Geomorphic features extraction from high-resolution topography: landslide crowns and bank erosion |
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Authors: | Paolo Tarolli Giulia Sofia Giancarlo Dalla Fontana |
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Institution: | 1.Department of Land and Agroforest Environments,University of Padova,Legnaro,Italy;2.Institute of Inland Waters,Hellenic Center for Marine Research,Anavyssos,Greece |
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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. |
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