Coastal landscapes are one of the most changeable areas of the earth's surface. Given this spatial complexity and temporal variability, the construction of reference maps useful for geo-engineering is a challenge. In order to improve the performance of geomorphic models, reliable multiscale and multi-temporal base maps and Digital Elevation Models (DEM) are needed. The work presented in this paper addresses this issue using an inter-geo-disciplinary approach to optimize the processing of multisource and multi-temporal data and DEMs by using field surveys, conceptual model, and analytical computation on a test area. The data acquired with two surveying techniques were analyzed and compared: Aerial Laser Scanning (ALS) and photogrammetry from stereo pairs of High-Resolution Satellite Images (HRSI). To assess the reliability of the DEMs produced from point clouds, the residuals between the point cloud and the interpolated filtered surface were identified and analyzed statistically. In addition to the contour maps, some feature maps such as slope, planar, and profile curvature maps were produced and analyzed. The frequency distribution of the slope and curvature values were compared with the diffusion, advection, and stream power model, revealing a good agreement with the past and present geomorphic processes acting on the different parts of the study area. Moreover, the integrated geomatics–geomorphic analysis of the outliers’ map showed a good correspondence (more than 75%) between the identified outliers and some specific geomorphological features, such as micro-landforms, which are significant for erosive and gravity-driven mechanisms. The different distribution of the above singularities by different data sources allowed us to attribute their spatial model to the temporal variation of the topography and, consequently, to the geomorphic changes, rather than to the different accuracy. For monitoring purposes and risk mitigation activities, the methodology adopted seems to meet the requirements to make a digital mapping of the coast analyzed, characterized by a rapid evolution of the surface, and can be extended to other stretches of coast with similar characteristics.
Topographic base maps from remote sensing data for engineering geomorphological modelling: An application on coastal mediterranean landscape
Barbarella M.;Cuomo A.;Di Benedetto A.;Fiani M.;Guida D.
2019-01-01
Abstract
Coastal landscapes are one of the most changeable areas of the earth's surface. Given this spatial complexity and temporal variability, the construction of reference maps useful for geo-engineering is a challenge. In order to improve the performance of geomorphic models, reliable multiscale and multi-temporal base maps and Digital Elevation Models (DEM) are needed. The work presented in this paper addresses this issue using an inter-geo-disciplinary approach to optimize the processing of multisource and multi-temporal data and DEMs by using field surveys, conceptual model, and analytical computation on a test area. The data acquired with two surveying techniques were analyzed and compared: Aerial Laser Scanning (ALS) and photogrammetry from stereo pairs of High-Resolution Satellite Images (HRSI). To assess the reliability of the DEMs produced from point clouds, the residuals between the point cloud and the interpolated filtered surface were identified and analyzed statistically. In addition to the contour maps, some feature maps such as slope, planar, and profile curvature maps were produced and analyzed. The frequency distribution of the slope and curvature values were compared with the diffusion, advection, and stream power model, revealing a good agreement with the past and present geomorphic processes acting on the different parts of the study area. Moreover, the integrated geomatics–geomorphic analysis of the outliers’ map showed a good correspondence (more than 75%) between the identified outliers and some specific geomorphological features, such as micro-landforms, which are significant for erosive and gravity-driven mechanisms. The different distribution of the above singularities by different data sources allowed us to attribute their spatial model to the temporal variation of the topography and, consequently, to the geomorphic changes, rather than to the different accuracy. For monitoring purposes and risk mitigation activities, the methodology adopted seems to meet the requirements to make a digital mapping of the coast analyzed, characterized by a rapid evolution of the surface, and can be extended to other stretches of coast with similar characteristics.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
