Ten years of volcanic activity at Mt Etna: High-resolution mapping and accurate quantification of the morphological changes by Pleiades and Lidar data

Abstract The topography of Mt. Etna, Italy, is subjected to continuous modifications depending on intensity and magnitude of eruptions that frequently occur at the volcano summit and flanks. In order to make high-resolution maps of morphological changes and accurately calculate the overall volume of the erupted products (e.g., lava flows, tephra fall out, scoriae cones) in ten years, we have compared the altimetry models of Mt. Etna derived from 2005 Airborne Laser Scanning data and 2015 Pleiades stereo satellite imagery. Both models cover a common area of 400 km2 with spatial resolution of 2 m and comparable vertical accuracy (RMS

Development of an automatic delineation of cliff top and toe on very irregular planform coastlines (CliffMetrics v1.0)

We describe a new algorithm that automatically delineates the cliff top and toe of a cliffed coastline from a digital elevation model (DEM). The algorithm builds upon existing methods but is specifically designed to resolve very irregular planform coastlines with many bays and capes, such as parts of the coastline of Great Britain. The algorithm automatically and sequentially delineates and smooths shoreline vectors, generates orthogonal transects and elevation profiles with a minimum spacing equal to the DEM resolution, and extracts the position and elevation of the cliff top and toe. Outputs include the non-smoothed raster and smoothed vector coastlines, normals to the coastline (as vector shape files), xyz profiles (as comma-separated-value, CSV, files), and the cliff top and toe (as point shape files). The algorithm also automatically assesses the quality of the profile and omits low-quality profiles (i.e. extraction of cliff top and toe is not possible). The performance of the proposed algorithm is compared with an existing method, which was not specifically designed for very irregular coastlines, and to manually digitized boundaries by numerous professionals. Also, we assess the reproducibility of the results using different DEM resolutions (5, 10 and 50m), different user-defined parameter sets related to the degree of coastline smoothing, and the threshold used to identify the cliff top and toe. The model output sensitivity is found to be smaller than the manually digitized uncertainty. The code and a manual are publicly available on a GitHub repository

High-Resolution and Accurate Topography Reconstruction of Mount Etna from Pleiades Satellite Data

The areas characterized by dynamic and rapid morphological changes need accurate topography information with frequent updates, especially if these are populated and involve infrastructures. This is particularly true in active volcanic areas such as Mount (Mt.) Etna, located in the northeastern portion of Sicily, Italy. The Mt. Etna volcano is periodically characterized by explosive and effusive eruptions and represents a potential hazard for several thousands of local people and hundreds of tourists present on the volcano itself. In this work, a high-resolution, high vertical accuracy digital surface model (DSM) of Mt. Etna was derived from Pleiades satellite data using the National Aeronautics and Space Administration (NASA) Ames Stereo Pipeline (ASP) tool set. We believe that this is the first time that the ASP using Pleiades imagery has been applied to Mt. Etna with sub-meter vertical root mean square error (RMSE) results. The model covers an area of about 400 km2 with a spatial resolution of 2 m and centers on the summit portion of the volcano. The model was validated by using a set of reference ground control points (GCP) obtaining a vertical RMSE of 0.78 m. The described procedure provides an avenue to obtain DSMs at high spatial resolution and elevation accuracy in a relatively short amount of processing time, making the procedure itself suitable to reproduce topographies often indispensable during the emergency management case of volcanic eruptions

Bayesian Automating Fitting Functions for Spatial Predictions

A Bayesian predictive model for automating mapping of background radiation has the advantage of fully accounting for all uncertainties in the inferred data. Ten training datasets of background radiation were used to set up the model. The model is robust for data containing only close outliers but fails to accurately predict values when the input data is contaminated with extreme outliers, which are the result of a different random underlying process than the background data. For an integrated decision support system for automating mapping when data contamination is expected, a two stage approach is required in which background data are modeled with one set of equations and the contaminated data with a different set of equations

Bayesian Automating Fitting Functions for Spatial Predictions

A Bayesian predictive model for automating mapping of background radiation has the advantage of fully accounting for all uncertainties in the inferred data. Ten training datasets of background radiation were used to set up the model. The model is robust for data containing only close outliers but fails to accurately predict values when the input data is contaminated with extreme outliers, which are the result of a different random underlying process than the background data. For an integrated decision support system for automating mapping when data contamination is expected, a two stage approach is required in which background data are modeled with one set of equations and the contaminated data with a different set of equations

SaTSeaD: Satellite Triangulated Sea Depth Open-Source Bathymetry Module for NASA Ames Stereo Pipeline

We developed the first-ever bathymetric module for the NASA Ames Stereo Pipeline (ASP) open-source topographic software called Satellite Triangulated Sea Depth, or SaTSeaD, to derive nearshore bathymetry from stereo imagery. Correct bathymetry measurements depend on water surface elevation, and whereas previous methods considered the water surface horizontal, our bathymetric module accounts for the curvature of the Earth in the imagery. The process is semiautomatic, reliable, and repeatable, independent of any external bathymetry data eliminating user bias in selecting bathymetry calibration points, and it can generate a fully integrated and seamless topo-bathymetry digital elevation model (TBDEM) in the same coordinate system, comparable with the band-ratio method irrespective of the regression method used for the band-ratio algorithm. The ASP output can be improved by applying a camera bundle adjustment to minimize reprojection errors and by alignment to a more accurate topographic (above water) surface without any bathymetric input since the derived TBDEM is a rigid surface. These procedures can decrease bathymetry root mean square errors from 30 to 80 percent, depending on environmental conditions, the quality of satellite imagery, and the spectral band used (e.g., blue, green, or panchromatic)

High-Resolution and Accurate Topography Reconstruction of Mount Etna from Pleiades Satellite Data

The areas characterized by dynamic and rapid morphological changes need accurate topography information with frequent updates, especially if these are populated and involve infrastructures. This is particularly true in active volcanic areas such as Mount (Mt.) Etna, located in the northeastern portion of Sicily, Italy. The Mt. Etna volcano is periodically characterized by explosive and e usive eruptions and represents a potential hazard for several thousands of local people and hundreds of tourists present on the volcano itself. In this work, a high-resolution, high vertical accuracy digital surface model (DSM) of Mt. Etna was derived from Pleiades satellite data using the National Aeronautics and Space Administration (NASA) Ames Stereo Pipeline (ASP) tool set. We believe that this is the first time that the ASP using Pleiades imagery has been applied to Mt. Etna with sub-meter vertical root mean square error (RMSE) results. The model covers an area of about 400 km 2 with a spatial resolution of 2 m and centers on the summit portion of the volcano. The model was validated by using a set of reference ground control points (GCP) obtaining a vertical RMSE of 0.78 m. The described procedure provides an avenue to obtain DSMs at high spatial resolution and elevation accuracy in a relatively short amount of processing time, making the procedure itself suitable to reproduce topographies often indispensable during the emergency management case of volcanic eruptions.Published29835IT. Osservazioni satellitariJCR Journa

Inundation Exposure Assessment for Majuro Atoll, Republic of the Marshall Islands Using A High-Accuracy Digital Elevation Model

Majuro Atoll in the central Pacific has high coastal vulnerability due to low-lying islands, rising sea level, high wave events, eroding shorelines, a dense population center, and limited freshwater resources. Land elevation is the primary geophysical variable that determines exposure to inundation in coastal settings. Accordingly, coastal elevation data (with accuracy information) are critical for assessments of inundation exposure. Previous research has demonstrated the importance of using high-accuracy elevation data and rigorously accounting for uncertainty in inundation assessments. A quantitative analysis of inundation exposure was conducted for Majuro Atoll, including accounting for the cumulative vertical uncertainty from the input digital elevation model (DEM) and datum transformation. The project employed a recently produced and validated DEM derived from structure-from-motion processing of very-high-resolution aerial imagery. Areas subject to marine inundation (direct hydrologic connection to the ocean) and low-lying lands (disconnected hydrologically from the ocean) were mapped and characterized for three inundation levels using deterministic and probabilistic methods. At the highest water level modeled (3.75 ft, or 1.143 m), more than 34% of the atoll study area is likely to be exposed to inundation (68% chance or greater), while more than 20% of the atoll is extremely likely to be exposed (95% chance or greater). The study demonstrates the substantial value of a high-accuracy DEM for assessing inundation exposure of low-relief islands and the enhanced information from accounting for vertical uncertainty