Dynamics of Forest Structure under Different Silvicultural Regimes in the Acadian Forest

Research plots in many long-term studies of forest ecosystems often cannot be used for spatial modeling because of their small scale and nested inventory design. This has been unfortunate as these plots represent some of the best records of structural development as affected by forest management. I developed methodologies to reconstruct both tree height growth and spatial pattern in these types of plots from historical inventory records and stem-mapped data, and then retrospectively investigated 3-dimensional structural development as affected by five silvicultural and harvesting treatments (unmanaged natural area, commercial clearcut, fixed-diameter limit, 5-year selection, and 3-stage shelterwood— with and without precommercial thinning) in a long-term, USDA Forest Service study in Bradley, ME. In order to capture site variation and account for the hierarchical inventory design, mixed-effects, nonlinear heightdiameter models were developed for the nine most common tree species in 50 stemmapped plots: Abies balsamea (L.) Mill., Acer rubrum L., Betula papyrifera Marsh., B. populifolia Marsh., Picea rubens Sarg., P. mariana (Mill.) B.S.P., Pinus strobus L., Populus tremuloides Michx., and Tsuga canadensis (L.) Carr. Height-diameter models for the remaining species were fit with generalized nonlinear least squares. A morphing algorithmn was developed and then tested on both simulated and actual point patterns, to scale the spatial pattern from nested, sapling subplots (0.020 ha) to the scale of the larger tree plots (0.081 ha). Differences in spatial pattern, species mingling, height differentiation, and relative stand complexity index (rSCI) were compared among treatments. Regeneration events, whether induced through natural stand breakup or by harvesting, increased aggregation in spatial pattern and reduced species mingling. This pattern was heightened when treatments shifted species composition more towards hardwood species. Variation in height differentiation and rSCI was generally highest in the natural areas and 5-year selection compartments, intermediate in commercial clearcut and fixed diameter-limit compartments, and lowest in 3-stage shelterwood compartments. Divergence in spatial structure between the two natural areas reflects natural stand development within this forest and is an appropriate benchmark for management. The reconstruction model developed here can be applied to other long-term studies where the lengthy temporal scale can substitute for small spatial scale

Performance evaluation of future wireless networks: node cooperation and aerial networks

Perhaps future historians will only refer to this era as the \emph{information age}, and will recognize it as a paramount milestone in mankind progress. One of the main pillars of this age is the ability to transmit and communicate information effectively and reliably, where wireless radio technology became one of the most vital enablers for such communication. A growth in radio communication demand is notably accelerating in a never-resting pace, pausing a great challenge not only on service providers but also on researches and innovators to explore out-of-the-box technologies. These challenges are mainly related to providing faster data communication over seamless, reliable and cost efficient wireless network, given the limited availability of physical radio resources, and taking into consideration the environmental impact caused by the increasing energy consumption. Traditional wireless communication is usually deployed in a cellular manner, where fixed base stations coordinate radio resources and play the role of an intermediate data handler. The concept of cellular networks and hotspots is widely adopted as the current stable scheme of wireless communication. However in many situations this fixed infrastructure could be impaired with severe damages caused by natural disasters, or could suffer congestions and traffic blockage. In addition to the fact that in the current networks any mobile-to-mobile data sessions should pass through the serving base station that might cause unnecessary energy consumption. In order to enhance the performance and reliability of future wireless networks and to reduce its environmental footprint, we explore two complementary concepts: the first is node cooperation and the second is aerial networks. With the ability of wireless nodes to cooperate lays two main possible opportunities; one is the ability of the direct delivery of information between the communicating nodes without relaying traffic through the serving base station, thus reducing energy consumption and alleviating traffic congestion. A second opportunity would be that one of the nodes helps a farther one by relaying its traffic towards the base station, thus extending network coverage and reliability. Both schemes can introduce significant energy saving and can enhance the overall availability of wireless networks in case of natural disasters. In addition to node cooperation, a complementary technology to explore is the \emph{aerial networks} where base stations are airborne on aerial platforms such as airships, UAVs or blimps. Aerial networks can provide a rapidly deployable coverage for remote areas or regions afflicted by natural disasters or even to patch surge traffic demand in public events. Where node cooperation can be implemented to complement both regular terrestrial coverage and to complement aerial networks. In this research, we explore these two complementary technologies, from both an experimental approach and from an analytic approach. From the experimental perspective we shed the light on the radio channel properties that is hosting terrestrial node cooperation and air-to-ground communication, namely we utilize both simulation results and practical measurements to formulate radio propagation models for device-to-device communication and for air-to-ground links. Furthermore we investigate radio spectrum availability for node cooperation in different urban environment, by conductive extensive mobile measurement survey. Within the experimental approach, we also investigate a novel concept of temporary cognitive femtocell network as an applied solution for public safety communication networks during the aftermath of a natural disaster. While from the analytical perspective, we utilize mathematical tools from stochastic geometry to formulate novel analytical methodologies, explaining some of the most important theoretical boundaries of the achievable enhancements in network performance promised by node cooperation. We start by determining the estimated coverage and rate received by mobile users from convectional cellular networks and from aerial platforms. After that we optimize this coverage and rate ensuring that relay nodes and users can fully exploit their coverage efficiently. We continue by analytically quantifying the cellular network performance during massive infrastructure failure, where some nodes play the role of low-power relays forming multi-hop communication links to assist farther nodes outside the reach of the healthy network coverage. In addition, we lay a mathematical framework for estimating the energy saving of a mediating relay assisting a pair of wireless devices, where we derive closed-form expressions for describing the geometrical zone where relaying is energy efficient. Furthermore, we introduce a novel analytic approach in analyzing the energy consumption of aerial-backhauled wireless nodes on ground fields through the assistance of an aerial base station, the novel mathematical framework is based on Mat\'{e}rn hard-core point process. Then we shed the light on the points interacting of these point processes quantifying their main properties. Throughout this thesis we relay on verifying the analytic results and formulas against computer simulations using Monte-Carlo analysis. We also present practical numerical examples to reflect the usefulness of the presented methodologies and results in real life scenarios. Most of the work presented in this dissertation was published in-part or as a whole in highly ranked peer-reviewed journals, conference proceedings, book chapters, or otherwise currently undergoing a review process. These publications are highlighted and identified in the course of this thesis. Finally, we wish the reader to enjoy exploring the journey of this thesis, and hope it will add more understanding to the promising new technologies of aerial networks and node cooperation

Large-scale Mobile Traffic Analysis: a Survey

International audienceThis article surveys the literature on analyses of mobile traffic collected by operators within their network infrastructure. This is a recently emerged research field, and, apart from a few outliers, relevant works cover the period from 2005 to date, with a sensible densification over the last three years. We provide a thorough review of the multidisciplinary activities that rely on mobile traffic datasets, identifying major categories and sub-categories in the literature, so as to outline a hierarchical classification of research lines. When detailing the works pertaining to each class, we balance a comprehensive view of state-of-the-art results with punctual focuses on the methodological aspects. Our approach provides a complete introductory guide to the research based on mobile traffic analysis. It allows summarizing the main findings of the current state-of-the-art, as well as pinpointing important open research directions

Disruption analytics in urban metro systems with large-scale automated data

Urban metro systems are frequently affected by disruptions such as infrastructure malfunctions, rolling stock breakdowns and accidents. Such disruptions give rise to delays, congestion and inconvenience for public transport users, which in turn, lead to a wider range of negative impacts on the social economy and wellbeing. This PhD thesis aims to improve our understanding of disruption impacts and improve the ability of metro operators to detect and manage disruptions by using large-scale automated data. The crucial precondition of any disruption analytics is to have accurate information about the location, occurrence time, duration and propagation of disruptions. In pursuit of this goal, the thesis develops statistical models to detect disruptions via deviations in trains’ headways relative to their regular services. Our method is a unique contribution in the sense that it is based on automated vehicle location data (data-driven) and the probabilistic framework is effective to detect any type of service interruptions, including minor delays that last just a few minutes. As an important research outcome, the thesis delivers novel analyses of the propagation progress of disruptions along metro lines, thus enabling us to distinguish primary and secondary disruptions as well as recovery interventions performed by operators. The other part of the thesis provides new insights for quantifying disruption impacts and measuring metro vulnerability. One of our key messages is that in metro systems there are factors influencing both the occurrence of disruptions and their outcomes. With such confounding factors, we show that causal inference is a powerful tool to estimate unbiased impacts on passenger demand and journey time, which is also capable of quantifying the spatial-temporal propagation of disruption impacts within metro networks. The causal inference approaches are applied to empirical studies based on the Hong Kong Mass Transit Railway (MTR). Our conclusions can assist researchers and practitioners in two applications: (i) the evaluation of metro performance such as service reliability, system vulnerability and resilience, and (ii) the management of future disruptions.Open Acces

Extreme Rainfall Events: Incorporating Temporal and Spatial Dependence to Improve Statistical Models

The proper design of protective measurements against floods related to heavy precipitation has long been a question of interest in many fields of study. A crucial component for such design is the analysis of extreme historical rainfall using Extreme Value Theory (EVT) methods, which provide information about the frequency and magnitude of possible future events. Characterizing an entire basin or geographical catchment requires the extension of univariate EVT methods to capture the spatial variability of the data. This extension requires that the similarity of the data for nearby stations be included in the model, resulting in more efficient use of the data. This dissertation focuses on using statistical models incorporating spatial dependence for modeling annual rainfall maxima. Additionally, we present ways of adapting the models to capture the dependence between rainfall of different time scales. These models are used in order to pursue two aims. The first aim is to improve our understanding of the mechanisms that lead to dependence on extreme rainfall. The second aim is to improve the resulting estimates when incorporating the dependence into the models. Two published studies make up the main findings of this dissertation. The models used in both studies involve the use of Brown-Resnick max-stable processes, allowing the models to explicitly account for the dependence on either the temporal or the spatial domain. These conditional models are compared for both cases to a model that ignores the dependence, allowing us to determine the impact of the dependence in both situations. Contributions to three other studies using the concept of dependence are also summarized. In the first study, we assess the impact of including the dependence between rainfall series of different aggregation durations when estimating Intensity-Duration-Frequency curves. This assessment was done in a case study for the Wupper catchment in Germany. This study found that including the dependence in the model had a positive effect on the prediction accuracy when focusing on rainfall with short durations (d <= 10h) and large probabilities of non-exceedance. Therefore, we recommend using max-stable processes when a study focuses on short-duration rainfall. In the second study, we investigate how the spatial dependence of extreme rainfall in Berlin-Brandenburg changes seasonally and how this change could impact the estimates from a max-stable model that includes this dependence. The seasonality was determined by estimating the parameters of a summer and winter semi-annual block maxima model. The results from this study showed that, for the summer maxima, the dependence structure was adequately captured by an isotropic Brown-Resnick model. On the contrary, the same model performed poorly for the winter maxima, suggesting that a change in the assumptions is needed when dealing with typical winter events, typically frontal or stratiform for this region. These results show that accounting for the meteorological properties of the rainfall-generating processes can provide useful information for the design of the models. Overall, our findings show that including meteorological knowledge in statistical models can improve their resulting estimations. In particular, we find that, under certain conditions, using statistical dependence to incorporate knowledge about the differences in temporal and spatial scales of rainfall-generating mechanisms can lead to a positive impact in the models.Die richtige Auslegung von Schutzmaßnahmen gegen Überschwemmungen im Zusammenhang mit Starkniederschlägen ist seit langem eine Frage, die in vielen Studienbereichen von Interesse ist. Eine entscheidende Komponente für eine solche Planung ist die Analyse extremer historischer Niederschläge mit Methoden der Extremwertstatistik, die Informationen über die Häufigkeit und das Ausmaß möglicher künftiger Ereignisse liefern. Die Charakterisierung eines ganzen Einzugsgebiets oder einer geografischen Einheit erfordert die Erweiterung der univariaten Extremwerstatistik-Methoden, um die räumliche Variabilität der Daten zu erfassen. Diese Erweiterung erfordert, dass die Ähnlichkeit der Daten für nahe gelegene Stationen in das Modell einbezogen wird, was zu einer effizienteren Nutzung der Daten führt. Diese Dissertation konzentriert sich auf die Verwendung statistischer Modelle, die die räumliche Abhängigkeit bei der Modellierung von jährlichen Niederschlagsmaxima berücksichtigen. Darüber hinaus werden Möglichkeiten zur Anpassung der Modelle vorgestellt, um die Abhängigkeit zwischen Niederschlägen auf verschiedenen Zeitskalen zu erfassen. Diese Modelle werden zur Verfolgung zweier Ziele eingesetzt. Das erste Ziel besteht darin, unser Verständnis der Mechanismen zu verbessern, die zur Abhängigkeit von extremen Niederschlägen führen. Das zweite Ziel besteht darin, die resultierenden Schätzungen zu verbessern, wenn die Abhängigkeit in die Modelle einbezogen wird. Zwei veröffentlichte Studien bilden die wichtigsten Ergebnisse dieser Dissertation. Die in beiden Studien verwendeten Modelle basieren auf max-stabilen Brown-Resnick-Prozessen, die es den Modellen ermöglichen, die Abhängigkeit entweder auf der zeitlichen oder auf der räumlichen Ebene ausdrücklich zu berücksichtigen. Diese bedingten Modelle werden für beide Fälle mit einem Modell verglichen, das die Abhängigkeit ignoriert, so dass wir die Auswirkungen der Abhängigkeit in beiden Situationen bestimmen können. Es werden auch Beiträge zu drei anderen Studien zusammengefasst, die das Konzept der Abhängigkeit verwenden. In der ersten Studie bewerten wir die Auswirkungen der Einbeziehung der Abhängigkeit zwischen Niederschlagsreihen unterschiedlicher Aggregationsdauern bei der Schätzung von Intensitäts-Dauer-Frequenz-Kurven. Diese Bewertung wurde in einer Fallstudie für das Einzugsgebiet der Wupper in Deutschland durchgeführt. Diese Studie ergab, dass sich die Einbeziehung der Abhängigkeit in das Modell positiv auf die Vorhersagegenauigkeit auswirkt, wenn man sich auf Niederschläge mit kurzen Dauern (d <= 10 h) und großer Nichtüberschreitungwahrscheinlichkeit konzentriert. Daher empfehlen wir die Verwendung von max-stabilen Prozessen, wenn sich eine Studie auf Regenfälle von kurzer Dauer konzentriert. In der zweiten Studie untersuchen wir, wie sich die räumliche Abhängigkeit von Extremniederschlägen in Berlin-Brandenburg saisonal verändert und wie sich diese Veränderung auf die Schätzungen eines max-stabilen Modells auswirken könnte, das diese Abhängigkeit berücksichtigt. Die Saisonalität wurde durch die Schätzung der Parameter eines halbjährlichen Sommer- und Winter-Blockmaxima-Modells bestimmt. Die Ergebnisse dieser Studie zeigten, dass die Abhängigkeitsstruktur für die Sommermaxima durch ein isotropes Brown-Resnick-Modell angemessen erfasst wurde. Im Gegensatz dazu zeigte dasselbe Modell eine schlechte Leistung für die Wintermaxima, was darauf hindeutet, dass eine Änderung der Annahmen erforderlich ist, wenn es um typische Winterereignisse geht, die in dieser Region typischerweise frontal oder stratiförmig sind. Diese Ergebnisse zeigen, dass die Berücksichtigung der meteorologischen Eigenschaften der Niederschlagsprozesse nützliche Informationen für die Gestaltung der Modelle liefern kann. Insgesamt zeigen unsere Ergebnisse, dass die Einbeziehung von meteorologischem Wissen in statistische Modelle die daraus resultierenden Schätzungen verbessern kann. Insbesondere stellen wir fest, dass unter bestimmten Bedingungen die Nutzung der statistischen Abhängigkeit zur Einbeziehung von Wissen über die Unterschiede in den zeitlichen und räumlichen Skalen der regenerzeugenden Mechanismen zu einer positiven Wirkung in den Modellen führen kann