With the rapid advances in technologies, strategies for efficiently operating the spatial data are needed. The spatial data consists of points, lines, rectangles, regions, surface, and volumes. In this thesis, we focus on the region data. There are many important and efficient operations for the region data, such as neighbor finding, rotation, and mirroring. The nearest neighbor (NN) finding is frequently used in geographic information system (GIS). We can find the specific point (e.g., a park, a department store, etc.) that is the closest to our position in geographical information systems. In any representation for the region data, it is not instinctive and easy for nearest neighbor finding, since the coordinate information has been lost. Voros, Chen, and Chang have proposed the strategies for the nearest neighbor finding based on the quadtree in eight directions. Chen and Chang have proposed the nearest neighbor finding based on the Peano curves. These strategies for the nearest neighbor finding based on the quadtree and the Peano curve use a looping process, which is time-consuming. On the other hand, in recent years, many researchers have also focused on finding efficient strategies for the rotating and mirroring operations, which is useful when the animation is performed by computers. The boolean function-based encoding is a considerable amount of space-saving with respect to the other binary image representation. The CBLQ representation saves memory space as compared to the other binary image representations that have proposed the strategies of the set operations. However, the processes for obtaining the rotated or mirrored code based on these two representations are time-consuming, since the coordinate information of all pixels has been lost. Therefore, in this thesis, first, for the nearest neighbor finding based on the quadtrees and the Peano curve, we propose the strategy which uses the bitwise and arithmetic operations, and it is more efficient than the strategies based on the looping processes. Next, we propose efficient strategies for rotating and mirroring images based on the boolean function-based encoding and constant bit-length linear quadtrees (CBLQ) representations. From our simulation study, first, we show that our strategies based on the quadtree and the Peano curve require the least CPU-time and our strategy based on the Hilbert curve requires the least total time (the CPU-time + the I/O time) among the strategies for the nearest neighbor finding based on the quadtree and the three space-filling curves. Next, in most of cases, when the black density is no larger than 50%, the CPU-time based on the boolean function-based encoding is less than that based on CBLQ.

Numerous studies project that climate change will accelerate the rise in global sea levels, leading to increased coastal inundation, greater potential damage from storm surge events, beach erosion and other coastal impacts which threaten vital infrastructure and facilities that currently support the economies of island nations. There is a broad consensus amongst experts that small island developing states (SIDS) face the greatest risk to the projected impacts of climate change. Unfortunately, few sea level rise (SLR) impact assessment studies have been conducted in SIDS due to the limitations of the geospatial data with regard to currency, accuracy, relevance and completeness. This research improves upon previous SLR impact assessment research by utilizing advanced global digital elevation models to create coastal inundation scenarios in one metre increments for 19 Caribbean Community (CARICOM) nations and member states, and then examine the implications for seven key impact indicators (land area, population, economic activity, urban areas, tourism resorts, transportation infrastructure and beach erosion). The results indicate that a one metre SLR would have serious consequences for CARICOM nations. For example under this scenario over 10% of the 73 identified study area airports and 30% of the 266 major tourism resorts were identified as prone to flooding. Projected effects were not found to be uniform across the region; low-lying island nations and mainland countries with coastal plains below ten metres were identified as the most vulnerable countries. Recommendations for adaptive actions and policies are provided.

CoordenaÃÃo de AperfeiÃoamento de Pessoal de NÃvel Superior / Este trabalho teve como objetivo principal analisar os parÃmetros hidrossedimentolÃgicos da bacia hidrogrÃfica do SÃo Josà (BHSJ), avaliar a produÃÃo de sedimentos e definir o mapa de susceptibilidade dos solos à erosÃo, a partir da EquaÃÃo Universal de Perdas do Solo (USLE). Para isso determinou-se: (i) a erosividade da chuva na bacia, a partir de formulaÃÃes desenvolvidas com base em Wischmeier e Smith (1958) e Fournier (1960); (ii) a erodibilidade do solo na bacia, desenvolvida a partir do nomograma de Wischmeier et al. (1971); (iii) o fator topogrÃfico da bacia, atravÃs do mÃtodo de Bertoni e Lombardi Neto (1990), utilizando o Modelo NumÃrico do Terreno (MNT) gerado atravÃs de dados do Shuttle Radar Topography Mission (SRTM); (iv) a identificaÃÃo, delimitaÃÃo e descriÃÃo dos tipos de uso e ocupaÃÃo dos solos na bacia, atravÃs de SIGs, utilizando a classificaÃÃo de imagens de satÃlite multitemporais prÃ-processadas. A razÃo de aporte de sedimentos (RAS) da bacia foi estimada a partir de equaÃÃes propostas por Maner (1958), Roehl (1962), Vanoni (1975), Renfro (1975) e Williams e Brendt (1972), a partir das quais escolheu-se a que apresentou resultado mais prÃximo do valor de descarga sÃlida em suspensÃo medido no exutÃrio para o ano hidrolÃgico monitorado de Setembro de 2011 a Setembro de 2012. Os resultados mostraram que: (i) a erosividade, determinada a partir da equaÃÃo desenvolvida nesse trabalho e validada com coeficiente de Nash e Sutcliffe de 0,81; apresentou resultados satisfatÃrios no ajuste da equaÃÃo da USLE para a BHSJ, com melhor ajuste dentre as metodologias comparadas, que apresentaram erros percentuais de pelo menos 50%. (ii) a erodibilidade determinada foi espacializada na bacia por Krigagem, utilizando modelo Gaussiano testado e validado; (iii) o fator topogrÃfico foi determinado e espacializado na bacia, considerando-se valores mÃdios para seis classes distintas de declividades; (iv) os tipos de uso e ocupaÃÃo dos solos na bacia foram divididos em agricultura, caatinga, cerrado, floresta Ãmida, solo exposto, Ãrea urbana e vegetaÃÃo rasteira. A partir da USLE estimou-se uma taxa de erosÃo na bacia de 364 t.ha-1.ano-1, correspondendo a uma produÃÃo de 1,48 x 106 t de sedimentos no ano hidrolÃgico. O mapa de susceptibilidade à erosÃo mostrou que a bacia apresentou grau de erosÃo: baixo, mÃdio, alto e muito alto, respectivamente, em 26,39, 34,34, 30,37 e 8,90% da Ãrea. A SDR determinada pelas equaÃÃes propostas por Maner (1958) e Vanoni (1975) apresentaram valores bem prÃximos da descarga sÃlida em suspensÃo medida no exutÃrio durante os eventos monitorados, com erros percentuais de â12,4 e â 2,6%, respectivamente. / This study aimed to analyse the SÃo Josà river catchment (BHSJ) hydrosedimentological parameters, evaluate the sediment yield and define the susceptibility of soils to erosion based on the Equation Universal Soil Loss (USLE). Therefore, it was determined: (i) the rainfall erosivity (R - factor) at catchment, based on the formulations proposed by Wischmeier and Smith (1958) and Fournier (1960), (ii) the soil erodibility (K â factor) at catchment, it was calculated using the nomograph developed by Wischmeier et al. (1971), (iii) the topographic factor (LS â factor) was obtained through the method of Bertoni and Lombardi Neto (1990), using the Digital Elevation Model (DEM) generated by the Shuttle Radar Topography Mission (SRTM), (iv) identification, delineation and description of the land cover (C â factor), using GIS, through by the classification techniques of multitemporal satellite images. The sediment delivery ratio (SDR) of the catchment was estimated based in equations proposed by Maner (1958), Roehl (1962), Vanoni (1975), Renfro (1975) and Williams and Brendt (1972). The equation that presented the best fit was selected, comparing modelled and measured data at the catchment outlet, for hydrological year monitored since September 2011 to September 2012. The results show the following: (i) the rainfall erosivity, determined by the equation developed in this study and validated with Nash and Sutcliffe coefficient of 0.81; presented satisfactory results in the adjustment of the USLE equation for BHSJ, with best adjustments among the compared methods, that presented percentage errors of at least 50%; (ii) the soil erodibility determined was spatialized in the catchment by Kriging, using the method of interpolation Gaussian model tested and validated; (iii) the topographic factor was determined and spatialized in the catchment, considering average values for six distinct classes of slopes; (iv) the use types and occupation of the basin were divided into agriculture, caatinga, cerrado, tropical rainforest, bare soil, urban and undergrowth. In the catchment, the rate of erosion was estimated at 364 t ha-1.year-1 from the USLE, corresponding to a sediment yield of 1.48 x 106 t in the hydrological year. The map of erosion susceptibility showed that the degree of erosion basin presented: low, medium, high and very high, respectively, 26.39, 34.34, 30.37 and 8.90% of the area. SDR determined by the equations proposed by Maner (1958) and Vanoni (1975) presented values near suspended sediment discharge measured at the catchment outlet during the monitored events, with percentage errors of -12.4 and -2.6%, respectively.

Flash flooding, as a result of excessive rainfall in a short period, is considered as one of the worst environmental hazards in arid regions. Areas located in the western provinces of Saudi Arabia have experienced catastrophic floods. Geomorphologic evaluation of hydrographic basins provides necessary information to define basins with flood hazard potential in arid regions, especially where long-term field observations are scarce and limited. Six large basins (from North to South: Yanbu, Rabigh, Khulais, El-Qunfza, Baish and Jizan) were selected for this study because they have large surface areas and they encompass high capacity dams at their downstream areas. Geographic Information System (GIS) and remote sensing techniques were applied to conduct detailed morphometric analysis of these basins. The six basins were further divided into 203 sub-basins based on their drainage density. The morphometric parameters of the six basins and their associated 203 sub-basins were calculated to estimate the degree of flood hazard by combining normalized values of these parameters. Thus, potential flood hazard maps were produced from the estimated hazard degree. Furthermore, peak runoff discharge of the six basins and sub-basins were estimated using the Snyder Unit Hydrograph and three empirical models (Nouh’s model, Farquharson’s model and Al-Subai’s model) developed for Saudi Arabia. Additionally, recommendations for flood mitigation plans and water management schemes along these basins were further discussed.

In developing countries rural communities are normally geographically isolated contributing to both poverty levels and the deficiency in the participation of social and economic activities. Accessibility to education constitutes one of the primordial links between the economic growth of a country and the development of high skilled population. Given South Africa's unique history, divisions throughout the landscape incapacitate inhabitants of rural communities in reaching opportunities and services, therefore, aggravating issues related to social exclusion and inequality. This study aims to determine accessibility levels in South African rural regions by looking at different aspects that entangle the theory behind it, specifically: the zone attractiveness and impedance. With that in mind, the investigations carried out are firstly directed towards accessibility at the provincial level and thereafter a focus area is determined. At the provincial level, it was found that the Northern Cape presented the greatest disadvantages. However, given insufficient resources and data related to this province, the Cape Winelands Municipality District was chosen as the area to extend the investigations. When assessing the focus area, the study deployed a GIS-based analysis wherein potential and real accessibility were determined. Initially using the gravity measure, and subsequently using a survey carried out in the region. The study has revealed that Stellenbosch and Robertson are the towns experiencing high accessibility levels. Notwithstanding, most principal towns still experience critically low accessibility indexes. The findings of this study can, therefore, be useful in indicating areas that need further studies or are experiencing disadvantages regarding accessibility.

Harmful Algae Blooms (HABs) in inland waterbodies (e.g., lakes and ponds) pose serious threat to human health and natural ecosystem. Thus, it is imperative to assess HABs and their potential triggering factors over broader spatiotemporal scales. This study utilizes Chlorophyll-a (Chl-a) concentration in water samples collected from lakes in Illinois as an indirect measurement of HABs. The major objectives were to assess the spatiotemporal pattern of HABs over Illinois regions in recent decades, and to examine different machine learning models for predicting the Chl-a concentration based on publicly available water quality datasets. The Chl-a dataset was compiled from two different sources, the regular monitoring program by Illinois Environmental Protection Agency (IEPA) and the Voluntary Lake Monitoring Program (VLMP), the latter of which was primarily collected by citizen participants. Seven environmental and water quality zones were selected for spatial analyses. Additionally, the temporal patterns were assessed using time-series decomposition of monthly Chl-a concentration datasets. The machine learning pipeline includes two tasks: a regression modeling task for predicting Chl-a concentration, and a classification task for estimating lake trophic status. Different meteorological, land use and land cover, and lake morphometry variables were used as independent variables. Four regression models, i.e., Partial Least Squares Regression (PLSR), Support Vector Machine Regression (SVR), Artificial Neural Network Regression (ANNR), and Random Forest Regression (RFR) were used for the first task of the modeling pipeline, and four classification models, i.e., Logistic Regression Classification (LRC), Support Vector Machine Classification (SVC), Artificial Neural Network Classification (ANNC), and Random Forest Classification (RFC), were used for the second task. Results indicate that: a) the Collinsville region in southwestern part of Illinois exhibited higher mean concentration of Chl-a in its lakes than any other regions from 1998 to 2018; b) the lakes that showed increasing trends in their monthly mean Chl-a concentrations were also clustered in the southwestern region; c) Random Forest outperformed all other models in both classification (Accuracy=60.06%) and regression (R2=38.88%); and d) the land use and land cover variables were found as the most important set of variables in Random Forest models.

Please read the abstract in the section 00front of this document / Dissertation (MSc)--University of Pretoria, 2001. / Geography, Geoinformatics and Meteorology / MSc / Unrestricted

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