BC-Department of Geography

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Browsing BC-Department of Geography by Author "Inyele, Juliet"

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    Prioritization of Soil Erosion Prone Areas Based on Morphometric and Land Use / Cover Parameters in River Thiririka Watershed, Kiambu County Kenya
    (Kenyatta University, 2024-05) Inyele, Juliet
    Morphometric studies and land use / land cover analysis play a key role in integrated watershed management. Sustainable resource utilization at a watershed level requires an in-depth understanding of the vegetation characteristics, land surface features, land use, drainage and hydrological patterns of the watershed. In developing countries, poverty have led to unsuitable land management practices (e.g. deforestation, continuous tillage), contributing to increased runoff causing land degradation and increased soil erosion in watersheds. This inhibits the achievement of the Sustainable Development Goals (SDGs) of zero hunger, access to clean water, and sanitation. To reduce soil erosion at the watershed level, watershed managers need to make informed decisions such as developing vegetative cover, agroforestry, and terracing. However, this is limited in Kenya due to lack of readily available data to guide the process. This study explored the potential use of basin and drainage network properties, land use / land cover characteristics with Geographic Information Systems (GIS) and Remote Sensing (RS) tools to identify sub watersheds susceptible to soil erosion in Thiririka watershed in Kenya. Five sub watersheds were delineated and assigned a code from SW1 to SW5 using the Shuttle Radar Topographic Mission (SRTM) 30 meter resolution Digital Elevation Model (DEM) with Arc Hydro tools in ArcGIS 10.8 software. These was followed by the analysis of morphometric parameters of linear, aerial, and relief characteristics of the watershed. Land use / land cover classes were generated from an annual median composite of Sentinel-2 image for the year 2020, collected using Google Earth Engine (GEE). The training polygons were systematically sampled from the field using handheld GPS. A supervised classification scheme was used to develop a random forest classifier to perform the classification. In addition, the Normalized Difference Vegetation Index (NDVI) extracted from a median composite of Sentinel-2 image for 2020 and the SRTM-DEM were incorporated to improve the classification accuracy. The overall accuracy was 0.88, and Kappa statistics of the classifications was 0.86. Further, to understand the spatial distribution of water in the catchment, the Topographic Wetness Index (TWI) values were extracted from the SRTM DEM. The effect of land use / land cover, vegetation cover and soil moisture to soil erosion tested using a two way ANOVA showed that all the parameters have a positive correlation with soil erosion. Finally, the effects of morphometric parameters, land use/ land cover, vegetation characteristics and soil moisture on soil erosion were assessed and assigned ranks 1 to 5. The ranks assigned for all the parameters were averaged to get the compound priority value (CP). Results showed that sub watershed 5 (SW5) and sub watershed 1 (SW1) are highly susceptible to soil erosion needing immediate management actions, while sub watershed 4 (SW4) and sub watershed 3 (SW3) show less susceptibility to soil erosion. This study provides information on sub watersheds exposed to soil erosion, which is important for all the stakeholders in watershed management such as agricultural officers, farmers, planners, and policymakers to focus the appropriate sustainable watershed management practices.