# The 2014 Effusive Eruption at Stromboli: New Insights from In Situ and Remote-Sensing Measurements
## 2 Materials and Methods
### Topographic Data and Co-Registration  
Topographic change detection of Stromboli island due to the 2014 eruption was performed by comparing pre- and post- eruptive DEMs, the former generated from airborne LiDAR data and the post-eruption DEM from PLEIADES-1 satellite data. The pre-2014 LiDAR-DEM was obtained elaborating the 3D point cloud acquired during an airborne survey carried on May 2012 by \"BLOM Compagnia Generale Ripreseaeree S.P.A.\" (www.blomasa.com). The data were acquired using the Leica ADS80 sensor which has instrumental vertical and horizontal accuracy of \(\pm\)10/20 cm and \(\pm\)25 cm, respectively (see [36] for 2012 DEM features). The acquired point cloud has a mean point density of 8 pt/m\({}^{2}\). The post-2014 DEM was derived from the very high-resolution (VHR) tri-stereo optical imagery from the PLEIADES-1 satellites. PLEIADES-1 constellation is composed by two satellites, PLEIADES-1A (PHR1A) and PLEIADES-1B (PHR1B). These satellites can sense three or more synchronous images of the same area, with angles variable between \(\sim\)6\({}^{\circ}\) e \(\sim\)-28\({}^{\circ}\). The stereoscopic triplet is composed of three nearly simultaneously acquired images, one backward looking, one forward looking, plus a third near-nadir image [37; 38; 39; 40; 41]. Tri-stereo images are 100% cloud free and were acquired by PHR1A on 27 May 2017, with a total areal coverage of 58 km\({}^{2}\). This acquisition mode permits to obtain a DEM using the photogrammetric processing of three images (tri-stereo mode), with a pixel xy resolution of 1 m \(\times\) 1 m. The dataset comprised also VHR optical imagery (0.5 m \(\times\) 0.5 m resolution for Panchromatic + 2 m \(\times\) 2 m Multispectral data). To assess the accuracy of the heights and their horizontal position in the Pleiades-1 DEM, six Ground-Control Points (GCPs) were collected on the map database (Cartographic XY standard deviation: 0.15 m). Nine tie points were automatically collected in the images. A block adjustment including all the satellite scenes was performed. The block adjustment was validated when the following accuracy was achieved: (i) pixel xy bias smaller than 0.3 pixels; (ii) pixel xy standard deviation smaller than 0.3 pixels; (iii) pixel xy maximum smaller than 2 pixels. Topographic change detection using multi-temporal DEMs was performed by differencing two DEMs of the same area derived from data taken at different time. This calculation is often affected by errors depending on mismatches between the two DEMs, which lead to artefact \(\Delta z\)[42]. This error can be detected and reduced by measuring and minimizing the DEM differences in areas where the two DEMs are supposed to be equal, i.e., those areas not reasonably affected by relevant natural changes. The 2012 and 2017 topographic data were here co-registered by minimizing the root mean square (RMS) error between two DEMs. The methods used iteratively vary the three angles of rotation, the translation, and the magnification or reduction factor of one DEM using a custom-made algorithm based on the MINUIT minimization library [43], as described by [44; 45; 46]. MINUIT is a tool to find the minimum value of multi-parameter functions and can be freely downloaded ([[http://www.cern.ch/minuit](http://www.cern.ch/minuit)]([http://www.cern.ch/minuit](http://www.cern.ch/minuit))). The LiDAR-PLEIADES DEMs co-registration was performed by calculating the minimization parameters for the whole volcano with the exclusion of the investigated areas (i.e., the SdF). The RMS error in this case is given by the MINUIT output. The 2012 LiDAR was used as reference for co-registering the PLEIADES DEM. It is worth emphasizing that this RMS error is the root mean square residuals (in elevation) between a 3D point cloud and the reference DEM, rather than a true absolute error.