Estimation of Ground Deformation in Landslide Prone Areas Using GPS: A Case Study of Bududa, Uganda

Landslides are a frequent phenomenon on mountain Elgon, particularly in Bududa district on the SW side of this extinct shield volcano. Landslides have led to the destruction of property and loss of life we, therefore, need to monitor them. Monitoring how landslides build-up makes it possible to timely evacuate people and build barriers to protect property against damage by landslides. Residents in Bududa have reported cracks developing in the ground and houses. These cracks continue to grow, suggesting a future catastrophic event. Such an event may resemble the 2010 landslide in Bududa, which killed approximately 450 people and destroyed much property. In order to mitigate the consequences of a new landslide as much as possible, we monitored ground motion in Bududa in eleven stations from June 2018 to June 2019. Six-hour session GPS observations were made, and deformation was determined over the observation period, June to September 2018, September to November 2018, November 2018 to February 2019 and February to June 2019. A congruency test was performed to determine how significant the deformation was. It appeared that the ground deformation differed largely at various monitored stations, ranging from 0.004 to 0.076 m, 0.001 to 0.067 m and 0 to 0.078 m in the East, North and vertical directions respectively. The values indicate that most slopes in the district are unstable, particularly in the wet seasons, which implies that future landslides pose a high risk for society.

Estimation of Ground Vertical Displacement in Landslide Prone Areas Using PS-InSAR. A Case Study of Bududa, Uganda

Estimation of ground displacement in landslide susceptible regions is very critical to understanding how landslides develop. The knowledge of ground displacement rates and magnitudes helps plan for the safety of the people and infrastructure. The early detection of landslides in Bududa is still a challenge due to the limited technology, hard to access, and a need for an affordable technique that can monitor a wide area continuously. In recent studies, the use of Persistent Scatterer Interferometry Synthetic Aperture Radar (PS-InSAR) has provided vital information on landslide monitoring through the measurement of ground displacement. In this study, Synthetic Aperture Radar (SAR) band C series of Sentinel 1-A and 1-B Satellite images were acquired between 2019 and 2020 along ascending and descending orbit paths. The Line of Sight Sight (LOS) displacement was determined for both satellite tracks, and then the LOS displacement was projected to the vertical direction. The PS-InSAR derived vertical displacement was then compared with GPS vertical displacement magnitudes over three GPS stations in the area. It was observed that vertical displacement velocity reached 20 cm/yr in Mountain Elgon. This displacement rate showed that there are points in the region that are highly unstable. The displacement velocity and magnitude in Bududa reached 6 cm/yr and 13 cm in two years. This rate and magnitude showed that Bududa is highly unstable compared with displacement velocities and magnitudes in landslide susceptible areas globally. The displacement was generally subsidence over the observation period. The vertical displacement estimated by PS-InSAR was comparable with GPS based on the estimated RMSE. The vertical displacement was highest at slopes between 32° and 60° and lowest between 0° and 9°. The vertical ground displacement was highly correlated with the rainfall that was received. The soil texture in Bududa has high clay content, with clay layering hence low drainage rates, field capacity, saturation and bulk density. It was observed that ground displacement was highly influenced by slope, rainfall and soil texture. Displacement could be estimated in three dimensions using PS-InSAR in the future if sufficient SAR images in ascending and descending tracks are made available with significantly different geometries. This would add to the knowledge of displacement patterns in the east and north directions at a large spatial scale.

Evaluation of CHIRPS Satellite Gridded Dataset as an Alternative Rainfall Estimate for Localized Modelling over Uganda

The Ugandan economy is largely dependent on rural-based and rain-fed agriculture. This creates a critical need to understand the rainfall dynamics at the local scale. However, the country has a sternly sparse and unreliable rain gauge network. This research, therefore, sets out to evaluate the use of the CHIRPS satellite gridded dataset as an alternative rainfall estimate for local modelling of rainfall in Uganda. Complete, continuous and reliable in situ station observations for the period between 2012 and 2020 were used for the comparison with CHIRPS satellite data models in the same epoch. Rainfall values within the minimum 5 km and maximum 20 km radii from the in situ stations were extracted at a 5 km interval from the interpolated in situ station surface and the CHIRPS satellite data model for comparison. Results of the 5 km radius were adopted for the evaluation as it’s closer to the optimal rain gauge coverage of 25 km2. They show the R2 = 0.91, NSE = 0.88, PBias = -0.24 and RSR = 0.35. This attests that the CHIRPS satellite gridded datasets provide a good approximation and simulation of in situ station data with high collinearity and minimum deviation. This tallies with related studies in other regions that have found CHIRPS datasets superior to interpolation surfaces and sparse rain gauge data in the comprehensive estimation of rainfall. With a 0.05° * 0.05° (Latitude, longitude) spatial resolution, CHIRPS satellite gridded rainfall estimates are therefore able to provide a comprehensive rainfall estimation at a local scale. Essentially these results reward research science in regions like Uganda that have sparse rain gauges networks characterized by incomplete, inconsistent and unreliable data with an empirically researched alternative source of rainfall estimation data. It further provides a platform to scientifically interrogate the rainfall dynamics at a local scale in order to infuse local policy with evidence-based formulation and application.

Socio-Economic Vulnerability to COVID-19: The Spatial Case of Greater Kampala Metropolitan Area (GKMA)

COVID-19 has presented itself with an extreme impact on the resources of its epi-centres. In Uganda, there is uncertainty about what will happen especially in the main urban hub, the Greater Kampala Metropolitan Area (GKMA). Consequently, public health professionals have scrambled into resource-driven strategies and planning to tame the spread. This paper, therefore, deploys spatial modelling to contribute to an understanding of the spatial variation of COVID-19 vulnerability in the GKMA using the socio-economic characteristics of the region. Based on expert opinion on the prevailing novel Coronavirus, spatially driven indicators were generated to assess vulnerability. Through an online survey and auxiliary datasets, these indicators were transformed, classified, and weighted based on the BBC vulnerability framework. These were spatially modelled to assess the vulnerability indices. The resultant continuous indices were aggregated, explicitly zoned, classified, and ranked based on parishes. The resultant spatial nature of vulnerability to COVID-19 in the GKMA sprawls out of major urban areas, diffuses into the peri-urban, and thins into the sparsely populated areas. The high levels of vulnerability (24.5% parishes) are concentrated in the major towns where there are many shopping malls, transactional offices, and transport hubs. Nearly half the total parishes in the GKMA (47.3%) were moderately vulnerable, these constituted mainly the parishes on the outskirts of the major towns while 28.2% had a low vulnerability. The spatial approach presented in this paper contributes to providing a rapid assessment of the socio-economic vulnerability based on administrative decision units-parishes. This essentially equips the public health domain with the right diagnosis to subject the highly exposed and vulnerable communities to regulatory policy, increase resilience incentives in low adaptive areas and optimally deploy resources to avoid the emancipation of high susceptibility areas into an epicentre of Covid-19.