Modelling the Impacts of Predicted Environmental Change on the Frequency and Magnitude of Rainfall Induced Landslides in Central Kenya

The central highlands of Kenya frequently suffer the impacts of rainfall-induced landslides resulting from the interaction of slope stability and elements of environmental change (land-use and climatic variables). The impacts of rainfall-induced landslides affect the country’s fight against poverty, bearing in mind the limited budgets to cope with the socioeconomic losses incurred by landslide hazards. On the other hand, a fast population growth rate puts pressure on the country’s resources which is majorly agricultural based, thus contributing to more people settling on steep slopes and increasing their vulnerability to rainfall landslide hazards. Thus, this research sought to contribute to the mitigation measures by mapping the landslide areas, performing landslide susceptibility assessment, and investigating the impacts of predicted environmental change on the frequency and magnitude of rainfall-induced landslides. The role of environmental change was investigated using specific objectives which assessed the impacts of land-use on slope stability, and the impact of precipitation characteristics on landslide susceptibility. Several data types ranging from topographic, soil and geology, land-use land-cover (LULC), hydrology, and precipitation landslide controlling factors were mapped and used in the modelling process. The methodology comprised of LULC change detection with Landsat multitemporal data for the years 1995, 2002, 2010 and 2014; structural geology and soil mapping; landslide inventory creation with Landsat multitemporal data for the years 1995, 2000, 2010 and 2014; landslide susceptibility mapping with Combined Hydrological and Slope stability Model (CHASM) and landslide modelling with Artificial Neural Network (ANN) model. The success of mapping and visualizing geology lineaments was owed to the digital image enhancement methods involving band ratioing, False Colour Composites (FCC), feature data transformation and data reduction methods of principal and independent component analysis. In addition to the feature data transformation and data reduction, the landslide inventory mapping was enhanced by utilizing a Normalized Difference Mid-Red (NDMIDR) spectral index involving Landsat geology and red bands. The key results of this research indicated that human activities relating to land-use (mostly agricultural) did aggravate the landslide processes on the sloppy terrain. This was confirmed by the CHASM model results where forested slopes maintained low landslide susceptibility levels. In addition, the ANN model rated LULC, rainfall, and proximity to drainage network factors high in contributing to landslide occurrence in the study area. Thus, majorly shallow types of landslides dominated, although the ANN model mapped some areas with deep-seated landslide areas along lineament features. The impacts of heavy precipitation were observed to increase slope instability, especially in bare land covers and high density drainage network areas due to rapid soil saturation, while prolonged precipitation increased infiltration thus maintaining high landslide susceptibility levels. The effects of climatic variables were associated with increased rock weathering observed on bare volcanic rocks, hence high instability rates around such areas. Landslide hazard zonation with ANN model captured several landslide types and the stability classification. The results of this study can guide targeted policies on land-use management as it has been established that rainfall induced landslides are a result of the interactions of land-use, slope and rainfall landslide conditioning factors. Moreover, creating a landslide inventory which can be updated with landslide attributes was a success since this had not been done in this geographical location to indicate the potential of landslide reactivation

Spectroscopy-supported digital soil mapping

Global environmental changes have resulted in changes in key ecosystem services that soils provide. It is necessary to have up to date soil information on regional and global scales to ensure that these services continue to be provided. As a result, Digital Soil Mapping (DSM) research priorities are among others, advancing methods for data collection and analyses tailored towards large-scale mapping of soil properties. Scientifically, this thesis contributed to the development of methodologies, which aim to optimally use remote and proximal sensing (RS and PS) for DSM to facilitate regional soil mapping. The main contributions of this work with respect to the latter are (I) the critical evaluation of recent research achievements and identification of knowledge gaps for large-scale DSM using RS and PS data, (II) the development of a sparse RS-based sampling approach to represent major soil variability at regional scale, (III) the evaluation and development of different state-of-the-art methods to retrieve soil mineral information from PS, (IV) the improvement of spatially explicit soil prediction models and (V) the integration of RS and PS methods with geostatistical and DSM methods. A review on existing literature about the use of RS and PS for soil and terrain mapping was presented in Chapter 2. Recent work indicated the large potential of using RS and PS methods for DSM. However, for large-scale mapping, current methods will need to be extended beyond the plot. Improvements may be expected in the fields of developing more quantitative methods, enhanced geostatistical analysis and improved transferability to other areas. From these findings, three major research interests were selected: (I) soil sampling strategies, (II) retrieval of soil information from PS and (III) spatially continuous mapping of soil properties at larger scales using RS. Budgetary constraints, limited time and available soil legacy data restricted the soil data acquisition, presented in Chapter 3. A 15.000 km2 area located in Northern Morocco served as test case. Here, a sample was collected using constrained Latin Hypercube Sampling (cLHS) of RS and elevation data. The RS data served as proxy for soil variability, as alternative for the required soil legacy data supporting the sampling strategy. The sampling aim was to optimally sample the variability in the RS data while minimizing the acquisition efforts. This sample resulted in a dataset representing major soil variability. The cLHS sample failed to express spatial correlation; constraining the LHS by a distance criterion favoured large spatial variability over short distances. The absence of spatial correlation in the sampled soil variability precludes the use of additional geostatistical analyses to spatially predict soil properties. Predicting soil properties using the cLHS sample is thus restricted to a modelled statistical relation between the sample and exhaustive predictor variables. For this, the RS data provided the necessary spatial information because of the strong spatial correlation while the spectral information provided the variability of the environment (Chapter 3 and 6). Concluding, the RS-based cLHS approach is considered a time and cost efficient method for acquiring information on soil resources over extended areas. This sample was further used for developing methods to derive soil mineral information from PS, and to characterize regional soil mineralogy using RS. In Chapter 4, the influences of complex scattering within the mixture and overlapping absorption features were investigated. This was done by comparing the success of PRISM’s MICA in determining mineralogy of natural samples and modelled spectra. The modelled spectra were developed by a linearly forward model of reflectance spectra, using the fraction of known constituents within the sample. The modelled spectra accounted for the co-occurrence of absorption features but eluded the complex interaction between the components. It was found that more minerals could be determined with higher accuracy using modelled reflectance. The absorption features in the natural samples were less distinct or even absent, which hampered the classification routine. Nevertheless, grouping the individual minerals into mineral categories significantly improved the classification accuracy. These mineral categories are particularly useful for regional scale studies, as key soil property for parent material characterization and soil formation. Characterizing regional soil mineralogy by mineral categories was further described in Chapter 6. Retrieval of refined information from natural samples, such as mineral abundances, is more complex; estimating abundances requires a method that accounts for the interaction between minerals within the intimate mixture. This can be done by addressing the interaction with a non-linear model (Chapter 5). Chapter 5 showed that mineral abundances in complex mixtures could be estimated using absorption features in the 2.1–2.4 µm wavelength region. First, the absorption behaviour of mineral mixtures was parameterized by exponential Gaussian optimization (EGO). Next, mineral abundances were successfully predicted by regression tree analysis, using these parameters as inputs. Estimating mineral abundances using prepared mixes of calcite, kaolinite, montmorillonite and dioctahedral mica or field samples proved the validity of the proposed method. Estimating mineral abundances of field samples showed the necessity to deconvolve spectra by EGO. Due to the nature of the field samples, the simple representation of the complex scattering behaviour by a few Gaussian bands required the parameters asymmetry and saturation to accurately deconvolve the spectra. Also, asymmetry of the EGO profiles showed to be an important parameter for estimating the abundances of the field samples. The robustness of the method in handling the omission of minerals during the training phase was tested by replacing part of the quartz with chlorite. It was found that the accuracy of the predicted mineral content was hardly affected. Concluding, the proposed method allowed for estimating more than two minerals within a mixture. This approach advances existing PS methods and has the potential to quantify a wider set of soil properties. With this method the soil science community was provided an improved inference method to derive and quantify soil properties The final challenge of this thesis was to spatially explicit model regional soil mineralogy using the sparse sample from Chapter 3. Prediction models have especially difficulties relating predictor variables to sampled properties having high spatial correlation. Chapter 6 presented a methodology that improved prediction models by using scale-dependent spatial variability observed in RS data. Mineral predictions were made using the abundances from X-ray diffraction analysis and mineral categories determined by PRISM. The models indicated that using the original RS data resulted in lower model performance than those models using scaled RS data. Key to the improved predictions was representing the variability of the RS data at the same scale as the sampled soil variability. This was realized by considering the medium and long-range spatial variability in the RS data. Using Fixed Rank Kriging allowed smoothing the massive RS datasets to these ranges. The resulting images resembled more closely the regional spatial variability of soil and environmental properties. Further improvements resulted from using multi-scale soil-landscape relationships to predict mineralogy. The maps of predicted mineralogy showed agreement between the mineral categories and abundances. Using a geostatistical approach in combination with a small sample, substantially improves the feasibility to quantitatively map regional mineralogy. Moreover, the spectroscopic method appeared sufficiently detailed to map major mineral variability. Finally, this approach has the potential for modelling various natural resources and thereby enhances the perspective of a global system for inventorying and monitoring the earth’s soil resources. With this thesis it is demonstrated that RS and PS methods are an important but also an essential source for regional-scale DSM. Following the main findings from this thesis, it can be concluded that: Improvements in regional-scale DSM result from the integrated use of RS and PS with geostatistical methods. In every step of the soil mapping process, spectroscopy can play a key role and can deliver data in a time and cost efficient manner. Nevertheless, there are issues that need to be resolved in the near future. Research priorities involve the development of operational tools to quantify soil properties, sensor integration, spatiotemporal modelling and the use of geostatistical methods that allow working with massive RS datasets. This will allow us in the near future to deliver more accurate and comprehensive information about soils, soil resources and ecosystem services provided by soils at regional and, ultimately, global scale.</p

Spectral indices derived, non-parametric Decision Tree Classification approach to lithological mapping in the Lake Magadi area, Kenya

Here, we demonstrate the application of Decision Tree Classification (DTC) method for lithological mapping from multi-spectral satellite imagery. The area of investigation is the Lake Magadi in the East African Rift Valley in Kenya. The work involves the collection of rock and soil samples in the field, their analyses using reflectance and emittance spectroscopy, and the processing and interpretation of Advanced Spaceborne Thermal Emission and Reflection Radiometer data through the DTC method. The latter method is strictly non-parametric, flexible and simple which does not require assumptions regarding the distributions of the input data. It has been successfully used in a wide range of classification problems. The DTC method successfully mapped the chert and trachyte series rocks, including clay minerals and evaporites of the area with higher overall accuracy (86%). Higher classification accuracies of the developed decision tree suggest its ability to adapt to noise and nonlinear relations often observed on the surface materials in space-borne spectral image data without making assumptions on the distribution of input data. Moreover, the present work found the DTC method useful in mapping lithological variations in the vast rugged terrain accurately, which are inherently equipped with different sources of noises even when subjected to considerable radiance and atmospheric correction

Environmental modeling and remote sensing linked to lacustrine climate proxies: the rift basin Chew Bahir (Ethiopia) during the late Pleistocene

Eastern Africa, a hotspot of hominin evolution, is a diverse and fragile landscape, shaped by the ongoing rifting and the large orographic and climatic gradients between green and lush mountainous regions and dry and hot rift floors, which are partly deserted today, such as the Chew Bahir basin, in southern Ethiopia and north-east of Lake Turkana. Orbital-induced climate change caused the last African Humid Period (AHP) from 15–5 ka (thousand years before present), leading to large rift lakes, such as the paleo-lake Chew Bahir with up to 2500 km² in size. Drilled lacustrine sediment proxies from the Chew Bahir Basin revealed the climate dynamics for the last AHP and the past 620 ka. Because proxies provide qualitative spatially point-wise and temporally continuous data about the past climate and environment, this thesis aims to complement these results using catchment-scaled environmental models to gain quantitative and spatial data about the past hydroclimate and vegetation in the vicinity of Chew Bahir. Therefore, this cumulative thesis provides a collection of model applications in the Chew Bahir vicinity and discusses the consequences for early humans living in this region. First, a Lake Balance Model (LBM) extrapolated the necessary paleo-precipitation of +25–41% compared to today during the AHP and revealed the high importance of paleo-hydro-connectivity between different lake basins in the East African Rift System. Model results showed paleo-lake Chew Bahir may have desiccated within decades and flooded within the same time afterward as soon as humid conditions prevailed. Second, a Predictive Vegetation Model (PVM) linked with the LBM provided spatial estimates of the paleo-vegetation during the Last Glacial Maximum (LGM) time and the AHP. A comparison with the archeological record indicates a human preference for open landscapes in southern Ethiopia. The model yields a precipitation reduction of -17.5% during LGM times. Third, a comprehensive perspective on the lake level evolution within the Turkana Depression indicates that besides paleo-lake Chew Bahir, there was a paleo-lake Chalbi, similar in size and, presumably, even more climate-sensitive than paleo-lake Chew Bahir. Fourth, developing an isotope-enabled quantitative model using the geological gradient between the modern-day catchment and the extended paleo-catchment may be possible for the Chew Bahir lacustrine sediments, as this modern-analog study shows. Fifth, the interdisciplinary discussion about the risk assessment of humans living close-by Chew Bahir and Lake Turkana during the termination of the AHP is shown. Sixth and last, the application of a hydrological model is shown exemplary in a different landscape in Georgia, and the impact of human behavior on the hydrosphere during the Holocene is revealed