Geomorphic features extraction from high-resolution topography: landslide crowns and bank erosion

Abstract

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

An objective approach for feature extraction: distribution analysis and statistical descriptors for scale choice and channel network identification

“…The minimum width value (3 cells) relies on the fact that sampling window needs to be centered on the cell of interest, thus the smallest area must comprise this cell and its eight nearest neighbors. Literature review (Pirotti and Tarolli, 2010;Tarolli et al, 2010) demonstrated that considering a window width range of 3-33 cells, quality of extracted features tends to a progressive worsening when windows are greater than ∼25 cells. We decided to apply the same kernel size range (3-33) to account also for a margin of uncertainty considering the different interpolation techniques of the study DTMs and different morphological characterization of the study cases.…”

An integrated airborne laser scanning approach to forest management and cultural heritage issues: a case study at Porolissum, Romania

“…However, features were easily identified only when illuminated from the side, a shortcoming reported in many other studies (Štular 2011, Tarolli et al 2012, Tarolli 2014. Thus, road features were well defined on one aspect but difficult to distinguish on others.…”

A study on the use of planarity for quick identification of potential landslide hazard

“…This implies that landslide initiation zones have a lower planarity compared to other areas of consideration, which is based on two pre-defined conditions: (a) the boundary of landslide initiation zone delineated as a circle which ignores the real extent of the initiation zone and (b) the appropriate size of the moving window when the planarity is calculated. Tarolli et al (2012) introduced a logical assumption for determining the moving window size for the channel network recognition in order to reduce the noise and noted that a proper window size is the critical element for extract- ing appropriate geomorphological features. In our case, the moving window used in this study was 3 by 3, which is relatively optimal for 1 m LiDAR-derived DEM.…”