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Abstract and Figures

Structure-from-motion (SfM) algorithms greatly facilitate the generation of 3-D topographic models from photographs and can form a valuable component of hazard monitoring at active volcanic domes. However, model generation from visible imagery can be prevented due to poor lighting conditions or surface obscuration by degassing. Here, we show that thermal images can be used in a SfM workflow to mitigate these issues and provide more continuous time-series data than visible counterparts. We demonstrate our methodology by producing georeferenced photogrammetric models from 30 near-monthly overflights of the lava dome that formed at Volcán de Colima (Mexico) between 2013 and 2015. Comparison of thermal models with equivalents generated from visible-light photographs from a consumer digital single lens reflex (DSLR) camera suggests that, despite being less detailed than their DSLR counterparts, the thermal models are more than adequate reconstructions of dome geometry, giving volume estimates within 10% of those derived using the DSLR. Significantly, we were able to construct thermal models in situations where degassing and poor lighting prevented the construction of models from DSLR imagery, providing substantially better data continuity than would have otherwise been possible. We conclude that thermal photogrammetry provides a useful new tool for monitoring effusive volcanic activity and assessing associated volcanic risks.
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http://dx.doi.org/10.1016/j.jvolgeores.2017.03.022
Thermal Photogrammetric Imaging: A New Technique for Monitoring
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Dome Eruptions
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Samuel T. Thielea,b*, Nick Varleya & Mike R. Jamesc
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aColima Intercambio e Investigación en Vulcanología, Universidad de Colima, av. Universidad 333, Las
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Viboras C.P. 28040, Colima, México
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bSchool of Earth, Atmosphere and Environment, Monash University, Clayton VIC 3800, Australia
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cLancaster Environment Centre, Lancaster University, Lancaster LA1 4YQ, UK
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*Corresponding Author: sam.thiele01@gmail.com
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Abstract
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Structure-from-motion (SfM) algorithms greatly facilitate the generation of 3-D topographic models
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from photographs and can form a valuable component of hazard monitoring at active volcanic domes.
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However, model generation from visible imagery can be prevented due to poor lighting conditions or
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surface obscuration by degassing. Here, we show that thermal images can be used in a SfM workflow
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to mitigate these issues and provide more continuous time-series data than visible-light equivalents.
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We demonstrate our methodology by producing georeferenced photogrammetric models from 30
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near-monthly overflights of the lava dome that formed at Volcán de Colima (Mexico) between 2013
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and 2015. Comparison of thermal models with equivalents generated from visible-light photographs
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from a consumer digital single lens reflex (DSLR) camera suggests that, despite being less detailed than
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their DSLR counterparts, the thermal models are more than adequate reconstructions of dome
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geometry, giving volume estimates within 10% of those derived using the DSLR.
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Significantly, we were able to construct thermal models in situations where degassing and poor
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lighting prevented the construction of models from DSLR imagery, providing substantially better data
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continuity than would have otherwise been possible. We conclude that thermal photogrammetry
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provides a useful new tool for monitoring effusive volcanic activity and assessing associated volcanic
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risks.
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Key words: Lava Dome, Photogrammetry, Thermal Imaging, Volcán de Colima
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1. Introduction
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Lava domes are known to pose significant volcanic hazards, due to their tendency to generate collapse
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related pyroclastic flows and their association with explosive eruptions (Fink and Anderson, 2000). For
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example, successive dome collapses at Soufrière Hills on the island of Montserrat, starting in 1995,
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caused the evacuation and eventual abandonment of the capital Plymouth and surrounding areas
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(Wadge et al., 2014), while the 1994 collapse of Mount Merapi (Indonesia) resulted in 95 deaths and
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damage to several villages (Abdurachman et al., 2000). A similar event at Volcán de Colima in 2015
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generated pyroclastic flows that travelled ~10 km, fortunately causing only minor damage.
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Monitoring of dome geometry (e.g. volume and height), growth rate and deformation is key to
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forecasting such dome collapse events (Voight, 2000), and photogrammetry and structure from
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motion (SfM) are increasingly being used for this purpose (e.g. Herd et al., 2005; Ryan et al., 2010;
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Diefenbach et al., 2012; James and Varley, 2012; Diefenbach et al., 2013). Using these techniques,
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morphological and geometric data can be safely and inexpensively acquired, and used to track
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eruption progress, identify signs of instability or changes in effusion rate, and forecast changes in
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volcanic risk. These methods, however, rely on clear viewing conditions and so are highly sensitive to
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degassing, cloud and poor lighting conditions.
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Thermal imaging techniques are also widely used for monitoring purposes (Spampinato et al., 2011),
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as they allow quantitative evaluation of heat flux from volcanic vents (e.g. Harris and Stevenson, 1997;
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Sahetapy-Engel et al., 2008), domes (e.g. Hutchison et al., 2013; Pallister et al., 2013), flows (e.g.
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Calvari et al., 2003; James et al., 2006) and fumaroles (e.g. Stevenson and Varley, 2008; Harris et al.,
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2009). Importantly, the spatial distribution of heat flux can reveal features that are difficult to detect
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using reflected visible light, such as fumaroles, fractures and rock fall traces (Hutchison et al., 2013;
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Mueller et al., 2013).
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Changes in the distribution and intensity of thermal anomalies can also precede volcanic eruptions or
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changes in eruptive style (Spampinato et al., 2011) and thus have potential for hazard forecasting.
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However, to facilitate inter-survey comparisons, thermal data need to be spatially referenced, and
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producing orthorectified thermal maps usually requires additional topographic data, knowledge of the
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camera location and viewing direction (e.g. James et al., 2006; James et al., 2009; Lewis et al., 2015).
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This study demonstrates a method for deriving topographic data and georeferenced thermal maps
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directly from oblique thermal imagery using SfM techniques and imagery captured during an episode
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of dome growth at Volcán de Colima (Mexico) between 2013 and 2015. We suggest that the resulting
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three-dimensional thermal models provide intuitive and georeferenced representations of dome
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surface temperature and valuable measurements of dome geometry. Furthermore, we demonstrate
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that despite the lower spatial resolution of thermal images, dome volume estimates are comparable
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to those estimated using SfM reconstructions deriving from visible-light digital single lens reflex (DSLR)
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photographs, and that unlike the DSLR models, the thermal models can be constructed during periods
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of poor lighting and extensive degassing.
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Volcán de Colima is an andesitic and frequently erupting stratovolcano, located at the western limit
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of the Trans-Mexican Volcanic Belt. During the most recent eruptive periods, six episodes of dome
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growth have been observed at the volcano (19981999, 20012003, 2004, 20072011, 20132015
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and an ongoing episode initiated in February 2016). This represents the most active period at the
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volcano since its last catastrophic eruption in 1913. A range of effusion rates have been estimated,
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with the longer-lived eruptions associated with rates as low as 0.01 m3 s-1. During the current eruption
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the volcano has exhibited the continuous generation of small Vulcanian explosions with a frequency
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of the order of hours. Larger magnitude explosions usually follow periods of dome emplacement,
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which re-excavate the summit crater.
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The episode of dome growth investigated in this study began in January 2013 when lava erupted into
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the base of a ~150 m wide and ~50 m deep crater formed (by several large explosions that same
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month) on top of a previous (2007 to 2011) lava dome. The new dome proceeded to fill this crater and
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by April 2013 overflowed to form several lava flows and eventually fill the entire summit crater (~300
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m across). Several partial collapses (accompanied by increased volcanic activity) resulted in dome
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destruction on 10 11 July 2015; pyroclastic density currents generated by these collapses travelled
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up to ~10.6 km along the ravine of Montegrande, threatening several ranches and the town of
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Quesaría (pop. 8611 in 2010). This eruption was the largest (by volume) at Volcán de Colima since
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1913.
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2. Methods
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2.1. Image capture and pre-processing
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Images (Fig. 1) were acquired using a consumer DSLR (Nikon D90) and a thermal camera (Jenoptik
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VarioCAM HiRes) from a light aircraft during 30 observation flights, conducted at intervals of
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approximately one month. The DSLR had an 18105 mm zoom lens (most images were captured using
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the 105 mm setting), while the thermal camera used a 75 mm fixed-focal lens. Thermal images had an
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order of magnitude lower resolution than the DSLR images (640×480 pixels and 4288×2848 pixels
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respectively).
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Observational flights involved 23 circuits around the crater at a slightly higher elevation than the
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summit. Typical viewing distances varied between ~13 km, corresponding to ground sampling
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distances of ~5-15 cm/pixel for the DSLR camera (at full zoom) and ~25-75 cm/pixel for the thermal
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camera. Both cameras were operated by hand, with DSLR photographs captured every ~510 seconds
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and the thermal camera programmed to take an image every 3.5 seconds.
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Blurry and poorly exposed images were manually removed from the resulting image sets (of ~100
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DSLR images and ~200400 thermal images) prior to photogrammetric processing. Normally, ~5075
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DSLR images and ~100200 thermal images were considered usable, though this varied substantially
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with viewing conditions.
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The thermal images were converted from Jenoptic’s proprietary IRB format to JPEG (using a colour
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scale selected to maximise the amount of detail visible on the dome and volcanic flanks) before
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photogrammetric processing. A second set of JPEG images were additionally created from the thermal
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images using a fixed colour scale, and later projected onto the photogrammetric model to create a
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thermal texture map that can be compared between models.
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Figure 1. Examples of typical DSLR (left) and thermal (right) images from two different observation
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flights. Both views are looking to the north-west, and the summit region is ~300 m across.
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2.2. Structure from Motion
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Photogrammetric processing of both the DSLR and thermal datasets was performed using Agisoft
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Photoscan Professional Edition (v1.2.3). Prior to 3D reconstruction, both photosets were masked to
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remove degassing plumes, aircraft parts and unnecessary background, ensuring that only the edifice
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region was reconstructed. SfM methods were applied to estimate camera locations, orientations and
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internal parameters and produce a ‘sparse point cloud’ containing the location of tens of thousands
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of automatically detected features. These data were then used to constrain a detailed reconstruction
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of the volcanic edifice, producing a ‘dense point cloud’ typically containing 10 20 million points for
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the DSLR models and ~0.5 million points for the thermal models.
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Finally, a continuous triangulated surface model was derived from the dense point cloud for image
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rendering. For practical reasons, we limited the model to 1 million triangles, prior to texturing by
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projecting the original images onto its surface. For the thermal models, the photoset used to construct
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the model was exchanged with the photoset with a consistent colour-scale prior to the texturing step.
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2.3. Georeferencing and Alignment
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Due to difficult access and high risk, ground control points were not available for any of the models.
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Instead, similar to the approach used by James and Varley (2012), models generated from the DSLR
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images were georeferenced (within Agisoft Photoscan) by minimising the distance between features
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identified on the models and equivalent features located in Google Earth imagery. Here, we
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additionally used 1-arc second SRTM (Shuttle Radar Topographic Mission) data from February 2000 to
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derive elevations. As the morphology of the summit area changed substantially over the study period,
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it was necessary to use Google Earth imagery from different dates for some models, causing relative
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translations of the results (reflecting the georeferencing error within Google Earth; coordinates of
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some static features changed by >20 meters between imagery from different dates).
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To improve the registration between models and facilitate direct comparisons, the relative
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georeferencing of each model was optimised by aligning to one reference model (from 11 January
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2013) using the iterative closest point (ICP) alignment implementation in Cloud Compare (Girardeau-
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Montaut, 2015). Model location, orientation and scale was allowed to vary during this step, during
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which areas known to have changed (i.e. the dome and associated flows) were manually excluded.
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Models constructed using thermal images could not generally be georeferenced from the Google
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Earth imagery due to difficulties identifying corresponding features in the thermal data. Instead, they
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were aligned to the DSLR model from the same flight (or from a previous flight if the DSLR model had
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failed), using a manual 3-D point-matching approach in Meshlab (Cignoni et al., 2008) to achieve an
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initial alignment that was then optimised using ICP.
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Where possible, the similarity (and alignment) of the DSLR and thermal models was assessed by
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comparison with DSLR models generated from the same flight. As the ICP alignment algorithm only
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applies a scaling and rigid body transformation, similarities between the DSLR and thermal models
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suggest that the photogrammetric reconstructions converge on a consistent surface shape, adding
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confidence to the results. Note that while this assessment provides an indication of uncertainty in the
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overall model shapes, it cannot evaluate the full geospatial uncertainty because the thermal models
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are not independently georeferenced.
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2.4. Volume Calculation
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Dome volume was estimated by determining the difference between each photogrammetric model
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and the pre-dome reference model created photogrammetrically using data from a flight on 11
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January 2013. The difference calculations (performed using a Java implementation of the signed
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tetrahedral method; Zhang and Chen, 2001) determined the volume between the surfaces in up to
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four ‘regions of interest’ (ROI; Fig. 2). In this instance, a ROI containing the lava dome was defined for
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each of the photogrammetric models (both DSLR and thermal), and the volume of the dome estimated
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by comparison with a reference surface representing the pre-dome topography. Where the dome
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overflowed the crater (and transitioned into a lava flow), a consistent (but visually estimated) ‘dome
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boundary’ was defined (the boundary between regions a and b in Fig. 2), and the volume of the upper
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portion of a lava flow was also determined.
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In order to better evaluate the uncertainty of the volume estimates, change within two stable
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reference areas on the flanks of the volcano was also calculated; because these areas should not vary,
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detecting volume change within them suggests greater uncertainty in the topographic models or their
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relative registration. These changes were expressed as mean vertical offsets that could then be used
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to estimate the dome volume (positive or negative) that likely resulted from alignment errors. Note
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however, that these reference areas were always located on the eastern flanks of the volcano, as the
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western flanks changed substantially over the study period (due to lava flows), and hence are not
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equally sensitive to all types of alignment error (e.g. translations or rotations) in dome area.
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Figure 2: Oblique view of the ‘regions of interest’ defined for the 27/4/13 photogrammetric model.
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Region (a) contains the growing lava dome, and (b) the incipient lava flow. Regions (c) and (d) are the
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reference areas. The colour map represents the vertical distance between the comparison and
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reference surfaces. The dome region (a) is ~140 m across.
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3. Results and Discussion
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The 30 survey flights allowed the construction of 19 usable models from visible imagery and 22 models
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from the thermal imagery, although thermal data was only available for 23 flights. These datasets
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provide a reconstruction of the summit lava dome geometry at ~monthly intervals for the entire
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dome-forming eruption.
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3.1. Comparison of Thermal and DSLR models
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Both sets of photogrammetric models (thermal and DSLR) reconstructed the crater and dome complex
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on Volcán de Colima with varying degrees of completeness, detail and accuracy (Fig. 3). It is clear that,
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in general, models constructed using the thermal images were substantially less detailed than DSLR
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equivalents. This will be due to a combination of the thermal images having a lower spatial resolution
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than the DSLR images and a lack of high-frequency image texture, due to low thermal contrast on the
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volcano flanks.
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Figure 3. Selected DSLR and thermal photogrammetric models illustrated by hillshade (top and middle),
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and associated thermal orthomosaics (bottom). Grid cells are 50×50 m and oriented north-south and
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east-west. The model shown in (a) was captured before lava dome growth and was used as the
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reference model in volume calculations.
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Nevertheless, 3D reconstruction using the thermal images was found to be far more robust to poor
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photography conditions than the DSLR models. In particular, thermal models could be constructed in
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situations where degassing made useful reconstruction from the DSLR images impossible. This is
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because water droplets in the degassing plume cause near complete scattering of visible light (and
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hence the plumes appear white), whilst the thermal infrared radiation (7.5-14 μm) is less affected (Fig.
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4). Of all flights for which both thermal and DSLR data was available, ~30% of the DSLR surveys failed
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to generate a model while only ~5% of the thermal models failed, even though image locations and
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overlap were approximately the same. Hence, in addition to providing a useful map of estimated
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temperature across the crater complex, the thermal models provide greater data continuity than the
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models from the DSLR.
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Figure 4. Thermal (a) and DSLR (b) images captured at approximately the same time and location
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(looking towards the east), under strong degassing conditions. The dome is generally resolvable in the
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thermal image, but is substantially obscured in the DSLR image. A photogrammetric model of the dome
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was successfully reconstructed from the thermal images, and is of particular importance as it was
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captured on 5/7/15, days before the major July 2015 eruption. A model was not attempted using the
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DSLR data due to the degassing.
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Shortest distance comparisons between associated DSLR and thermal models show generally good
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agreement (Fig. 5a and b). As thermal models tend to be smoother than the DSLR models (Fig. 3),
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differences tend to be focused around sharp topographic features such as the crater rim. However,
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in a few cases, the thermal models did differ significantly from their DSLR counterparts (Table 1). The
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largest dome volume difference was observed at the time when the dome area was largest (Fig. 5b),
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but the second largest observed dome volume difference resulted from the thermal model locating
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the dome surface ~5 m higher than the DSLR model (Fig. 5c). The reason for this difference is
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unclear, but highlights our ability to identify uncertainty by comparing the different datasets. A few
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of the thermal models also contained substantial error (±10 m; Fig. 5d), which was mostly apparent
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in areas of low thermal contrast, where image alignments and surface reconstructions are likely to
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be weakest. Although the active lava surfaces were not directly influenced by this effect, the noisy
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surfaces did impair the ICP process and probably increased registration error.
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Figure 5. The shortest distance between corresponding DSLR and thermal models. Regions where the
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thermal model is above the DSLR model are yellow-red, while areas where the DSLR model is on top
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are shaded green-blue. Histograms showing the distribution of the difference values are shown below
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each map. Examples of typical models are shown in (a) and (b), with few large differences except along
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sharp features (e.g. the crater rim) and towards model boundaries. Examples of models showing
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greater differences are presented in (c) and (d), where reconstructed dome geometries do not match
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well (c) or where substantial error is present (d).
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Table 1: The five largest differences between the thermal and DSLR volume estimates. Volumes and
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differences are in million m3. Percentages are relative to the DSLR volume estimate.
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Model
DSLR Volume
Thermal Volume
% Difference
7/06/2015
1.16
1.05
9%
4/02/2015
1.10
1.20
9%
20/06/2013
0.51
0.56
9%
14/02/2014
0.51
0.55
7%
2/12/2013
0.54
0.52
3%
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Using consumer cameras, and in the absence of ground control points sufficient to help constrain
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photogrammetric processing, SfM-based data have been previously shown to provide topographic
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data with an overall precision of ~1/1000 of the viewing distance (James and Robson, 2012). Thus,
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over viewing distances of ~1-3 km, the 1-5 m differences between models are in line with this rule of
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thumb. These results are reasonable given the low resolution of the thermal camera and the relatively
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narrow angular field of view of both cameras (12° for the DSLR camera at full zoom and 10° for the
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thermal camera), which can cause difficulties for precise photogrammetric reconstruction. It is likely
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that the orbital flight paths play a strong role in helping to reduce error by naturally providing
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convergent imagery, which mitigates systematic model deformation effects when ground control
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points cannot be incorporated into the photogrammetric processing (Wackrow and Chandler, 2008;
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James and Robson, 2014).
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3.2. Dome volume calculations
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Dome volumes calculated independently using the DSLR and thermal models generally correspond
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well (Fig. 6), and differ by <10%. Likewise, the volume difference within the reference areas tended to
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be small, averaging 5% of the dome volume estimates.
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While the volcanological significance and the implications of these results for understanding the 2013
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2015 eruption are beyond the scope of this paper, it is clear that they provide valuable information
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on phases of dome growth (and volume loss) at Volcán de Colima between 2013 and 2015 (Fig. 6).
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Average effusion rates could also be estimated from the rate of dome volume change, although the
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effect of volume loss through explosive activity and lava flows would need to be accounted for.
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Finally, where both DSLR and thermal models were successful, the two independent reconstructions
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also provide a valuable indication of uncertainty in model shape. Future studies could extend this
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approach and use GPS devices to “geotag” image locations at the time of capture, allowing additional
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evaluation of georeferencing uncertainty as the thermal models would no longer rely on ICP
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registration against a similar visible-light model for their georeferencing. For high quality camera
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position data, this ‘direct georeferencing’ approach has been shown capable of delivering decametric
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accuracies (Nolan et al., 2015). Alternatively, where sufficient topographic features are recognisable
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in the thermal models, a single georeferenced model or high resolution digital elevation model of
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known accuracy could be used for georeferencing, avoiding the need for closely associated DSLR
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models.
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Figure 6. Volume of the lava dome (dotted) and lava flow top (dashed) between initiation of dome
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growth in January 2013 and dome collapse in July 2015. Where both DSLR (squares) and thermal
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(circles) models were available, the lines represent an average estimate. It is clear that there is
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generally good agreement between volumes calculated with the DSLR and thermal models. Reference
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area volumes (which would be zero under error-free conditions) are shown in grey to give an indication
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of relative accuracy.
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4. Conclusions
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We have successfully used SfM techniques and oblique thermal images to produce a time-series of
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georeferenced, three-dimensional thermal models of an active lava dome at Volcán de Colima.
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Comparisons between these models and equivalents derived from DSLR images suggest that, while
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less detailed, the thermal models provide a valuable representation of dome geometry. Estimates of
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the lava dome volume correspond well between the DSLR and thermal datasets.
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The thermal models were found to be substantially more robust to the adverse effects of degassing
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and poor lighting. Because degassing is common at Volcán de Colima (as at many other volcanoes)
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thermal imaging provided important data continuity at times when DSLR image quality was restricted.
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Where both DSLR and thermal models were available, the thermal models provided a useful
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complementary geometry estimate, helping to identify uncertainty in the models, and a
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georeferenced map of temperature distribution that allows identification of thermally active regions
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on the dome surface.
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The combined DSLR and thermal datasets provided detailed information about the evolution of the
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dome on Volcán de Colima between 2013 and 2015. It is possible that, if employed as a monitoring
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technique (rather than retrospectively), the rapid change in dome volume, morphology and
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temperature distribution documented by the models in the months leading up to July 2015 may have
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provided prior warning of the dome collapse.
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Acknowledgements
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The authors would like to acknowledge the multitude of past CIIV students who participated in data
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collection for this study and helped to finance flights. Some flights were financed by NERC Urgency
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Grant NE/L000741/1 (PI: Paul Cole). NV was supported by Universidad de Colima FRABA grants. Paul
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Cole and an anonymous reviewer are thanked for their useful feedback during the review process.
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... Camera positions and scene geometries are simultaneously solved, and a sparse 3D point cloud is generated (Westoby et al., 2012). Prior research using SfM processing with TIR imagery has focused on active volcanoes (Thiele et al., 2017), measuring the relative temperature distribution of a building envelope structure , and modeling forest canopies (Webster et al., 2018). However, we are not aware of past research that focused on identifying wildlife in 3D models generated with UAS-TIR imagery and SfM approaches. ...
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An important component of wildlife management and conservation is monitoring the health and population size of wildlife species. Monitoring the population size of an animal group can inform researchers of habitat use, potential changes in habitat and resulting behavioral adaptations, individual health, and the effectiveness of conservation efforts. Arboreal monkeys are difficult to monitor as their habitat is often poorly accessible and most monkey species have some degree of camouflage, making them hard to observe in and below the tree canopy. Surveys conducted using uninhabited aerial vehicles (UAVs) equipped with thermal infrared (TIR) cameras can help overcome these limitations by flying above the canopy and using the contrast between the warm body temperature of the monkeys and the cooler background vegetation, reducing issues with impassable terrain and animal camouflage. We evaluated the technical and procedural elements associated with conducting UAV-TIR surveys for arboreal and terrestrial macaque species. Primary imaging missions and analyses were conducted over a monkey park housing approximately 160 semi-free-ranging Japanese macaques (Macaca fuscata). We demonstrate Repeat Station Imaging (RSI) procedures using co-registered TIR image pairs facilitate the use of image differencing to detect targets that were moving during rapid sequence imaging passes. We also show that 3D point clouds may be generated from highly overlapping UAV-TIR image sets in a forested setting using structure from motion (SfM) image processing techniques. A point cloud showing area-wide elevation values was generated from TIR imagery, but it lacked sufficient point density to reliably determine the 3D locations of monkeys.
... Structure-from-motion photogrammetry has emerged over the past decade as an increasingly powerful and reliable means to survey and analyze lava dome growth (James and Varley, 2012;Thiele et al., 2017;Carr et al., 2019a;James et al., 2020b;Zorn et al., 2020;Andaru et al., 2021;Kelfoun et al., 2021;Moussallam et al., 2021). Our results based on differencing of DEMs created using SfM photogrammetry with images from UAS surveys serve to further constrain the erupted volume and effusion rates at Sinabung when compared to previous estimates using ground- based SfM photogrammetry (Carr et al., 2019a), laser distance measurements , and satellite images (Yulianto et al., 2016;Nakada et al., 2019;Pallister et al., 2019;Kriswati and Solikhin, 2020). ...
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Lava domes form by the effusive eruption of high-viscosity lava and are inherently unstable and prone to collapse, representing a significant volcanic hazard. Many processes contribute to instability in lava domes and can generally be grouped into two categories: active and passive. Active collapses are driven directly by lava effusion. In contrast, passive collapses are not correlated with effusion rate, and thus represent a hazard that is more difficult to assess and forecast. We demonstrate a new workflow for assessing and forecasting passive dome collapse by examining a case study at Sinabung Volcano (North Sumatra, Indonesia). We captured visual images from the ground in 2014 and from unoccupied aerial systems (UAS) in 2018 and used structure-from-motion photogrammetry to generate digital elevation models (DEMs) of Sinabung’s evolving lava dome. By comparing our DEMs to a pre-eruption DEM, we estimate volume changes associated with the eruption. As of June 2018, the total erupted volume since the eruption began is 162 × 10 ⁶ m ³ . Between 2014 and 2018, 10 × 10 ⁶ m ³ of material collapsed from the lava flow due to passive processes. We evaluate lava dome stability using the Scoops3D numerical model and the DEMs. We assess the passive collapse hazard and analyze the effect of lava material properties on dome stability. Scoops3D is able to hindcast the location and volume of passive collapses at Sinabung that occurred during 2014 and 2015, and we use the same material properties to demonstrate that significant portions of the erupted lava potentially remain unstable and prone to collapse as of late 2018, despite a pause in effusive activity earlier that year. This workflow offers a means of quantitatively assessing passive collapse hazards at active or recently active volcanoes.
... Drones have recently made significant advances in the field of geology in general, providing a great opportunity to reach remote outcrops that would otherwise be inaccessible or extremely dangerous for direct study or reconnaissance. Drones in geology have been used for both practical/industrial and pure research applications in the fields of mining (e.g., McLeod et al. 2013;Lee and Choi 2016;Dunnington and Nakagawa 2017;Kirsch et al. 2018), volcanology (e.g., Amici et al. 2013;Harvey et al. 2016;Thiele et al. 2017;Di Felice et al. 2018;Patrick et al. 2019;Walter et al. 2020), karst research (McFarlane et al. 2013, study of post-earthquake land changes (Gong et al. 2012), analysis and mapping of river systems (e.g., Flener et al. 2013;Casado et al. 2015;Langhammer et al. 2017), glaciology (e.g., Westoby et al. 2015;Bhardwaj et al. 2016;De Michele et al. 2016), landslides and rockfall analysis and mapping (e.g., Danzi et al. 2013;Giordan et al. 2015;Turner et al. 2015;Lindner et al. 2016;Mateos et al. 2017;Gupta and Shukla 2018;Devoto et al. 2020;Francioni et al. 2020;Godone et al. 2020), sedimentology and 3D facies modelling (e.g., Chesley et al. 2017;Hayes et al. 2017;Nieminski and Graham 2017;Cabello et al. 2018;Rubi et al. 2018;Behrman et al. 2019), structural geology (e.g., Bemis et al. 2014;Deffontaines et al. 2017;Giletycz et al. 2017;Trippanera et al. 2019) and geothermy (e.g., Nishar et al. 2016), and other reasons as discussed below. ...
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... The lack of GCPs on 3D model uncertainties could be also mitigated by the use of GNSS-RTK-enabled survey devices. Moreover, as successfully demonstrated by Thiele et al. [5], thermal images could be used in an SfM workflow to mitigate the adverse effects of degassing and poor visibility and provide more continuous time-series data than the visible-light equivalents. ...
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In July and August 2019, two paroxysmal eruptions dramatically changed the morphology of the crater terrace that hosts the active vents of Stromboli volcano (Italy). Here, we document these morphological changes, by using 2259 UAS-derived photographs from eight surveys and Structure-from-Motion (SfM) photogrammetric techniques, resulting in 3D point clouds, orthomosaics, and digital surface models (DSMs) with resolution ranging from 8.1 to 12.4 cm/pixel. We focus on the morphological evolution of volcanic features and volume changes in the crater terrace and the upper part of the underlying slope (Sciara del Fuoco). We identify both crater terrace and lava field variations, with vents shifting up to 47 m and the accumulation of tephra deposits. The maximum elevation changes related to the two paroxysmal eruptions (in between May and September 2019) range from +41.4 to −26.4 m at the lava field and N crater area, respectively. Throughout September 2018–June 2020, the total volume change in the surveyed area was +447,335 m3. Despite Stromboli being one of the best-studied volcanoes worldwide, the UAS-based photogrammetry products of this study provide unprecedented high spatiotemporal resolution observations of its entire summit area, in a period when volcanic activity made the classic field inspections and helicopter overflights too risky. Routinely applied UAS operations represent an effective and evolving tool for volcanic hazard assessment and to support decision-makers involved in volcanic surveillance and civil protection operations.
... Additionally, when there is a lack of encoded metadata containing exterior orientation parameters of cameras (as in this case study) target positioning represents the only solution for a reliable 3D reconstruction of the scanned scene. Consequently, when inaccessible or high-risk sites represent monitoring areas, especially in engineering geological issues, the task of target positioning may not be feasible [45,46]. A possible approach to partially solve this problem is the employment of UAV platforms equipped with Real Time Kinematic Global Positioning Systems (RTKGPS) [47] that can provide camera positions with high accuracy (i.e., errors in the order of centimeters). ...
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The study of strain effects in thermally-forced rock masses has gathered growing interest from engineering geology researchers in the last decade. In this framework, digital photogrammetry and infrared thermography have become two of the most exploited remote surveying techniques in engineering geology applications because they can provide useful information concerning geomechanical and thermal conditions of these complex natural systems where the mechanical role of joints cannot be neglected. In this paper, a methodology is proposed for generating point clouds of rock masses prone to failure, combining the high geometric accuracy of RGB optical images and the thermal information derived by infrared thermography surveys. Multiple 3D thermal point clouds and a high-resolution RGB point cloud were separately generated and co-registered by acquiring thermograms at different times of the day and in different seasons using commercial software for Structure from Motion and point cloud analysis. Temperature attributes of thermal point clouds were merged with the reference high-resolution optical point cloud to obtain a composite 3D model storing accurate geometric information and multitemporal surface temperature distributions. The quality of merged point clouds was evaluated by comparing temperature distributions derived by 2D thermograms and 3D thermal models, with a view to estimating their accuracy in describing surface thermal fields. Moreover, a preliminary attempt was made to test the feasibility of this approach in investigating the thermal behavior of complex natural systems such as jointed rock masses by analyzing the spatial distribution and temporal evolution of surface temperature ranges under different climatic conditions. The obtained results show that despite the low resolution of the IR sensor, the geometric accuracy and the correspondence between 2D and 3D temperature measurements are high enough to consider 3D thermal point clouds suitable to describe surface temperature distributions and adequate for monitoring purposes of jointed rock mass.
... Within the natural hazards field, there are several studies developed to address the characteristics and dynamics of floods (Murphy et al. 2016;Serban et al. 2016;Izumida et al. 2017;Cescutti et al. 2018;Langhammer and Vackova 2018;Yalcin 2018;Leal-Alves et al. 2020), earthquakes and tsunamis (Li et al. 2011;Nedjati et al. 2016;Vollgger and Cruden 2016;Dominici et al. 2017;Valkaniotis et al. 2018;Mavroulis et al. 2019;Koukouvelas et al. 2020), fires (Merino et al. 2012), volcanic processes (Mori et al. 2016;Thiele et al. 2017;Darmawan et al. 2018;Favalli et al. 2018;De Beni et al. 2019;Kazahaya et al. 2019) and landslides, which are the object of the present study (Stumpf et al. 2013;Lucieer et al. 2014;Barlow et al. 2017;Tanteri et al. 2017;Chang et al. 2018;Comert et al. 2018). ...
Article
On a global scale, from 2005 to 2019, there were 275 high-magnitude, low-frequency disasters that involved 14,172 fatalities and four million affected people. Similar patterns have taken place during longer periods of time in recent decades. This paper aims to analyse the contribution of the international landslide research community to disaster risk reduction and disaster risk management in reference to the use of Unmanned Aerial Vehicles (UAVs) in a literature review. The first section notes the relevance of disaster risk research contributions for the implementation of initiatives and strategies concerning disaster risk management. The second section highlights background information and current applications of drones in the field of hazards and risk. The methodology, which included a systematic peer review of journals in the ISI Web of Science and SCOPUS, was presented in the third section, where the results include analyses of the considered data. This study concludes that most current scholarly efforts remain rooted in hazards and post-disaster evaluation and response. Future landslide disaster risk research should be transdisciplinary in order to strengthen participation of the various relevant stakeholders in contributing to integrated disaster risk management at local, subnational, national, regional and global levels.
... UAVs are commonly used in active, dormant, and extinct volcanic settings in order to map volcanic deposits (e.g., Dering et al., 2019;Thiele et al., 2017;Gomez and Kennedy, 2018;Carr et al., 2019;Jordan, 2019;Smith et al., 2019). For reviews in using UAVs to survey volcanic settings see Dering et al. (2019) and Jordan (2019). ...
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Volcaniclastic stratigraphy can be difficult to map and describe due to its complex nature. However, such stratigraphy preserves information about fluctuations in volcanic activity and sedimentation and is vital to understanding volcanic systems. Uncrewed aerial vehicle (UAV) based analysis of volcanic stratigraphy can enhance mapping and analysis, especially on vertical surfaces where outcrop exposure is greatest. Here we present a method for using small UAVs to produce vertical grain size and bedding measurement logs, or quantitative stratigraphic columns, of vertical volcaniclastic stratigraphy. We demonstrate the range of high-accuracy measurements and parameters that can be collected for building measurement logs using consumer grade UAVs through a case study in the Marysvale volcanic field where we collected 34,422 grain measurements from 21 individual units. The purpose of producing such measurement logs is to enhance lithofacies analysis through the use of large quantitative datasets and improve the reproducibility of data reporting. Whereas descriptions of volcaniclastic units such as those describing grading are often reported qualitatively, we describe methods for calculating numerical parameters for enhanced lithologic analysis including grain size, grading, clast to matrix ratios, and shape characteristics. The methods described in this paper can enhance field data acquisition, mapping, and quantitative analysis of volcaniclastic deposits and are applicable to a wide range of other geologic settings where coarse-grained clastic sedimentary deposits exist.
... Optical and thermal cameras transported on UAVs have been used for identifying meter to sub-meter topography changes and for the detection of thermal anomalies (Nakano et al., 2014;Thiele et al., 2017;Nakano et al., 2014;Müller et for collecting immediate and real-time aerial data in volcanic environments during and after an eruption is provided by Jordan (2019), highlighting its advantages for mapping, sample collection, thermal imaging, magnetic surveys, slope stability studies, and also as platforms for carrying outgassing measurement sensors. ...
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Fogo in the Cape Verde archipelago off Western Africa is one of the most prominent and active ocean island volcanoes on Earth, posing an important hazard to both local populations and at a regional level. The last eruption took place between 23 November 2014 and 8 February 2015 in the Chã das Caldeiras area at an elevation close to 1,800 m above sea level The eruptive episode gave origin to extensive lava flows that almost fully destroyed the settlements of Bangaeira, Portela and Ilhéu de Losna. In December 2016 a survey of the Chã das Caldeiras area was conducted using a fixed-wing unmanned aerial vehicle and RTK GNSS, with the objective of improving the mapping accuracy derived from satellite platforms. The main result is an ultra-high resolution 3D point cloud with a Root Mean Square Error of 0.08 m in X, 0.11 m in Y and 0.12 m in Z, which provides unprecedented accuracy. The survey covers an area of 23.9 km2 and used 2909 calibrated images with an average ground sampling distance of 7.2 cm. A digital surface model and an orthomosaic with 25 cm resolution are provided, together with elevation contours with an equidistance of 50 cm and a 3D texture mesh for visualization purposes. The delineation of the 2014–15 lava flows shows an area of 4.53 km2 by lava, which is smaller but more accurate than the previous estimates from 4.8 to 4.97 km2. The difference in the calculated area, when compared to previously reported values, is due to a more detailed mapping of flow geometry and the exclusion of the areas corresponding to kīpukas. Our study provides an ultra high-resolution dataset of the areas affected by Fogo's latest eruption – crucial for local planning – and provides a case study to determine the advantages of ultra high-resolution UAV surveys in disaster-prone areas. The dataset is available for download at http://doi.org/10.5281/zenodo.4035038 (Vieira et al., 2020).
Chapter
Technological developments in the last few decades allow generation of increasingly high-resolution digital elevation models (DEMs), useful in many fields of Earth and environmental science, and especially for tectonic geomorphic studies. Combined with falling costs and the improved accuracy of geo-referencing using satellite geodetic tools based on Global Navigation Satellite Systems (GNSS), such as Global Positioning System (GPS), these developments have moved DEMs from the realm of computer equivalents of a topographic map to sophisticated tools for process understanding. Four techniques for the production of high-resolution DEMs are notable: light detection and ranging (LIDAR), interferometric synthetic aperture radar (InSAR), terrestrial radar interferometry (TRI), and structure from motion (SfM) photogrammetry. With the exception of TRI, restricted to ground-mounted platforms, the instrumentation can be hosted on satellites, piloted aircraft, or Unoccupied Aerial Vehicles (UAVs). Calibration with GNSS enables merging, or comparison of data sets acquired by different techniques, as well as change detection at the centimeter level.
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The most recent eruptive phase of Volcán de Colima, Mexico, started in 1998 and was characterized by episodic dome growth with a variable effusion rate, interrupted intermittently by explosive eruptions. Between November 2009 and June 2011, growth at the dome was limited to a lobe on the western side where it had previously started overflowing the crater rim, leading to the generation of rockfall events. This meant that no significant increase in dome volume was perceivable and the rate of magma ascent, a crucial parameter for volcano monitoring and hazard assessment, could no longer be quantified via measurements of the dome's dimensions. Here, we present alternative approaches to quantify the magma ascent rate. We estimate the volume of individual rockfalls through the detailed analysis of sets of photographs (before and after individual rockfall events). The relationship between volume and infrared images of the freshly exposed dome surface and the seismic signals related to the rockfall events was then investigated. Larger events exhibited a correlation between the previously estimated volume of a rockfall and the surface temperature of the freshly exposed dome surface as well as the mean temperature of rockfall masses distributed over the slope. We showed that for larger events, the volume of the rockfall correlates with the maximum temperature at the newly formed cliff as well as the seismic energy. By calibrating the seismic signals using the volumes estimated from photographs, the count of rockfalls over a certain period was used to estimate the magma extrusion flux for the period investigated. Over the course of the measurement period, significant changes were observed in number of rockfalls, rockfall volume and hence averaged extrusion rate. The extrusion rate was not constant: it increased from 0.008 m3 s−1 to 0.02 m3 s−1 during 2010 and dropped down to 0.008 m3 s−1 again in March 2011. In June 2011, magma extrusion had come to a halt. The methodology presented represents a reliable tool to constrain the growth rate of domes that are repeatedly affected by partial collapses. There is a good correlation between thermal and seismic energies and rockfall volume. Thus it is possible to calibrate the seismic records associated with the rockfalls (a continuous monitoring tool) to improve both volcano monitoring at volcanoes with active dome growth and hazard management associated with rockfalls specifically.
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Airborne photogrammetry is undergoing a renaissance: lower-cost equipment, more powerful software, and simplified methods have significantly lowered the barriers-to-entry and now allow repeat-mapping of cryospheric dynamics at spatial resolutions and temporal frequencies that were previously too expensive to consider. Here we apply these techniques to the measurement of snow depth from manned aircraft. The main airborne hardware consists of a consumer-grade digital camera coupled to a dual-frequency GPS. The photogrammetric processing is done using a commercially-available implementation of the Structure from Motion (SfM) algorithm. The system hardware and software, exclusive of aircraft, costs less than USD 30 000. The technique creates directly-georeferenced maps without ground control, further reducing costs. To map snow depth, we made digital elevation models (DEMs) during snow-free and snow-covered conditions, then subtracted these to create difference DEMs (dDEMs). We assessed the accuracy (geolocation) and precision (repeatability) of our DEMs through comparisons to ground control points and to time-series of our own DEMs. We validated these assessments through comparisons to DEMs made by airborne lidar and by another photogrammetric system. We empirically determined an accuracy of ± 30 cm and a precision of ± 8 cm (both 95% confidence) for our methods. We then validated our dDEMs against more than 6000 hand-probed snow depth measurements at 3 test areas in Alaska covering a wide-variety of terrain and snow types. These areas ranged from 5 to 40 km2 and had ground sample distances of 6 to 20 cm. We found that depths produced from the dDEMs matched probe depths with a 10 cm standard deviation, and these depth distributions were statistically identical at 95% confidence. Due to the precision of this technique, other real changes on the ground such as frost heave, vegetative compaction by snow, and even footprints become sources of error in the measurement of thin snow packs (< 20 cm). The ability to directly measure such small changes over entire landscapes eliminates the need to extrapolate isolated field measurements. The fact that this mapping can be done at substantially lower costs than current methods may transform the way we approach studying change in the cryosphere.
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The 1995–present eruption of Soufrière Hills Volcano on Montserrat has produced over a cubic kilometre of andesitic magma, creating a series of lava domes that were successively destroyed, with much of their mass deposited in the sea. There have been five phases of lava extrusion to form these lava domes: November 1995–March 1998; November 1999–July 2003; August 2005–April 2007; July 2008–January 2009; and October 2009–February 2010. It has been one of the most intensively studied volcanoes in the world during this time, and there are long instrumental and observational datasets. From these have sprung major new insights concerning: the cyclicity of magma transport; low-frequency earthquakes associated with conduit magma flow; the dynamics of lateral blasts and Vulcanian explosions; the role that basalt–andesite magma mingling in the mid-crust has in powering the eruption; identification using seismic tomography of the uppermost magma reservoir at a depth of 5.5 > 7.5 km; and many others. Parallel to the research effort, there has been a consistent programme of quantitative risk assessment since 1997 that has both pioneered new methods and provided a solid evidential source for the civil authority to use in mitigating the risks to the people of Montserrat.
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We investigate high-resolution digital photographs and infrared images of the lava dome eruption at Volcán de Colima, from 2007 to 2010. Qualitative observations provide insight into active volcanic processes (e.g. rockfalls and fracturing) and show that, as the dome advances a substantial cooled talus apron develops, which stabilizes the structure. Progressive collapse of the talus apron as it reaches the crater rim corresponds with the development of a lava lobe, extruding hot lava from deeper within the dome. Quantitative dome surface temperature timeseries show that the highest temperature hotspots migrate from the dome sides (250-380 °C) to the top (150-300 °C) and finally to the lava lobe (220-400 °C) as the structurally unstable areas expose fresh material. Net surface heat loss from the dome ranges from 5 to 30 MW, comparable to other dome forming systems. Heat budget calculations confirm that the lava dome retained a hot viscous core throughout the period 2007-2010. We propose that the mechanical stability of the Volcán de Colima dome arises from the shear strength of flanking talus which stabilizes the hot viscous core.
Article
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The most recent eruptive phase of Volc´ an de Colima, Mexico, started in 1998 and was characterized by dome growth with a variable effusion rate, interrupted intermittently by explosive eruptions. Between November 2009 and June 2011, activity at the dome was mostly limited to a lobe on the western side where it had previously started overflowing the crater rim, leading to the generation of rockfall events. As a consequence of this, no significant increase in dome volume was perceivable and the rate of magma ascent, a crucial parameter for volcano monitoring and hazard assessment could no longer be quantified via measurements of the dome’s dimensions. Here, we present alternative approaches to quantify the magma ascent rate. We estimate the volume of individual rockfalls through the detailed analysis of sets of photographs (before and after individual rockfall events). The relationship between volume and infrared images of the freshly exposed dome surface and the seismic signals related to the rockfall events were then investigated. Larger rockfall events exhibited a correlation between its previously estimated volume and the surface temperature of the freshly exposed dome surface, as well as the mean temperature of rockfall mass distributed over the slope. We showed that for larger events, the volume of the rockfall correlates with the maximum temperature of the newly exposed lava dome as well as a proxy for seismic energy. It was therefore possible to calibrate the seismic signals using the volumes estimated from photographs and the count of rockfalls over a certain period was used to estimate the magma extrusion flux for the period investigated. Over the course of the measurement period, significant changes were observed in number of rockfalls, rockfall volume and hence averaged extrusion rate. The extrusion rate was not constant: it increased from 0.008 ±0.003 to 0.02 ± 0.007 m3 s-1 during 2010 and dropped down to 0.008 ± 0.003 m 3 s-1 again in March 2011. In June 2011, magma extrusion had come to a halt. The methodology presented represents a reliable tool to constrain the growth rate of domes that are repeatedly affected by partial collapses. There is a good correlation between thermal and seismic energies and rockfall volume. Thus it is possible to calibrate the seismic records associated with the rockfalls (a continuous monitoring tool) to improve volcano monitoring at volcanoes with active dome growth.
Article
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Topographic measurements for detailed studies of processes such as erosion or mass movement are usually acquired by expensive laser scanners or rigorous photogrammetry. Here, we test and use an alternative technique based on freely available computer vision software which allows general geoscientists to easily create accurate 3D models from field photographs taken with a consumer-grade camera. The approach integrates structure-from-motion (SfM) and multi-view-stereo (MVS) algorithms and, in contrast to traditional photogrammetry techniques, it requires little expertise and few control measurements, and processing is automated. To assess the precision of the results, we compare SfM-MVS models spanning spatial scales of centimeters (a hand sample) to kilometers (the summit craters of Piton de la Fournaise volcano) with data acquired from laser scanning and formal close-range photogrammetry. The relative precision ratio achieved by SfM-MVS (measurement precision : observation distance) is limited by the straightforward camera calibration model used in the software, but generally exceeds 1:1000 (i.e. centimeter-level precision over measurement distances of 10s of meters). We apply SfM-MVS at an intermediate scale, to determine erosion rates along a ~50-m-long coastal cliff. Seven surveys carried out over a year indicate an average retreat rate of 0.70±0.05 m a-1. Sequential erosion maps (at ~0.05 m grid resolution) highlight the spatio-temporal variability in the retreat, with semivariogram analysis indicating a correlation between volume loss and length scale. Compared with a laser scanner survey of the same site, SfM-MVS produced comparable data and reduced data collection time by ~80%.
Article
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Slope failures resulting from structural instability of andesitic volcanic edifices can generate mobile debris avalanche that travel long distances down or beyond the flanks of volcanoes. More than 20 major slope failures have occurred worldwid over the past 500 years, a rate exceeding that of caldera collapse. Hazards derive from the debris avalanches themselves from associated explosive activity that ranges from vertical eruptions (often accompanied by pyroclastic currents) to devastatin directed blasts, from associated lahars, and from tsunamis. Collapses of growing lava domes are more frequent, are similar in many ways, to edifice collapse, and can directly generate devastating pyroclastic currents. This paper examines some aspects of current understanding of edifice and lavadome instability. The primary focus of the presentatio is on mechanisms and factors associated with collapse, the geometric factors, augmented loading by magma, localized strengt reduction by physical and chemical changes (the latter commonly associated with hydrothermal processes), strain weakening pore-fluid (water or gas) pressure enhancement, retrogressive failure, time-dependent failure, and seismic shaking. Some aspect of material property evaluation, analysis procedures, and implications on monitoring are also discussed. Case examples discusse include edifice instability at Mt St Helens, USA, and Soufriere Hills volcano, Montserrat, the stability of lava spines a Mont Pelee, Martinique, and Lamington, Papua New Guinea, and lavadome stability at Soufriere Hills. The topics bear on understandin hazardous edifice and dome failures, and the measures to anticipate such failures.
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