The Cettiidae, a family of primarily small, insectivorous, Asiatic and Austronesian, mountain birds have been the subject of acoustic analysis in the past. However, until this point, an in-depth review of the songs of the entire family had yet to be undertaken. In an effort to resolve this shortcoming, the songs of 29 Cettiidae species were examined through the usage of acoustic analysis software, with specific factors such as bandwidth, frequency, and strophe duration being statistically recorded. In total 286 individuals and over 800 strophes were analyzed, with the collected data being displayed in various PCA plots. These PCA graphs were then compared to both a dated phylogenetic tree specifically created for this study, and a Mahalanobis distance vs. genetic distance plot, created using the acoustic data as well as Cytochrome b genetic data. Based on these plots, several notable trends could be observed across the entire family. While largescale divergence from the norm was noted in several pairwise comparisons of species, as well as large scale conservation within clades such as the island Horornis species, examples of convergent evolution of their songs was rather scant. It was also noted that despite the strong divergence of certain species, each genus occupied its own area of multivariate space within the PCAs. Strong statistical divergence between island and continental species was also noted in both the PCAs and the Mahalanobis graph. Meanwhile, the statistical analysis of these species unfortunately provided no clues as to the ancestral state of their songs. However, a visual analysis of every species song, mapped on the dated phylogenetic tree, suggested that two distinct linages of simple and complex songs could be traced back approximately 10 million years. This allows for speculation as to the songs of now long extinct Cettiidae species as far back as the Miocene.

Debris flows occur frequently in remote areas of Great Smoky Mountains National Park, Tennessee. Rainfall gauges are not adequate for modeling infiltration required for triggering debris flows. Weather radar, providing frequently updated, continuous coverage, is a valuable tool for estimating rainfall intensity, duration, runoff, and infiltration. Daily rainfall from a sole gauge was compared with hourly rainfall from the Digital Precipitation Array weather radar product to model infiltration on August 5, 2012, the day before a debris flow was known to have occurred in the 91-km2West Prong Little Pigeon River watershed. Additionally, both gauge and radar data were used for rainfall-runoff-infiltration modeling for a 42-day period in July and August 2012. Runoff and infiltration were simulated using the conventional semi-distributed hydrological model HEC-HMS. A local bias correction of radar rainfall at the gauge location improved correlation between the radar rainfall and the gauge data. Peak daily rainfall for the August 5 storm was 93 mm (gauge) and 98 mm (radar), whereas average daily rainfall for the 42-day period was 10 mm and 7.75 mm, respectively. Over the study period, simulated daily infiltration declined from 28 mm to 0.5 mm for the gauge and from 15 mm to 0.14 mm for radar, indicating essentially saturated conditions on the day of the debris flow.

Volunteered Geographic Information, data contributed by community scientists, is an increasingly popular tool to collect scientific data, involve the community in scientific research, and provide information and education about a prominent issue. Johnson City, Tennnessee, USA has a long history of downtown flooding, and recent redevelopment of two land parcels has created new city parks that mitigate flooding through floodwater storage, additional channel capacity, and reduced impervious surfaces. At Founders Park, a project to collect stage data using text messages from community scientists has collected 1479 stage measurements from 597 participants from May 2017 through July 2021. Text messages were parsed to extract the stage and merged with local precipitation data to assess the stream’s response to precipitation. Of 1479 observations, 96.7% were correctly parsed. Only 3% of observations were false positives (parser extracted incorrect stage value) or false negatives (parser unable to extract correct value but usable data were reported). Less than 2% of observations were received between 11 p.m. and 7 a.m., creating an overnight data gap, and fewer than 7% of observations were made during or immediately following precipitation. Regression models for stage using antecedent precipitation explained 21.6% of the variability in stream stage. Increased participation and development of an automated system to record stage data at regular intervals will provide data to validate community observations and develop more robust rainfall–runoff models.

The integration of information from multiple geophysical and other prospection surveys of archaeological sites and regions leads to a richer and more complete understanding of subsurface content, structure, and physical relationships. Such fusions of information occur within a single geophysical data set or between two or more geophysical and other prospection sources in one, two, or three dimensions. An absolute requirement is the accurate coregistration of all information to the same coordinate space. Data integrations occur at two levels. At the feature level, discrete objects that denote archaeological features are defined, usually subjectively, through the manual digitization of features interpreted in the data, although there is growing interest in automated feature identification and extraction. At the pixel level, distributional issues of skewness and outliers, high levels of noise that obfuscate targets of interest, and a lack of correlation between largely independent dimensions must be confronted. Nevertheless, successful fusions occur using computer graphic methods, simple arithmetic combinations, and advanced multivariate methods, including principal components analysis and supervised and unsupervised classifications. Four case studies are presented that illustrate some of these approaches and offer advancement into new domains.

Risk assessments in volcanic contexts are complicated by the multi-hazard nature of both unrest and eruption phases, which frequently occur over a wide range of spatial and temporal scales. As an attempt to capture the multi-dimensional and dynamic nature of volcanic risk, we developed an integrAteD VolcanIc risk asSEssment (ADVISE) model that focuses on two temporal dimensions that authorities have to address in a volcanic context: short-term emergency management and long-term risk management. The output of risk assessment in the ADVISE model is expressed in terms of potential physical, functional, and systemic damage, determined by combining the available information on hazard, exposed systems and vulnerability. The ADVISE model permits qualitative, semi-quantitative and quantitative risk assessment depending on the final objective and on the available information. The proposed approach has evolved over a decade of study on the volcanic island of Vulcano (Italy), where recent signs of unrest combined with uncontrolled urban development and significant seasonal variations of exposed population result in highly dynamic volcanic risk. For the sake of illustration of all the steps of the ADVISE model, we focus here on the risk assessment of the transport system in relation to the tephra fallout associated with a long-lasting Vulcanian cycle.

The mining industry generates billions of tonnes of waste annually, which is often stored in tailings storage facilities (TSF). This waste is generated from the extraction of ore from surface or underground mines, as well as from metallurgical processing and low-grade stockpiles. TSF can have significant environmental impacts, as they can cause acid mine drainage resulting in the leaching and transport of heavy metals into ground and surface waters. With increasing demand for critical raw material, recent studies have shown that the valorisation of mine waste can be a potential secondary source of critical raw materials. The valorisation of mine waste is possible when the waste is accurately characterised.A novel method that uses multispectral satellite remote sensing and machine learning to estimate the mineral resource in a defunct TSF in the Witwatersrand Basin, South Africa is proposed in this research. Four machine learning models: 1) random forest (RF); 2) adaptive boosting (AB); 3) extra trees (ET); and 4) k-nearest neighbours are developed using supervised machine learning. The models are trained using training data acquired from a TSF with known gold concentration located 3 kilometres from the TSF and deployed on the TSF to predict the gold grades. The results of the machine learning model predictions indicates that machine learning models had high performances for predicting gold grades in the TSF. The AB, RF and ET, models performed best. Their performances were evaluated using the coefficient of determination (R2) value. The R2 values for the machine learning models were 0.95, 0.92, 0.87 and 0.70 for AB, ET, RF and kNN respectively. The mean gold grade predicted was 0.44 g/t by all machine learning models. This was compared to a 2D surficial geostatistical model which estimated 0.35g/t gold in the TSF using ordinary kriging and a 2D vertically averaged geostatistical model with an estimated 0.4 g/t mean gold grade. The short-wave infrared (SWIR) - band 11 at a 20 m spatial resolution had the highest correlation with the reflectance of gold in the TSF. This study demonstrated the value of leveraging multi-spectral remote sensing data and machine learning to perform mineral resource estimation in defunct TSF.

The way of representing Earth has changed; two dimensional (2D) maps have turned into three Dimensional (3D) representations. There are many studies in order to create 3D city maps as well as areas where these are applied such as 3D cadastral, 3D visualization and flood simulation, etc. These can be created with the aid of different data sources using photogrammetric Digital Surface Model (DSM) derived from image matching and from Light Detection and Ranging (LiDAR) point clouds or both of them combining orthophotos and building footprints. Several software has been developed to ease and speed up this process. In this study, a current state-of-the art in the 3D city modeling with particular interest to commercial software was analyzed. DSMs from image matching (Satellite and Airborne) were used in order to create a 3D city model of Trento, Italy. The objectivewas to examine the degree of automation and the computation time of two available software: Feature Manipulation Engine (FME) and Building Reconstruction (BREC). Other problem such as the quality of the DSM needed would be discussed for each software and the results would be compared to those achieved using LiDAR data. Recommendations and possible problems would also be addressed. In order to create a 3D city model, the ALDPAT software (Airborne LiDAR Data Processing and Analysis Tool) has been used to separate the ground regions (Digital Terrain Model, DTM) from the man-made objects and trees (Normal Digital Surface Model, nDSM). FME and BREC software have been used to reconstruct the 3D city model. The output results from FME and BREC have been analyzed qualitatively and quantitatively. The comparison between the models generated from photogrammetric and LiDAR DSMs have been performed. The results show that buildings generated by satellite images have poorest quality compared with buildings from LiDAR and airborne data. In particular, the performed tests will be shown that among the Level of Details (LoD), a LoD1 and LoD2 3D city models can be generated using a DSM by image matching. A deeper study should be done in order to analyze the level of detail qualitatively.

Streets provide the framework of a city and they are necessary for human life. Some underlying patterns of street networks cannot be directly recognized by people. In this study, topological analysis of urban street networks was adopted to build up new insight into urban morphology. Space syntax, which has been integrated into GIS, was applied for the analysis of spatial configuration, and fifty Swedish cities were chosen as samples to uncover various urban patterns. Street connectivity was the focus of the analysis and axial lines were the main analytical tools. The aim of this study was to hierarchically represent the cities’ streets and classify the sample cities into different types by urban morphology. Street data for Swedish cities were collected from OpenStreetMap. ArcGIS 10, with the Axwoman extension, provided a platform to carry out the topological analysis. Natural roads, axial lines and space syntax parameters were generated automatically with the functions of Axwoman. Hierarchical levels of streets were visually represented and the underlying pattern of each city was gotten from the hierarchical representation. Based on street hierarchy, the fifty sample cities were classified into nine groups, wherein cities of the same group had uniform hierarchical levels. Using the hierarchical pattern of each group’s axial lines, the nine city groups were further reclassified into three types. It was found that, for the street network of most sample cities represented with axial lines, not more than 40% of their streets have connectivity larger than the average value. The hierarchical representation also revealed that streets with high connectivity, which provide greater accessibility, were only minorities in the sample cities. Moreover, minor streets with high connectivity were almost distributed in city centers. In some of the studied cities, axial lines made better representation of the hierarchical patterns of streets, while in others, it did not provide a suitable way of uncovering urban patterns compared to natural roads. A limitation of axial lines manifested in this study was that it chopped curved roads into several segments, thus, disrupting the continuity of streets. In general, axial lines can provide a way to uncover urban patterns. They have meaningful effect to city residents and these patterns can help people gain better understanding of the urban structure. In addition, the hierarchical patterns of streets can be used to model pedestrian and traffic flows, predict crime occurrences, and make spatial plans. The hierarchical representation of streets can also contribute to people’s wayfinding performance.

The global temperature is at an all-time high, the polar ice is melting, the sea levels are rising and the associated disasters are a time bomb. These variations in temperature are thought to trace roots to anthropogenic sources. In order to mitigate these changes and slow down the rate of warming, several efforts have been made locally and internationally. One of the agreed up-on way to do this is by using forests as reservoirs for carbon since carbon is one of those greenhouses gasses responsible for the warming. Mau forest, in Kenya, is one of those ecosystems where degradation has happened tremendously, though still viewed as a potential site for reclamation. Using GIS and remote sensing analysis of Landsat images, the study sought to compare various change detection techniques, find the amount of biomass lost or gained in the forest and the possible income accrued in case the forest is placed under the Kyoto protocol’s Clean Development Mechanism (CDM). Various vegetation ratios were used in the study ranging from NDVI, NDII to RSR. The results obtained from these ratios were not quite convincing as setting threshold for the ratios to separate dense forest from other forms of vegetation was not straightforward. As a consequence, the three ratios NDVI, NDII and RSR were combined and substituted for RGB bands respectively. A classification was done using this combination and the results compared to classifications based on tasselled cap and principal component analysis (PCA). The results of the various methods showed that the forest has lost its biomass over time. The methods indicated that the section of the forest studied lost between 8088 ha and 9450 ha of dense forest land between 1986 and 2010. This is between 29% and 35% of forest cover lost depending on the various methods of change detection used. This acreage when converted into forest biomass at a rate of 236 Mg.ha-1 gives a value of between 1908768 tons and 2230200 tons of carbon. If the Mau forest were registered as Kyoto compliant, then in the carbon market, this would have been a loss of between $24.1m and $ 28.2m according to California carbon dashboard (28th, May 2015). This is a huge sum of money if paid to a rural community as benefits from carbon sequestration via forestry. Such are the amounts that a community can earn by protecting a forest for the purposes of carbon sequestration and trading.

Our understanding of how magma-poor rifts accommodate strain remains limited largely due to sparse geophysical observations from these rift systems. To better understand magma-poor rifting processes, chapter 1 of this dissertation is focused on investigating the lithosphere-asthenosphere interactions beneath the Malawi Rift, a segment of the magma-poor Western Branch of the East African Rift (EAR). Chapter 2 and 3 are focused on investigating the sources of melt beneath the Rungwe Volcanic Province (RVP), an anomalous volcanic center located at the northern tip of the Malawi Rift. In chapter 1, we use the lithospheric structure of the Malawi Rift derived from the World Gravity Model 2012 to constrain three-dimensional (3D) numerical models of lithosphere-asthenosphere interactions, which indicate ~3 cm/yr asthenospheric upwelling beneath the thin lithosphere (115-125 km) of the northern Malawi Rift and the RVP from lithospheric modulated convection (LMC) that is decoupling from surface motions. We suggest that the asthenospheric upwelling may generate decompression melts which weakens the lithosphere thereby enabling extension. The source of asthenospheric melt for the RVP is still contentious. Some studies suggest the asthenospheric melt beneath the RVP arises from thermal perturbations in the upper mantle associated with plume head materials, while others propose decompression melting from upwelling asthenosphere due to LMC where the lithosphere is thin. Chapter 2 of this dissertation is focused on testing the hypothesis that asthenospheric melt feeding the RVP can be generated from LMC using realistic constraints on the mantle potential temperature (Tp). We develop a 3D thermomechanical model of LMC beneath the RVP and the entire Malawi Rift that incorporates melt generation. We find decompression melt associated with LMC upwelling (~3 cm/yr) occurs at a maximum depth of ~150 km localized beneath the RVP. Studies of volcanic rock samples from the RVP indicate plume signatures which are enigmatic since the RVP is highly localized, unlike the large igneous provinces in the Eastern Branch of the EAR. In chapter 3, we test the hypothesis that the melt beneath the RVP is generated from plume materials. We investigate melt generation from plume-lithosphere interactions (PLI) beneath the RVP by developing a 3D seismic tomography-based convection (TBC) model beneath the RVP. The seismic constraints indicate excess temperatures of ~250 K in the sublithospheric mantle beneath the RVP suggesting the presence of a plume. We find a relatively fast upwelling (~10 cm/yr) beneath the RVP which we interpret as a rising plume. The TBC upwelling generates decompression melt (~0.25 %) at a maximum depth of ~200 km beneath the RVP where the lithosphere is thinnest (~100 km). Our results demonstrate that an excess heat source from may be plume materials is necessary for melt generation in the sublithospheric mantle beneath the RVP because passive asthenospheric upwelling of ambient mantle will require a higher than normal Tp to generate melt. Studies of volcanic rock samples from the RVP indicate plume signatures which are enigmatic since the RVP is highly localized, unlike the large igneous provinces in the Eastern Branch of the EAR. In chapter 3, we test the hypothesis that the melt beneath the RVP is generated from plume materials. We investigate melt generation from plume-lithosphere interactions (PLI) beneath the RVP by developing a 3D seismic tomography-based convection (TBC) model beneath the RVP. The seismic constraints indicate excess temperatures of ≈ 250K in the sublithospheric mantle beneath the RVP suggesting the presence of a plume. We find a relatively fast upwelling (≈10 cm/yr) beneath the RVP which we interpret as a rising plume. The TBC upwelling generates decompression melt (≈0.25 %) at a maximum depth of ≈200 km beneath the RVP where the lithosphere is thinnest (≈100 km). Our results demonstrate that an excess heat source from may be plume materials is necessary for melt generation in the sublithospheric mantle beneath the RVP because passive asthenospheric upwelling of ambient mantle will require a higher than normal Tp to generate melt. / Doctor of Philosophy / Studies suggest the presence of hot, melted rock deep in the continents makes them weaker and easier to break apart, however, our understanding of how continents with less melted rock break apart remains limited largely due to sparse geophysical observations from these dry areas. To better understand how continents with less melted rock break apart, chapter 1 of this dissertation is focused on investigating the interactions between the rigid part of the Earth, called lithosphere, and the underlying lower viscosity rock layer called asthenosphere beneath the Malawi Rift, a segment of the magma-poor Western Branch of the East African Rift (EAR). Chapter 2 and 3 are focused on investigating the sources of melt beneath the Rungwe Volcanic Province (RVP), an anomalous volcanic center located at the northern tip of the Malawi Rift. In chapter 1, we use the lithospheric structure of the Malawi Rift derived from gravity data to constrain three-dimensional (3-D) numerical models of lithosphere-asthenosphere interactions, which indicate ~3 cm/yr asthenospheric upwelling beneath the thin lithosphere (115-125 km) of the northern Malawi Rift and the RVP that does not seem to drive movements at the surface. We suggest that the asthenospheric upwelling may generate melted rock which weakens the lithosphere thereby enabling extension. However, the source of asthenospheric melt for the RVP is still contentious. Some studies suggest the asthenospheric melt beneath the RVP arises from thermal perturbations in the upper mantle associated with rising mantle rocks or plume head materials, while others propose melting occurs from upwelling asthenosphere due to lithospheric modulated convection (LMC) where the lithosphere is thin. Chapter 2 of this dissertation is focused on testing the hypothesis that asthenospheric melt feeding the RVP can be generated from LMC. We develop a 3D thermomechanical model of LMC beneath the RVP and the entire Malawi Rift that incorporates melt generation. We find decompression melt associated with LMC upwelling (~3 cm/yr) occurs at a maximum depth of ~150 km localized beneath the RVP. Studies of volcanic rock samples from the RVP indicate plume signatures which are enigmatic since the RVP is highly localized, unlike the large igneous provinces in the Eastern Branch of the EAR. In chapter 3, we investigate melt generation from plume-lithosphere interactions (PLI) beneath the RVP. We develop a 3D model of convection using information from seismology we call tomography-based convection (TBC) beneath the RVP. The seismic data indicate excess temperatures of ~250 K beneath the RVP suggesting the presence of a plume. We find a relatively fast upwelling (~10 cm/yr) beneath the RVP which we interpret as a rising plume. The TBC upwelling generates decompression melt at a maximum depth of ~200 km beneath the RVP. Our results demonstrate that an excess heat source from may be plume materials is necessary for melt generation in the sublithospheric mantle beneath the RVP because passive asthenospheric upwelling of ambient mantle will require a higher than normal mantle potential temperatures to generate melt.