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.

Soil erosion is one of the most important environmental problems distributed worldwide. In the last decades, numerous studies have been published on the assessment of soil erosion and the related processes and forms using empirical, conceptual and physically based models. For the prediction of the spatial distribution, more and more sophisticated stochastic modelling approaches have been proposed – especially on smaller spatial scales such as river basins. In this work, we apply a maximum entropy model (MaxEnt) to evaluate badlands (calanchi) and rill–interrill (sheet erosion) areas in the Oltrepo Pavese (Northern Apennines, Italy). The aim of the work is to assess the important environmental predictors that influence calanchi and rill–interrill erosion at the regional scale. We used 13 topographic parameters derived from a 12 m digital elevation model (TanDEM‐X) and data on the lithology and land use. Additional information about the vegetation is introduced through the normalized difference vegetation index based on remotely sensed data (ASTER images). The results are presented in the form of susceptibility maps showing the spatial distribution of the occurrence probability for calanchi and rill–interrill erosion. For the validation of the MaxEnt model results, a support vector machine approach was applied. The models show reliable results and highlight several locations of the study area that are potentially prone to future soil erosion. Thus, coping and mitigation strategies may be developed to prevent or fight the soil erosion phenomenon under consideration. © 2020 John Wiley & Sons, Ltd.

Gully erosion is a major threat concerning landscape degradation in large areas along the northern Tanzanian Rift valley. It is the dominant erosion process producing large parts of the sediments that are effectively conducted into the river network. The study area is located in the Lake Manyara-Makuyuni River catchment, Arusha, northern Tanzania. During fieldwork, we measured topographic data of eight gully systems close to Makuyuni Town. The main focus of this study is to assess gully erosion dynamics using improved DEMs with original resolutions of 30 and 20 m, respectively. We assessed terrain characteristics to extract information on environmental drivers. To improve the DEM, we integrated information deduced from satellite images as well as from acquired GPS field data. Topographic indices such as Stream Power Index or Transport Capacity Index were derived from the re-interpolated DEM. To evaluate gully evolution, we assessed also the longitudinal slope profiles. Finally, the gully evolution phases of each gully were classified according to the concept proposed by Kosov et al. (Eksperimental'naya geomorfologiya, vol 3. Moscow University, Moskva, pp 113-140, 1978). The reinterpolated DEMs revealed a positive response especially for the more developed gullies. We show that the extraction of information on this spatial process scale based on ''lowresolution'' data is feasible with little additional fieldwork and image interpretation. In fact, areas identified as having a greater risk of gully erosion have been confirmed by observations and surveys carried out in the field.

The Lake Manyara area is the focus of several paleo-archeological investigations. The Manyara basin is located approximately 70 km east of Olduvai Gorge, where important paleoanthropological artifacts are traced back to Homo habilis. In the Manyara basin itself, two hominin-bearing sites (0.78-0.633 Ma) and plenty of vertebrate bones and teeth as well as stone artifacts from different periods were discovered, especially close to the Makuyuni River. Different methodological approaches with a main emphasis on remote sensing were utilized to contribute to the understanding of the paleo-landscape development. In order to investigate the morphotectonic evolution of the study area, lineaments were detected from Synthetic Aperture Radar satellite scenes. The complex lacustrine development of the Lake Manyara and its paleo-stages was investigated by delineating the extent of paleo-lake sediments (older than 0.633 Ma) with multispectral ASTER data. In addition, lake terraces and shorelines on different levels (up to 80 m above today's lake level) and an outlet to the neighboring Engaruka basin were detected by analyzing the backscattered intensity of TerraSAR-X data. The distribution of topsoils, identified from multisensory remote sensing datasets, indicates soil formation as well as erosional and depositional processes. The fossils and artifacts were then