The Long Point wetland complex is one of the most significant coastal wetland systems in the Great Lakes, containing a diverse mosaic of wetland vegetation communities that have developed in response to water level fluctuations due to natural climate variability. Natural short-term water level variations are important for promoting wetland productivity and diversity, but long-term water level changes resulting from human-induced climate change can have serious and long-term consequences on the integrity and health of wetlands. The historical response of the wetland to water level fluctuations was quantified and modelled to provide an indication of how the wetland may respond to future projected water level changes - water level fluctuations are used as a surrogate for climate change. A spatiotemporal trend analysis was conducted within a geographic information system (GIS) to determine the effects of water level conditions on wetland vegetation and land cover at the wetland complex at Long Point, Ontario for seven years from 1945 to 1999. The spatiotemporal trend analysis documented changes in the structure and composition of the wetland complex in response to declining and rising water level conditions. During drier periods, there were significant increases in the amount of drier emergent and meadow vegetation, especially within the Inner Bay and northern portion of the outer peninsula. There was less fragmentation and complexity in the wetland as these drier communities expanded forming larger continuous patches of vegetation. During wetter periods, open water increased and there was a predominance of wetter emergent and meadow communities in the wetland. Drier vegetation communities became interspersed with water creating a more fragmented convoluted wetland landscape. The historical response of the wetland vegetation and land cover to water level fluctuations was then simulated with three different wetland models developed in the GIS. A rule-based model, a probability model, and a transition model were developed to assess wetland response to future water level changes. The models were evaluated using simple statistical methods. The transition and rule-based models performed the best and were successful in predicting over 80 % of the wetland vegetation distribution correctly. The probability model was the least successful, predicting only 55 % of the response correctly. The GIS proved successful in documenting wetland response to historical water level fluctuations and providing insight into the potential impacts of future climate change though water level fluctuations on the Long Point coastal wetland complex. The spatiotemporal analysis and wetland modelling advance the role of GIS in wetland management and analysis. They are practical methods within a GIS that can be used to assess the impacts of climate change on wetland systems and to document and model wetland change in other coastal wetlands of the Great Lakes.

The Long Point wetland complex is one of the most significant coastal wetland systems in the Great Lakes, containing a diverse mosaic of wetland vegetation communities that have developed in response to water level fluctuations due to natural climate variability. Natural short-term water level variations are important for promoting wetland productivity and diversity, but long-term water level changes resulting from human-induced climate change can have serious and long-term consequences on the integrity and health of wetlands. The historical response of the wetland to water level fluctuations was quantified and modelled to provide an indication of how the wetland may respond to future projected water level changes - water level fluctuations are used as a surrogate for climate change. A spatiotemporal trend analysis was conducted within a geographic information system (GIS) to determine the effects of water level conditions on wetland vegetation and land cover at the wetland complex at Long Point, Ontario for seven years from 1945 to 1999. The spatiotemporal trend analysis documented changes in the structure and composition of the wetland complex in response to declining and rising water level conditions. During drier periods, there were significant increases in the amount of drier emergent and meadow vegetation, especially within the Inner Bay and northern portion of the outer peninsula. There was less fragmentation and complexity in the wetland as these drier communities expanded forming larger continuous patches of vegetation. During wetter periods, open water increased and there was a predominance of wetter emergent and meadow communities in the wetland. Drier vegetation communities became interspersed with water creating a more fragmented convoluted wetland landscape. The historical response of the wetland vegetation and land cover to water level fluctuations was then simulated with three different wetland models developed in the GIS. A rule-based model, a probability model, and a transition model were developed to assess wetland response to future water level changes. The models were evaluated using simple statistical methods. The transition and rule-based models performed the best and were successful in predicting over 80 % of the wetland vegetation distribution correctly. The probability model was the least successful, predicting only 55 % of the response correctly. The GIS proved successful in documenting wetland response to historical water level fluctuations and providing insight into the potential impacts of future climate change though water level fluctuations on the Long Point coastal wetland complex. The spatiotemporal analysis and wetland modelling advance the role of GIS in wetland management and analysis. They are practical methods within a GIS that can be used to assess the impacts of climate change on wetland systems and to document and model wetland change in other coastal wetlands of the Great Lakes.

A lack of empirical evidence to understand neighborhood and residential development processes within neighborhoods has challenged urban planners’ ability to influence the course of future land development. The main objectives of this study were to examine neighborhood and residential development patterns and investigate dynamic processes in northwest Harris County, Texas, along the U.S. Highway 290 transportation corridor from 1945 to 2006. Researchers have identified different patterns of land development: leapfrog, contagion and infill development. However, because of the fuzziness in neighborhood and residential development patterns, the nominal classifications of development patterns are limited in their potential to characterize development patterns both on neighborhood and parcel levels; their applications for development processes and its impacts are even more limited. This study presents a quantitative approach for measuring development patterns by characterizing neighborhood development patterns as a function of spatial distance and temporal lapse time from the closest existing neighborhood to new neighborhood(s). The analysis in this study was based on disaggregated parcel data provided by the Harris County Appraisal District (HCAD) real estate and property records. The quantitative measures of neighborhood development patterns and processes within each pattern of neighborhood were derived by aggregating parcel level data into neighborhood level. This study developed the Long-term Trend of Development Model (LTDM) to classify neighborhood and residential development patterns based on spatial distance and temporal lapse time from existing neighborhoods to new neighborhood(s) each year to examine development processes. Regression analysis was used to identify the relationship between neighborhood patterns and residential development processes. This study found that development patterns can be measured quantitatively with spatial and temporal relationships between prior and new development at the neighborhood level. Empirical evidence supported the hypothesis that leapfrog neighborhood development triggers neighborhood development, contagion follows leapfrog neighborhood quickly, and infill follows contagion after a lapsed time. Residential development patterns in each pattern of neighborhood showed discrete development processes. Age of neighborhood can be used to predict development pressures and growth. In this process, physical and social infrastructure is involved, therefore, development process is best observed on the neighborhood level.

The main goal of this research was to investigate the spatial and temporal impacts of water siltation caused by Artisanal Small-scale Gold Mining (ASGM) on the underwater light field of the Tapajós River and its main tributaries (Jamanxim, Novo, Tocantinzinho, and Crepori rivers). In order to accomplish this, two fieldwork research trips were undertaken to collect data associated with water quality and radiometric data. This data provided information to quantify the underwater light field in water affected by a gradient of mining tailings intensity, clustered into five major classes ranging from 0 to 120 mg/L of total suspended solids (TSS) (Chapter 3). In general, with increased TSS from mining operations such as in the Crepori, Tocantinzinho, and Novo rivers, the scattering process prevails over absorption coefficient and, at sub-surface, scalar irradiance is reduced, resulting in a shallower euphotic zone where green and red wavelengths dominate. The effects of light reduction on the phytoplankton community was not clearly observed, which may be attributed to a low number of samples for proper comparison between impacted and non-impacted tributaries and/or to general low phytoplankton productivity in all upstream tributaries. In Chapter 4, aiming to extend the information derived from Chapter 3 over a 40-year period (1973-2012), the TSS concentration along the Tapajós River and its main tributaries was quantified based on in situ data and historical Landsat-MSS/TM/OLI data. Measurements of radiometric data were used to calibrate satellite atmospheric correction and establish an empirical relationship with TSS. The regression estimates TSS with high confidence from surface reflectance (ρ_surf (red)) up to 25%, which corresponds to approximately 110 mg.L-1. The combination of the atmospheric correction and the robust reflectance-based TSS model allowed estimation of TSS in the Tapajós River from the historical Landsat database (40 years). In Chapter 5, the role of the temporal changes of ASGM area in the water siltation over the last 40 years was investigated in four sub-basins: the Crepori, Novo, and Tocantins sub-basins (mined); and the Jamanxim sub-basin (non-mined), considering the landscape characteristics such as soil type and proximity to drainage system. ASGM areas were mapped for five annual dates (1973, 1984, 1993, 2001, and 2012) based on Landsat satellite images. Results showed that ASGM increased from 15.4 km2 in 1973, to 166.3 and 261.7 km2 in 1993 and 2012, respectively. The effects of ASGM areas on water siltation depends on several factors regarding ASGM activities, such as the type of mining, type of gold deposits, and intensity of gold mining, represented by number of miners and gold production. / Graduate / 0373 / 0768 / 0775 / lobo@uvic.ca

The semi-arid and arid Andes of South America are characterized by large areas with glacial and periglacial environments. This study focusses on the distribution of glacial and periglacial landforms in the Las Veguitas catchment, Cordillera Frontal, Argentina. A detailed geomorphological map of the Las Veguitas catchment is presented, based on high-resolution elevation data (ALOSPALSAR), satellite imagery (Landsat 8, World View 2, Google Earth), and field studies. First, a general topographical analysis is performed for the entire Las Veguitas catchment, including elevation, slope and aspect characteristics. Second, the altitudinal range of glacial features (glaciers, debris covered glaciers and thermokarst ponds on glaciers) and the altitudinal range and predominant aspect of periglacial features (active, inactive and fossil rock glaciers) are analyzed. Currently, glaciers are restricted to ≥ 4300m, but moraines are identified to elevations of c. 3200m. Active rock glaciers extend down to c. 3450m and have a more southern aspect then both inactive and fossil rock glaciers. Third, a temporal analysis has been performed of glacier and rock glacier flow using a time series of remote sensing images. Glacier flow traced by the displacement of thermokarst lake features (2006-2016) had a mean velocity of 6.66m/yr. The mean velocity of rock glaciers (1963-2017) was much lower at 0.63m/yr. Finally, the thesis discusses limitations and uncertainties in study methods and suggestions for further research activities.

Natural disasters have become more severe and frequent than previous assessments with global warming. The increasing risk of natural disasters presents different groups of populations with diverse vulnerabilities, particularly those underrepresented social groups which need specific support before, during, and after extreme disasters. Hence, it is highly desired to examine vulnerability quantitatively and qualitatively across different social groups in risk to natural disasters. This dissertation study aims to investigate the measure of social vulnerability to two types of climate change-related natural disasters: sea-level-rise floodings and wildfires. In the study of sea-level-rise floodings, high-risk flooding areas are first identified for a coastal city. Then, we measure social vulnerability index (SVI) using a new SVI metric to identify vulnerable social groups which should be paid more attention for coastal flooding disaster mitigation. Compared to existing SVI methods, the new SVI leverages principal component analysis and analytic hierarchy process to achieve a better social vulnerability analysis. In the study of wildfires, we focus on the understanding of minority vulnerabilities and their disparities to wildfires over time and space. Minority vulnerabilities are analyzed with spatial clustering methods including Local Moran’s I and Getis-Ord Gi*. The vulnerability disparity is measured based on a reference point from which the quantity separates a minority group on a particular place. Both location quotient and location amplitude index are used to quantitively measure the vulnerability disparity among different minorities. Lastly, in addition to the “direct” impact of disasters on vulnerable population, this dissertation study also conducts vulnerability analysis to failed infrastructure (e.g., power systems) due to disasters, i.e., the “indirect” impact of disasters on different social groups. Recently, scheduled power outages known as Public Safety Power Shutoff (PSPS) are becoming increasingly common to mitigate threats of wildfires to power systems. However, current PSPS decision making processes do not consider the unequal distribution of various social groups, particularly those who are more vulnerable to the power outage. This study investigates the measure of social vulnerability in high-risk fire areas to PSPS, which will help decision makers to better determine the efficiency of a PSPS event for wildfire mitigation.

Abstract: Road traffic accidents claim more than 1,500 lives each year in Canada and affect society adversely, so transport authorities must reduce their impact. This is a major concern in Quebec, where the traffic-accident risks increase year by year proportionally to provincial population growth. In reality, the occurrence of traffic crashes is rarely random in space-time; they tend to cluster in specific areas such as intersections, ramps, and work zones. Moreover, weather stands out as an environmental risk factor that affects the crash rate. Therefore, traffic-safety engineers need to accurately identify the location and time of traffic accidents. The occurrence of such accidents actually is determined by some important factors, including traffic volume, weather conditions, and geometric design. This study aimed at identifying hotspot locations based on a historical crash data set and spatiotemporal patterns of traffic accidents with a view to improving road safety. This thesis proposes two new methods for identifying hotspot locations on a road network. The first method could be used to identify and rank hotspot locations in cases in which the value of traffic volume is available, while the second method is useful in cases in which the value of traffic volume is not. These methods were examined with three years of traffic-accident data (2011–2013) in Sherbrooke. The first method proposes a two-step integrated approach for identifying traffic-accident hotspots on a road network. The first step included a spatial-analysis method called network kernel-density estimation. The second step involved a network-screening method using the critical crash rate, which is described in the Highway Safety Manual. Once the traffic-accident density had been estimated using the network kernel-density estimation method, the selected potential hotspot locations were then tested with the critical-crash-rate method. The second method offers an integrated approach to analyzing spatial and temporal (spatiotemporal) patterns of traffic accidents and organizes them according to their level of significance. The spatiotemporal seasonal patterns of traffic accidents were analyzed using the kernel-density estimation; it was then applied as the attribute for a significance test using the local Moran’s I index value. The results of the first method demonstrated that over 90% of hotspot locations in Sherbrooke were located at intersections and in a downtown area with significant conflicts between road users. It also showed that signalized intersections were more dangerous than unsignalized ones; over half (58%) of the hotspot locations were located at four-leg signalized intersections. The results of the second method show that crash patterns varied according to season and during certain time periods. Total seasonal patterns revealed denser trends and patterns during the summer, fall, and winter, then a steady trend and pattern during the spring. Our findings also illustrated that crash patterns that applied accident severity were denser than the results that only involved the observed crash counts. The results clearly show that the proposed methods could assist transport authorities in quickly identifying the most hazardous sites in a road network, prioritizing hotspot locations in a decreasing order more efficiently, and assessing the relationship between traffic accidents and seasons. / Les accidents de la route sont responsables de plus de 1500 décès par année au Canada et ont des effets néfastes sur la société. Aux yeux des autorités en transport, il devient impératif d’en réduire les impacts. Il s’agit d’une préoccupation majeure au Québec depuis que les risques d’accidents augmentent chaque année au rythme de la population. En réalité, les accidents routiers se produisent rarement de façon aléatoire dans l’espace-temps. Ils surviennent généralement à des endroits spécifiques notamment aux intersections, dans les bretelles d’accès, sur les chantiers routiers, etc. De plus, les conditions climatiques associées aux saisons constituent l’un des facteurs environnementaux à risque affectant les taux d’accidents. Par conséquent, il devient impératif pour les ingénieurs en sécurité routière de localiser ces accidents de façon plus précise dans le temps (moment) et dans l’espace (endroit). Cependant, les accidents routiers sont influencés par d’importants facteurs comme le volume de circulation, les conditions climatiques, la géométrie de la route, etc. Le but de cette étude consiste donc à identifier les points chauds au moyen d’un historique des données d’accidents et de leurs répartitions spatiotemporelles en vue d’améliorer la sécurité routière. Cette thèse propose deux nouvelles méthodes permettant d’identifier les points chauds à l’intérieur d’un réseau routier. La première méthode peut être utilisée afin d’identifier et de prioriser les points chauds dans les cas où les données sur le volume de circulation sont disponibles alors que la deuxième méthode est utile dans les cas où ces informations sont absentes. Ces méthodes ont été conçues en utilisant des données d’accidents sur trois ans (2011-2013) survenus à Sherbrooke. La première méthode propose une approche intégrée en deux étapes afin d’identifier les points chauds au sein du réseau routier. La première étape s’appuie sur une méthode d’analyse spatiale connue sous le nom d’estimation par noyau. La deuxième étape repose sur une méthode de balayage du réseau routier en utilisant les taux critiques d’accidents, une démarche éprouvée et décrite dans le manuel de sécurité routière. Lorsque la densité des accidents routiers a été calculée au moyen de l’estimation par noyau, les points chauds potentiels sont ensuite testés à l’aide des taux critiques. La seconde méthode propose une approche intégrée destinée à analyser les distributions spatiales et temporelles des accidents et à les classer selon leur niveau de signification. La répartition des accidents selon les saisons a été analysée à l’aide de l’estimation par noyau, puis ces valeurs ont été assignées comme attributs dans le test de signification de Moran. Les résultats de la première méthode démontrent que plus de 90 % des points chauds à Sherbrooke sont concentrés aux intersections et au centre-ville où les conflits entre les usagers de la route sont élevés. Ils révèlent aussi que les intersections contrôlées sont plus à risque par comparaison aux intersections non contrôlées et que plus de la moitié des points chauds (58 %) sont situés aux intersections à quatre branches (en croix). Les résultats de la deuxième méthode montrent que les distributions d’accidents varient selon les saisons et à certains moments de l’année. Les répartitions saisonnières montrent des tendances à la densification durant l’été, l’automne et l’hiver alors que les distributions sont plus dispersées au cours du printemps. Nos observations indiquent aussi que les répartitions ayant considéré la sévérité des accidents sont plus denses que les résultats ayant recours au simple cumul des accidents. Les résultats démontrent clairement que les méthodes proposées peuvent: premièrement, aider les autorités en transport en identifiant rapidement les sites les plus à risque à l’intérieur du réseau routier; deuxièmement, prioriser les points chauds en ordre décroissant plus efficacement et de manière significative; troisièmement, estimer l’interrelation entre les accidents routiers et les saisons.