Browsing by Author "Yumbu, Alex"

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    Determination and analysis of land deformation in major landslide areas in Kenya using geospatial techniques: A case study of Murang a and Nyeri counties
    (2024-03) Yumbu, Alex; Odera, Patroba
    This thesis presents a meticulous investigation into determining and analysing land deformation in the prominent landslide-prone areas of Kenya, specifically within the central highlands encompassing Murang'a and Nyeri counties. The research employs a spectrum of areal deformation methods, including Small Baseline Sub-set (SBAS) Interferometric Synthetic Aperture RADAR (InSAR) from processed ALOS PALSAR and Sentinel-1 datasets, Gravity Recovery and Climate Experi-ment (GRACE/GRACE-FO) satellite data, and hydrological modelling data from the Global Land Data Assimilation System (GLDAS), to elucidate and dissect the time series deformation trends prevalent in the study area. A multi-faceted methodology was employed to investigate the deformation induced by landslides in the study area. Various datasets were harnessed to provide a compre-hensive understanding of the phenomena. Satellite gravimetry data from GRACE and GRACE-FO, in conjunction with hydrological modelled data from GLDAS, delivered valuable insights into vertical deformation rates at selected points of inter-est. These points were identified through InSAR processing using RADAR imagery from Sentinel-1 and ALOS PALSAR, which offered finer spatial resolution. While Sentinel-1 imagery was acquired and processed in both the ascending and descend-ing nodes, ALOS PALSAR imagery was solely processed in the ascending node due to dataset availability constraints. In the Sentinel-1 SBAS analysis, temporal and perpendicular baselines were constrained to 60 days and approximately 200 metres, respectively. For ALOS PALSAR imagery, the temporal and perpendicular baselines were constrained to 875 days and 2500 metres, respectively. Furthermore, to discern the most probable causes of deformation, a range of factors were considered. These encompassed precipitation data, which was critical in as- sessing the role of rainfall in triggering landslides. Additionally, geomorphological factors, such as slope, aspect, and surface roughness, extracted from SRTM Arcsec- ond DEM were incorporated. Land Use Land Cover data spanning from 1973 to 2022 were analysed to understand the impact of human activities and land cover changes on the occurrence of landslides. The amalgamation of these datasets and analyses facilitated a comprehensive examination of the factors contributing to deformation and landslides in the study area. Results reveal distinctive deformation patterns over the study area from various datasets. Sentinel-1 Ascending imagery indicates varying uplift rates from 19.8 to 189.3 mm/yr and varying subsidence rates from -169.4 to -63.8 mm/yr over the area Iv of study. Sentinel-1 Descending data corroborates these trends, with varying uplift rates from 59.5 to 209.9 mm/yr, and varying subsidence rates from -200.8 to -90.7 mm/yr. Results from the ALOS PALSAR dataset contributes additional complexity but with lower values than Sentinel -1 data, probably due to data gaps. The rates of uplift observed from ALOS PALSAR dataset vary from 16.5 to 100.8 mm/yr, while the rate of subsidence vary from -715.9 to -60.8 mm/yr. GRACE/GRACE-FO and GLDAS datasets provide smaller values of uplift and subsidence at a larger scale, devoid of large deformations at smaller scales captured by InSAR. Among investigated possible causes of landslides, geomorphological factors emerge as prominent influencers of deformation patterns, while changes in land use and land cover, high and extreme rainfall events and lithology and soil properties also play pivotal roles. Crucially, this thesis underscores that landslides and deformation are rarely attributed to single causative agents; rather, they result from an intricate interplay of various factors, often occurring simultaneously.
  • No Thumbnail Available
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    Open Access
    Determination and analysis of land deformation in major landslide areas in Kenya using geospatial techniques: A case study of Murang a and Nyeri counties
    (2024) Yumbu, Alex; Odera, Patroba
    This thesis presents a meticulous investigation into determining and analysing land deformation in the prominent landslide-prone areas of Kenya, specifically within the central highlands encompassing Murang'a and Nyeri counties. The research employs a spectrum of areal deformation methods, including Small Baseline Sub-set (SBAS) Interferometric Synthetic Aperture RADAR (InSAR) from processed ALOS PALSAR and Sentinel-1 datasets, Gravity Recovery and Climate Experi-ment (GRACE/GRACE-FO) satellite data, and hydrological modelling data from the Global Land Data Assimilation System (GLDAS), to elucidate and dissect the time series deformation trends prevalent in the study area. A multi-faceted methodology was employed to investigate the deformation induced by landslides in the study area. Various datasets were harnessed to provide a compre-hensive understanding of the phenomena. Satellite gravimetry data from GRACE and GRACE-FO, in conjunction with hydrological modelled data from GLDAS, delivered valuable insights into vertical deformation rates at selected points of inter-est. These points were identified through InSAR processing using RADAR imagery from Sentinel-1 and ALOS PALSAR, which offered finer spatial resolution. While Sentinel-1 imagery was acquired and processed in both the ascending and descend-ing nodes, ALOS PALSAR imagery was solely processed in the ascending node due to dataset availability constraints. In the Sentinel-1 SBAS analysis, temporal and perpendicular baselines were constrained to 60 days and approximately 200 metres, respectively. For ALOS PALSAR imagery, the temporal and perpendicular baselines were constrained to 875 days and 2500 metres, respectively. Furthermore, to discern the most probable causes of deformation, a range of factors were considered. These encompassed precipitation data, which was critical in as- sessing the role of rainfall in triggering landslides. Additionally, geomorphological factors, such as slope, aspect, and surface roughness, extracted from SRTM Arcsec- ond DEM were incorporated. Land Use Land Cover data spanning from 1973 to 2022 were analysed to understand the impact of human activities and land cover changes on the occurrence of landslides. The amalgamation of these datasets and analyses facilitated a comprehensive examination of the factors contributing to deformation and landslides in the study area. Results reveal distinctive deformation patterns over the study area from various datasets. Sentinel-1 Ascending imagery indicates varying uplift rates from 19.8 to 189.3 mm/yr and varying subsidence rates from -169.4 to -63.8 mm/yr over the area Iv of study. Sentinel-1 Descending data corroborates these trends, with varying uplift rates from 59.5 to 209.9 mm/yr, and varying subsidence rates from -200.8 to -90.7 mm/yr. Results from the ALOS PALSAR dataset contributes additional complexity but with lower values than Sentinel -1 data, probably due to data gaps. The rates of uplift observed from ALOS PALSAR dataset vary from 16.5 to 100.8 mm/yr, while the rate of subsidence vary from -715.9 to -60.8 mm/yr. GRACE/GRACE-FO and GLDAS datasets provide smaller values of uplift and subsidence at a larger scale, devoid of large deformations at smaller scales captured by InSAR. Among investigated possible causes of landslides, geomorphological factors emerge as prominent influencers of deformation patterns, while changes in land use and land cover, high and extreme rainfall events and lithology and soil properties also play pivotal roles. Crucially, this thesis underscores that landslides and deformation are rarely attributed to single causative agents; rather, they result from an intricate interplay of various factors, often occurring simultaneously.