Abstract
Rwanda has a high density of landslides, heavy precipitation events and a shortage of resources to study them, making it an excellent candidate for study using satellite-based remote sensing data. To assess landslide hazards countrywide, I first built a landslide inventory of 254 landslides and used a statistical methodology. Using logistic regression on 24 test variables, I determined that slope and population density are statistically most relevant to landslide occurrence in Rwanda. A preliminary predictive hazard map for Rwanda was produced, with an overall predictive accuracy of 79.6%. Second, I worked to define a relationship between precipitation intensity and landslide density for a landslide-prone study area in western Rwanda. In the 1180 km2 study area, I mapped 577 landslides, using CNES/Astrium and WorldView satellite imagery in Google Earth over a study period of 2000 to 2015. One 400 km2 part of the study area has a high landslide density of 1.4 landslides/km2, while another 780 km2 part with identical geology, soils, land-use, and vegetation has a much lower landslide density. To identify possible triggering events for these landslides, I analyzed a 16 year record of TRMM (Tropical Rainfall Measuring Mission) satellite precipitation data. The high landslide density region and the low landslide density region were not notably different in rainfall, as quantified by recurrence interval analysis. A relationship between precipitation and landslide density could therefore not be developed, and the null hypothesis cannot be ruled out. This apparent lack of connection could result from a variety of factors including TRMM grid size, satellite imagery temporal resolution, antecedent soil moisture, or vegetation regrowth rates. Approval _________________________________________ __________________ Dr. Adam Booth, Thesis Advisor Date _________________________________________ __________________ Dr. Martin J. Streck, Department Chair Date
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