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Drainage networks in agrarian landscape withinfloodplains constitute surface’s discontinuities that are expected to affect hydrological response during floods. Drainage network recognition and quanti fi cation of water storage capacity within channels are, therefore, crucial for watershed planning and management. These evaluations require accurate spatial information for the area of interest and in most cases, when studying large catchments, broad datasets of ditches locations and descriptions are not available. In order to characterize drainage networks for large areas, the availability of high resolution topography derived by airborne laser scanner (LiDAR) represents a new and effective tool. Nowadays LiDAR DTMs covering large areas are readily available for public authorities, and there is a greater and more widespread interest in the application of such information for the development of automated methods aimed at solving geomorphological and hydrological problems. While LiDAR DTMs reliability in steep landscape has been proven by several recent studies, only few researches have been conducted to take into account the effectiveness of these data in agrarian low relief landscapes. The goal of this research is to propose a semi-automatic approach based on a LiDAR DTM to (1) detect drainage networks in agrarian/ fl oodplain contexts, and (2) to estimate some of the network summary statistics (network length, width, drainage density and storage capacity). The procedure is applied in two typical alluvial-plain areas in the North East of Italy, and tested comparing automatically derived network with surveyed ones. The results underline the capability of high resolution DTMs for drainage network detection and characterization in the context of agrarian landscapes within floodplains, opening at the same time new challenges to evaluate some hydrological processes in these areas

Developmental dyslexia (DD) is a complex neurodevelopmental deficit characterized by impaired reading acquisition, in spite of adequate neurological and sensorial conditions, educational opportunities and normal intelligence. Despite the successful characterization of DD-susceptibility genes, we are far from understanding the molecular etiological pathways underlying the development of reading (dis)ability. By focusing mainly on clinical phenotypes, the molecular genetics approach has yielded mixed results. More optimally reduced measures of functioning, that is, intermediate phenotypes (IPs), represent a target for researching disease-associated genetic variants and for elucidating the underlying mechanisms. Imaging data provide a viable IP for complex neurobehavioral disorders and have been extensively used to investigate both morphological, structural and functional brain abnormalities in DD. Performing joint genetic and neuroimaging studies in humans is an emerging strategy to link DD-candidate genes to the brain structure and function. A limited number of studies has already pursued the imaging–genetics integration in DD. However, the results are still not sufficient to unravel the complexity of the reading circuit due to heterogeneous study design and data processing. Here, we propose an interdisciplinary, multilevel, imaging–genetic approach to disentangle the pathways from genes to behavior. As the presence of putative functional genetic variants has been provided and as genetic associations with specific cognitive/sensorial mechanisms have been reported, new hypothesis-driven imaging–genetic studies must gain momentum. This approach would lead to the optimization of diagnostic criteria and to the early identification of ‘biologically at-risk’ children, supporting the definition of adequate and well-timed prevention strategies and the implementation of novel, specific remediation approach.