Rwanda Journal of Engineering, Science, Technology and Environment

Estimating Soil Erosion to Highlight Potential Areas for Conservation Priority in Rukarara Catchment, South-western Rwanda

Mon, 12 Jun 2023 00:00:00 +0000

Soil erosion is one of the major environmental problems in tropical ecosystems; however, the lack of information on the amount of eroded soils in Rwandan mountainous watersheds hinders effective decision-making toward sustainable soil management. This study aimed at predicting soil erosion in the Rukarara River watershed, one of the mountainous watersheds in Rwanda, and identifying potential areas of high erosion risk using the revised universal soil loss equation (RUSLE) implemented in a GIS environment. The annual soil loss was estimated by computing and performing a spatial overlay analysis of relevant layers including rainfall erosivity (R), soil erodability (K), slope length and steepness (LS), cover management (C), and conservation practice (P) factors. The results indicate that the annual soil erosion varies from 54 to 134 t ha⁻¹ year⁻¹ (95% confidence interval) with a mean of 39.96 t ha⁻¹ year⁻¹. The study area is generally characterized by very low and low soil erosion classes and the mean erosion correlated with literature results in the tropics. Agricultural lands are the hotspots of soil erosion with a mean of soil loss of 61.29 t ha⁻¹ year⁻¹. The produced maps and soil loss estimates can facilitate informed decision-making toward sustainable soil management in mountainous areas of Rwanda, especially in the Rukarara River watershed.

Leveraging the Potential of Convolutional Neural Network and Satellite Images to Map Informal Settlements in Urban Settings of the City of Kigali, Rwanda

Esaie Dufitimana, Theophile Niyonzima — Mon, 12 Jun 2023 00:00:00 +0000

The urban population is rapidly increasing and more than half of the world's population is currently living in cities. About 1 billion of city dwellers are living in slums and informal settlements. Addressing the issue of slums and informal settlements require information on these areas. This study explored the potential of Convolutional Neural Network (CNN) and Very-High Resolution satellite image to map the informal settlements in urban areas of the city of Kigali, Rwanda. The study applied modified U-Net model with MobileNetV2 model as the base model to discriminate areas with informal settlements from other areas. The model was obtained by modifying the original U-Net architecture to incorporate dilated convolutional operations at the beginning of the network. The findings demonstrate that based on the spatial characteristics of informal settlements, the model was able to detect informal settlements in urban areas with a recall of 0.862, a precision of 0.810 and an F1-Score of 0.809. Based on these results, the study can be a basis for finding relevant information about informal settlements that are of concern in the implementation of SDGs, especially goal 11 addressing the issues of safe and inclusive cities and human settlements.

Measuring disparities in access to district and referral hospitals in the city of Kigali, Rwanda

Gilbert Nduwayezu, Emmanuel Ingabire, Jean Pierre Bizimana — Mon, 12 Jun 2023 00:00:00 +0000

The study focused on the identification of theoretical and methodological constructs that can be used to analyse and improve the spatial performance of public health service delivery systems and the development of a corresponding spatial-analysis and GIS-based planning approach using the city of Kigali as a case study. Access to health care as a multi-tiered system was measured by using five dimensions of access, namely geographic accessibility, availability, affordability, acceptability and accommodation. Geo-statistical analysis was performed to measure and evaluate access to the district and referral health care. For geographic access, the travel time for every trip is approximated for the two main modes of transport (walking and public transport). To realistically represent the friction of distance, the street network was categorised into several road classes for which different travel speeds are estimated for each transport mode on the basis of local knowledge. A factor analysis model was used for availability, affordability, acceptability, and accommodation dimensions. Different indicators were evaluated in various units and scales and standardized to see how satisfaction levels varied with each dimension. Based on the type of indicator, for the cost or benefit, the score was zero (0) or one (1). One (1) was considered a positive impact, while zero (0) was considered a negative impact. Summary scores were developed for all indicators by combining more than one indicator into a single value. The created summary score also ranges from 0 to 1, and the higher the score, the better the access attainment. To find areas that need intervention, the comparison of all five indicators and the scores of access for each indicator was made. The dimensions of geographic accessibility and availability appeared to be the most problematic. The district health care dimensions have revealed a low performance level in geographic accessibility (0.64) and a high-performance level in affordability (0.94). The referral health care dimensions have revealed a low performance level of geographic accessibility (0.17) and a high-performance level of acceptability (0.92). Health planners and policy makers will commonly require such a planning method, particularly in the developing world where spatial contexts are highly dynamic as a result of rapid urbanisation.

Groundwater Potential Mapping using Geospatial and AHP Techniques in Eastern Province of Rwanda

Mon, 12 Jun 2023 00:00:00 +0000

The article presents an analysis of the potential groundwater recharge zones in the Eastern Province of Rwanda using Geographic Information System (GIS) technology. The groundwater potential zones (GWPZs) in the area are influenced by several factors. To conduct the spatial analysis, seven theme layers were created and integrated, including geology, drainage density, rainfall, slope, soil, land use and land cover (LULC), and normalized difference vegetation index (NDVI). The Analytical Hierarchy Process (AHP), based on multiple criteria, was utilized to assign weights to each layer. By overlaying the theme layers with the prescribed weights, a map of potential groundwater recharge zones was generated. These zones were classified into five categories: poor, fair, medium, good, and excellent, representing 173 km² (1.9 %), 1002 km² (11.3%), 5976 km² (67.2%), 1732 km² (19.5%), and 12 km² (0.1%), respectively. The findings revealed that a significant portion of the study area exhibited good to moderate potential zones for groundwater. Among the seven districts in the Eastern Province, Rwamagana district had the highest coverage of good and excellent groundwater potential zones, accounting for 59% and 1% of the area, respectively. The accuracy of the GWPZ map was assessed by comparing it with borehole yield data, demonstrating the reliability of the chosen methodology. These validated results provide valuable support for the sustainable management and strategic utilization of groundwater resources in the study area. The study outcomes can guide decision-makers in making informed choices regarding the conservation of groundwater resources in the research area.

Spatial Assessment of Urban Growth on Green Spaces in Rwanda: An insight from Rebero Mountain Landscape in Kicukiro District, City of Kigali

Mon, 12 Jun 2023 00:00:00 +0000

Green space is a crucial component of sustainable urban development that provides urban dwellers with several environmental and social benefits. This study investigates the impact of urban growth on green space management on hilly landscape using a case study of Rebero Mountain in Kicukiro District, Kigali City. The study used medium resolution satellite imagery (Landsat for 2000, 2010, and 2021) and high-resolution imagery (Quick Bird 2010 and Worldview 2 images of 2015) to assess Land Uses Land Covers (LULC) dynamics. To investigate the local community's perception on the effects of urban growth on green space, interviews were conducted. The LULC changes detection revealed a significant decrease in green space with an increase in the built-up areas. The urbanized area has been increasing from 7,422.10 Ha up to 14,730.94 Ha during the last two decades. The increasing built-up area was in parallel with reduction of green space, specifically 44 Ha (-6.7%) of urban forest and 255 Ha (- 26.2%) of agricultural land, for the period between 2000 and 2021. The reduction of green cover had a negative impact on the environment, such as soil erosion and biodiversity loss. In order to support decision-makers in fostering the development of sustainable and green smart cities, a spatially explicit assessment of the availability, management, and accessibility of urban green areas is also important. Based on this research findings, the restoration of green spaces, including the need for land-use and land-cover change monitoring, practical government physical planning interventions, and an inclusive strategy for managing the city's green spaces that involves NGOs and citizens, and advocates for green city development are strongly recommended.

Geospatial Assessment of Urban Flood Susceptibility Using AHP-Based Multi-Criteria Technique: Case Study of Musanze, Rwanda

Mon, 12 Jun 2023 00:00:00 +0000

Over the last decade, flood events in the northern and western urban areas of Rwanda have increased due to anthropogenic activities and the effects of climate change. This study aims to use the Analytic Hierarchy Process (AHP) geospatial multi-criteria technique to identify flood susceptibility areas and exposed assets in Musanze City. Seven factors were considered such as Topographic Wetness Index (TWI), Rainfall, Land Use Land Cover (LULC), Slope, Soil Texture, River, and Digital Elevation Model (DEM) to create a flood susceptibility map with 5 classes (from very low to very high). To validate the final map, 26 GPS points were collected from areas with a history of flooding, and the susceptibility classes were compared. Results showed that 33% of the area (~2367 ha) was classified as high to very high risk, 23% (~1645 ha) was classified as moderate, and 44% (~3113 ha) was classified as low and very low susceptibility. The study also identified physical infrastructure and land use exposed in high and very high flooding areas, including 24 schools, 2 markets, and 1 health facility, 18668 km of roads, and 3400 buildings that are highly exposed to flooding. The applied methods in this study are useful for urban planners and government officials in developing flood mitigation policies, strategies, and plans.