The quantification of pebble shape has been of interest to geomorphologists for decades. Several authors developed parameters to describe pebble shapes from their images. The extraction of this information from images involves two steps: the segmentation of pebble contours and the application of a computational geometry algorithm to estimate shape parameters. When images are taken in the field, unavoidable shadows might hinder the possibility of using automatic segmentation methods. This paper introduces a new method for automatic segmentation of pebbles that improves segmentation accuracy in the presence of shadows. The method is based on the Canny edge detection algorithm which uses a double thresholding process to provide a classification of the strength of the detected edges. The proposed method applies this algorithm with an ensemble of thresholding values, estimating, for each pixel, the probability of being an edge. The resulting pebble contours were analysed using two computational geometry algorithms to obtain shape parameters. The algorithm was calibrated on a sample of five pebbles and then validated on a sample of 1696 pebbles. Its accuracy has been estimated by comparing the resulting shape parameters with those obtained using reference software, which was used as ground truth (GT). The proposed segmentation method was capable of accurately segmenting around 91% of the sample with a relative error for roundness of −1.7% and −0.4%; for elongation of −0.2% and −0.3% and for circularity of 0.2% and 0.1%, when shape parameters were computed using the algorithms of Zheng or Roussillon, respectively. The method could therefore be used to segment images of pebbles collected in the field with low contrast and shadowing, providing comparable accuracy with ‘manual’ segmentation, while removing operator bias.

The Lower Mara River and Wetland, Tanzania, is an important ecosystem and unique water resource for a vast semi-arid area. The river, an affluent of Lake Victoria, and the wetland are experiencing morphological and vegetation changes resulting in channel avulsions and wetland expansion. This study analyses the changes over the last 100 years and investigates natural and anthropogenic behaviors to explain the increase of the Mara Wetland area. We collated historical topographic maps and satellite images. We conducted two field surveys in low and high flow condition with an unmanned aerial vehicle, a sonar and an ADCP. We mapped selected areas as well as the bed topography in some stretches of the river, measured discharges, and collected river bed and suspended sediment samples. The analysis of the sediments shows that the wetland system, dominated by papyrus sp., is very efficient in trapping sediment, releasing clear water to the Lake Victoria. The historical reconstruction using topographic maps, satellite images and a multivariable analysis including hydrology and land cover, shows that 4 major avulsions occurred in the last 70 years due to a combination of natural behaviors, hydrological fluctuations and anthropogenic factors such as basin deforestation, farming and grazing along the river banks and in the wetland. Each avulsion led to substantial expansion of the wetland. Combined, they increased the wetland area by a factor of 3.6. Describing the Lower Mara River dynamic behavior, this work provides relevant information for sustainable future water and sediment management in order to preserve wetland habitats and natural resources.

Sediment accumulation hampers optimal water resources management of reservoirs. In the Roseires Reservoir across the Blue Nile River, in Sudan, about 30% of the storage capacity has been lost by sedimentation before dam heightening (2012), despite regular sediment sluicing and flushing. At the same time, increasing soil erosion in the upper river basin in Ethiopia is significantly reducing land productivity.