School of Built Environment (SBE) Collections

Permanent URI for this collection

http://10.29.1.53:4000/handle/10570/179

Browse

Recent Submissions

Now showing 1 - 5 of 515
  • Item
    Upgrading of Kijjuna HC11 to HC11 in Kijjuna subcounty, Kassanda district.
    (Makerere University, 2025) Lutimba, Arnold
    This report covers the positive aspects of construction project management exercised by the author and the project team at large while implementing the construction works for Upgrading of Kijjuna HCII to HCIII in Kijjuna Sub County under UGIFT. The report details contract administration skills, contract back ground, and application of project management knowledge areas during project implementation. The report also looks at contract performance in details putting into consideration the aspects of scope, time cost and quality management during implementation of the works. Construction supervision for this project was undertaken jointly by the Ministry of Health, Ministry of Works and Transport and Kassanda District Local Government. The author as the Civil Engineer for Kassanda District, I was the assistant Project Manager for this project and was responsible for the day to day supervision of works at the site. The actual construction works took place between March 2022 and August 2023 (15months). The project management team for implementation of Upgrading works for Kijjuna HCII to HCIII in Kijjuna Sub County was headed by the project manager who was the District Engineer, assisted by the Civil Engineer (buildings), an environment Officer, and the sociologist.
  • Item
    Maize yield prediction using earth observation data at different phenological phases using machine learning. A case study of Lugore prison farm-Gulu District
    (Makerere University, 2026) Adero, Lydia
    Accurate crop yield prediction is of great importance to global food production; however, its inaccuracy remains a persistent and critical challenge for the agricultural sector. The increasing availability of satellite-based earth observation data plays a pivotal role in crop yield prediction, providing spatially extensive and temporal insights, enabling early and accurate yield prediction. Currently, most maize yield predictions are statistical in nature and focus on maize yield prediction based on aggregation of all the seasonal variables required for maize yield prediction and therefore, do not account for the phase-specific dynamics since each growth phase is characterized by unique physiological processes and environmental sensitivities that ultimately determine yield potential. This research therefore, aims to explore the use of earth observation to predict maize yield, specifically during the vegetative and reproductive phases of the maize crop growth, using machine learning models and a case study of Lugore Prison Farm from 2018 to 2024. The research utilised Sentinel-2 data for Vegetation Indices, MODIS data for temperature and CHIRPS data for precipitation. The study utilised NDVI time series curves smoothed with the Savitzky–Golay filter to determine the temporal patterns of the vegetative and reproductive phases using the relative threshold method and three machine learning algorithms: random forest, Gaussian Process Regression, and Extreme Gradient Boost for maize yield prediction at the vegetative and reproductive phases of maize. The results revealed a longer vegetative phase than the reproductive phase with interannual variations in the onsets, durations and end of the different phases, but these were mainly dependent on the prevailing meteorological factors. For the maize yield estimation, the Extreme Gradient Boost model demonstrated the most superior performance with Root Mean Square (RMSE) of 50,010 kg and 4,270 kg in the vegetative and reproductive phase of season one, respectively and 3 kg and 5 kg in the vegetative and reproductive phase of season two, respectively. The Gaussian Process Regression model had the least accurate results with RMSE of 127,264 kg in the vegetative phase and 127,924 kg in the reproductive phase of season one, and 74,163 kg in the vegetative phase and 66,681 kg in the reproductive phase of season two. The study demonstrates the potential of leveraging earth observation data and machine learning models for accurate and phase-specific prediction of maize yield. The results from the study can be used for strategic planning by policy makers and farmers, especially those at the vegetative phase, since they can be attained earlier than the actual harvest time. Further research can use the models on different crops, geographic locations and also use different machine learning models, deep learning models and artificial intelligence for maize yield prediction.
  • Item
  • Item
    Factors influencing the implementation of the Uganda National Land Information System in Kampala and Wakiso Ministry Zonal Offices
    (Makerere University, 2026) Nyangkori, Maurice
    Land information systems are central to land sector reforms worldwide, aimed at improving efficiency, transparency, tenure security, and service delivery. In many developing countries, the transition from manual to digital land systems has delivered benefits, yet implementation encounters institutional, financial, technological, and social constraints. In Uganda, the Government has implemented the Uganda National Land Information System as part of land governance reforms to digitize records, streamline services, and reduce fraud. Despite this progress, concerns persist regarding effectiveness in high-demand service environments. The Kampala and Wakiso Ministry Zonal Offices, which handle a large share of land transactions, continue to face challenges related to governance arrangements, sustainability of funding, reliability of information and communications technology infrastructure, and inclusiveness of stakeholder participation. These challenges raise questions about the system’s ability to deliver land administration outcomes. This study examined the factors influencing the implementation of the Uganda National Land Information System in Kampala and Wakiso, with particular focus on governance, budgetary support, information and communications technology infrastructure, and stakeholder involvement. A sequential explanatory mixed-methods approach was adopted. Stratified random sampling ensured representation of key staff categories and external user groups. Purposive sampling targeted respondents with specialized knowledge and experience. The study involved 52 internal staff from the Ministry of Lands, Housing and Urban Development and 170 external users, including surveyors, valuers, lawyers, planners, and landowners. Data were collected through structured questionnaires and interviews and analyzed using descriptive statistics and thematic analysis. Findings indicate that governance structures provide an oversight framework, but centralized decision-making limits responsiveness at ministry zonal offices. Budgetary support remains donor-dependent, information and communications technology infrastructure faces reliability constraints, and stakeholder engagement is uneven. The study concludes that the Uganda National Land Information System has advanced land administration but remains institutionally and financially fragile.
  • Item
    A GIS assessment of the effect of uncontrolled urbanisation on ECO system services in Wakiso, Uganda : a case study of Kyengera Town Council, Wakiso District
    (Makerere University, 2025) Sitenda, Nicodemus Magulu
    This study investigated the trend of the LULC and variation of ESV in Kyengera Town Council using remote sensing data for twenty years, from 2000 to 2020. Also, the study clearly states the importance of remote sensing and satellite images in quantifying land cover changes and ecosystem conservation that was covered in the objectives of the study. The study area was centred and rotated on Kyengera Town Council situated along Kampala – Masaka road and its headquarters are located at Nsangi – Mukono in Nsangi parish, approximately 15km from Kampala Capital City Authority. The primary data included GPS coordinates picked to aid in accuracy assessment and field photos and the tools used included, Google Earth Engine, ENVI software, Arc GIS and Microsoft office for the tasks summarized; also, those tools were used to extract urbanization and vegetation cover data. The methodology used mainly included the pre-processing methods that were done to remove flaws and deficiencies in the images due to atmospheric and electric noise and, included operations such as atmospheric and radiometric corrections, tasseled cap and PCA. LULC change was being driven by a combination of factors, including growing urban populations and their livelihoods, unplanned urban settlement, transportation congestion, air pollution, unmanaged solid waste disposal, and global climate change. Also, the findings of this study suggested the current value of ecosystem services; also suggested that policymakers should consider the regional heterogeneity of ES supply and the gradient analysis for a more accurate definition of ES supply. However, the study provided the new insight into variation in ESV in the region over the past 20 years of the study period. In summary, the study recommended the integrating nature-based solutions in urban development plans, policies, and financial support for implementing smart interventions; some of the recommended plans were green roof space, rainwater harvesting, sufficient use of clean and green energy, and plantation in available spaces at large scales with the active participation of communities and coordination with governmental bodies to enhance the ecosystem services by increasing LULC dynamics. The results of this study were useful in land use, and land cover model analysis tests alternate approaches for the determination how they (land use, and land cover model analysis tests) affected the ecosystem. ESV calculation was a conclusive and suitable method for valuing the ecosystem in terms of money, giving the scientific foundation for directing the policies.