software to several long reaches of free-flowing meandering rivers allows me to develop a consistent set of observations on the temporal and spatial evolution of channel width and curvature with unprecedented level of detail. Furthermore, model outcomes indicate that meander-averaged width and slope invariably decrease during meander development, and that the temporal adjustment of the hydraulic geometry is controlled by the ratio between the evolutionary timescales of planform and riverbed, quantified from the analyzed meandering rivers dataset. The nonlinear perturbation model indicates that width and curvature co-evolve according to a hysteretic behavior in time and predicts that the meander belt width dramatically decreases when the meander resonance threshold is crossed. The modelling approach predicts wider-at-bend meanders when the bank pull is dominant with respect to bar push, which in turn promotes meander bends that are wider at inflections. Analytical modeling and remote sensing analysis are mostly integrated through a statistical approach; bend-scale evolutionary analysis of planform descriptors such as channel width, width oscillations and curvature in large pristine meandering rivers exhibit good agreement with the outcomes of the proposed analytical models. Finally, the integration between analytical modeling and remote sensing analysis allows me to identify the key processes controlling the interaction between migrating sediment bars and planform-driven steady point bars. The conditions for the formation of migrating bars in meandering rivers are mostly related to the production of sediment supply by the basin, contrarily to the widespread idea that meandering rivers exhibiting migrating bars typically display lower values of the channel curvature.

As part of our work on the NSF funded Integrated Digital Event Archiving and Library (IDEAL) project and the Global Event and Trend Archive Research (GETAR) project, we collected over 1.4 billion tweets using over 1,000 keywords, key phrases, mentions, or hashtags, starting from 2009. Since many tweets talk about events (with useful location information), such as natural disasters, emergencies, and accidents, it is important to geo-locate those tweets whenever possible. Due to possible location ambiguity, finding a tweet's location often is challenging. Many distinct places have the same geoname, e.g., "Greenville" matches 50 different locations in the U.S.A. Frequently, in tweets, explicit location information, like geonames mentioned, is insufficient, because tweets are often brief and incomplete. They have a small fraction of the full location information of an event due to the 140 character limitation. Location indicative words (LIWs) may include latent location information, for example, "Water main break near White House" does not have any geonames but it is related to a location "1600 Pennsylvania Ave NW, Washington, DC 20500 USA" indicated by the key phrase 'White House'. To disambiguate tweet locations, we first extracted geospatial named entities (geonames) and predicted implicit state (e.g., Virginia or California) information from entities using machine learning algorithms including Support Vector Machine (SVM), Naive Bayes (NB), and Random Forest (RF). Implicit state information helps reduce ambiguity. We also studied how location information of events is expressed in tweets and how latent location indicative information can help to geo-locate tweets. We then used a machine learning (ML) approach to predict the implicit state using geonames and LIWs. We conducted experiments with tweets (e.g., about potholes), and found significant improvement in disambiguating tweet locations using a ML algorithm along with the Stanford NER. Adding state information predicted by our classifiers increased the possibility to find the state-level geo-location unambiguously by up to 80%. We also studied over 6 million tweets (3 mid-size and 2 big-size collections about water main breaks, sinkholes, potholes, car crashes, and car accidents), covering 17 months. We found that up to 91.1% of tweets have at least one type of location information (geo-coordinates or geonames), or LIWs. We also demonstrated that in most cases adding LIWs helps geo-locate tweets with less ambiguity using a geo-coding API. Finally, we conducted additional experiments with the five different tweet collections, and found significant improvement in disambiguating tweet locations using a ML approach with geonames and all LIWs that are present in tweet texts as features. / Ph. D. / As part of our work on the projects “Integrated Digital Event Archiving and Library (IDEAL)” and “Global Event and Trend Archive Research (GETAR),” funded by NSF, we collected over 1.4 billion tweets using over 1,000 keywords, key phrases, mentions, or hashtags, starting from 2009. Since many tweets talk about events (with useful location information), such as natural disasters, emergencies, and accidents, it is important to geolocate those tweets whenever possible. Due to possible location ambiguity, finding a tweet’s location often is challenging. Many distinct places have the same geoname, e.g., “Greenville” matches 50 different locations in the U.S.A. Frequently, in tweets, explicit location information, like geonames mentioned, is insufficient, because tweets are often brief and incomplete. They have a small fraction of the full location information of an event due to the 140 character limitation. Location indicative words (LIWs) may include latent location information, for example, “Water main break near White House” does not have any geonames but it is related to a location “1600 Pennsylvania Ave NW, Washington, DC 20500 USA” indicated by the key phrase ‘White House’. To disambiguate tweet locations, we first extracted geonames, and then predicted implicit state (e.g., Virginia or California) information from entities using machine learning (ML) algorithms (wherein computers learn from examples what state is appropriate). Implicit state information helps reduce ambiguity. We also studied how location information of events is expressed in tweets and how latent location indicative information can help to geo-locate tweets. We then used a ML approach to predict the implicit state using geonames and LIWs. We conducted experiments with tweets (e.g., about potholes), and found significant improvement in disambiguating tweet locations using a ML algorithm along with the Stanford Named Entity Recognizer. Adding state information predicted by our classifiers increased the ability to find the state-level geo-location unambiguously by up to 80%. We also studied over 6 million tweets (in three mid-size and two big collections, about water main breaks, sinkholes, potholes, car crashes, and car accidents), covering 17 months. We found that up to 91.1% of tweets have at least one type of location information (geocoordinates or geonames), or LIWs. We also demonstrated that in most cases adding LIWs helps geo-locate tweets with less ambiguity using a geo-coding Web application (that converts addresses into geographic coordinates). Finally, we conducted additional experiments with the five different tweet collections, and found significant improvement in disambiguating tweet locations using a ML approach wherein the features considered are the geonames and all LIWs that are present in the tweet texts.