Channel inception in cohesionless sediment by seepage erosion

This study presents a series of systematic experiments on channelization by seepage erosion with various sediment layer depths and chamber slopes. When the depth of the sediment layer increases, the threshold discharge for channel initiation decreases, but the channel width increases. This is due to the fact that an increase in the weight of sediment induces an increase in a gravitational driving force along the chamber bed. When the chamber slope increases, we also found a similar effect as an increase in the sediment layer depth that the threshold discharge decreases, but the width increases. While channel bifurcation was never found in the previous study using plastic pellets as sediment, it was observed in many experiments in the present study, in which very coarse sand was used. We hypothesized that the characteristics of groundwater flow field and the resistibility of sediment material to slope failure play important roles on the channel bifurcation.     

Land-use land-cover classification analysis of Giba catchment using hyper temporal MODIS NDVI satellite images

Landsat-based land-use land-cover (LULC) mapping studies were previously conducted in Giba catchment, comprising an area of 4019 km². No attempt has been done to map LULC of this catchment through the analysis of Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI) time-series data. This article is aimed to see whether time-series MODIS NDVI data set is applicable for LULC mapping of Giba catchment or not. MODIS NDVI data sets of the year 2010 were used for classification analysis. The original data were subjected to MODIS Reproduction Tool and stacking. The re-projected and stacked images were filtered using Harmonic Analysis of Time-Series filtering algorism to remove the effects of cloud and other noises. The MODIS NDVI data sets (16-day maximum value composite) were classified using the ISODATA clustering algorithm available under ERDAS IMAGINE software. A series of unsupervised classification runs were carried out with a pre-defined number of classes (5–24). From this classification, the optimal numbers of classes were determined to be eight after checking for average divergence analysis. The classification result became eight LULC classes namely: bare land, grass land, irrigated land, cultivated land, area closure, shrub land, bush land, and forest land with an overall accuracy of 87.7%. It was therefore concluded that MODIS NDVI time-series image is applicable for mapping large watersheds.