Riverbed material has multiple functions in river ecosystems, such as habitats, feeding grounds, spawning grounds, and shelters for aquatic organisms, and particle size of riverbed material reflects the tractive force of the channel flow. Therefore, regular surveys of riverbed material are conducted for environmental protection and river flood control projects. The field method is the most conventional riverbed material survey. However, conventional surveys of particle size of riverbed material require much labor, time, and cost to collect material on site. Furthermore, its spatial representativeness is also a problem because of the limited survey area against a wide riverbank. As a further solution to these problems, in this study, we tried an automatic classification of riverbed conditions using aerial photography with an unmanned aerial vehicle (UAV) and image recognition with artificial intelligence (AI) to improve survey efficiency. Due to using AI for image processing, a large number of images can be handled regardless of whether they are of fine or coarse particles. We tried a classification of aerial riverbed images that have the difference of particle size characteristics with a convolutional neural network (CNN). GoogLeNet, Alexnet, VGG-16 and ResNet, the common pre-trained networks, were retrained to perform the new task with the 70 riverbed images using transfer learning. Among the networks tested, GoogleNet showed the best performance for this study. The overall accuracy of the image classification reached 95.4%. On the other hand, it was supposed that shadows of the gravels caused the error of the classification. The network retrained with the images taken in the uniform temporal period gives higher accuracy for classifying the images taken in the same period as the training data. The results suggest the potential of evaluating riverbed materials using aerial photography with UAV and image recognition with CNN.
Information about land cover is required for economic, agricultural and environmental policy making. Therefore, reliable up-to-date information is always called upon. In this study, we developed a new approach for land cover mapping based on the information of vegetation phenology. The main objective of this approach was to generate a land cover map of large cropland dominated area with high classification accuracy. Our approach consisted of two steps: first, we divided the study area into three land use groups depending on the phenology trend of cereals. Second, we applied a supervised classification for each group using the Maximum Likelihood Classifier and multi-date satellite images. Recent multi-temporal Landsat 8 images and field survey data were used for the classification process. To assess the robustness of this approach, a conventional supervised classification was performed using single date and multi-date images. Results indicated that the proposed approach is able to discriminate between different land cover types which have a similar spectral reflectance such as cereals, vegetables and pasture with high accuracy. The accuracy assessment showed very promising results with an overall accuracy of 86 % and a Kappa of 0.85 (good agreement) as compared to the single date (54-55 %) and the multi-date approach (78 %). Indeed, the application of this method provides accurate information for ecologists, hydrologists and the land development decision-makers. It can also improve the accuracy of environmental models that require high resolution land cover maps as input data.
Sedimentation occupies the storage capacity of reservoirs and reduces the amount of available surface water resource. The countermeasure to the sedimentation is required especially in arid land where the land erosion is very severe due to low vegetation in the catchment area, and even fine particles are deposited because of the low water rotation of the reservoirs under the climatic condition of clear difference between rainy season and drought season. However, it has not been carried out because the conventional technologies against the sedimentation, such as dredging or bypass for sediment inflow, are quite costly.The authors proposed the exploitation idea which can valorize the sediment and will financially assist the cost of dredging or other countermeasures to the sedimentation. One of the exploitation ways is producing construction bricks. Sediment in Tunisian reservoirs is fine and sticky clay or silt, so there is a potential of the material for producing ceramics. In this study, the current situation of the production of the construction bricks in Tunisia was surveyed; price of raw material, a wholesale price, market price, processing cost and material flow. This information defines the market of construction bricks and the possibility of reservoir sediment for the production of construction bricks can be evaluated.Physical feasibility of the sediment for construction bricks was also investigated by the trial production of small pieces of slate and carrying out the flexure test with them. The slate samples made from the sediment gave almost same strength as the slate made from clay which is used in a brick factory in Tunisia.
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