In recent years, new remote-sensed technologies, such as airborne and terrestrial laser scanner, have improved the detail and the quality of topographic information, providing topographical high-resolution and high-quality data over larger areas better than other technologies. A new generation of high-resolution (≤3 m) digital terrain models (DTMs) is now available for different areas and is widely used by researchers, offering new opportunities for the scientific community. These data call for the development of a new generation of methodologies for an objective extraction of geomorphic features, such as channel heads, channel networks, bank geometry, debris-flow channel, debris-flow deposits, scree slope, landslide and erosion scars, etc. A high-resolution DTM is able to detect the divergence/convergence of areas related to unchannelized/channelized processes with better detail than a coarse DTM. In this work, we tested the performance of new methodologies for an objective extraction of geomorphic features related to shallow landsliding processes (landslide crowns), and bank erosion in a complex mountainous terrain. Giving a procedure that automatically recognizes these geomorphic features can offer a strategic tool to map natural hazard and to ease the planning and the assessment of alpine regions. The methodologies proposed are based on the detection of thresholds derived by the statistical analysis of variability of landform curvature. The study was conducted on an area located in the Eastern Italian Alps, where an accurate field survey on shallow landsliding, erosive channelized processes, and a high-quality set of both terrestrial and airborne laser scanner elevation data is available. The analysis was conducted using a high-resolution DTM and different smoothing factors for landform curvature calculation in order to test the most suitable scale of curvature calculation for the recognition of the selected features. The results revealed that (1) curvature calculation is strongly scale-dependent, and an appropriate scale for derivation of the local geometry has to be selected according to the scale of the features to be detected; (2) such approach is useful to automatically detect and highlight the location of shallow slope failures and bank erosion, and it can assist the interpreter/operator to correctly recognize and delineate such phenomena. These results highlight opportunities but also challenges in fully automated methodologies for geomorphic feature extraction and recognition

The Earth's surface morphology, in an abiotic context, is a consequence of major forcings such as tectonic uplift, erosion, sediment transport, and climate. Recently, however, it has become essential for the geomorphological community to also take into account biota as a geomorphological agent that has a role in shaping the landscape, even if at a different scale and magnitude from that of geology. Although the modem literature is flourishing on the impacts of vegetation on geomorphic processes, the study of anthropogenic pressures on geomorphology is still in its early stages. Topography emerges as a result of natural driving forces, but some human activities (such as mining, agricultural practices and the construction of road networks) directly or indirectly move large quantities of soil, which leave dear topographic signatures embedded on the Earth's morphology. These signatures can cause drastic changes to the geomorphological organization of the landscape, with direct consequences on Earth surface processes. This review provides an overview of the recent literature on the role of humans as a geological agent in shaping the morphology of the landscape. We explore different contexts that are significantly characterized by anthropogenic topographic signatures: landscapes affected by mining activities, road networks and agricultural practices. We underline the main characteristics of those landscapes and the implications of human impacts on Earth surface processes. The final section considers future challenges wherein we explore recent novelties and trials in the concept of anthropogenic geomorphology. Herein, we focus on the role of high-resolution topographic and remote-sensing technologies. The reconstruction or identification of artificial or anthropogenic topographies provides a mechanism for quantifying anthropogenic changes to landscape systems. This study may allow an improved understanding and targeted mitigation of the processes driving geomorphic changes during anthropogenic development and help guide future research directions for development-based watershed studies. Human society is deeply affecting the environment with consequences on the landscape. Therefore, establishing improved management measures that consider the Earth's rapidly changing systems is fundamental. (C) 2015 Elsevier B.V. All rights reserved

Abstract.A statistical approach to LiDAR derived topographic attributes for the automatic extraction of channel network and for the choice of the scale to apply for parameter evaluation is presented in this paper. The basis of this approach is to use distribution analysis and statistical descriptors to identify channels where terrain geometry denotes significant convergences. Two case study areas with different morphology and degree of organization are used with their 1 m LiDAR Digital Terrain Models (DTMs). Topographic attribute maps (curvature and openness) for various window sizes are derived from the DTMs in order to detect surface convergences. A statistical analysis on value distributions considering each window size is carried out for the choice of the optimum kernel. We propose a three-step method to extract the network based (a) on the normalization and overlapping of openness and minimum curvature to highlight the more likely surface convergences, (b) a weighting of the upslope area according to these normalized maps to identify drainage flow paths and flow accumulation consistent with terrain geometry, (c) the standard score normalization of the weighted upslope area and the use of standard score values as non subjective threshold for channel network identification. As a final step for optimal definition and representation of the whole network, a noise-filtering and connection procedure is applied. The advantage of the proposed methodology, and the efficiency and accurate localization of extracted features are demonstrated using LiDAR data of two different areas and comparing both extractions with field surveyed networks.

Vineyard landscapes are a relevant part of the European culture, and several authors concluded that they are the agricultural practice that causes the highest soil loss. Grape quality depends on the availability of water, and soil erosion is an important parameter dictating the vineyard sustainability; therefore, soil and water conservation measures are often implemented. Among them, the construction of terraces is the most widely used system. However, while favouring agricultural activities, terraces if not properly maintained can lead to local instabilities creating hazards for settlements and cultivations, and for the related economy. Terraced fields are also served by agricultural roads that can have deep effects on water flows triggering surface erosion. The goal of this research is to use lidar elevation data for a hydro‐geomorphological analysis of terraced vineyards. The work is divided in two parts. At first, the Relative Path Impact Index is tested in two vineyards to identify terrace‐induced and road‐induced erosions. Statistical thresholds of the Relative Path Impact Index are then defined to label the most critical areas. On the second step, using the index and the defined thresholds, we simulate different scenarios of soil conservation measures, establishing the optimal solution to reduce erosion. The results highlight the effectiveness of high‐resolution topography in the analysis of surface erosion in terraced vineyards, when the surface water flow is the main factor triggering the instabilities. The proposed analysis can help in scheduling a suitable planning to mitigate the consequences of the anthropogenic alterations induced by the terraces and agricultural roads. Copyright © 2014 John Wiley & Sons, Ltd.

In floodplains, anthropogenic features such as levees or road scarps, control and influence flows. An up-to-date and accurate digital data about these features are deeply needed for irrigation and flood mitigation purposes. Nowadays, LiDAR Digital Terrain Models (DTMs) covering large areas are available for public authorities, and there is a widespread interest in the application of such models for the automatic or semiautomatic recognition of features. The automatic recognition of levees and road scarps from these models can offer a quick and accurate method to improve topographic databases for large-scale applications. In mountainous contexts, geomorphometric indicators derived from DTMs have been proven to be reliable for feasible applications, and the use of statistical operators as thresholds showed a high reliability to identify features. The goal of this research is to test if similar approaches can be feasible also in floodplains. Three different parameters are tested at different scales on LiDAR DTM. The boxplot is applied to identify an objective threshold for feature extraction, and a filtering procedure is proposed to improve the quality of the extractions. This analysis, in line with other works for different environments, underlined (1) how statistical parameters can offer an objective threshold to identify features with varying shapes, size and height; (2) that the effectiveness of topographic parameters to identify anthropogenic features is related to the dimension of the investigated areas. The analysis also showed that the shape of the investigated area has not much influence on the quality of the results. While the effectiveness of residual topography had already been proven, the proposed study underlined how the use of entropy can anyway provide good extractions, with an overall quality comparable to the one offered by residual topography, and with the only limitation that the extracted features are slightly wider than the investigated one