Geomorphological response to volcanic activity at Stromboli volcano using multi-platform remote sensing

Author(s):  
Federico Di Traglia ◽  
Alessandro Fornaciai ◽  
Massimiliano Favalli ◽  
Teresa Nolesini ◽  
Nicola Casagli

<p>Steep volcano flanks are geomorphological systems highly responsive to both exogenous dynamics and endogenous forcing. While the external (gravitational) processes lead to a shift of material from steeper slopes to areas with lower gradients (erosion of loose deposits, rockfall of lavas/welded material), magmatic and tectonic activity can have either a constructional (accumulation) or a destructive effect (triggering moderate- to large-scale mass-wasting). Remotely sensed data have often been used to map areas affected by lithological and morphological changes, i.e. to identify areas impacted by eruptive and post-eruptive (landslides or floods) phenomena, as well as to quantify topographic changes.</p><p>In this work, the geomorphological evolution of the Sciara del Fuoco (SdF) depression on the Island of Stromboli (Italy) between July 2010 and October 2019 has been reconstructed by using multi-temporal, multi-platform remote sensing data. Digital elevation models (DEMs) from PLEIADES-1 tri-stereo images and from LiDAR acquisitions allowed the topographic changes estimation. Data comprised also high-spatial-resolution (QUICKBIRD) and moderate spatial resolution (SENTINEL-2) satellite images allowing to map areas affected by major lithological and morphological changes. SdF was selected being the optimal test-site for monitoring the effect of volcanic eruption on steep-slope volcano flank, since: i) it is affected by persistent volcanic activity, ii) it is prone to mass-wasting phenomena, and iii) it is one of the best studied and, among all, monitored volcano on Earth, providing exceptional validation data and ground-truth constrains.</p><p>During the analysed period, the volcano experienced two eruptions (summer 2014 and summer 2019), with the emplacement of two lava flow fields on the SdF. Before the 2014 effusion and in between the two eruptions, geomorphological changes consisted of volcanoclastic sedimentation and some overflows outside the crater. The effusive (and partially explosive) activity produced larger topographic changes, related to the emplacement of the two lava flow fields and to the accumulation of a volcaniclastic wedge on the SdF. This work shows that, at Stromboli, the emplacements of lava flow fields were preceded and accompanied by the accumulation of volcanoclastic wedges on the SdF. The quantification of these volcanoclastic wedges is relevant because they are composed of the same material that was involved in the 30 December 2002 tsunamigenic landslide, besides being located in the same area.</p><p>PLEIADES tri-stereo and LiDAR DEMs have been quantitatively and qualitatively compared, providing a first indication on the differences between two largely used methods for modelling topography. Although there are small artefacts in smaller ridges and valleys, there is still a clear consistency between the two DEMs for the main valleys and ridges. This analysis can be used by the volcanological community and the civil protection authorities in case of a cost-benefit analysis for planning the best method for updating topography and quantify morphological changes of an active volcano.</p>

2020 ◽  
Author(s):  
Gary Llewellyn ◽  
Loreena Jaouen ◽  
Jennifer Killeen ◽  
Chloe Barnes ◽  
Luke Platts ◽  
...  

<p>Rhododendron (Rhododendron ponticum) has been identified as an invasive non-native species (INNS) in the UK and a potential carrier of Phytophthora ramorum and therefore needs management.  This study identified the presence and location of rhododendron from airborne hyperspectral data and compared the results with Random Forests classifications of Sentinel-2 and Pleiades satellite data. The multispectral satellite systems had two limitations. The first limitation was insufficient spectral resolution to identify individual understorey species in a deciduous woodland (e.g. rhododendron, cherry laurel and holly). In this instance the satellite systems were only able to identify the presence of ‘potential rhododendron’, rather than actual rhododendron, where the term ‘potential rhododendron’ included any understorey evergreen species in a deciduous woodland. The second was insufficient spatial resolution (10m and 2m, respectively) to discriminate individual understorey plants; which resulted in the understorey being represented by a majority of mixed pixels. In this situation no more than percentage estimates of ‘potential rhododendron’ in an area could be obtained.</p><p>The airborne data used in this study were collected using a HySpex hyperspectral VNIR sensor and Phase One (80MB) survey camera; these provided a spatial resolution of 0.32m and 0.07m, respectively. The HySpex VNIR sensor had 186 bands with a full-width-half-maximum of 4.5nm. This sensor combination was shown to have sufficient spectral and spatial resolution to identify individual understorey species. Discrimination of different understorey species was achieved using a combination of spectral analysis techniques, including spectral angle mapper (SAM), and object-based-image analysis (OBIA). Furthermore, overstorey and understorey canopies were separated through the inclusion of a separate airborne LiDAR dataset, collected earlier that year.</p><p>Remotely sensed optical data were collected in leaf-off conditions to minimise the influence of the overstorey vegetation canopy. However, this introduced specific issues relating to weak sunlight and low solar illumination angles; these influenced data quality, data analysis and validation of the final classification. Methods to mitigate these issues were developed (e.g. use of masks to remove long shadows cast by trees), but challenging obstacles remained (e.g. steep north-facing terrain casting large areas in shadow). Meanwhile, validation required botanical expertise, careful consideration of the relative dates when remotely sensed data and field validation data were collected, the geographical precision of field data and an awareness of any bias incurred by shadow.  As with other remote sensing studies, the number and distribution of validation samples and the selection of training data were major considerations. However, this multi-scale study demonstrates the advantages of using airborne hyperspectral systems for species mapping in complex environments. </p>


2020 ◽  
Vol 12 (3) ◽  
pp. 438 ◽  
Author(s):  
Federico Di Traglia ◽  
Alessandro Fornaciai ◽  
Massimiliano Favalli ◽  
Teresa Nolesini ◽  
Nicola Casagli

The geomorphological evolution of the volcanic Island of Stromboli (Italy) between July 2010 and June 2019 has been reconstructed by using multi-temporal, multi-platform remote sensing data. Digital elevation models (DEMs) from PLÉIADES-1 tri-stereo images and from Light Detection and Ranging (LiDAR) acquisitions allowed for topographic changes estimation. Data were comprised of high-spatial-resolution (QUICKBIRD) and moderate spatial resolution (SENTINEL-2) satellite images that allowed for the mapping of areas that were affected by major lithological and morphological changes. PLÉIADES tri-stereo and LiDAR DEMs have been quantitatively and qualitatively compared and, although there are artefacts in the smaller structures (e.g., ridges and valleys), there is still a clear consistency between the two DEMs for the larger structures (as the main valleys and ridges). The period between July 2010 and May 2012 showed only minor changes consisting of volcanoclastic sedimentation and some overflows outside the crater. Otherwise, between May 2012 and May 2017, large topographic changes occurred that were related to the emplacement of the 2014 lava flow in the NE part of the Sciara del Fuoco and to the accumulation of a volcaniclastic wedge in the central part of the Sciara del Fuoco. Between 2017 and 2019, minor changes were again detected due to small accumulation next to the crater terrace and the erosion in lower Sciara del Fuoco.


2018 ◽  
Vol 80 (3) ◽  
Author(s):  
Massimiliano Favalli ◽  
Alessandro Fornaciai ◽  
Luca Nannipieri ◽  
Andrew Harris ◽  
Sonia Calvari ◽  
...  

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2407
Author(s):  
Hojun You ◽  
Dongsu Kim

Fluvial remote sensing has been used to monitor diverse riverine properties through processes such as river bathymetry and visual detection of suspended sediment, algal blooms, and bed materials more efficiently than laborious and expensive in-situ measurements. Red–green–blue (RGB) optical sensors have been widely used in traditional fluvial remote sensing. However, owing to their three confined bands, they rely on visual inspection for qualitative assessments and are limited to performing quantitative and accurate monitoring. Recent advances in hyperspectral imaging in the fluvial domain have enabled hyperspectral images to be geared with more than 150 spectral bands. Thus, various riverine properties can be quantitatively characterized using sensors in low-altitude unmanned aerial vehicles (UAVs) with a high spatial resolution. Many efforts are ongoing to take full advantage of hyperspectral band information in fluvial research. Although geo-referenced hyperspectral images can be acquired for satellites and manned airplanes, few attempts have been made using UAVs. This is mainly because the synthesis of line-scanned images on top of image registration using UAVs is more difficult owing to the highly sensitive and heavy image driven by dense spatial resolution. Therefore, in this study, we propose a practical technique for achieving high spatial accuracy in UAV-based fluvial hyperspectral imaging through efficient image registration using an optical flow algorithm. Template matching algorithms are the most common image registration technique in RGB-based remote sensing; however, they require many calculations and can be error-prone depending on the user, as decisions regarding various parameters are required. Furthermore, the spatial accuracy of this technique needs to be verified, as it has not been widely applied to hyperspectral imagery. The proposed technique resulted in an average reduction of spatial errors by 91.9%, compared to the case where the image registration technique was not applied, and by 78.7% compared to template matching.


Sign in / Sign up

Export Citation Format

Share Document