Gas piston activity of the Nyiragongo lava lake: First insights from a Stereographic Time-Lapse Camera system

Journal of African Earth Sciences xxx (2016) 1e14

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Gas piston activity of the Nyiragongo lava lake: First insights from a Stereographic Time-Lapse Camera system Benoît Smets a, b, c, *, Nicolas d'Oreye a, d, Matthieu Kervyn b, François Kervyn c a

European Center for Geodynamics and Seismology, 19 Rue Josy Welter, L-7256, Walferdange, G.D., Luxembourg Department of Geography, Earth System Science, Vrije Universiteit Brussel, 2 Pleinlaan, B-1050, Brussels, Belgium c Department of Earth Sciences, Royal Museum for Central Africa, 13 Leuvensesteenweg, B-3080, Tervuren, Belgium d Department of Geophysics/Astrophysics, National Museum of Natural History, 19 Rue Josy Welter, L-7256, Walferdange, G.D., Luxembourg b

a r t i c l e i n f o

a b s t r a c t

Article history: Received 15 September 2015 Received in revised form 12 April 2016 Accepted 13 April 2016 Available online xxx

Nyiragongo volcano (D.R. Congo), in the western branch of the East African Rift System, is one of the most active volcanoes on Earth. Its eruptive activity is mainly characterized by the presence of a persistent lava lake in its main crater. As observed at other persistent lava lakes, the Nyiragongo lava lake level exhibits metric vertical variations in the form of minutes-to hour-long cycles, which we infer to be gas piston activity. To study this activity, we developed and tested a Stereographic Time-Lapse Camera (STLC) system, which takes stereo-pairs of photographs of the Nyiragongo crater at regular intervals. Each pair of gas- and steam-free images during daytime allows the production of a 3D point cloud. The comparison of the point clouds provides a measurement of topographic changes related to variations in lava lake level. The processing of a first dataset acquired between 18 and 20 September 2011, at an acquisition rate of 1 pair of images every 2 min, revealed cycles of vertical lava lake level variations reaching up to 3.8 m. Lava lake level variations >0.5 m are detected significantly. They are interpreted to result from gas accumulation and release in the lava lake itself. The limitations of the STLC approach are related to the number of cameras used and the atmospheric masking by steam and volcanic gas in the Nyiragongo crater. The proposed photogrammetric approach could be applied elsewhere or in other disciplines, where frequent topographic changes occur. © 2016 Elsevier Ltd. All rights reserved.

Keywords: Virunga Nyiragongo Lava lake Gas piston Photogrammetry

1. Introduction Nyiragongo (3470 m a.s.l.) is one of the eight main volcanic edifices of the Virunga Volcanic Province, in the western branch of the East African Rift System. Located in the eastern part of the Democratic Republic of Congo, close to the border with Rwanda (Fig. 1), this volcano is among the most active on Earth (Wright et al., 2015). Because the surrounding area is densely populated, Nyiragongo is considered one of the most dangerous African volcanoes (e.g., Favalli et al., 2009). Its eruptive activity since at least the beginning of the 20th Century is primarily characterized by the presence of a molten lava lake in its main crater, from which a SO2rich gas plume escapes continuously (e.g., Tedesco et al., 2007). In

* Corresponding author. European Center for Geodynamics and Seismology, 19 Rue Josy Welter, L-7256, Walferdange, G.D., Luxembourg. E-mail addresses: [email protected], [email protected], benoit.smets@ africamuseum.be (B. Smets), [email protected] (N. d'Oreye), [email protected] (M. Kervyn), [email protected] (F. Kervyn).

1977 and 2002, flank eruptions occurred, draining the lava lake and producing fast-moving lava flows that proved deadly and destructive (e.g., Tazieff, 1977; Komorowski et al., 2004). Months to years after these flank eruptions, the lava lake reappeared in the main crater. The size, shape and elevation of the Nyiragongo lava lake evolved through time, modifying the morphology of the main crater. Today's crater topography is characterized by remnants of solidified levels of the lava lake, forming platforms attached to the inner flanks (Fig. 2). The uppermost platform P1 corresponds to the highest level reached by the lava lake before the 1977 eruption. This former lava lake level, located ~150 m below the crater rim, was first reached in April 1972 (Durieux, 2004). The 4 remaining sections of that platform have a cumulated surface of ~55,000 square metres. The best-preserved platform P2 represents the highest level reached by the here encrusted lava lake prior to the 2002 eruption, this level being reached in December 1995 (Tedesco, 2004). P2 is located ~230 m below the crater rim and covers ~245,000 square metres. The bottom of the crater, P3, is formed by

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Please cite this article in press as: Smets, B., et al., Gas piston activity of the Nyiragongo lava lake: First insights from a Stereographic Time-Lapse Camera system, Journal of African Earth Sciences (2016), http://dx.doi.org/10.1016/j.jafrearsci.2016.04.010

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Fig. 1. Location map of Nyiragongo volcano (North Kivu, Democratic Republic of Congo).

successive overflows of the currently active lava lake nested in its

centre. The altitude of P3 is evolving with these lava lake overflows.

Please cite this article in press as: Smets, B., et al., Gas piston activity of the Nyiragongo lava lake: First insights from a Stereographic Time-Lapse Camera system, Journal of African Earth Sciences (2016), http://dx.doi.org/10.1016/j.jafrearsci.2016.04.010

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Fig. 2. Map of the Nyiragongo main crater. The three levels of platform are referred as P1, P2 and P3, the latter being the active platform that delimits the pit of the current lava lake. The two green dots are the location of the two cameras of the Stereographic Time-Lapse Camera (STLC) system. The thick black line in the lava lake locates the profile A-A0 shown in Fig. 6a. Blue squares represent samples used to assess the relative elevation of the lava lake and a stable zone on P3. The rim of the platform was mapped on the field, with a differential GPS. The lava lake and the platforms were mapped using the SPOT-5 image (© CNES, 2009, Distribution AIRBUS DS) and the orthophoto shown in Fig. 4. The elevation of platforms was determined using the reference point cloud described in Section 2.3. (For interpretation of the references to colour in this figure caption, the reader is referred to the web version of this article.)

P3 has a shield-like morphology, with gently outward-dipping slopes (<10
) surrounding the active pit of the lava lake. A temporary levee exceeding a height of 10 m sometimes forms from overflows and fountaining during periods of intense activity. As reported by Tazieff (1966), two types of lava lake level variations occur at Nyiragongo. The first one corresponds to single events of level increase or decrease reaching up to several tens of metres within a few tens of minutes or a few hours. These large variations usually modify the mean lava lake level for weeks or months. These changes of the lava lake level were sparsely reported by scientists during field observations on top of the volcano (e.g., Sahama and Meyer, 1958; Tazieff, 1966, 1975; Durieux, 2004). The second type of variation corresponds to cyclic metre-scale changes commonly lasting few minutes. Similar lava lake level variations are also observed at Hawaiian persistent lava lakes and are referred as “gas piston activity” (e.g., Orr and Rea, 2012 and references therein). Only briefly mentioned by Tazieff (1966), such activity at Nyiragongo has never been measured or studied. Photogrammetric and videogrammetric techniques are suitable approaches to study such fast-changing eruptive activity. In the field of volcanology, photogrammetry is commonly used to study dome growths, lava flow movements and volcano instabilities, or for analogue modelling, either using single-, stereo- or multi-image techniques (e.g., Lagmay et al., 2000; Cecchi et al., 2003; Donnadieu et al., 2003; James et al., 2006; Delcamp et al., 2008; Walter, 2011;

Walter et al., 2013). To study lava lakes, Orr and Rea (2012) used time-series of single images from a camera aimed at a specific point
o
along the inner wall of the pit containing a lava lake at the Puʻu ʻOʻ vent (K
ılauea Volcano, Hawai'i) and located at a known distance from that point. This configuration of image acquisition allowed the authors to assess the gas piston activity of that lava lake using a simple trigonometric approach. In the last few years, with the improvement of computing power, as well as the recent developments in digital photogrammetry and remotely piloted aircraft systems (RPAS), new and more accurate stereo point matching algorithm techniques appeared (Dall'Asta and Roncella, 2014). These new techniques along with “Structure-from-Motion” (SfM) algorithms (Ullman, 1979) exploited in computer vision can be used for 3D object reconstruction without ground control points. The result of this approach is a sparse 3D point cloud of the targeted object, used as input for a multi-view stereo approach from which a dense and accurate 3D point cloud is produced. The SfM approach was recently exploited on volcanic zones to study dome growths and the progression of active lava flows (James and Varley, 2012; James et al., 2011; James and Robson, 2014a). This technique has recently been used to monitor K
ılauea's summit lava lake for the last couple of years (T. Orr, written communication), but not yet on other persistent lava lakes. To study the Nyiragongo lava lake, we developed a Stereographic Time-Lapse Camera (STLC) system to measure lava lake

Please cite this article in press as: Smets, B., et al., Gas piston activity of the Nyiragongo lava lake: First insights from a Stereographic Time-Lapse Camera system, Journal of African Earth Sciences (2016), http://dx.doi.org/10.1016/j.jafrearsci.2016.04.010

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level variations. The STLC comprises two time-lapse cameras that synchronously take photographs of the lava lake. The main goal of this instrument is to regularly collect stereo-pairs of photographs, in order to create a time-series of 3D point clouds using close-range SfM-based photogrammetry. The comparison of these 3D models can then be used to measure topographic changes related to the lava lake activity. Installed in September 2011 on P1, along the southern inner flank of the crater (Fig. 2), the STLC system acquired data during 48 h (18e20 September 2011), at a rate of one pair of images every 2 min, allowing the first measurements of gas piston cycles at the Nyiragongo lava lake. After a description of the methodology, the quality of the produced 3D point clouds (i.e., noise, georeferencing) and the measured sequences of lava lake level variations are analysed. The analysis of gas piston cycles is complemented by a description of the surface activity of the lava lake (i.e., surface velocity and evolution of degassing) during the measured level variations. Finally, the suitability of the STLC system to measure lava lake variations and the technical challenges encountered during its application in the Nyiragongo crater are discussed, and the obtained results are compared to observations and interpretations of gas piston activity at other persistent lava lakes. 2. Methodology 2.1. The Stereographic Time-Lapse Camera system The STLC system consists of a pair of digital single-lens reflex (DSLR) Nikon D5100 cameras with 18e55 mm lenses physically fixed to 18 mm. Each camera is protected by a housing system made of fiberglass and installed on a ballasted tripod with buried pods, ensuring an all-weather, stable and identical acquisition view. The cameras are powered by 12 V/9 Ah batteries charged by 20 W solar panels. An additional car battery is used to provide extra power during frequent period of successive cloudy days. The two cameras are controlled by synchronized Digisnap 2700 intervalometers (Harbortronics, 2010) connected to each other using a 100-m-long cable. Captured images are stored on SDHC memory cards. No data transmission system was developed to save power and reduce the cost of the developed system. The time clock of the cameras was synchronized with a handheld GPS before deployment and the acquisition time of the first and last camera shot of each session of acquisition was checked with a GPS to correct for clock drift. In order to have adequate convergent camera observations, we installed the two time-lapse cameras of the STLC system 90 m from each other, on a remnant of P1 (Fig. 2). This configuration offers an adequate base-to-height (B/H) ratio of almost 0.2. The B/H ratio is the ratio between the distance that separates two image acquisitions (i.e., the base or baseline) and the distance of this image pair to the photographed object (i.e., the height). In the resulting 3D model, this ratio strongly influences the accuracy in the direction of the field of view (i.e., orthogonal accuracy). When the B/H ratio is very small (i.e., <0.1) or very large (i.e., >1), the orthogonal accuracy is low (Hasegawa et al., 2000). B/H ratios between 0.5 and 0.9 would theoretically offer the best 3D reconstructions (Hasegawa et al., 2000; EOS Systems Inc., 2012), but this range of values limit the quality of the stereo matching, as point correlation becomes harder (Hasegawa et al., 2000). Hence, B/H ratios between 0.1 and 0.5 offer the best balance between dense image matching and orthogonal accuracy. The STLC system acquired pairs of images during nearly two consecutive days at a sampling rate of two minutes. As the weather conditions were relatively bad during the expedition (e.g., heavy rains, hail storms and clouds frequently shrouding the summit) and because steam and passive volcanic degassing often obstructed the

visibility of the lava lake from the camera viewpoints, only 15.4% of the daytime image acquisitions were usable for the photogrammetric processing; this representing 102 pairs among the 661 acquisitions. 2.2. Data processing workflow The acquired stereo-pairs of images were processed using the photogrammetric software Agisoft® PhotoScan Pro 1.1.6. In addition to computing 3D models (i.e., point clouds, meshes, digital elevation models) and orthophotos, the software offers co-registration and batch processing options to easily compute time-series of 3D models, also sometimes referred as 4D modelling. The photogrammetric processing workflow used here contains 2 main steps. First, the photo alignment (or photo orientation) step estimates interior and exterior orientations using an SfM algorithm. According to Leon et al. (2015), PhotoScan Pro uses its own algorithm, which is relatively similar to the SIFT algorithm of Lowe (2004). The photo alignment is performed using a selection of keypoints invariant to scale and rotation. When this processing step is performed with only two different images, interior and exterior orientations are poorly modelled. During the photo alignment processing with Photoscan, variability in the calculated orientations was observed, depending on the processing run. Such variability is likely related to iterative steps in the applied SIFT-like method. For camera calibration parameters (i.e., interior orientation), including parameters used to correct the optical distortion of the image, this may lead to significantly different results and, hence, to potentially large errors in the final 3D model. This is well illustrated by the estimated radial distortion curves in Figs. 3ae5, which results from identical processing using exactly the same pair of images. The pair of test images was acquired on a 1/1300-scaled reduced model of the Nyiragongo crater used to assess the feasibility of the STLC technique (not presented here). Given the pixel size of the Nikon D5100 cameras (i.e., 0.005 mm on the camera sensor) and the configuration of image acquisitions in the Nyiragongo crater, the error in the radial distortion correction during the auto-calibration of cameras may induce an error of elevation of about 5 m related to doming effect in the point cloud created by the badly estimated calibration parameters. Such problem of doming deformation in point clouds associated with lens distortions is well illustrated by James and Robson (2014b). It is worth noting that when four images instead of two are acquired at different locations, with adequate B/H ratios and convergent view angles between the images, the error of distortion associated with the auto-calculation of the camera calibration parameters decreases under the pixel size (Figs. 3be5) and, hence, can be used in the processing. To avoid this problem with our STLC system, which used only two cameras, we fixed the camera calibration parameters during the entire processing workflow, using in-lab calibration values. This in-lab calibration was based on a multi-sheet approach. First, fifteen A4 paper sheets with coded targets printed on them were fixed on the ground, four of them being fixed 5 cm higher to produce relief. Next, these sheets were photographed from different viewpoint orientations at a minimum distance of 1 m. Finally, the acquired images were used to derive the camera calibration parameters. It is worth noting that such in-lab calibration is realized with a different focus setting than during the STLC acquisition. €be and Fo €rstner (2004), geometric instaHowever, according to La bility related to focus changes is much smaller than with zoom changes and is only significant for camera-target distances shorter than 0.5 m. Calibration errors may also remain in the image corners because coded targets are mainly located in the central part of the images, providing a less constrained distortion correction at largest radial distances in the images. As the Nyiragongo lava lake is also

Please cite this article in press as: Smets, B., et al., Gas piston activity of the Nyiragongo lava lake: First insights from a Stereographic Time-Lapse Camera system, Journal of African Earth Sciences (2016), http://dx.doi.org/10.1016/j.jafrearsci.2016.04.010

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Fig. 3. Radial symmetric lens distortion of the STLC cameras, calculated by the PhotoScan Pro software during the Photo Alignment step, for images acquired on a reduced model of the Nyiragongo crater. The pixel size is 0.005 mm. (a) Radial distortion curves (50 iterations) calculated with the same stereo-pair of images. (b) Radial distortion curves (50 iterations) calculated with two additional cameras, i.e., with 4 simultaneous images acquired from 4 different view angles. This graph shows how adding two cameras to the STLC system improves the self-calibration during the photogrammetric processing.

located in the central part of the images, this source of error is assumed to be limited. Once the photo alignment step is finished, a sparse point cloud is produced. This first point cloud is edited using one of the filters available in PhotoScan Pro, in order to remove inaccurately located points and, hence, improve the quality of the calculated orientations. The filtering option used is the ‘reprojection error’ filter, which allows selecting and deleting points poorly constrained by the calculated interior and exterior orientations. The other filtering options of PhotoScan Pro were not used, as they are dedicated to point clouds produced with more than two images. The second main step in the processing of the STLC data

corresponds to the production of the dense point cloud, here using a method similar to the semi-global dense matching method of Hirschmüller (2005, 2008) (Remondino et al., 2014). This step exploits the previously produced sparse point cloud and the derived exterior orientation as inputs to calculate a pixel-wise 3D point cloud for each stereo-pair. The identification of the lava lake level on each point cloud was difficult due to the fact that outgassing masked different parts of the lake on several stereo-pairs. Hence, to assess the lava lake level variations, we selected a zone of 40  40 square metres of the lava lake (Fig. 2) where outgassing rarely occurred during the acquisition of the selected images. This avoids the need of manually

Please cite this article in press as: Smets, B., et al., Gas piston activity of the Nyiragongo lava lake: First insights from a Stereographic Time-Lapse Camera system, Journal of African Earth Sciences (2016), http://dx.doi.org/10.1016/j.jafrearsci.2016.04.010

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Fig. 4. 3D view of the reference point cloud used to georeference the produced point cloud time series. This reference model covers most of the Nyiragongo crater. The blue rectangles represent the image acquisition viewpoints during helicopter flights over the volcano. Note that the present 3D model shows the state of the lava lake on 5 July 2014, which was almost 70 m lower than the rim of the pit. (For interpretation of the references to colour in this figure caption, the reader is referred to the web version of this article.)

removing, from the time-series, the groups of points that reflect volcanic outgassing instead of the lava lake level. In order to detect systematic errors due to instability in the relative orientation of the cameras, we also selected a zone of 40  40 square metres in the NE side of P3 (Fig. 2), which is used as a stable reference. The mean elevation of this reference zone was calculated and provided with the calculated mean elevation of the lava lake. In order to detect if the observed lava lake level variations indeed affect the entire modelled lava lake surface, we next visualized these variations using the open-source CloudCompare v2 software (http://www.cloudcompare.com), which offers several techniques for 3D point cloud comparison. We selected the M3C2 comparison algorithm (Lague et al., 2013), which allows a direct 3D cloud-to-cloud comparison by measuring the mean surface change between normals calculated for points located within an area with a given radius adapted to the surface roughness of the point cloud. According to Lague et al. (2013), this algorithm offers the highest accuracy for direct point cloud comparison. In the present study, we configured the M3C2 algorithm to measure vertical changes based on normals calculated for areas with a radius of 2.5 m.

crater. The images were acquired with a Nikon D7000 DSLR camera equipped with a Nikkor 20 mm f/2.8D lens and a Solmeta Geotagger N3 GPS receiver. The camera calibration is, here, automatically assessed during the photo alignment process. Because some parts of the main crater were not accessible to measure adequate ground control points, the geocoding of the dense point cloud is performed using the sole integration of the GPS geolocation tag recorded in the EXIF metadata of each photograph. In practice, the STLC data were divided into two temporal subsets during all processing steps, as cameras slightly moved in the housing box during a manual data transfer on 19 September 2011. The two subsets were georeferenced using the image-based alignment option of PhotoScan Pro. In order to finely co-register the two subsets and reduce any georeferencing error between them, we used the Iterative Closest Point algorithm (Besl and McKay, 1992; Chen and Medioni, 1992) available in the CloudCompare software, which iteratively finds the best fit for two point clouds representing the same object or surface.

2.3. Georeferencing and dataset alignment

3.1. Reference model and georeferencing of the time-series

The STLC data were georeferenced using the image-based alignment option of PhotoScan Pro, which co-registers the exterior orientation of each dataset by detecting similar pixels in the photographs. In order to produce a reference model for the scaling and geocoding of the time-series of point clouds, we used a set of 50 photos acquired during a helicopter flight over the Nyiragongo

The produced dense point cloud used as reference for the geocoding shows most of the Nyiragongo main crater (Fig. 4) and allows the production of a DEM and an orthophoto with a resolution of 25 cm and 15 cm, respectively. According to the used geographic coordinates of the geotagged photographs, the relative accuracy of the geocoding is 16.8 m, i.e. 10.3 m, 10.9 m and 7.7 m, in X, Y and Z,

3. Results

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Fig. 5. Map of the Nyiragongo crater showing the 10 control points used to estimate the horizontal geocoding error of the reference point cloud described in Section 2.3. The grayscale image in the background corresponds to the SPOT-5 image used to assess the horizontal geocoding error. The colour image corresponds to the orthophoto derived from the reference point cloud. (For interpretation of the references to colour in this figure caption, the reader is referred to the web version of this article.)

respectively. Despite a geotagging resolution of 1 m in elevation, the best accuracy is along the vertical (Z) axis, as the flight paths over the crater were close to horizontal. There is no other georeferenced 3D model available to assess the absolute horizontal and vertical accuracy of the produced model of the Nyiragongo crater. However, using a SPOT-5 image (pixel resolution of 2.5 m) of the Nyiragongo crater acquired in July 2009, which is the only existing high-resolution optical image that allows seeing a major part of the Nyiragongo crater, we can at least assess its horizontal accuracy. For that, we selected 10 control points on the SPOT-5 image and compared their location on an orthophoto derived from the 3D model of the crater. As the SPOT-5 image was orthorectified using the SRTM-1 DEM (© NGA/NASA; Farr et al., 2007) and accurately georeferenced using a series of 24 ground control points acquired with a differential GPS in the urbanized areas surrounding the volcano, the accuracy of the control points selected on the SPOT-5 image is assumed to be of the order of the pixel size (i.e., 2.5 m). The 10 control points revealed a rotation error in the geocoding, which is linked to the movement of the helicopter during the geotagged-image acquisition (Fig. 5). This induces a large horizontal location error of maximum 45 m. However, by comparing the horizontal distances between the selected control points (i.e. 90 baseline distances) measured on the SPOT-5 image and the orthophoto, a scaling error of maximum 2.9% is detected, which is relatively good regarding the geocoding method applied here. Hence, the produced 3D model of the Nyiragongo main crater provides a suitable reference for the georeferencing of STLC data,

when the latter data are used for the relative measurement of topographic changes linked to the activity of the lava lake. 3.2. Quality of produced dense point clouds In the produced dense point clouds, the lava lake surface is locally affected by volcanic outgassing. Together with 3D modelling errors related to the stereo-photogrammetric approach and possible errors due to heat-related refraction, it introduces a vertical dispersion of points in the clouds, also referred to noise. In the reference point cloud, a vertical profile through the lava lake surface revealed a surface deformation, which has the shape of a wave on the profile (Fig. 6a), but rather corresponds to modelling errors triggering higher elevation values in some parts of the lava lake. The amplitude of the surface variation is 2e2.5 m and is mostly related to strong outgassing at the lava lake surface. Lava lake movements between helicopter paths and heat shimmer may also have influenced the 3D reconstruction of the lava lake surface. In the point cloud samples used to estimate the lava lake level variations, the vertical point dispersion can reach several metres. This is highlighted by standard deviations values, which are usually around 0.6 m, but range from 0.27 to 1.38 m. However, points are symmetrically distributed around the mean elevation value, as highlighted by standard errors of the mean ranging from 0.004 to 0.012 m. An example of vertical point dispersion in a 40  40 m point cloud sample is shown in Fig. 6b. The standard deviations outside of the lava lake, in the reference zone on P3, are of the same order, but vary in a narrower range (i.e., 0.53e0.9 m), suggesting

Please cite this article in press as: Smets, B., et al., Gas piston activity of the Nyiragongo lava lake: First insights from a Stereographic Time-Lapse Camera system, Journal of African Earth Sciences (2016), http://dx.doi.org/10.1016/j.jafrearsci.2016.04.010

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Fig. 6. Graphs showing profiles in the produced point clouds, over the Nyiragongo lava lake. (a) Profile A-A0 crossing the lava lake in the reference point cloud produced for the geocoding of the point cloud time-series (see Fig. 2 for profile location). The point cloud sample is a N-S-oriented, 5-m-wide and 90-m-long vertical slice of the model. This profile shows that the modelled surface of the lava lake is affected by a wave-like deformation with amplitude of 2e2.5 m. (b) Vertical projection of a 40  40 m point cloud sample of the lava lake along the N-S axis. This profile illustrates the typical vertical noise in point clouds of the lava lake. This noise results from the combination of vertical movements at the lava lake surface, surface outgassing, possible refraction related to heat shimmer and modelling errors. Thick black dashed lines in graphs represent the mean elevation value of the point cloud samples. Thick blue dashed lines in Graph (b) are the mean elevation value ± 2 standard deviations (i.e., ±2s). (For interpretation of the references to colour in this figure caption, the reader is referred to the web version of this article.)

that noise in the point cloud mostly results from the stereophotogrammetric approach, but sometimes increase over the lava lake due to effects of outgassing and possible heat shimmer. It is worth noting that the standard deviations in the reference zone are also affected by the fact that the modelled surface is not absolutely horizontal. In the produced time-series, the mean elevation of the reference zone on P3 (3057.86 m) shows maximum variations of ±0.39 m (Table 1 in Appendix). Such variability centred on the mean value is representative of the instability of the camera orientations, indicating the minimum threshold of detectable elevation change we could expect with the developed methodology. 3.3. Lava lake level variation cycles The 102 dense point clouds produced 6 continuous time sequences with durations ranging from 12 to 86 min, during which clear lava lake level variations are observed (Figs. 7e9; Table 1 in Appendix). These level changes have various durations ranging

from 6 to more than 20 min and reach vertical changes of maximum 3.8 m. Also, lake level variation cycles of different durations are sometimes combined, forming more complex apparent variations (Fig. 9a,c). When dense point cloud differences are performed with the M3C2 algorithm, it appears that lake level changes >0.5 m are detected, although variations between 0.5 and 1 m are often of the same range as the point cloud noise. However, the noise shows high spatial frequency variations, whereas lava lake level changes are uniform throughout the lava lake surface (Figs. 8 and 9b-e). According to the few lava lake level variations observed with the STLC, the velocity of vertical variations have a similar mean value of 0.14 m/min during both increasing and decreasing lake levels. But, speeds of rising level show a wider range of values (i.e., 0.04e0.34 m/min) than for decreasing levels (i.e., 0.1e0.17 m/min). These velocities of lava lake level variation must however be interpreted with caution, as both the number of observations and the temporal resolution of measurements are limited. According to the produced 3D model of the Nyiragongo main

Please cite this article in press as: Smets, B., et al., Gas piston activity of the Nyiragongo lava lake: First insights from a Stereographic Time-Lapse Camera system, Journal of African Earth Sciences (2016), http://dx.doi.org/10.1016/j.jafrearsci.2016.04.010

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rising level, outgassing is frequently concentrated in a random restricted zone of the lake. The intensity of both outgassing and spattering strongly decrease a few minutes before reaching the highest level of a gas-piston cycle. Strong outgassing typically appears at the beginning of a lava lake level decrease. The size of crust pieces tends to be dependant on the intensity of outgassing and spattering, but no systematic rule came out from this descriptive analysis of the photographs. 4. Discussion 4.1. Potential and limits of the STLC technique

Fig. 7. Four of the six sequences of the lava lake variations recorded by the STLC system in September 2011. Time is UTC ±00:00. The two other sequences are shown in Figs. 8 and 9. Lava lake level estimates (red squares) correspond to the mean elevation value of the selected 40  40 m point cloud sample (Fig. 2). The relative levels of the reference 40  40 m point cloud sample on P3 (Fig. 2) are represented by black dots. (For interpretation of the references to colour in this figure caption, the reader is referred to the web version of this article.)

crater, the lava lake covers a surface of 47 ± 1.4  103 m2. Thus, a lava lake level variation of 3.8 m, similar to the maximum variation observed in our measurements, would represent a volume change of ~0.18  106 m3. By comparing lava lake level changes with observations of the lava lake activity on photographs, it is difficult to distinguish clear systematic behaviours in terms of outgassing, spattering and size of crust's pieces at the lava lake surface. However, during periods of

The STLC system successfully assessed the vertical changes in lava lake level of Nyiragongo, with a detection threshold < 1 m. Such accuracy is a few decimetres above the expected minimum threshold (i.e., ±0.39 m) indicated by the maximum variability of the mean elevation recorded in a stable zone of the crater. However, this system suffers from serious limitations. First, produced dense point clouds are relatively noisy, mostly due to modelling errors related to the stereo-photogrammetric approach. Adding two more cameras to the system would strongly improve the results (Fig. 3). Second, gas escaping from the lava lake often partly or entirely hides the lava lake surface or locally introduces an additional noise in point clouds. As a consequence, only a small portion of the daytime image pairs we acquired (15.4%) was available for photogrammetric modelling and parts of the lava lake were often not modelled. This might be improved by removing the IR-blocking filter from the CCD camera sensors and adding a suitable lens filter to enhance the IR spectrum which enables to partly see though gas and steam. Adding more cameras at different locations would also improve the quality of 3D modelling. Furthermore, more frequent acquisitions would improve the characterization of the gas piston cycles of the Nyiragongo lava lake. For better performance in modelling small features of the lava lake surface (waves, bubbles, lava crust, etc.), the cameras of the STLC system could also be installed closer to the lava lake, or at least equipped with larger zoom lenses. Indeed, closer or zoomed views of the lava lake would improve the spatial resolution of the dense point clouds. In addition, closer cameras would reduce the likelihood of intervening fume and steam. It is important, though, that at least a small part of the images retain a view of a stable part of the crater so that the 3D models can be georeferenced based on similar image points or using a fine point cloud registration algorithm. With a suitable orientation of the cameras and accurate measurements, in the field or using the 3D reference model, of view angle and orthogonal distance between cameras and the lava lake pit rim, the single-image technique used by Orr and Rea (2012) could be also exploited to assess lava lake level variations. However, we were not able to perform the required measurements in the field and the morphology of P3 was modified between the acquisition of STLC data (September 2011) and the acquisition of images used to create the reference point cloud (July 2014). For these reasons, we cannot easily apply this single-image approach in our STLC data set. 4.2. Gas piston activity of the Nyiragongo lava lake As reported by Oppenheimer et al. (2009), the best model that explains the existence of long-lived lava lakes and persistent eruptive activity is a bi-directional flow of magma in the feeder conduit (e.g., Tazieff, 1994; Stevenson and Blake, 1998; Huppert and Hallworth, 2007). In this model, the convection is primarily controlled by density contrasts between gas-rich and outgassed magmas, but also by the cooling of magma at the lake surface by

Please cite this article in press as: Smets, B., et al., Gas piston activity of the Nyiragongo lava lake: First insights from a Stereographic Time-Lapse Camera system, Journal of African Earth Sciences (2016), http://dx.doi.org/10.1016/j.jafrearsci.2016.04.010

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Fig. 8. Lava lake level decrease on 18 September 2011. (a) Graph of lava lake level measurements. The blue area in the background corresponds to the lava lake level decrease illustrated in Frame (b). Lava lake level estimates (red squares) correspond to the mean elevation value of the selected 40  40 m point cloud sample (Fig. 2). The relative levels of the reference 40  40 m point cloud sample on P3 (Fig. 2) are represented by black dots. Time is UTC ±00:00. (b) Vertical point cloud comparison illustrating the lava lake level decrease observed in Frame (a). The colour scale is in metre. (For interpretation of the references to colour in this figure caption, the reader is referred to the web version of this article.)

crystallisation and gas dynamics (Harris, 2008; Pansino, 2012). Two models exist to explain gas piston activity (Sawyer et al., 2008; Orr and Rea, 2012; Peters et al., 2014; Jones et al., 2015). The first model interprets gas piston activity as the surface expression of degassing and outgassing mechanisms and magma convection, which occur in a system with a magma chamber connected to a lava lake through an open conduit. Lava lake level changes are, thus, explained by gas slugs rising towards the surface, either by bubble ascent and coalescence (Parfitt and Wilson, 1995; Parfitt, 2004), as a result of a foam collapse (Jaupart and Vergniolle, 1988), or by gas bubble driven pressurisation changes (Witham et al., 2006). The second model for gas piston activity suggests that the vertical variations of lava lakes are related to gas accumulation in the lava lake itself, beneath a relatively impermeable solidified crust

(Swanson et al., 1979; Patrick et al., 2011) or as a foam layer that develops at the top of a column of lava (Dibble, 1972; Orr and Rea, 2012). In the case of the Nyiragongo lava lake, the very limited temporal changes in the composition of the gas plume escaping from the lava lake (Sawyer et al., 2008) directly discard the foam collapse model of Jaupart and Vergniolle (1988), in which gas slugs would trigger variations in the CO2/SO2 ratio (Sawyer et al., 2008). Our results are also less favourable to the pressure balance model of Witham et al. (2006), as contrary to the experimental results of these authors, no decreasing rate of lava level rise is observed, though the number and resolution of the measurements are limited. Furthermore, this model would imply that outgassing stops or at least strongly decreases during the retreating lava lake level

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Fig. 9. Lava lake level variations on 20 September 2011. (a) Graph of lava lake level measurements. The measurement pinpointed as “outlier” (green arrow) illustrates how volcanic gas and steam can cause measurement errors, highlighting the need to manually control each point cloud when lava lake level changes are measured. The red and blue areas in the background correspond to the lava lake level increases (red) and decreases (blue) illustrated in Frames (b) to (e). Lava lake level estimates (red squares) correspond to the mean elevation (Fig. 2). The relative levels of the reference 40  40 m point cloud sample on P3 (Fig. 2) are represented by black dots. Time is UTC ±00:00. Frames (b) to (e) are vertical point cloud comparisons illustrating the lava lake level variations observed in Frame (a). The related colour scales are in metre. (For interpretation of the references to colour in this figure caption, the reader is referred to the web version of this article.)

phase of the gas piston cycle, while the contrary is observed, at least during the first minutes of the lava level decrease. Velocity of lava lake level rise and fall movements in the Nyiragongo are relatively similar, though speeds during rising movements display a wider range of values. As gas escapes continuously, though with striking variations, during gas piston cycles, with, according to Sawyer et al. (2008), the gas rising as fast as magma in the feeder conduit, and because changes in outgassing at the lava lake surface only appear within a few minutes before and after the highest lava lake level is reached, we suggest that gas piston activity is controlled by gas entrapment in the lava lake itself. This interpretation is in accordance with those of Swanson et al. (1979) and Orr and Rea (2012) for K
ılauea, though observations are not totally similar. Indeed, these authors observed clear decreasing speeds before reaching the highest lava lake levels and constant speeds during decreasing levels, while such variations are not highlighted by our measurements. Hence, according to the gas piston model proposed by Swanson et al. (1979) and Orr and Rea (2012), superficial gas accumulation would be related to density contrasts and convection in the lava lake, while variations in the amplitude and duration of gas piston cycles would be associated with changes in the gas volume fraction

of the ascending magma. Further gas measurements coupled with higher rate time-lapse or video acquisitions are however required to provide a better description of the gas piston cycles of Nyiragongo and their relationship with the outgassing activity.

5. Conclusions In the present study, we developed a stereo-photogrammetric instrument, i.e., the STLC system, to monitor the variations of the Nyiragongo lava lake level. Using the stereo-pairs acquired during 48 h, in September 2011, a time-series of 3D point clouds were produced using close-range photogrammetric techniques, allowing the first measurements of gas piston activity of the Nyiragongo lava lake. The STLC system proved to be able to measure the lava lake level variations with an unprecedented resolution of 0.5e1 m. However, the technique also showed several major limitations related to the instability of the camera orientations and atmospheric masking in the main crater (e.g., by steam and volcanic fume), which prevented the production of long uninterrupted time-series of 3D point clouds. A single-photo approach for lava lake level measurement (not developed here) and improvements of the STLC system (e.g., modified camera sensors, lens filters,

Please cite this article in press as: Smets, B., et al., Gas piston activity of the Nyiragongo lava lake: First insights from a Stereographic Time-Lapse Camera system, Journal of African Earth Sciences (2016), http://dx.doi.org/10.1016/j.jafrearsci.2016.04.010

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additional cameras, etc.) would partly solve the problem. Additional modifications such as a higher acquisition rate, the choice of more suitable camera orientations for single-photo measurements (e.g., Orr and Rea, 2012) and/or the synchronous measurement of the lava lake outgassing would also improve the possibilities that could offer the developed photogrammetric approach. The selection of steam- and gas-free images available for the photogrammetric processing could also be determined automatically using the mean image spectrum over the lava lake. Results show that the lava lake exhibited metre-scale vertical gas piston cycles that commonly lasted a few tens of minutes. According to our observations and the resolution of measurements, gas piston cycles at Nyiragongo show similarities with those observed in Hawaii by Swanson et al. (1979) and Orr and Rea (2012). Additional investigations are however required to better constrain the relationship between those level variations and the dynamics of outgassing. With the proposed adaptations and a real-time transfer of images acquired by the cameras, the STLC technique could provide a tool for a long-term monitoring of the Nyiragongo lava lake. The proposed approach could be also used for other case studies, where frequent topographic changes occur.

Acknowledgements The present study was performed in the framework of the NYALHA (AFR PhD Grant no 3221321, FNR Luxembourg), GeoRisCA (Contract SD/RI/02A, SSD Program, Belgian Science Policy Office) and Vi-X (Contract SR/00/150, STEREO II Program, Belgian Science Policy Office; INTER Program, FNR Luxembourg; Grant NTI_INSA0525, DLR) projects. The STLC system was built thanks to a grant of VOCATIO asbl, the Belgian foundation for the vocation (www.vocatio.be), and the precious support of Nikon BelgiumLuxembourg. The authors would like to thank the Goma Volcano Observatory and the “Institut Congolais pour la Conservation de la Nature” (ICCN) for their logistical help and fruitful collaboration during the Sept. 2011 field expedition on top of Nyiragongo. The Belgian Alpine Club, REAj asbl and L'Escale climbing gym (Arlon, Belgium) are thanked for the climbing gears used to descent in the Nyiragongo crater. The MONUSCO (UN mission in D.R. Congo) is thanked for the provided helicopter flights over Nyiragongo. J.P. Smets is thanked for his help in developing the STLC system. The authors would like to thank Mike R. James and Tim R. Orr for their very constructive review, which helped improve the manuscript. Appendix

Table 1 Relative elevation and related standard deviation for a 40  40 m sample of the lava lake and a 40  40 m sample of P3 serving as stable reference. The first column indicates the figure picturing the relative elevations. Graph

Fig. 7a

Fig. 8a

Fig. 7b

Date and time

18/09/11 18/09/11 18/09/11 18/09/11 18/09/11 18/09/11 18/09/11 18/09/11 18/09/11 18/09/11 18/09/11 19/09/11 19/09/11 19/09/11 19/09/11 19/09/11 19/09/11 19/09/11 19/09/11 19/09/11 19/09/11 19/09/11 19/09/11 19/09/11 19/09/11 19/09/11 19/09/11 19/09/11 19/09/11 19/09/11 19/09/11 19/09/11 19/09/11 19/09/11 19/09/11 19/09/11 19/09/11 19/09/11 19/09/11 19/09/11

13:24 13:26 13:28 13:30 13:32 13:34 13:36 13:38 13:40 13:42 13:44 04:10 04:12 04:14 04:16 04:18 04:20 04:22 04:24 04:26 04:28 04:30 04:32 04:34 04:36 04:38 04:40 04:42 05:44 06:16 06:18 06:24 06:26 06:28 06:30 06:32 06:36 06:38 06:40 06:42

Lava lake sample

P3 sample

Relative mean level

STDEV

Relative mean level

STDEV

2.46 2.52 2.11 1.97 2.18 2.09 2.29 2.40 3.02 3.10 3.42 6.15 5.67 5.31 4.88 4.58 4.25 4.03 3.81 3.26 2.98 2.59 2.32 2.44 2.37 2.35 2.60 2.17 3.45 3.15 2.86 3.42 3.08 2.93 2.48 2.67 2.49 2.77 2.95 3.04

0.65 0.70 0.70 0.60 0.60 0.68 0.68 0.56 0.69 0.57 0.52 0.73 0.77 0.66 0.66 0.73 0.80 0.64 0.61 0.65 0.68 0.82 0.74 0.82 0.70 0.72 0.77 0.59 0.83 0.28 0.27 0.37 0.42 0.31 0.31 0.33 0.44 0.39 0.39 0.47

0.01 0.11 0.20 0.14 0.11 0.00 0.04 0.23 0.03 0.20 0.09 0.10 0.07 0.02 0.01 0.11 0.04 0.06 0.18 0.03 0.03 0.14 0.05 0.13 0.03 0.26 0.16 0.21 0.07 0.12 0.27 0.23 0.06 0.00 0.03 0.02 0.02 0.02 0.07 0.06

0.86 0.78 0.77 0.78 0.65 0.80 0.68 0.73 0.76 0.56 0.77 0.82 0.77 0.83 0.84 0.82 0.86 0.81 0.86 0.84 0.71 0.76 0.85 0.82 0.83 0.74 0.83 0.74 0.74 0.76 0.70 0.71 0.77 0.74 0.77 0.71 0.73 0.71 0.72 0.71

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Table 1 (continued ) Graph

Fig. 7c

Fig. 7d

Fig. 9a

Date and time

19/09/11 19/09/11 19/09/11 19/09/11 19/09/11 19/09/11 19/09/11 19/09/11 19/09/11 19/09/11 19/09/11 19/09/11 19/09/11 19/09/11 19/09/11 19/09/11 19/09/11 19/09/11 19/09/11 19/09/11 19/09/11 19/09/11 19/09/11 19/09/11 19/09/11 19/09/11 19/09/11 19/09/11 19/09/11 19/09/11 20/09/11 20/09/11 20/09/11 20/09/11 20/09/11 20/09/11 20/09/11 20/09/11 20/09/11 20/09/11 20/09/11 20/09/11 20/09/11 20/09/11 20/09/11 20/09/11 20/09/11 20/09/11 20/09/11 20/09/11 20/09/11 20/09/11 20/09/11 20/09/11 20/09/11 20/09/11 20/09/11 20/09/11 20/09/11 20/09/11 20/09/11 20/09/11

06:46 06:48 06:50 06:52 06:54 06:58 07:00 07:02 07:04 07:06 07:08 07:10 08:26 08:28 08:30 08:32 08:34 08:36 08:38 08:58 09:42 09:46 09:48 09:50 09:52 09:54 09:56 09:58 10:00 10:02 04:22 04:24 04:26 04:28 04:30 04:32 04:34 04:36 04:38 04:40 04:42 04:44 04:46 04:48 04:50 04:52 04:54 04:56 04:58 05:00 05:02 05:04 05:06 05:08 05:10 05:22 05:24 05:26 05:28 05:30 05:34 06:14

Lava lake sample

P3 sample

Relative mean level

STDEV

Relative mean level

STDEV

2.30 2.17 2.03 2.20 2.52 4.06 4.48 4.03 3.59 3.45 3.52 3.67 3.19 2.68 2.90 2.58 2.63 2.98 3.16 3.64 2.71 2.92 3.38 3.16 3.36 3.23 2.97 3.32 3.21 3.75 2.12 2.95 2.09 2.07 1.90 2.07 2.11 1.95 1.67 1.86 2.51 3.35 4.49 5.08 4.53 4.30 4.49 4.63 4.30 4.12 3.52 3.35 3.27 3.56 4.08 4.90 4.45 4.04 3.78 3.77 3.26 2.89

0.43 0.43 0.38 0.38 0.33 0.31 0.34 0.32 0.37 0.37 0.37 0.36 0.33 0.48 0.41 0.32 0.35 0.36 0.35 0.37 0.56 0.36 0.62 0.47 0.54 0.57 0.78 1.06 0.93 0.95 0.91 1.38 0.76 1.10 1.04 1.21 1.16 1.02 1.14 0.82 0.84 0.84 1.09 1.02 0.80 0.83 0.77 0.91 1.04 1.09 0.65 0.51 0.47 0.48 0.57 0.46 0.50 0.58 0.51 0.52 0.44 0.49

0.06 0.00 0.02 0.06 0.02 0.19 0.12 0.00 0.15 0.12 0.09 0.03 0.09 0.38 0.05 0.13 0.01 0.06 0.09 0.10 0.08 0.17 0.20 0.01 0.09 0.12 0.08 0.39 0.01 0.34 0.06 0.22 0.12 0.14 0.13 0.11 0.02 0.15 0.27 0.24 0.07 0.13 0.12 0.18 0.04 0.12 0.03 0.00 0.24 0.04 0.18 0.13 0.22 0.17 0.15 0.20 0.12 0.24 0.27 0.26 0.17 0.03

0.72 0.71 0.74 0.70 0.71 0.68 0.71 0.74 0.73 0.72 0.69 0.70 0.74 0.87 0.64 0.63 0.68 0.61 0.63 0.62 0.61 0.60 0.64 0.59 0.60 0.60 0.73 0.79 0.90 0.74 0.73 0.74 0.83 0.74 0.77 0.71 0.78 0.64 0.74 0.72 0.90 0.84 0.80 0.81 0.68 0.69 0.62 0.80 0.62 0.86 0.70 0.63 0.55 0.54 0.67 0.67 0.65 0.69 0.62 0.61 0.60 0.53

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Please cite this article in press as: Smets, B., et al., Gas piston activity of the Nyiragongo lava lake: First insights from a Stereographic Time-Lapse Camera system, Journal of African Earth Sciences (2016), http://dx.doi.org/10.1016/j.jafrearsci.2016.04.010