Innovative analysis and use of high‐resolution DTMs for quantitative interrogation of Earth‐surface processes

This is the era of digital landscapes; the widespread availability of powerful sensing technologies has revolutionized the way it is possible to interrogate landscapes in order to understand the processes sculpting them. Vastly greater areas have now been acquired at ‘high resolution’: currently tens of metres globally to millimetric precision and accuracy locally. This permits geomorphic features to be visualized and analysed across the scales at which Earth-surface processes operate. Especially exciting is the capturing of process dynamics in repeated surveying, which will only become more important with low-cost accessible data generation through techniques such as Structure from Motion (SfM). But the key challenge remains; to interpret high resolution Digital Terrain Models (DTMs), particularly by extracting geomorphic features in robust and objective ways and then linking the observed features to the underlying physical processes. In response to the new data and challenges, recent years have seen improved processing of raw data into DTMs, development of data fusion techniques, novel quantitative analysis of topographic data, and innovative geomorphological mapping. The twelve papers collected in this volume sample this progress in interrogating Earth-surface processes through the analysis of DTMs. They cover a wide range of disciplines and spatio-temporal scales, from landslide prone landscapes, to agriculturally modified regions, to mountainous landscapes, and coastal zones. They all, however, showcase the quantitative exploitation of information contained in high-resolution topographic data that we believe will underpin the improvement of our understanding of many elements of Earth-surface processes. Most of the papers introduced here were first presented in a conference session at the European Geosciences Union General Assembly in 2011. Copyright © 2014 John Wiley & Sons, Ltd.

[1]  P. Reichenbach,et al.  Identification and mapping of recent rainfall-induced landslides using elevation data collected by airborne Lidar , 2007 .

[2]  Efi Foufoula-Georgiou,et al.  Channel network extraction from high resolution topography using wavelets , 2007 .

[3]  Two‐dimensional nonlinear diffusive numerical simulation of geomorphic modifications to cinder cones , 2013 .

[4]  William Eugene Carter,et al.  Geodetic imaging with airborne LiDAR: the Earth's surface revealed , 2013, Reports on progress in physics. Physical Society.

[5]  R. Barneveld,et al.  Assessment of terrestrial laser scanning technology for obtaining high‐resolution DEMs of soils , 2013 .

[6]  Stephanie S. Day,et al.  Landscape Evolution in South-Central Minnesota and the Role of Geomorphic History on Modern Erosional Processes , 2011 .

[7]  P. Tarolli High-resolution topography for understanding Earth surface processes: Opportunities and challenges , 2014 .

[8]  Time-variable 3 D ground displacements from High-Resolution Synthetic 1 Aperture Radar ( SAR ) . Application to La Valette landslide ( South French Alps ) , 2022 .

[9]  Wolfgang Schwanghart,et al.  Flow network derivation from a high resolution DEM in a low relief, agrarian landscape , 2013 .

[10]  R. Colwell Remote sensing of the environment , 1980, Nature.

[11]  J. Hillier,et al.  Testing 3D landform quantification methods with synthetic drumlins in a real digital elevation model , 2012 .

[12]  E. Foufoula‐Georgiou,et al.  Automatic geomorphic feature extraction from lidar in flat and engineered landscapes , 2011 .

[13]  Bernhard Höfle,et al.  Fusion of multi‐resolution surface (terrestrial laser scanning) and subsurface geodata (ERT, SRT) for karst landform investigation and geomorphometric quantification , 2013 .

[14]  D. J. Chadwick,et al.  Analysis of LiDAR-derived topographic information for characterizing and differentiating landslide morphology and activity , 2006 .

[15]  G. Bertoldi,et al.  On the assessment of the management priority of sediment source areas in a debris‐flow catchment , 2014 .

[16]  W. Marcus,et al.  Remote sensing of rivers: the emergence of a subdiscipline in the river sciences , 2010 .

[17]  B. Bookhagen,et al.  Channel planform geometry and slopes from freely available high-spatial resolution imagery and DEM fusion: Implications for channel width scalings, erosion proxies, and fluvial signatures in tectonically active landscapes , 2013 .

[18]  J. Roering,et al.  Automated landslide mapping using spectral analysis and high-resolution topographic data: Puget Sound lowlands, Washington, and Portland Hills, Oregon , 2008 .

[19]  Michael B. Gousie,et al.  The Cookie Cutter: A method for obtaining a quantitative 3D description of glacial bedforms , 2009 .

[20]  P. Tarolli,et al.  Geomorphic features extraction from high-resolution topography: landslide crowns and bank erosion , 2012, Natural Hazards.

[21]  D. Benson,et al.  Particle tracking and the diffusion‐reaction equation , 2013 .

[22]  J. Huthnance,et al.  Threshold of erosion of submarine bedrock landscapes by tidal currents , 2013 .

[23]  Paola Passalacqua,et al.  Testing space‐scale methodologies for automatic geomorphic feature extraction from lidar in a complex mountainous landscape , 2010 .

[24]  Lorenzo Marchi,et al.  Characterisation of the surface morphology of an alpine alluvial fan using airborne LiDAR , 2008 .

[25]  J. Hillier,et al.  Seamount detection and isolation with a modified wavelet transform , 2008 .

[26]  J. Wasowski,et al.  Using COSMO/SkyMed X-band and ENVISAT C-band SAR interferometry for landslides analysis , 2012 .

[27]  D. Staley,et al.  Surficial patterns of debris flow deposition on alluvial fans in Death Valley, CA using airborne laser swath mapping data , 2006 .

[28]  Colin P. Stark,et al.  Application of a multi‐temporal, LiDAR‐derived, digital terrain model in a landslide‐volume estimation , 2013 .

[29]  Giulia Sofia,et al.  An objective approach for feature extraction: distribution analysis and statistical descriptors for scale choice and channel network identification , 2011 .

[30]  R. Detrick,et al.  Effect of the Galápagos hotspot on seafloor volcanism along the Galápagos Spreading Center (90.9–97.6°W) , 2004 .

[31]  Alessandro Corsini,et al.  Automated classification of Persistent Scatterers Interferometry time series , 2013 .

[32]  A. Corsini,et al.  Integrating airborne and multi‐temporal long‐range terrestrial laser scanning with total station measurements for mapping and monitoring a compound slow moving rock slide , 2013 .

[33]  M. J. Smith,et al.  Testing techniques to quantify drumlin height and volume: synthetic DEMs as a diagnostic tool , 2014 .

[34]  Zhong Lu,et al.  Pre-, co-, and post- rockslide analysis with ALOS/PALSAR imagery: a case study of the Jiweishan rockslide, China , 2013 .

[35]  K. L. Frankel,et al.  Characterizing arid region alluvial fan surface roughness with airborne laser swath mapping digital topographic data , 2007 .

[36]  Guillermo Sapiro,et al.  A geometric framework for channel network extraction from lidar: Nonlinear diffusion and geodesic paths , 2010 .

[37]  J. Roering,et al.  Using DInSAR, airborne LiDAR, and archival air photos to quantify landsliding and sediment transport , 2009 .

[38]  J. McKean,et al.  Objective landslide detection and surface morphology mapping using high-resolution airborne laser altimetry , 2004 .

[39]  J. Hillier Testing techniques to quantify drumlin height and 1 volume ; synthetic DEMs as a diagnostic tool , 2013 .

[40]  Peter Dorninger,et al.  Automated recognition of quasi‐planar ignimbrite sheets as paleosurfaces via robust segmentation of digital elevation models: an example from the Central Andes , 2014 .

[41]  P. Tarolli,et al.  Drainage network detection and assessment of network storage capacity in agrarian landscape , 2013 .

[42]  P. Tarolli,et al.  Variations in multiscale curvature distribution and signatures of LiDAR DTM errors , 2013 .

[43]  P. Belmont,et al.  TerEx Toolbox for semi‐automated selection of fluvial terrace and floodplain features from lidar , 2014 .

[44]  Jon D. Pelletier,et al.  A robust, two‐parameter method for the extraction of drainage networks from high‐resolution digital elevation models (DEMs): Evaluation using synthetic and real‐world DEMs , 2013 .

[45]  Martin Rutzinger,et al.  Digital elevation models derived from airborne laser scanning point clouds: appropriate spatial resolutions for multi‐temporal characterization and quantification of geomorphological processes , 2014 .