Browsing by Author "Christ, Sven"

Now showing 1 - 2 of 2
  • Thumbnail Image
    Item
    The influence of topographical variability on wildfire occurrence and propagation
    (Stellenbosch : Stellenbosch University, 2024-03) Christ, Sven; De Klerk, Helen; Stellenbosch University. Faculty of Arts and Social Sciences. Dept. of Geography and Environmental Studies.
    ENGLISH ABSTRACT: Wildfires have increasingly become a point of concern, especially with notable incidents like the 2017 Knysna fire. These naturally occurring phenomena, despite their disruptive nature, are crucial for the sustainability of certain ecosystems. At the heart of understanding wild-fires lies the relationship between climate, vegetation, topography, and human land use, with topography standing out as a significant determinant. This thesis delves into the fundamen-tal role of topography, emphasizing its effect on the ignition, propagation, and behaviour of wildfires. Utilizing Digital Elevation Models (DEMs), the research extracts invaluable topographic data aiming to augment the understanding of wildfires, especially in mixed natural forest and fyn-bos ecosystems. Existing fire models have shown certain shortcomings, often overlooking crucial localized wind data, which has profound implications for predicting fire behaviour. By bridging this gap, the study explores the potential of computational fluid dynamics in modelling surface winds based on topography for fire research. The research systematically addresses several key objectives: Mapping the current land-scape of topography-cantered wildfire research and investigating the utility of DEM-derived surface wind in refining fire propagation models, identifying and analysing historical fire patterns to pinpoint fire refugia in the Knysna/Tsitsikhama region, employing machine learning techniques, to determine if topographic variables extracted from DEMs can antici-pate fire refugia. The findings underscore the salience of topography in wildfires. Especially significant is the role of aspect in determining fire refugia, emphasizing that a combination of multiple variables offers the most accurate insights. Machine learning, notably the XGBoost model, showcases potential in identifying critical topographical features impacting fire behaviours. Furthermore, the research sheds light on the pivotal influence of wind chan-nels, formed by topographical features, in both the inception and spread of wildfires. In summary, this thesis underscores the integral role of topography in understanding wild-fires. It charts a roadmap for future research, emphasizing the importance of high-quality validation data, a more comprehensive mapping of fire refugia, and an acknowledgement of the influence of human activity on fire regimes. By building on the methodologies and in-sights presented, there lies an opportunity to advance sustainable wildfire management so-lutions that benefit both ecosystems and human communities.
  • Thumbnail Image
    Item
    Spatial visualization of uncertainty
    (Stellenbosch : Stellenbosch University, 2017-03) Christ, Sven; Munch, Zahn; Stellenbosch University. Faculty of Arts and Social Sciences. Dept. of Geography and Environmental Studies.
    ENGLISH ABSTRACT: Geospatial information has become more accessible since the early 2000s. Uncertainty has remained a constant in data, due to various factors, including scale and real world conceptualization. Geospatial products are frequently used to inform decision makers on key decisions, with little understanding of the quality of the data. However, accuracy assessments have improved significantly since the visual screening that was used in the 1950s, now providing statistics such as the Kappa coefficient, root mean square error (RMSE) and the confusion matrix. Two questions thus arise: 1) do those using the data inform themselves about the quality of data; and 2) can visualization of the uncertainty in spatial data aid in the communication of the data quality? This research was achieved in three tasks: 1) evaluate the South African perception on data quality; 2) develop an uncertainty visualization tool; 3) evaluate the uncertainty visualization tool. The first task was achieved through a quantitative survey of people working in the South African geospatial industry. Despite a limited response, the findings indicated that those working with geospatial data do not always seek to verify the quality of the data they are using. It also came to light that most of those who do not verify the quality of their data, would like to have the uncertainty in the data visualized. Task 2 aimed at developing a tool for the visualization of spatial uncertainty (Uview). Uview was based on the findings from Task 1 supplemented by recommendations from literature and other uncertainty visualization tools. The tool was developed for continuous raster datasets only and uses the z-score and modified z-score as its main statistics for visualization. Standard accuracy assessment statistics (global data quality statistics), such as RMSE and mean absolute error (MAE) have also been included in Uview to make it an accuracy assessment and uncertainty visualization tool for continuous raster data. Lastly Task 3, the evaluation of Uview was done using a two-pronged approach. The first part encompassed investigating the usability of the tool. In this phase the visualizations were used to derive relationships between digital elevation models (DEM), uncertainty and a watershed product. It was found that Uview does provide useful information, and watersheds are sensitive to deviations from true value at key locations more than the magnitude of the deviation. When Uview was evaluated by twelve people in the geospatial industry they all agreed that though improvements can be made, as it presents itself currently it is already a useable product that can add value. All respondents agreed that the visualization improves the comprehension of the statistics, and so of uncertainty.