Isa Kabenge

Biographic Data

ID9058590
NAMEIsa Kabenge
GIVEN NAMESIsa
FAMILY NAMEKabenge
SIGNATUREKABENGE I
AFFILIATIONSMakerere University
ORCID0000-0003-1578-4568
VERIFIEDYes
TOTAL WORKS6
TOTAL CITATIONS0
WORKS AS AUTHOR6
WORKS AS EDITOR0
FIRST PUBLICATION YEAR2023
LATEST PUBLICATION YEAR2026
H-INDEX0

  • Applying the Adaptive Systemic Approach to activate community participation in waste management and pollution control in the Inner Murchison Bay, Uganda

    Open AccessDavid Gwapedza, Andrew State et al.ARTICLEConservation Ecology2026

    Managing waste and preventing water pollution are urgent and interconnected challenges in rapidly urbanizing African cities. In Uganda, unregulated waste disposal is threatening the ecological and social health of Lake Victoria, particularly in Inner Murchison Bay, near Kampala’s Ggaba Landing Site. This study examines how the Adaptive Systemic Approach (ASA), developed within a multi-country African project: Unlocking Resilient Benefits from Afr…

  • Dimensions and drivers of social vulnerability to flood risk in Manafwa catchment, Eastern Uganda

    Open AccessGodwin Erima, Yazidhi Bamutaze et al.ARTICLECogent Social Sciences2025

    Over the past decade, Uganda has experienced a notable increase in extreme meteorological hazards, yet the country lacks a comprehensive disaster management strategy. Despite the critical role it plays in disaster risk reduction, there is a shortage of approaches that contribute to our understanding of social vulnerability within dynamic contextual conditions. This study therefore offers a comprehensive assessment of both flood exposure and socia…

  • Quantifying forest above ground biomass

    Open AccessFortunate Kemigyisha, Evet Naturinda et al.ARTICLETrees Forests and People2025

    This review compares traditional and remote sensing methods for biomass estimation. • Traditional methods are more accurate but arduous, costly on a large-scale. • Remote sensing techniques are suitable for large-scale forest analysis. • Artificial Neural Networks performed best with coefficient of determination of 0.8. • Further exploration of artificial and convolutional neural networks is recommended. Forests are important in sequestering carb…

  • Spatiotemporal Analysis of the Hydrological Responses to Land-Use Land-Cover Changes in the Manafwa Catchment, Eastern Uganda

    Godwin Erima, Anthony Gidudu et al.ARTICLEThe Professional Geographer2024

    This study evaluated the impact of land-use land-cover (LULC) change (LULCC) on the hydrologic processes of the Manafwa Basin in eastern Uganda using the Soil and Water Assessment Tool (SWAT) model. Model inputs included the 1995, 2008, and 2017 LULC maps; digital elevation model; and soil, climatological, and hydrologic data. The Calibration and Uncertainty Program Sequential Uncertainty Fitting algorithm was used to calibrate and validate the S…

  • Characterization of briquettes developed from banana peels, pineapple peels and water hyacinth

    Open AccessTadeo Mibulo, Denis Nsubuga et al.ARTICLEEnergy Sustainability and Society2023

  • Determinants of households' flood risk coping strategies in a high exposure system of the Manafwa catchment and Lake Kyoga Basin

    Open AccessGodwin Erima, Anthony Egeru et al.ARTICLEWater Policy2023

    The impact of floods on households in Uganda is becoming increasingly severe. It is often assumed that people who reside in a riverine area have adapted to flood pulses. However, in most cases, household-level risk reduction strategies are inadequate for ensuring a livelihood resilient to floods. The objective of this study was to investigate the determinants of households' decisions on coping strategies in the Manafwa catchment, Eastern Uganda. …