Browsing by Author "Turyahabwe, Remigio"

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Assessment of landslide susceptibility and settlement exposure via Geospatial techniques in Bulambuli distrcit, eastern Uganda
(Applied Environmental Research, 2025-10-15) Mulabbi, Andrew; Esagu, John Calvin; Akello, Gertrude; Turyahabwe, Remigio
Landslide susceptibility is a significant concern in Elgon County, Uganda, particularly during the rainy season. This vulnerability is attributable to several factors, including steep slopes, fertile soils, and dense settlements on volcanic ridges. Landslide susceptibility maps are important in mitigating the risk particularly at the local level. The objectives of this study were 1) to model landslide susceptibility via an interpretable machine-learning model, 2) to identify the most influential factors for landslide susceptibility in the study area, and 3) to assess the exposure of settlements to landslide risk. This study employed the XGBoost model trained on nine conditioning factors via GIS data. Exposure analysis was performed through the zonal statistics and spatial overlay of the landslide susceptibility map with the settlement footprint data and classified into four risk exposure classes. The results show that the XGBoost model attained an AUC of 95.2%, indicating its precision. The results further revealed that approximately 50% of the slopes are susceptible to landslides and that 76% of the settlements in the study area are highly exposed to landslide risk. Bulugunya, Sisiyi, Lusha, and Buginyanya subcounties located on the middle slopes are the most susceptible areas in Elgon County and have relatively high settlement exposure because of the overlap of dense settlements with unstable terrain. The SHAP analysis identified slope, elevation, and the NDVI as the key influencing factors of susceptibility. This study highlights the importance of conducting detailed, local-scale landslide susceptibility and risk exposure mapping as necessary for risk and vulnerability assessment. The generation of such maps has the potential to inform land-use planning and risk-reduction strategies, thus offering significant advantages over regional models. Furthermore, by interpreting the XGBoost model, this study provides valuable insights into the decision-making processes of machine learning models, promoting their practical application in designing appropriate disaster mitigation plans.
Factors affecting the adoption of soil and water conservation practices by small-holder farmers in Muyembe Sub-County, Eastern Uganda
(Ghana Journal of Geography, 2022-08) Turyahabwe, Remigio; Nabalegwa, Muhamud Wambede; Asaba, Joyfred; Mulabbi, Andrew; Gumisiriza, Loy Turyabanawe
Farmers in tropical rural areas are confronted with several challenges but outstanding among these challenges is soil degradation arising from soil erosion. This study involved identifying the dominant soil and water conservation practices and assessing the factors affecting their adoption in the Muyembe sub-county, Eastern Uganda. A total of 500 respondents were used to obtain primary data. As the study adopted a cross- sectional design, we used questionnaires, interviews, focus group discussions and field observations to collect the required data. Data were analyzed using descriptive statistics and the non-parametric (Chi-square) test. The results indicated that the dominant soil and water conservation practices adopted in the study area were, contour cropping (77%), mixed cropping (59% and crop rotation (51%). The remaining five practices had less than a 50% adoption rate. The chi-square test revealed that the age and gender of the farmers had a significant association with the levels of the adoption of soil and water conservation practices among farmers at P<0.001. We concluded that the adoption of soil and water conservation practices was low, which left the majority of farmers vulnerable to soil erosion effects such as low yields and crop failure. We recommend that stakeholders who work on soil and water conservation programs use model farmers in the area to educate and demonstrate the importance of soil and water conservation practices to other farmers.
Flood inundation and damage assessment of the degraded Semliki River plains using SAR data, Google Earth Engine, and GIS techniques
(Journal of Degraded and Mining Lands Management, 2025-06-21) Mulabbi, Andrew; Esagu, John Calvin; Akello, Gertrude; Turyahabwe, Remigio
The Semliki River valley in Ntoroko district has experienced devastating annual floods since 2019. Recurrent floods in Ntoroko District have displaced thousands and devastated pasturelands, disrupting livelihoods. Therefore, rapid assessment of flooded areas is crucial for developing effective mitigation strategies, disaster preparedness plans, and proactive policies to enhance resilience and mitigate the impact of future flood events. This study introduced a combined approach using Synthetic Aperture Radar (SAR) imagery and a digital elevation model (DEM) to map flood extent, depth, and building exposure in the Semliki Valley. Using Sentinel-1 SAR images taken both before and during the flood, combined with the ALOS PALSAR DEM, inundated areas and flood depths were determined, based on thresholding the SAR backscatter of the VH polarisation images. The flood extent maps were generated using Google Earth Engine and GIS techniques to create depth maps by subtracting the surface elevation from the height/surface of the flood waters. Building exposure and impact analysis for two flood events was ascertained through spatial join and overlay. The results showed that the 2023 flood event inundated approximately 1,968 hectares, including 1,553 hectares of pastureland and 74 buildings, while the 2024 event covered 1,139 hectares, equally inundating 1,050 hectares of pastureland and 54 buildings. Further analysis revealed that despite the smaller extent, the 2024 flood event caused a severe impact on the buildings compared to the 2023 flood disaster.
Understanding the drivers of adoption of organic banana farming technologies in Kajara County, South-western Uganda
(East African Journal of Agriculture and Biotechnology, 2022) Atwijukye, Dunstan; Turyahabwe, Remigio; Nabalegwa, Muhamud Wambede; Asaba, Joyfred
This study aimed at identifying and characterising the major organic banana farming technologies used and assessing the drivers of adoption of the same in Kajara County. A total of 360 respondents were used to obtain primary data. As the study adopted a cross-sectional design, we used questionnaires, interviews, focus group discussions, and field observations to collect the required data. Data was analysed using descriptive statistics and the non-parametric (Chi-square) tests. Results indicate that the major organic banana farming technologies adopted in the study area were mulching, cover cropping, farmyard manure application, pest and weed management. The chi-square test revealed that the marital status, gender, and level of education of the farmers had significant positive effects on the adoption of organic banana farming technologies among the farmers. We concluded that, generally, the rate of adoption of organic farming technologies in Kajara County was low, and therefore, there is a need for emphasising the training of the farmers at local levels so as to equip them with information on the organic farming technologies for sustainable banana farming. We recommend that stakeholders who work on agricultural programs use model farmers in the area to educate and demonstrate the importance of organic banana farming technologies.