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Natural and potentially hazardous events occur on the Earth’s surface every day. The most destructive of these processes must be monitored, because they may cause loss of lives, infrastructure, and natural resources, or have a negative effect on the environment. A variety of remote sensing technologies allow the recoding of data to detect these processes in the first place, partly based on the diagnostic landforms that they form. To perform this effectively, automatic methods are desirable.
Universal detection of natural hazards is challenging due to their differences in spatial impacts, timing and longevity of consequences, and the spatial resolution of remote-sensing data. Previous studies have reported that topographic metrics such as roughness, which can be captured from digital elevation data, can reveal landforms diagnostic of natural hazards, such as gullies, dunes, lava fields, landslides and snow avalanches, as these landforms tend to be more heterogeneous than the surrounding landscape. A single roughness metric is often limited in such detections; however, a more complex approach that exploits the spatial relation and the location of objects, such as object-based image analysis (OBIA), is desirable.
In this thesis, I propose a topographic roughness measure derived from an airborne laser scanning (ALS) digital terrain model (DTM) and discuss its performance in detecting landforms principally diagnostic of natural hazards. I further develop OBIA-based algorithms for the detection of snow avalanches using near-infrared (NIR) aerial images, and the size (changes) of mountain lakes using LANDSAT satellite images. I quantitatively test and document how the level of difficulty in detecting these very challenging landforms depends on the input data resolution, the derivatives that could be evaluated from images and DTMs, the size, shape and complexity of landforms, and the capabilities of obtaining the information in the data. I demonstrate that surface roughness is a promising metric for detecting different landforms in diverse environments, and that OBIA assists significantly in detecting parts of lakes and snow avalanches that may not be correctly assigned by applying only the thresholding of spectral properties of data and their derivatives.
The curvature-based surface roughness parameter allows the detection of gullies, dunes, lava fields and landslides with a user’s accuracy of 0.63, 0.21, 0.53, and 0.45, respectively. The OBIA algorithms for detecting lakes and snow avalanches obtained user’s accuracy of 0.98, and 0.78, respectively. Most of the analysed landforms constituted only a small part of the entire dataset, and therefore the user’s accuracy is the most appropriate performance measure that should be given in a such classification, because it tells how many automatically-extracted pixels in fact represent the object that one wants to classify, and its calculation does not take the second (background) class into account. One advantage of the proposed roughness parameter is that it allows the extraction of the heterogeneity of the surface without the need for data detrending. The OBIA approach is novel in that it allows the classification of lakes regardless of the physical state of their water, and also allows the separation of frozen lakes from glaciers that have very similar water indices used in purely optical remote sensing applications. The algorithm proposed for snow avalanches allows the detection of release zones, tracks, and deposition zones by verifying the snow heterogeneity based on a roughness metric evaluated from a water index, and by analysing the local relation of segments with their neighbouring objects. This algorithm contains few steps, which allows for the simultaneous classification of avalanches that occur on diverse mountain slopes and differ in size and shape.
This thesis contributes to natural hazard research as it provides automatic solutions to tracking six different landforms that are diagnostic of natural hazards over large regions. This is a step toward delineating areas susceptible to the processes producing these landforms and the improvement of hazard maps.
In this work, an approach of paleoclimate reconstruction for tropical East Africa is presented. After giving a short summary of modern climate conditions in the tropics and the East African climate peculiarity, the potential of reconstructing climate from paleolake sediments is discussed. As demonstrated, the hydrologic sensitivity of high-elevated closed-basin lakes in the Central Kenya Rift yields valuable guaranties for the establishment of long-term climate records. Temporal fluctuations of the limnological characteristics saved in the lake sediments are used to define variations in the Quaternary climate history. Based on diatom analyses in radiocarbon- and 40Ar/39Ar-dated sediments, a chronology of paleoecologic fluctuations is developed for the Central Kenya Rift -lakes Nakuru, Elmenteita and Naivasha. At least during the penultimate interglacial (around 140 to 60 kyr BP) and during the last interglacial (around 12 to 4 kyr BP), these lakes experienced several transgression-regression cycles on time intervals of about 11,000 years. Additionally, a long-term trend of lake evolution is found suggesting the general succession from deep freshwater lakes towards more saline waters during the last million years. Using ecologic transfer functions and a simple lake-balance model, the observed paleohydrologic fluctuations are linked to potential precipitation-evaporation changes in the lake basins. Though also tectonic influences on the drainage pattern and the effect of varied seepage are investigated, it can be shown that already a small increase in precipitation of about 30±10 % may have affected the hydrologic budget of the intra-rift lakes within the reconstructed range. The findings of this study help to assess the natural climate variability of East Africa. They furthermore reflect the sensitivity of the Central Kenya Rift -lakes to fluctuations of large-scale climate parameters, such as solar radiation and sea-surface temperatures of the Indian Ocean.