The objective of this thesis was to identify the mechanisms associated with the recurrence of rock-slope instability along the R518 and R523 roads in Limpopo. Advanced geotechnical methods and ASTER imagery were used for the purpose while a predictive rockfall hazard rating matrix chart and rock slope stability charts for unsaturated sensitive clay soil and rock slopes were to be developed. The influence of extreme rainfall on the slope stability of the sensitive clay soil was also evaluated. To achieve the above, field observations, geological mapping, kinematic analysis, and limit equilibrium were performed. The latter involved toppling, transitional and rotational analyses. Numerical simulation was finally resorted to. The following software packages were employed: SWEDGE, SLIDE, RocData, RocFall, DIPS, RocPlane, and Phase 2. The simulation outputs were analyzed in conjunction with ASTER images. The advanced remote sensing data paved the way for landslide susceptibility analysis. From all the above, rockfall hazard prediction charts and slope stability prediction charts were developed. Several factors were also shown by numerical simulation to influence slope instability in the area of study, i.e. sites along the R518 and R523 roads in the Thulamela Municipality. The most important factors are extreme rainfall, steep slopes, geological features and water streams in the region, and improper road construction. Owing to the complexity of the failure mechanisms in the study area, it was concluded that both slope stability prediction charts and rock hazard matrix charts are very useful. They indeed enable one to characterize slope instability in sensitive clay soils as well as rockfall hazards in the study area. It is however recommended that future work is undertaken to explore the use of sophisticated and scientific methods. This is instrumental in the development of predictive tools for rock deformation and displacement in landslide events. / Electrical and Mining Engineering / D. Phil. (Mining Engineering)

Indiana University-Purdue University Indianapolis (IUPUI) / The primary objective of this research involves mapping out and validating the existence of sub surface drainage tiles in a given cropland using Remote Sensing and GIS methodologies. The process is dependent on soil edge differentiation found in lighter versus darker IR reflectance values from tiled vs. untiled soils patches. Data is collected from various sources and a primary classifier is created using secondary field variables such as soil type, topography and land Use and land cover (LULC). The classifier mask reduces computational time and allows application of various filtering algorithms for detection of edges. The filtered image allows an efficient feature recognition platform allowing the tile drains to be better identified. User defined methods and natural vision based methodologies are also developed or adopted as novel techniques for edge detection. The generated results are validated with field data sets which were established using Ground Penetration Radar (GPR) studies. Overlay efficiency is calculated for each methodology along with omission and commission errors. This comparison yields adaptable and efficient edge detection techniques which can be used for similar areas allowing further development of the tile detection process.

Indiana University-Purdue University Indianapolis (IUPUI) / Urban forestry is a very important element of urban structures that can improve the environment and life quality within the urban areas. Having an accurate classification of urban forests and grass areas would help improve focused urban tree planting and urban heat wave mitigation efforts. This research project will compare the use of high – resolution aerial imagery and LiDAR data when used to classify canopy and grass areas. The high – resolution image, with 1 – meter resolution, was captured by The National Agriculture Imagery Program (NAIP) on 6/6/2012. Its coordinate system is the North American Datum of 1983 (NAD83). The LiDAR data, with 1.0 – meter average post spacing, was captured by Indiana Statewide Imagery and LiDAR Program from 03/13/2011 to 04/30/2012.The study area is called the Near East Side Community Organization (NESCO) neighborhood. It is located on the east side of downtown Indianapolis, Indiana. Its boundaries are: 65 interstate, East Massachusetts Avenue, East 21st Street, North Emerson Avenue, and the rail road tracks on the south of the East Washington Street. This research will also perform the accuracy assessment based on the results of classifications using high – resolution aerial imagery and LiDAR data in order to determine and explain which method is more accurate to classify urban canopy and grass areas.

Indiana University-Purdue University Indianapolis (IUPUI) / Over the past two decades, northern parts of the United States have experienced extreme heat conditions. Some of the notable heat wave impacts have occurred in Chicago in 1995 with over 600 reported deaths and in Philadelphia in 1993 with over 180 reported deaths. The distribution of extreme heat events in Indianapolis has varied since the year 2000. The Urban Heat Island effect has caused the temperatures to rise unusually high during the summer months. Although the number of reported deaths in Indianapolis is smaller when compared to Chicago and Philadelphia, the heat wave in the year 2010 affected primarily the vulnerable population comprised of the elderly and the lower socio-economic groups. Studying the spatial distribution of high temperatures in the vulnerable areas helps determine not only the extent of the heat affected areas, but also to devise strategies and methods to plan, mitigate, and tackle extreme heat. In addition, examining spatial patterns of vulnerability can aid in development of a heat warning system to alert the populations at risk during extreme heat events. This study focuses on the qualitative and quantitative methods used to measure extreme heat events. Land surface temperatures obtained from the Landsat TM images provide useful means by which the spatial distribution of temperatures can be studied in relation to the temporal changes and socioeconomic vulnerability. The percentile method used, helps to determine the vulnerable areas and their extents. The maximum temperatures measured using LST conversion of the original digital number values of the Landsat TM images is reliable in terms of identifying the heat-affected regions.