Abstract:The purpose of this paper is to describe the delineation of paleo-shorelines using high resolution microwave images and digital image processing tools, and with that to contribute to the understanding of the complex landscape evolution of the Lake Manyara Basin. The surroundings of Lake Manyara are the focus of several paleo-archeological investigations, since the location is close to Olduvai Gorge, where paleo-anthropological findings can be traced back to homo habilis. In the catchment of Lake Manyara two hominin-bearing sites (0.78 to 0.63 Ma), lots of vertebrate fossils and hand axes from different periods were found. Understanding the development and extent of the lake is crucial for understanding the regional paleo-environment of the Quaternary. Morphological structures of shorelines and terraces east of Lake Manyara were identified from TerraSAR-X StripMap images. By applying a Canny edge detector, linear features were extracted and revised for different image acquisitions using a contextual approach. Those features match literature and field references. A digital elevation model of the region was used to map the most distinct paleo-shorelines according to their elevation.

This study pursues the mapping of the distribution of topsoils and surface substrates of the Lake Manyara area of northern Tanzania. The nine soil and lithological target classes were selected through fieldwork and laboratory analysis of soil samples. High-resolution WorldView-2 data, TerraSAR-X intensity data, medium-resolution ASTER spectral bands and indices, as well as ENVISAT ASAR intensity and SRTM-X-derived topographic parameters served as input features. Objects were derived from image segmentation. The classification of the image objects was conducted applying a nonlinear support vector machine approach. With the recursive feature elimination approach, the most input-relevant features for separating the target classes were selected. Despite multiple target classes, an overall accuracy of 71.9% was achieved. Inaccuracies occurred between classes with high CaCO3 content and between classes of silica-rich substrates. The incorporation of different input feature datasets improved the classification accuracy. An in-depth interpretation of the classification result was conducted with three soil profile transects.

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Paleo-shorelines and ancient lake terraces east of Lake Manyara in Tanzania were identified from the backscatter intensity of TerraSAR-X StripMap images. Because of their linear alignment, edge detector algorithms were applied to delineate these morphological structures from those Synthetic Aperture Radar scenes. Due to the physical properties of microwave signals, this application has proven to be a challenging task for edge detectors. This study compares the performance of different combinations of speckle reduction techniques and edge operator in detecting linear paleo-shorelines. The Roberts, Sobel, Laplacian of Gaussian and the Canny edge detector algorithms were applied to extract and revise those linear structures. The comparison shows that the Canny edge detector is especially suitable for images with strong speckle noise. Canny achieves relatively high accuracies compared to the other operators. The stronger the filtering and speckle noise reduction, the better the performance of the other edge detection operators, compared to the Canny edge detector. The application of a wavelet transformation reduces the presence of artifacts resulting from speckle noise and emphasizes the detection of the target features.