New Insights from Applications of Lidar Technologies to Lava Flow Studies

Seeing the True Shape of Earth’s Surface themed issue

How lava ﬂ ows: New insights from applications of lidar technologies to lava ﬂ ow studies

K.V. Cashman1, S.A. Soule2, B.H. Mackey3, N.I. Deligne1, N.D. Deardorff 1, and H.R. Dietterich1 1Department of Geological Sciences, University of Oregon, Eugene, Oregon 97403, USA 2Geology and Geophysics, Woods Hole Oceanographic Institution, Woods Hole, Massachusetts 02543, USA 3Division of Geological and Planetary Sciences, California Institute of Technology, Pasadena, California 91125, USA

ABSTRACT availability of high-resolution digital topog- fi rst review the history, goals, and strategies of raphy is poised to revolutionize the study of lava fl ow research and describe the capabilities Mafi c lava fl ows are common; for this rea- mafi c lava fl ows. (and limitations) of both ALS and TLS imaging son, they have long been a focus of volcano- of volcanic landscapes before providing an over- logical studies. However, fi eld studies of both INTRODUCTION view of recent applications of lidar technology older and active fl ows have been hampered to lava fl ow studies. We then discuss the poten- by diffi culties in fi eld access; active fl ows are Mafi c lava fl ows are a persistent and wide- tial of lidar to improve our understanding of the hot, whereas older fl ows have rough and jag- spread form of volcanic activity that, while hazards and dynamics of mafi c lava fl ows. ged surfaces that are diffi cult to traverse. rarely fatal, pose a common threat to commu- As a result, morphometric studies of lava nities around the world. Central to lava fl ow BACKGROUND fl ows have generally lagged behind theoreti- hazard assessment is the construction of proba- cal studies of fl ow behavior. The advent of bilistic fl ow hazard maps and development of Lava fl ow studies have been critical to vol- laser scanning (LS) (i.e., lidar, light detection tools for real-time prediction of fl ow paths, fl ow canology as a science, as illustrated by early and ranging) technologies, both airborne advance rates, and fi nal fl ow lengths. Construc- founding of volcano observatories at Vesuvius, mapping (ALSM) and terrestrial (TLS), tion of lava fl ow hazard maps requires accurate Italy (in 1841, at a time when it was having fre- is promoting detailed studies of lava fl ows information on the areal distribution and tem- quent effusive eruptions) and Kilauea, Hawaii by generating data suitable for production poral history of effusive activity, in addition to (in 1912). The frequency of mafi c lava fl ows in of high-resolution digital elevation models probable vent locations. Accurate prediction of these locations, and at other locations around the (DEMs). These data are revolutionizing both fl ow paths and advance rates requires not only world (such Etna, Italy; Piton de la Fournaise, the visual and quantitative analysis of lava rapid assessment of eruption conditions (espe- Reunion; and Iceland), and the potential for fl ows. First and foremost, this technology cially eruption rate) but also improved mod- renewed volcanism near major population cen- allows accurate mapping of fl ow boundaries, els of lava fl ow emplacement. Together these ters (such as Auckland, New Zealand, and Mexico particularly in vegetated areas where bare dual goals of lava fl ow hazard mapping prior City, Mexico) pose unique hazards to surround- earth imaging dramatically improves map- to eruptive activity, and predictive modeling in ing populations. A compilation of eruptive ping capabilities. Detailed imaging of fl ow response to eruption initiation, demand accurate volumes (magnitude) and eruption rates (inten- surfaces permits mapping and measurement documentation of preexisting topography, past sity; Fig. 1) shows that, except in unusual cases of fl ow components, such as channels, surface fl ow volumes and areal coverage, syneruptive (e.g., Tazieff, 1977), historic lava fl ows have folds, cracks, blocks, and surface roughness. variations in lava fl ux, and an improved under- rarely exceeded mass eruption rates of 106 kg/s Differencing of preeruptive and posteruptive standing of the controls on fl ow advance. (an intensity index of 9; Pyle, 2000); this con- DEMs allows analysis of fl ow thickness vari- All of these needs are met by new laser altim- trasts with eruption rates of 107–109 kg/s (inten- ations, which can be related to the dynamics etry (lidar, light detection and ranging) tech- sity index 10–12) for most explosive eruptions. of lava emplacement. Multitemporal imaging nologies that are radically changing the ways in Figure 1 also shows that lava fl ows can persist of active fl ows provides information not only which we view and study volcanic landscapes from weeks to years, and thus pose a long-lived on the rates and locations of individual fl ow that are continually resurfaced by lava fl ows. hazard. However, the duration and frequency of lobes, but also measurement of pulsed lava The development of lidar using both airborne effusive activity also provide unique opportuni- transport. Together these new measurement (ALSM, airborne laser scanner mapping) and ties for volcanologists to study active volcanic capabilities can be used to test proposed mod- ground-based, or terrestrial (TLS) laser scan- processes. These dual motivations, i.e., hazard els of channel formation, lava tube formation, ners has dramatically improved the resolution and opportunity, work together to make loca- rates of fl ow advance, and fl ow conditions of digital topography available for lava fl ow tions of frequent lava fl ow activity the focal within lava channels; they also provide new research. This technology not only allows col- point for testing and applying new innovations ways to assess the hazard and risk posed by lection of better data on the spatial distribution in technology. Here we frame the primary ques- lava fl ow inundation. Early published stud- and volumes of older lava fl ows, but also pro- tions related to lava fl ows by reviewing both the ies illustrate the potential of applying lidar to vides views of active fl ows that offer new insights types of information required for hazard assess- volcanic terrain; it is clear, however, that the into the processes that formed them. Here we ment and, briefl y, some of the technological

Geosphere; December 2013; v. 9; no. 6; p. 1664–1680; doi:10.1130/GES00706.1; 16 ﬁ gures. Received 31 March 2011 ♦ Revision received 6 September 2011 ♦ Accepted 7 September 2011 ♦ Published online 11 October 2013

Downloaded from http://pubs.geoscienceworld.org/gsa/geosphere/article-pdf/9/6/1664/3346138/1664.pdf by guest on 26 September 2021 How lava ﬂ ows

12 Mauna Loa etry also affect fl ow length. To date, models of high resolution radiometer), and MODIS (mod- Kilauea fl ow coverage and dynamics have been limited, erate resolution imaging spectroradiometer); for Pyle 10 in part, by the resolution of both preeruptive and reviews, see Oppenheimer, 1998; Wright et al., posteruptive digital topography. Here we show 2004]. These satellites provide images with low 103s ways in which high-resolution lidar-generated spatial (1–4 km/pixel) but high temporal resolu- 8 DEMs can improve our understanding of how tion, and therefore can be used for whole fl ow lava fl ows resurface the landscape. fi eld monitoring. Although fl ow widths are com- 106s Intensity Index 6 monly smaller than individual pixels, the inten- Technological Advances in Lava Flow sity of thermal emissions records the fractional 109s 4 Mapping: Short History pixel areas occupied by active fl ows, allowing 0 2 468 fl ow area to be determined and converted to Magnitude Index Studies of lava fl ows have advanced in tan- effusion rate, given suffi cient calibration data Figure 1. Illustration of the range in magni- dem with technological advances. For much of (e.g., Pieri and Baloga, 1986; R. Wright et al., tudes and frequency of some historic mafi c human history, accounts of lava fl ow behavior 2001). Higher resolution thermal imaging data lava fl ows. Magnitude (M) and intensity (I) and impact were based on close observation, can be obtained using airborne (e.g., Realmuto indices are from Pyle (2000) and are defi ned and ranged in form from the rich oral traditions et al., 1992) and hand-held (e.g., Harris et al., as M = log erupted mass (kg) – 7 and I = of the Hawaiians (e.g., Cronin and Cashman, 2005; Ball and Pinkerton, 2006) thermal imag- log eruption rate (kg/s) + 3. Data are from 2007; Swanson, 2008) to the detailed draw- ing cameras that provide high spatial resolution, Lockwood and Lipman (1987), Wolfe et al. ings and careful descriptions of seventeenth and but usually at the expense of both spatial and (1987), and Pyle (2000). eighteenth century activity at Italian volcanoes temporal coverage. Etna and Vesuvius (e.g., Scarth, 2009). The early Satellite-based radar images have the advan- nineteenth century saw the addition of both a tages of both seeing through cloud cover and innovations that have contributed to lava fl ow theoretical framework for Earth history (deep having higher resolution than satellite-based studies. We return to lava fl ow hazards at the time) and technological advances in drafting thermal imaging techniques. Radar-generated end of this review, to illustrate ways in which and mapping techniques that propelled both the DEMs (e.g., from SRTM, Shuttle Radar Topog- lidar technology, in particular, is shaping new Scrope and Poulett (1825) study of the volcanoes raphy Mission; http://www2.jpl.nasa.gov/srtm/) methods of volcanic hazard and risk assessment. of central France, and Lyell’s (1830) compila- typically have a horizontal resolution of 90 m tion of observations of volcanic activity at Etna (although 30 m resolution is possible), which Lava Flow Hazards: Questions and Vesuvius (e.g., Rudwick, 2008). By the late limits applications of DEM analysis to changes nineteenth century, systematic observations of on the scale of the volcanic edifi ce (e.g., Wright From a hazards perspective, the questions frequent lava fl ows from Mauna Loa volcano, et al., 2006; Huggel et al., 2008). Radar correla- related to lava fl ows are simple. To construct Hawaii, provided a coherent observational tion imaging (e.g., using SIR-C, Space Shuttle hazard maps, we need to know which regions framework for the development of models of Imaging Radar), in contrast, provides image lava fl ows are most likely to cover, and how fre- lava fl ow behavior (e.g., Dana, 1891). Airplanes resolution suffi cient for monitoring individual quently this will occur. Hazard maps are gener- proved the fi rst major technological advance to lava fl ows (e.g., Zebker et al., 1996; Deitterich ated either by analysis of past eruption history lava fl ow studies, when aerial overviews (fi rst et al., 2012), as well as postemplacement fl ow (e.g., Heliker and Wright, 1992; Kauahikaua used in the 1919 eruption of Mauna Loa) gave volumes (e.g., Stevens et al., 1997; Lu et al., et al., 1995; Trusdell, 1995) or by simulations volcanologists new perspectives on the plan- 2003) and cooling-induced subsidence (Stevens that include both probable vent location (e.g., view complexity of individual lava fl ows. Start- et al., 2001). Airborne interferometric radar, Wadge et al., 1994) and the historic range of ing in the 1980s, routine helicopter support such as TOPSAR (topographic synthetic aper- eruption conditions (e.g., Crisci et al., 2010). allowed volcanologists to create detailed maps ture radar; e.g., Zebker et al., 1992), provides When an eruption is in progress, hazard man- of fl ow advance (e.g., Wolfe et al., 1988) to be suffi cient resolution (1–2 m vertical) to mea- agers want to know where the lava fl ow will go, supplemented with repeat measurements of fl ow sure the thickness of individual lava fl ows (e.g., how fast it will travel, and how long the erup- velocity and effusion rates from stations located Evans et al., 1992), although Mouginis-Mark tion is likely to last. Lava fl ows are confi ned along persistent lava channels (e.g., Lipman and and Garbeil (2005) recommended combining by topography, thus fl ow paths can be broadly Banks, 1987; Calvari et al., 1994) and robust lava TOPSAR with lidar data to obtain suffi ciently predicted from digital elevation models (DEMs) tube systems (e.g., Kauahikaua et al., 1996). high resolution DEMs for lava fl ow modeling. once the vent location is known (e.g., Kaua- The past few decades have seen an explosion Ground-based radar has a range of a few kilome- hikaua et al., 1995). Rates of lava fl ow advance of new tools that have been applied to lava fl ow ters and can measure topographic changes that depend on the volumetric effusion rate and prox- studies. The wide availability of global posi- exceed the instrument resolution of ~5 m; to imity to the vent (Kauahikaua et al., 2003; Soule tioning systems (GPS) provided detailed maps date, ground-based radar techniques have been et al., 2004). Flow length is also strongly depen- of fl ow extent, topographic maps gave way to used primarily for monitoring changes in slow- dent on eruption rate (Walker, 1973), although DEMs, and satellites carried instruments that moving blocky fl ows (Macfarlane et al., 2006) the slope traversed (Kilburn, 2004), steadiness allowed remote sensing of effusive activity. and lava domes (Wadge et al., 2008). of effusion from the vent (Guest et al., 1987), Of the satellite-based imaging capabilities, the extent of topographic confi nement (Soule et al., most thoroughly utilized for lava fl ow studies Lidar (Light Detection and Ranging) 2004), initial lava temperature (Riker et al., have been satellite systems that detect short 2009), emplacement conditions (open chan- wavelength infrared signals [such as GOES By the end of the twentieth century, this tech- nel or insulating tube; Cashman et al., 1999; (geostationary satellites), ATSR (along track nological explosion included implementation Griffi ths et al., 2003), and planform fl ow geom- scanning radiometer), AVHRR (advanced very of laser scanning (lidar) imaging, which was

Geosphere, December 2013 1665

Downloaded from http://pubs.geoscienceworld.org/gsa/geosphere/article-pdf/9/6/1664/3346138/1664.pdf by guest on 26 September 2021 Cashman et al.

fueled not only by instrument development but the target, because beam spreading can degrade sharing portals such as the National Science also by critical improvements in the compu- precision at large ranges. The benefi ts of TLS Foundation’s OpenTopography (http://www tational infrastructure required to collect and over ALS are its ability to resolve smaller length .opentopography.org/). Another challenge is the analyze large data sets (including the National scale features (decimeter to centimeter), its abil- huge numbers of individual x-y-z values (often Science Foundation–sponsored National Center ity to image in locations that are inaccessible to in excess of 109) in point clouds from typical for Airborne Laser Mapping). Airborne laser ALS (e.g., vertical faces and/or outcrops), lower ALSM or TLS surveys; such large data sets can swath mapping (ALSM) scans the ground sur- cost of operation, greater portability, and poten- present signifi cant computational challenges. face using laser pulses centered on the fl ight path tial for rapid deployment. As with ground-based This data management challenge is exacerbated of the plane; the scan direction is perpendicular radar, the primary limitations of TLS relative to for the enormous data sets generated from new- to the fl ight path and the swath width is a func- ALS are its much more restrictive spatial scale generation full waveform lidar systems (Bretar tion of distance from the ground. Conversion of and the diffi culty in achieving high-incidence et al., 2008). However, for most users, working the laser pulse returns to x-y-z points generates angles to the imaging target. The latter limita- with segments of larger data sets, or DTMs, and a point cloud that can be fi ltered and gridded tion means that acquiring complete coverage of choosing point spacing appropriate for the fea- to produce a high-resolution DEM. Details of a surface with TLS may require collecting mul- tures of interest, can mitigate these challenges. ALSM can be found in Shan and Toth (2008); tiple scans from different vantage points to fi ll in In addition, data analysis tools are widely avail- recent overviews include Jaboyedoff et al. line-of-sight shadows. In such cases, scans are able (e.g., through OpenTopography), and many (2010), Glennie et al. (2013), and Roering et al. registered relative to each other using surface students now come to the geosciences with geo- (2013). It is important that, depending on the fi l- matching algorithms (e.g., Besl and McKay, graphic information system (GIS) skills. tering, the DEM can show either bare earth eleva- 1992) where suffi cient overlap between scans is tion or the uppermost (fi rst return) surface, which available, or by georeferencing individual scans Classifi cation of Main Applications of Lidar can be used to map either urban or forest structure to a common geodetic reference frame. In both to Lava Flow Studies (e.g., Bisson et al., 2009; Deligne et al., 2012). cases, it is advantageous to use a network of The application of lidar-generated DEMs to Most ALSM systems operate at near-infrared widely distributed surveying targets that can be lava fl ow studies is a very recent phenomenon; frequencies with signifi cant absorption in water imaged from multiple vantage points and used to our knowledge, the fi rst lidar-based lava fl ow so that data cannot be collected through clouds; to coregister individual scans to a coherent geo- publications appeared in 2005. In subsequent areas with persistent cloud cover are therefore reference frame (e.g., Buckley et al., 2008). years the use of lidar has slowly increased as problematic and require either additional survey Looking to the future, opportunities for inno- more research groups obtain access to these data. time or pairing with radar imaging techniques vative digital terrain model (DTM) analysis Here we illustrate many important questions in to ensure complete coverage. The resolution of will only continue to grow as new lidar acqui- lava fl ow research that can be addressed with swath mapping using lidar systems is strongly sition and analysis techniques are developed. high-resolution digital topography. For the pur- dependent on the accuracy of the aircraft GPS For example, whereas many commercial lidar pose of this overview, we classify applications of control and inertial measurement unit, the eleva- systems measure only the fi rst and last return of lidar-derived high-resolution topog raphy (both tion of individual fl ight lines, and the extent and each laser pulse, new systems record the entire ALS and TLS) as follows: (1) mapping solidifi ed density of ground cover (vegetation). Of these, waveform of the energy pulse that is back- fl ows; (2) monitoring active fl ows; (3) modeling location accuracy is most important, particu- scattered from the refl ecting surface. Although lava fl ow emplacement; and (4) hazard mapping larly in referencing side-by-side fl ight swaths currently used primarily for forest research, full and risk assessment. This classifi cation allows (e.g., Favalli et al., 2009a); systematic relative waveform lidar offers the possibility of obtain- us to review the primary areas of lava fl ow stud- swath errors can be minimized by fl ying some ing more information on both the geometry and ies from the perspective of questions that can fl ight lines perpendicular to the main fl ight line refl ectance of illuminated surfaces (e.g., Bretar be addressed using very high resolution digital direction (Latypov, 2002) and by applying itera- et al., 2008; Mallet and Bretar, 2009). In addi- topo graphic data. We focus primarily on sub- tive closest point matching algorithms to over- tion, airborne lasers are now being developed aerial mafi c lava fl ows because (1) most appli- lapping swaths (Kumari et al., 2011). ALS lidar for measurement of bathymetry; water-imaging cations of lidar to lava fl ow studies to date have DEMs used in studies of young (unvegetated or systems use a short-wavelength (blue-green) been for mafi c fl ows, (2) the frequency of mafi c sparsely vegetated) lava fl ows are typically grid- laser capable of penetrating the water-air inter- eruptions at places like Hawaii and Mount Etna ded at <1 m, a substantial improvement over 30 face (C.W. Wright et al., 2001; Kinzel et al., both provides the opportunity for testing new or 10 m DEMs commonly used in current lava 2007; McKean et al., 2008; Glennie et al., 2013). models and new technologies, and (3) frequent fl ow hazard simulations. Although restricted to water depths of <10 m, resurfacing of these regions requires frequent Terrestrial laser scanning can provide com- this technique has the potential to image fl ows remapping of the volcanic edifi ce to obtain plementary three-dimensional (3D) data to erupting in shallow water, or, after emplace- up-to-date base maps for hazard assessment. airborne laser swath mapping (e.g., Petrie and ment, the shallow subaqueous extent of lava However, we also include examples from inter- Toth, 2008a, 2008b, 2008c). Depending on the (e.g., lava dams and lava deltas), which is often mediate composition lava fl ows, and from loca- range to the target, pulse rate of the laser, and ignored in fl ow volume measurements. tions other than Hawaii and Etna, to illustrate refl ectance of the surface, collecting TLS data It is important to discuss some of the chal- the full range of lidar-based techniques that can at subcentimeter point spacing with subcenti- lenges involved with lidar work. First, obtain- be applied to lava fl ow problems. meter accuracy is fairly routine. Commercially ing airborne lidar data is expensive. However, available TLS systems have a wide range of a single data set can often be used for a range MAPPING SOLIDIFIED LAVA FLOWS maximum target distances (from a few meters to of applications, and therefore can form the focal >1.5 km) and precisions are typically reported as point of collaborative projects. In addition, with Challenges to accurate lava fl ow mapping are subcentimeter, although precision depends not time the general availability of lidar data will both technological and intrinsic to the nature only on target refl ectivity but also on the range to certainly increase, particularly through data of lava fl ow resurfacing processes. Intrinsic

1666 Geosphere, December 2013

Downloaded from http://pubs.geoscienceworld.org/gsa/geosphere/article-pdf/9/6/1664/3346138/1664.pdf by guest on 26 September 2021 How lava ﬂ ows

problems include the decrease in the spatial and ever, is its unique contribution to fl ow mapping old West Crater fl ow) from the Owyhee River, the temporal accuracy of mapped lava fl ows in older vegetated terrains. In particular, raw Oregon (Brossy et al., 2008). In Figure 5, red with increasing fl ow age and distance from the point cloud data can be fi ltered to obtain bare (strong clustering) corresponds to smooth topog- vent. Because lava fl ows typically erupt from earth DEMs of lava fl ow surfaces in heavily raphy, while blue (strong scattering) corresponds localized vent areas, proximal parts of older forested areas (e.g., Hofton et al., 2006). This to rough topography. The contrast between these fl ows are covered by younger fl ows. For this capability of lidar alone is revolutionizing map- two fl ow surfaces refl ects primarily the differ- reason, accurate planimetric maps of lava fl ow ping of older lava fl ows. As an example, Fig- ence in age and consequent extensive infi lling coverage in near-vent regions are available only ure 3 shows aerial imagery, a 10 m DEM, a of the older West Crater fl ow surface with loess. for the most recent or historic fl ows. Although bare earth lidar hillshade, and a lava fl ow map some sense of the frequency of lava fl ow cover- from the upper McKenzie River in the central Measuring Erupted Volume age can be obtained from studies of drill cores Oregon Cascades. The lava fl ow map was cre- (e.g., Katz and Cashman, 2003; Stolper et al., ated from a lidar-generated DEM by mapping Another important parameter derived from 2009), assessments of expected fl ow coverage fl ow boundaries and variations in fl ow surface mapping lava fl ows is the total fl ow volume must rely on (1) past rates of surface coverage textures. Field checking of fl ow boundaries, (e.g., Fig. 1). Accurate measurements of lava (e.g., Behncke et al., 2005; Wright et al., 1992); combined with selective sampling and chemical fl ow volumes are surprisingly diffi cult to make. (2) simple probability models of surface cov- analysis, allows individual fl ows to be distin- A combination of digital orthophotos, GIS soft- erage through time (Kauahikaua et al., 1995); guished. Lidar mapping can also be combined ware, and/or GPS mapping of fl ow boundaries (3) mapping of lava sheds (i.e., drainage pat- with high-precision age dating to determine the generally provides excellent planimetric maps terns that will confi ne fl ow movement; Kaua- volcanic history of a region. For example, Crow of fl ow surface coverage. Conversion of area hikaua, 2007); or (4) Monte Carlo simulations et al. (2008) refi ned the history of lava fl ow to volume, however, relies on good estimates of future fl ow paths (e.g., Favalli et al., 2009b; inundation of, and removal from, the Grand of fl ow thickness. In the fi eld, fl ow thickness is Crisci et al., 2010). There are several mapping Canyon by mapping remnant terraces that mark typically estimated by averaging measurements challenges, however, that can be addressed by canyon-fi lling lava fl ows. Correlation of indi- of levee and/or fl ow front thickness. However, lidar, including both improved mapping of older vidual terraces through vertical elevation and Coltelli et al. (2007) showed that the use of fl ows, and quantitative mapping of younger age data provided a complete record of both the average fl ow thicknesses measured in this way fl ows and fl ow features. frequency and extent of canyon volcanism. may cause large errors in volume measure- Flow mapping and relative age determina- ments (25% or greater). For example, using Mapping Flows and Flow Units tion can be accomplished with lidar data alone the average thickness of the fl ow margin will by using either lidar-based intensity data or underestimate fl ow volumes where fl ows have When combined with historic accounts, roughness analysis of lidar DEMs. An example fi lled topographic depressions (e.g., Lu et al., existing fl ow maps, and high-resolution aerial of intensity mapping is provided in Figure 4, 2003) or undergone extensive syneruptive levee photos, lidar data can be used to generate very which shows fl ow-specifi c intensities for a construction (e.g., Sparks et al., 1976). These accurate maps of both fl ow boundaries and fl ow region of Mount Etna. For a given target dis- errors can be reduced using accurate data on surface morphology. An illustration of a lidar- tance, the intensity of the lidar return depends on both preeruption and posteruption topography. generated DEM and associated interpretive geo- surface roughness and/or texture, in turn a func- Unfortunately, preeruption high-resolution logic map is shown in Figure 2 (Pyle and Elliott, tion of original fl ow emplacement conditions topog raphy is rarely available and fl ow volumes 2006). Figure 2A illustrates the problem of (e.g., Peterson and Tilling, 1980; Rowland and are estimated either by differencing high-resolu- cloud cover (seen as the blurry center part of the Walker, 1990; Soule and Cashman, 2005) and tion lidar relative to existing (lower resolution) DEM, where a 15-m-resolution conventional subsequent surface weathering. At Mount Etna, DEMs (e.g., Ventura and Vilardo, 2008; Favalli DEM has been used to fi ll a cloud-generated the lidar signal intensity for a given fl ow surface et al., 2010a) or, for prehistoric fl ows, using data gap). However, Figure 2A also illustrates type fi rst decreases sharply because of fl ow cool- lidar data to compute thickness along the fl ow the exquisite topographic detail that lidar pro- ing, and then increases gradually with increas- length (e.g., Deardorff and Cashman, 2012). vides of complex lava fl ow surface morpholo- ing lava fl ow age as the fl ow surface weathers Where the preexisting topography is of suffi - gies. This lidar image shows a succession of and becomes vegetated. Where revegetation cient resolution, comparison of preemplacement historic dacitic lava fl ows on Nea Kameni Island is slow (e.g., arid climates), lava fl ow surfaces and postemplacement topography can pro- in the center of the Santorini caldera, Greece. can alter by infi lling with airborne dust (loess; vide important insight into fl ow emplacement The accompanying interpretive map (Fig. 2B) e.g., Vaughan et al., 2011). Surface infi lling will processes . For example, Figure 6 shows thick- shows the age of separate fl ows as well as the affect the intensity and the surface roughness, ness variations over a small segment of a lava locations of vents (domes and fi ssures), individ- which can be measured using the variability in fl ow erupted from Mauna Loa volcano in 1984. ual fl ow lobes, and fl ow surface features (levees slope and aspect in local patches of a DEM, as Here the preeruption DEM was constructed and surface folds). These individual features measured by vectors constructed to each DEM from high-resolution pre-1984 stereo-aerial can also be mapped separately, which is par- cell (McKean and Roering, 2004). Local vari- photos referenced to the lidar image outside the ticularly useful where solidifi ed fl ow features ability of vector orientation is measured using boundaries of the 1984 fl ow (e.g., Corsini et al., can be linked directly to observed emplacement eigenvalue ratios that record the extent and nature 2009). This method provides submeter vertical processes (e.g., Favalli et al., 2010a). of clustering of the vector orientations. We show accuracy, such that differencing preeruptive and The interpretive maps of Nea Kameni and an example of this type of analysis in Figure 5, posteruptive DEMs yields a detailed imaged of Etna rely not only on a wealth of observational which compares the surface textures of 1 km × fl ow thickness variations. In this channel seg- data, but also on the youth (and consequent lack 1 km DEM patches from a 3-k.y.-old lava fl ow ment of the 1984 lava fl ow, both the channel of vegetation) of the constituent lava fl ows. An from the upper McKenzie River (Fig. 4) with and levees are >6 m thick and much thicker than important advantage of lidar technology, how- a much older pahoehoe lava fl ow (the 60-k.y.- the fl ow margins, thus illustrating the problem

Geosphere, December 2013 1667

Downloaded from http://pubs.geoscienceworld.org/gsa/geosphere/article-pdf/9/6/1664/3346138/1664.pdf by guest on 26 September 2021 Cashman et al.

Figure 2. Lidar (light detection and ranging)-generated hillshade digital elevation model (top) and interpretive geologic map (bottom) of Nea Kameni, Santorini volcano, Greece (from Pyle and Elliott, 2006).

with using fl ow margin thicknesses to estimate rapidly enhancing our ability to map fl ow out- time goes on, coupled preeruptive and posterup- fl ow volumes. Thickening of the fl ow interior lines and/or areas of old and young fl ows alike, tive lidar surveys will provide increasingly accu- relative to the fl ow margin is also evident in to make detailed maps of fl ow surfaces, and to rate measurements of fl ow thickness distributions the distal segment of the 2001 Etna lava fl ow, track the evolution of those surfaces through and fl ow volumes produced by specifi c eruptions. where Favalli et al. (2010a) show that the early time. This ability will not only improve fl ow (rapidly emplaced) fl ow margins are less than hazard maps, particularly in regions of older MONITORING ACTIVE FLOWS half the thickness of the later, more slowly (and partially vegetated) lava fl ows, but will emplaced, channel fi ll. also allow new types of analysis, such as inves- Instantaneous lava fl ux is probably the most In summary, the use of lidar-generated data tigations of the ways in which lava fl ows are important parameter controlling both the rate of to make high-resolution bare earth DEMs is revegetated (Deligne et al., 2012). Moreover, as lava fl ow advance (e.g., Kauahikaua et al., 2003)

1668 Geosphere, December 2013

Downloaded from http://pubs.geoscienceworld.org/gsa/geosphere/article-pdf/9/6/1664/3346138/1664.pdf by guest on 26 September 2021 How lava ﬂ ows

A B

1.6

C 1.5 D

1.4 (wt%) 2 1.3

TiO 51 52 53 54

SiO2 (wt%)

Figure 3. Comparative maps of part of ~3000-yr-old mafi c lava fl ow fi eld in the upper McKenzie River basin, Oregon. (A) Google Earth image showing Clear Lake (dammed by the fl ow) and variable vegetation cover on the fl ows. (B) 10 m digital elevation model (DEM) hillshade. (C) Lidar (light detection and ranging)-generated DEM of the same region; note the detail provided of the fl ow surface. (D) Geologic map of individual fl ows constructed using the lidar data and associated mapping, sampling, and geochemical analysis.

Figure 4. Lidar (light detection and ranging) intensity values superimposed on shaded relief image of a portion of Etna volcano, Italy. Data are normalized to a standard aircraft elevation of 1000 m. Image illustrates the variation in intensity data with ﬂ ow age and surface texture (from Mazzarini et al., 2007, copyright 2007 American Geophysical Union. Reproduced by permission of American Geophysical Union.).

Geosphere, December 2013 1669

Downloaded from http://pubs.geoscienceworld.org/gsa/geosphere/article-pdf/9/6/1664/3346138/1664.pdf by guest on 26 September 2021 Cashman et al.

Figure 5. Surface analysis of 1 km × 1 km patches of lava fl ows from Sand Mountain and the Owyhee River, Oregon. (A) Lidar (light detection and ranging) hillshade of the ca. 3 ka basaltic andesite lava fl ows from the Sand Mountain volcanic chain, central Oregon Cas- cades. (B) Lidar hillshade of the West Crater lava fl ow, eastern Oregon. (C) Surface roughness analysis of A. (D) Surface roughness analysis of C. Surface roughness analysis uses method- CD 3 ology developed by McKean and Roering (2004). Rough- 2.5 ness scale goes from blue (more rough) to red (more smooth). 2 Note the signifi cant loess infi lling of the West Crater lava fl ow, as illustrated by the domi- 1.5 nance of warm colors in D relative to C. 1

0.5

and the distance a fl ow will travel (e.g., Walker, were used to measure the rates of lava fl ow days to image the entire fl ow fi eld at time inter- 1973). Accurate time-averaged volumetric effu- advance and to estimate the volumetric fl ux. vals ranging from 15 min to 25 h (Favalli et al., sion rates can be determined from measured fl ow Similar measurements have been employed in 2010b). The high spatial and temporal resolu- volumes and known eruption durations (e.g., monitoring the growth of a lava dome at Soufrière tion of this data set recorded short-term volu- Hofton et al., 2006; Ventura and Vilardo, 2008). Hills volcano, Montserrat (Wadge et al., 2008), metric increases of lava fl ux of ~5 times greater Reconstructing temporal variations in fl ux, how- where the combination of long viewing distance than the background level of fl ow (Fig. 7), con- ever, requires more than simple preeruption and and insensitivity to the persistent cloud cover has sistent with the order of magnitude fl ow front posteruption topographic data. For this reason, proved particularly valuable. Photogrammetric measurements of James et al. (2007, 2010). The acquisition of image time series of active fl ows time series obtained during recent eruptions of lidar data show simultaneous movement of these has been a recent priority in lava fl ow studies. Mount Etna (Coltelli et al., 2007; James et al., pulses through multiple parallel channels, pro- Here we briefl y review approaches that do not 2007, 2010) have also captured detailed infor- viding evidence that the fl ux changes originated rely on lidar before discussing the contributions mation on short-term variations in lava fl ux to near the vent and not by fl ow variations through of lidar to this important research topic. the fl ow front. These studies have documented individual channel reaches. The multitemporal Ground-based multitemporal studies of active order-of-magnitude changes in volumetric fl ux lidar images also document levee overfl ows lava fl ows have focused on changes at the fl ow at the fl ow front caused by pulses of lava that when the capacity of the channel is exceeded by front, a region that is relatively accessible and travel down the channel at rates of 0.16–0.33 m/s the temporary increase in lava volume caused safe for study. For example, a study of a blocky (James et al., 2007, 2010). by a pulse. This process of pulse-driven over- lava fl ow from Arenal volcano, Costa Rica, using Repeat airborne lidar surveys have the advan- fl ows may explain the excessive levee thick- ground-based millimeter wave radar (AVTIS, tage of allowing measurement of temporal nesses at the large channel bend in the Mauna All-Weather Volcano Topography Imaging Sen- variations in fl ow through lava channels over an Loa lava channel (shown in Fig. 6). sor; Macfarlane et al., 2006) allowed images entire fl ow; the cost of such surveys has limited Another example of the application of multi- to be obtained from a (safe) distance of 3 km. the applications to active lava fl ows in Italy. For temporal lidar imaging to active lava fl ows is a Recorded changes in both vertical elevation pro- example, a recent study of lava fl ow advance at study of fl ow emplacement on the steep slopes fi les and fl ux over an 8-day observation period Mount Etna used overfl ights carried out over 2 of Sciara del Fuoco, Stromboli volcano, Italy,

1670 Geosphere, December 2013

Downloaded from http://pubs.geoscienceworld.org/gsa/geosphere/article-pdf/9/6/1664/3346138/1664.pdf by guest on 26 September 2021 How lava ﬂ ows

250000 250500 251000 251500 1984 lava flow thickness (m)

2169000 - 2169000 High : 20.4

Figure 6. Preeruption and post- Low : –6.1 eruption eruption differencing of high-resolution digital elevation models (DEMs) for a region 2168500 of the 1984 Mauna Loa lava ﬂ ow. 2168500 Preeruption DEM was created from high-resolution aerial photos (e.g., Corsini et al., 2009); posteruption DEM is generated from lidar (light detection and ranging) data. Off-flow error is <1 m hori- 2168000 zontal and ~0.15 m vertical. 2168000 Channel construction is particularly evident at the location of a major breakout and/or ﬂ ow bifurcation (Flow1B of Lipman and Banks, 1987). 2167500 2167500

Mapped outline 0400m of 1984 flow 250000 250500 251000 251500

Figure 7. Volumetric changes across the distal portion of Etna lava channel measured by digital elevation model differenc- A ing. (A) At 919 s. (B) At 2817 s. (C) At 4706 s. (D) At 6621 s. (E) At 9290 s. (F) Total change B G over 2.5 h. Color scale denotes relative elevation differences (ﬂ ow thickness): red is a relative increase, blue is a relative C decrease. Black lines locate cross sections shown in G–I as variations in time-averaged discharge D H rate (TADR) along the channel, plotted as a function of distance from the flow front position in F. G–I: Measurements in E time steps. (G) 08:46–08:31. (H) 10:21–10:06. (I) 08:31–11:04. Red line in I gives the total vol- F I ume emplaced per unit length (L) (from Favalli et al., 2010b, copyright 2010 American Geophysical Union. Reproduced by permission of American Geophysical Union.). dV/dx—change in measured ﬂ ow volume per unit length along the channel.

Geosphere, December 2013 1671

Downloaded from http://pubs.geoscienceworld.org/gsa/geosphere/article-pdf/9/6/1664/3346138/1664.pdf by guest on 26 September 2021 Cashman et al.

during an eruption in 2007. Here repeat surveys that is ultimately delivered to the fl ow front. depth:width aspect ratios (e.g., Kauahikaua of the Sciara del Fuoco provide a temporal record They also demonstrate the necessity of repeat et al., 2002) or time-consuming (and thus lim- not only of lava effusion in an area that is inac- surveys, particularly near the beginning of an ited) differential GPS surveys of complete cessible to direct observation, but also evidence eruption when fl ux tends to be highest and con- channel sections (e.g., Zimbelman et al., 2008). of localized syneruptive horizontal deforma- ditions change rapidly. The paucity of accurate data on channel cross tion (Marsella et al., 2009). The latter presented sections is unfortunate given the demonstrated problems for fl ow volume calculations, because MODELING LAVA FLOW importance of lava channels as the primary con- the preeruptive (2006) lidar DEM was no longer EMPLACEMENT duits of lava transport. As a result, thermome- suitable for use as a topographic datum. In addi- chanical models of fl ow emplacement typically tion, a comparison of effusion rates obtained The studies described here demonstrate assume either that channels maintain a constant from lidar with those obtained from satellite- the use of lidar data to create high-resolution depth (e.g., Harris and Rowland, 2001) or a con- based thermal imaging (e.g., Wright et al., 2004) DEMs before, during, and after lava fl ow activ- stant width (e.g., Kilburn, 1996) from the vent showed that, because of the timing of the lidar ity, thereby providing critical information on to distal end of the fl ow; these assumptions are surveys, the lidar data did not capture the maxi- the spatial and temporal evolution of lava fl ow clearly oversimplifi cations for lava fl ow fi elds mum effusion rate that occurred early in the fi elds. Repeat surveys of an active lava fl ow that host multiple active channels, and in which 2007 eruption. This highlights a limitation of (Favalli et al., 2010b) further show the potential short-term changes in lava fl ux cause frequent lidar for monitoring active fl ows, which is that of using lidar for imaging short-term variations channel overfl ows. frequent repeat surveys are needed to capture the in the movement of lava through complex chan- Extraction of channel cross sections from subtleties of eruptive behavior, particularly early nel systems. These studies raise new questions lidar-generated high-resolution DEMs allows in an eruption. about channel development and the behavior of analysis of along-fl ow channel geometry. Together these pilot studies demonstrate the channelized lava fl ows, many of which can be Model predictions suggest that fl ow (and chan- remarkable near-real-time views of lava fl ow addressed by detailed analysis of lidar-gener- nel) geometry should refl ect both the material emplacement that can be obtained by multi- ated DEMs. properties of the lava and the volumetric fl ux temporal imagery of active lava fl ows. From the (e.g., Hulme, 1974; Lipman and Banks, 1987; perspective of fl ow modeling, they demonstrate Measuring Lava Channels Kilburn, 1996; Harris and Rowland, 2001). A that average fl uxes are a poor approximation of simple comparison of channel cross sections the lava available to supply any individual fl ow Prior to the advent of lidar technology, there from Hawaii (1984 Mauna Loa fl ow; Fig. 8 lobe. Specifi cally, these data show that (1) multi- were few systematic studies of lava channel [left]) and central Oregon (basaltic andesite Col- ple lava channels may be active simultaneously, geometry. Particularly challenging are measure- lier Cone lava fl ow; Deardorff and Cashman, and therefore the fl ux to any one channel will be ments of active channel depth, which is com- 2012; Fig. 8 [right]) confi rms that the (more vis- substantially less than the fl ux emerging from monly estimated by measuring the maximum cous) basaltic andesite lava channel is both wider the vent; (2) fl ow is unsteady within individual (assumed) neutral buoyancy height of rafted and deeper than the basaltic lava channel on channels, causing the local instantaneous fl ux lava fragments (Lipman and Banks, 1987) or, Mauna Loa. The two fl ows have roughly similar to the fl ow front to vary by a factor of fi ve or rarely, by probing lava streams within lava tubes volumes (0.12 km3 for the Collier Cone fl ow, as more; and (3) channel overfl ows during times of (Kauahikaua et al., 1998). Postemplacement compared to 0.22 km3 for the Mauna Loa fl ow); in increased fl ux will decrease the amount of lava channel measurements employ either simple addition, the Collier Cone fl ow traversed steeper

mm 0 150 300 0 150 300 Mauna Loa 1984 Channel Collier Cone Channel 2804 1660

1650 2802 25.6 m 4.8 m 1640 2800 173.8 m 51.9 m 1630 Elevation (m) Elevation Elevation (m) Elevation 2796 1620 0 20 40 60 80 100 120 140 160 180 200 0 50 100 150 200 250 300 350 400 Distance along profile (m) Distance along profile (m)

Figure 8. Example cross sections measured across channels. Left: The basaltic Mauna Loa 1984 lava ﬂ ow. Right: The ca. 1.5 ka basaltic andesite Collier Cone lava ﬂ ow. Note the differences in both horizontal and vertical scales of these two characteristic channels.

1672 Geosphere, December 2013

Downloaded from http://pubs.geoscienceworld.org/gsa/geosphere/article-pdf/9/6/1664/3346138/1664.pdf by guest on 26 September 2021 How lava ﬂ ows

average slopes (≤10° compared to ≤5°) and was probably erupted at signifi cantly lower effusion rates (Deardorff, 2011) than the Mauna Loa fl ow. As both of these factors should decrease, not increase, the channel dimensions, it appears that the lava rheology was the most important factor in determining channel geometry. If more detail is required, ALS imaging can be supplemented with TLS imaging of individual channel reaches. We fi nd that although channel dimensions from ALS are consistent with TLS measurements, ALS-derived channel depths are ~10% less, and widths ~20% more, than TLS-derived measurements of the same location (Fig. 9). Moreover, although gridding the ALS data at 0.5 m provides a better fi t than Figure 9. Topographic profi les across a rectangular-shaped channel in the Mauna Loa 1984 the 1 m gridding interval, the ALS model sur- fl ow by TLS (terrestrial laser scanning) and ALSM (airborne laser scanner mapping) at the face shows a U-shaped channel, whereas the same location. TLS data are gridded at 0.1 m (blue), and ALSM data are gridded at 1 m TLS resolves a rectangular channel, which is (black) and 0.5 m (red). The rectangular shape is resolved by the TLS data, but 0.5 m and more faithful to the actual channel shape (and 1 m ALSM data suggest a U-shaped channel. has important implications for models of fl ow through lava channels). This difference results from both wider point spacing in ALS relative to TLS data as well as larger grid spacing in DEM non-Newtonian fl uids when fl uid yield strength surface models that average elevations over limits lateral spreading (Johnson, 1970; Hulme, A small-scale features such as steep channel walls. 1974). Although this simple model has been Accurately capturing bank geometry is also a widely applied to lava fl ows (e.g., Fink and common problem with ALS in fl uvial studies Zimbelman, 1986; Moore, 1987), levees in (e.g., McKean et al., 2009). active lava fl ows clearly form by multiple mech- TLS imaging is also able to resolve small- anisms, many of which involve cooling and scale fl ow features that are not visible in ALS solidifi cation of the fl ow surface (Sparks et al., B gridded data. As an example, a TLS data set 1976; Lipman and Banks, 1987). To explore the from another portion of the Mauna Loa 1984 effect of cooling on fl ow channelization, Kerr channel system imaged “armadillo” (Naranjo et al. (2006) developed a model in which fl ow et al., 1992) structures lining the channel fl oor width (w) is determined by competition between (Fig. 10). These structures form during the late cross-slope fl ow spreading (controlled by the stages of fl ow emplacement due to shear fail- physical properties of the magma, the volumet- ure of partially solid, but ductile lava, and were ric fl ow rate, Q, and the slope) and cooling and observed to be the dominant mode of lava trans- crust formation: C port during the fi nal phase of emplacement of 113 ⎢⎡(gΔμρ)2 Q74cos9 θ⎥⎤ the 1988–1990 Lonquimay basaltic-andesite w = 2*⎢ ⎥ ⎢ σ6κ3 7 θ , (1) fl ow (Naranjo et al., 1992). The Mauna Loa ⎣ c sin ⎦⎥ structures are 50–75 cm in length and 5–15 cm in height, comparable to those described for where g is gravity, Δρ is the density contrast Lonquimay (~1–5 m in length, tens of centime- between the magma and surrounding medium σ ters in height). Their morphology is reminiscent (air), µ is magma viscosity, c is crust strength, of low-angle detachment faults with progres- κ is thermal diffusivity, and θ is slope. Figure 10. Imaging and measurements of sively rotated fault planes, steep breakaways, A superb lidar-generated data set for a 2004 “armadillo” (Naranjo et al., 1992) structures and fault-parallel corrugations (Cann et al., lava fl ow from Mount Etna (Mazzarini et al., on the fl oor of a lava channel in the Mauna 1997; Tucholke et al., 1998). Both the scale and 2005; Fig. 11A) provides along-fl ow data on Loa 1984 fl ow. (A) Point cloud viewed from the geometry of these structures provide infor- fl ow width, channel width, and slope that can above, with colors reflecting intensity of mation on rheological changes in late-erupted be used to illustrate the application of the Kerr returned laser pulses. (B) TLS (terrestrial magma that may provide clues to conditions that et al. (2006) model. Figure 11B shows the excel- laser scanning) point cloud gridded at 2 cm cause lava to stop fl owing. lent correspondence between calculated and with illumination from the west. Corruga- observed channel width for a fl ux of 0.6 m3/s, tions parallel to the direction of fl ow (left Modeling Lava Channels the best-fi t effusion rate determined by analy- to right) are well resolved. Red line shows sis of the root mean square error (RMSE, cal- profi le transect illustrated in C. (C) Profi le To fully understand lava fl ow emplacement, culated using the difference between predicted across the armadillo structures showing the we need to understand how and why lava fl ows and measured channel width). At this fl ux, the curved shear surface and steep breakaway construct channels. Channels develop in fl ows of RMSE is 2.7 m, ~15% of the average channel present on most individual structures.

Geosphere, December 2013 1673

Downloaded from http://pubs.geoscienceworld.org/gsa/geosphere/article-pdf/9/6/1664/3346138/1664.pdf by guest on 26 September 2021 Cashman et al.

40 A 30 20

RMSE (m) 10

0 012345 MER (m3/s)

Figure 11. Analysis of channel development in a 2004 Etna lava fl ow. (A) Projection of lidar (light detection and ranging) data showing the entire 2004 lava fl ow (light gray; modifi ed from Mazzarini et al., 2005, copyright 2005 American Geophysical Union. Modifi ed by permission of American Geophysical Union.). (B) 160 channel and fl ow width measurements reported by Mazzarini et al. (2005), compared with fl ow widths calculated using formulation presented by Kerr et al. (2006). The channel data match the predicted width values for a volumetric fl ux of 0.6 m3/s, with the only obvious discrepancy at the distal end of the fl ow, where there was insuffi cient lava to allow the channel widening predicted by the model. Parameters are derived from Kerr et al. (2006) and Harris et al. (2007): ΔΔρρ = 2000 kg/m3, µ = 3500 Pa s σ 5 κ –6 2 (proximal) to 8000 Pa s (distal), c = 2 × 10 Pa, = 10 m /s. Best-fi t fl ux Q (see text) was determined by minimizing the root mean square error (RMSE; inset) between the measured and predicted fl ow width. MER—mass eruption rate (m3/s).

width along the fl ow. A fl ux of 0.6 m3/s is istic length scale for advection, typically the regimes and channel geometries that could be slightly less than the minimum estimated total fl ow thickness). This balance can be combined easily exploited with lidar-derived DEMs to pre- 3 Ψ volumetric fl ow rate of 1 m /s (Mazzarini et al., into a single parameter = uts/h (Griffi ths, dict conditions of lava tube formation. 2005). The difference between the channel- 2000). Also important is the Rayleigh number Once formed, lava tubes can transport lava derived fl ow rate and minimum (1 m3/s) and of the fl ow: Ra = gβ(ΔT )h3/κν, where β, κ, and over great distances. Observations of active volume-averaged (2.2 m3/s) fl ow rates dem- ν are the thermal expansion coeffi cient, ther- tubes show that (1) tube systems are often onstrates that the channel transported only a mal diffusivity, and kinematic viscosity of the complex, with more than one branch active at a fraction of the total lava volume. Evidence of fl uid lava, respectively, and ΔT is the tempera- given time, (2) individual tube segments evolve fl ow outside of the well-defi ned channels can ture difference between the fl ow interior and in morphology with time, particularly through be found in the highly irregular fl ow margins, fl ow surface. downcutting by a combination of mechanical channel spillovers, and fl ow bifurcations around The applicability of this model to natural and thermal erosion, and (3) older tubes can topographic obstructions (Fig. 11A). lava fl ows has been tested in only a few cases. be reoccupied during renewed eruptive activity Soule et al. (2005) predicted Ψ using modeled (e.g., Greeley et al., 1998; Kauahikaua et al., From Lava Channels to Lava Tubes fl ow velocities for specifi c channel geometries 1998, 2003; Williams et al., 2004). Despite derived from high-resolution bathymetry. Loca- their importance, however, there are only a Most of the discussion here has focused on tions where a mobile crust was predicted cor- limited number of studies of either the physi- construction of, and fl ow through, well-defi ned related with the presence of wide autobrecciated cal characteristics of lava tubes or their mode lava channels. However, basaltic lava transport bands at the channel margins, a morphologic of formation (e.g., Peterson et al., 1994; Calvari can also occur through lava tubes, a common expression of open channel fl ow (Fig. 12A). and Pinkerton, 1998; Greeley, 1987; Dragoni feature of lava fl ows and a thermally effi cient Similarly Ventura and Vilardo (2008) applied et al., 1995; Kauahikaua et al., 1998; Kerr, form of lava transport (e.g., Keszthelyi, 1995; the model to the 1944 Somma-Vesuvius lava 2001). These studies have recognized a diver- Helz et al., 1995, 2003). Experimental models fl ow, where they modeled fl ow velocities from sity of tube morphologies that appear related to show that channel versus tube fl ow behavior is lidar-derived fl ow thickness and estimates of the dynamics of fl ow through the tubes, their a direct consequence of the physical conditions lava rheology. They found a similar correlation mechanisms of formation, the extent of thermal of fl ow emplacement (Griffi ths et al., 2003). between Ψ and fl ow morphology, with mobile erosion, and environmental conditions such Specifi cally, the extent to which a stable sur- crust regions characterized by autobrecciated as local slope. However, the almost complete face crust can be maintained on an advancing fl ow surfaces and stable crust regions by smooth absence of morphometric data on lava tube fl ow is determined by the relative time scales sheets (Fig. 12B). Additional experiments shapes along their lengths makes development

of surface cooling (ts) and fl ow advection (u/h, (Cashman et al., 2006) provide a framework and testing of hypotheses for tube formation where u is fl ow velocity and h is a character- for extending this approach to a variety of fl ow and evolution diffi cult.

1674 Geosphere, December 2013

Downloaded from http://pubs.geoscienceworld.org/gsa/geosphere/article-pdf/9/6/1664/3346138/1664.pdf by guest on 26 September 2021 How lava ﬂ ows

101 length) and the compressional stress (from the fold amplitude; e.g., Fink and Fletcher, 1978; Ψ A = 25( Gregg et al., 1998); for this reason, surface folds Ra/Ro have been used to estimate lava rheology, par- ) -1/3 ticularly in remote (or planetary) environments (Theilig and Greeley, 1986; Gregg et al., 1998; Figure 12. Relationship between mobile crust fl ow Rayleigh number (Ra) and Warner and Gregg, 2003). In practice, although the ratio between advective and Ψ 100 transitional fold wavelengths can be measured with reason- thermal time scales (Ψ) for fl ows able accuracy from high-resolution orthophotos, with difference surface mor- measurement of fold amplitude requires high- tube phologies imaged using high- resolution topographic data. resolution topographic data. Surface folds can be characterized using lidar- (A) Submarine channelized lava generated DEMs, either as linear transects taken fl ows erupted along the East perpendicular to the fold axes (e.g., Figs. 14 and 15A) or by generating spectrograms via Fourier Pacific Rise (following Soule 10-1 4 5 6 7 8 et al., 2005). (B) Channelized 10 10 10 10 10 analysis of digital surface data (Fig. 15B). The lava fl ow erupted from Vesu- Ra appropriate DEM accuracy depends on the scale of folding; for example, surface folds preserved vius (Italy) in 1944; here inter- 2 10 within the Mauna Loa 1984 fl ow channel, with mediate (IS) and distal (DS) B mobile crust fl ow segments show contrasting fold wavelengths of 5–10 m and amplitudes of (and unexpected) behavior with ~0.5 m, can be resolved by ALS data (Fig. 14A). 0 a transition from tube fl ow to 10 In contrast, ropy folds generated on pahoehoe open channel flow downslope fl ows from the coastal plain of Kilauea volcano Ψ are not visible with ALS, but can be resolved with because of an abrupt increase in tube slope (modifi ed following Ven- TLS (Fig. 14B). Here fold wavelengths of 2–3 cm 10-2 tura and Vilardo, 2008). and 5–10 cm are similar to the fi rst- and second- generation folds observed by Fink and Fletcher (1978). A third generation of folds that give the 10-4 pahoehoe ropes a braided appearance (Fig. 14C) cannot be resolved even by the TLS data. 6 7 8 9 10 10 10 10 10 10 Lidar-derived transects can be analyzed by Ra Fourier analysis to track changes in wavelength along the fl ow. The data must fi rst be interpo- lated to unit spacing and detrended to remove The portability of TLS systems make them completely new ways, and certainly represents the infl uence of slope. Pyle and Elliott (2006) excellent tools for detailed surveying of the a unique opportunity for advancing our under- also suggested using a cosine taper to improve structure and morphology of lava tubes; in standing of tube development in both terrestrial data quality; their analysis of surface folding particular, TLS systems are able to resolve and planetary environments. along one of the Nea Kameni lava fl ows (shown the cross-sectional tube geometries as well in their Fig. 3; see Fig. 15B herein) shows that as internal features such as lava falls and lava Morphological Measurements of the dominant wavelength of the surface folds highstands. In the example shown in Figure Lava Flow Surfaces increases from ~20 m near the vent to ~40 m 13, a 30 m section of the Thurston lava tube at the fl ow front (a distance of 2 km), suggest- on Kilauea volcano was scanned by an Optech Prominent morphological features of lava ing gradual thickening of the folded viscous Ilris 36D TLS. Two scans, one tilted up and the fl ows include not only lava channels and levees, layer. Qualitatively, the decrease in wave- other down, were collected from the same scan- but also surface folds, tumuli, fractures, blocks, length and amplitude from the more dacitic ner location. Tube-normal cross sections every and lobate fl ow fronts (see Fig. 2). These fea- Kameni lava fl ows to the basaltic but viscous 4 m illustrate the length scale (10–12 m) over tures record the dynamics of fl ow emplacement, Mauna Loa 1984 lava fl ow, to the low-viscosity which the tube cross section changes from a resulting from competition between the fl uid Kilauea coastal plain lava fl ow is consistent symmetric roughly circular shape to an asym- processes driving the fl ow (controlled primarily with viscosity-based models of fold generation; metric shape. The cyan dashed line in Figure 13 by lava fl ux and rheology) and the restraining however, a more comprehensive survey of these is at the same elevation in each cross section and presence of a growing (and brittle) crust (e.g., features, now easily afforded by ALS and TLS shows that the tube volume increases through Griffi ths, 2000; Applegarth et al., 2010). Of data, is necessary to develop quantitative mod- downdropping of the fl oor, possibly due to cap- these surface features, prominent surface folds els for fold formation. ture of the fl ow by a deeper tube or by thermal are common on lava fl ows of all compositions. and/or mechanical erosion of the tube fl oor They range in amplitude and wavelength from LIDAR APPLICATIONS TO HAZARD (e.g., Kauahikaua et al., 1998; Kerr, 2001). The centimeters (in Hawaiian pahoehoe) to tens AND RISK ASSESSMENT inset at the lower left illustrates TLS imaging of or hundreds of meters (in obsidian fl ows). In lava highstands. The ability to obtain accurate theory, the geometry of fl ow surface folds can The hazard posed by lava fl ows to exposed 3D images of lava tubes thus presents the oppor- be used to constrain the thickness and viscos- communities is often assessed by modeling the tunity to study lava tube structure and origin in ity of the folding layer (from the fold wave- probability of lava fl ow invasion into affected

Geosphere, December 2013 1675

Downloaded from http://pubs.geoscienceworld.org/gsa/geosphere/article-pdf/9/6/1664/3346138/1664.pdf by guest on 26 September 2021 Cashman et al.

Figure 13. TLS (terrestrial laser scanning) scan of a 30 m section of the Thurston lava tube, Kilauea volcano. Colors indicate intensity of refl ected laser pulse and are scaled 0–255. Flow-normal tube cross sections are sampled from the point cloud every 2 m and are shown by blue bands. Two-dimensional plots of tube cross section are shown at right with Z (in meters) on the y axis and Y (in meters) on the x axis. The cyan line is shown at the same elevation in each cross section and shows that the tube roof stays at the same height while the fl oor deepens. The distance of each cross section is measured from the origin at the scanner position. Two lava highstands can be seen in the tube walls, shown in the lower left inset, which is looking upfl ow toward the scanner position. The lack of data directly above and below the scanner results from limitations in the tilt and minimum range (~3 m) of the TLS.

areas. The traditional approach to hazard mod- Figure 14. Surface fold profi les eling has been to map hazard zones on the A showing resolution of measure- basis of past activity (e.g., Wright et al., 1992), ments from ALS (airborne laser an approach that assumes that future activ- scanning) and TLS (terrestrial ity will follow, statistically, the same patterns. laser scanning) images of Hawai- An alternative approach is to run simulations ian lava fl ows. (A) Detrended of future activity and use the results of those fold profi le from an 80 m sec- simulations to map the susceptibility of differ- tion of a lava channel within the ent areas to lava fl ow invasion (e.g., Felpeto medial portion of the Mauna et al., 2001; Rowland et al., 2005; Favalli et al., B Loa (ML) 1984 fl ow. Fold wave- 2009b, 2009c; Crisci et al., 2010). Critical for lengths are 5–10 m and ampli- these models are DEMs that are (1) updated suf- tudes are ~0.5 m. (B) Detrended fi ciently frequently to account for topographic fold profi le from an upturned changes caused by ongoing eruptive activity tumulus on the coastal plain of (both lava fl ows and scoria cones, e.g., Neri Kilauea (KIL) volcano. PHH— et al., 2008; Tarquini and Favalli, 2010), and pahoehoe. Two wavelengths of (2) of suffi ciently high resolution to characterize C folds are visible at 2–3 cm and expected activity. Most simulations are run on 5–10 cm with amplitudes of 10 m DEMs, even if the digital topographic data <1 cm and ~2 cm. Profi le A–A′ were originally collected at a higher resolution is generated from a 0.5 cm reso- (e.g., Tarquini and Favalli, 2010); this gridding lution grid from the point cloud is used for numerical convenience and, until shown in (C). Star in C indicates recently, because of limited high-resolution location of inset photograph, topographic data. The growing availability of which shows folds (centimeter- very high resolution lidar-generated topography, scale ruler).

1676 Geosphere, December 2013

Downloaded from http://pubs.geoscienceworld.org/gsa/geosphere/article-pdf/9/6/1664/3346138/1664.pdf by guest on 26 September 2021 How lava ﬂ ows

lava ﬂ ow hazards (Bisson et al., 2009). These 4 data can be analyzed by the percentage of land use within each hazard zone (Fig. 16A) or by 2 percentage of each hazard zone in urban versus forest environments (Fig. 16B).

0 CONCLUSIONS

Figure 15. Surface folding analy- -2 We have reviewed the application of ALS and sis of a dacitic lava ﬂ ow, Nea TLS data to mapping and morphologic interpre- Kameni, Greece. (A) Transect tations of lava ﬂ ows. It is important that collec- -4 perpendicular to surface folds tion of digital x-y-z point cloud data allows not

showing multiple characteris- Deviation in height from mean only (1) extraction of bare earth topography in tic fold wavelengths. (B) Spec- 200 400 600 800 1000 vegetated terrains, but also (2) detailed analysis tral analysis showing changes Distance (m) of surface features using spectrogram, Fourier in dominant ﬂ ow wavelength 11 transform, and vector analysis techniques, (indicated by spectral power, 10 (3) extraction and measurement of morphologic with red being high) from 9 features such as lava channels and bounding proximal (window 11) to distal 8 levees, and (4) quantitative data required for risk (window 1) parts of the ﬂ ow 7 assessment. The extraordinarily high resolution of TLS data makes TLS particularly useful for (from Pyle and Elliott, 2006). 6

Window detailed geomorphic studies. As the intensity of 5 the lidar return from the imaged surface is sensi- 4 tive to surface texture (as well as imaging dis- 3 tance), fl ows of different ages and emplacement 2 style can be easily distinguished using normal- 1 ized intensity data. Multitemporal topographic 10 20 30 40 50 80 data for a single lava fl ow provide detailed Wavelength (m) records of fl ow thickness distributions and inter- actions with topography (if collected before and after fl ow emplacement) as well as important information on conditions of fl ow advance (if however, raises the question of what scale of presented in the form of the percentage of land collected during emplacement). Together these topography is optimal for modeling fl ows of dif- area covered by a certain crop or population new capabilities allow testing of existing mod- ferent rheologies. density. However, a recent study of the risk of els of channel formation and fl ow advance and Modeling the risk of lava fl ow invasion lava fl ow invasion into the town of Zafferana improved capabilities to assess both the hazard requires combining inundation hazard prob- Etnea (on the slopes of Mount Etna) illustrates and risk of lava fl ow inundation; we expect that abilities with maps of land use and infrastruc- ways in which lidar-generated bare earth DEMs continued acquisition of lidar data will also pro- ture. Land use classifi cation is often undertaken can be combined with fi rst return (canopy) mote new ways of thinking about lava fl ows and using a combination of remote sensing (usu- DEMs to measure the volumes (rather than sim- fl ow fi elds. Additionally, it seems clear that new ally satellite imaging) and fi eld checking (e.g., ply area) of urban and forested areas, thereby analysis techniques and interpretive frameworks Harris et al., 2011); classifi cation is typically helping to quantify the risks associated with developed for ALS and TLS data on terrestrial lava fl ows will be rapidly transferred to interpretation of lava fl ows in submarine (e.g., Soule et al., 2005) and planetary (e.g., Glaze et al., ABurban 100 2003, 2009; Hiesinger et al., 2007) environments. 30 H1 ACKNOWLEDGMENTS urban 80 urban H2 We dedicate this review to Kurt Frankel, whose 60 20 forest H2 inspiration was the primary reason for our writing it. forest forest We thank the Eugene Water and Electric Board for urban 40 allowing us to add to their lidar survey of the upper % Coverage % Coverage 10 H3 H3 McKenzie River, Kyle House (U.S. Geological Sur- forest 20 vey) for the lidar image of West Crater fl ow, Owyhee ground ground River, and the National Center for Airborne Laser ground H4 Mapping for funding a student grant (to Deardorff) 0 0 H4 H1 H2 H3 H4 Urban Forest and for collecting the Hawaii data. We also thank F. Mazzarini and an anonymous reviewer for sug- Figure 16. Analysis of data shown visually in Figure 16. (A) Percentage of each hazard area gested improvements to the manuscript. This work was supported by National Science Foundation grants (H1–H4) occupied by the urban environment, forest, or bare earth. (B) Percentage of each EAR-0738894 (to Cashman and Soule) and EAR- land type (urban, forest) in each hazard area (following Bisson et al., 2009). 0739153 (to Soule).

Geosphere, December 2013 1677

Downloaded from http://pubs.geoscienceworld.org/gsa/geosphere/article-pdf/9/6/1664/3346138/1664.pdf by guest on 26 September 2021 Cashman et al.

REFERENCES CITED J., and Torrence, R., eds., Living under the shadow: Glennie, C.L., Carter, W.E., Shrestha, R.L., and Dietrich, The cultural impacts of volcanic eruptions: One World W.E., 2013, Geodetic imaging with airborne LiDAR: Applegarth, L.J., Pinkerton, H., James, M.R., and Calvari, Archaeology Series 53: Walnut Creek, California, Left The Earth’s surface revealed: Reports on Progress in S., 2010, Morphological complexities during the Coast Press, p. 175–202. Physics, v. 76, doi:10.1088/0034-4885/76/8/086801. emplacement of a lava fl ow fi eld: A study of the 2001 Crow, R., Karlstrom, K.E., McIntosh, W., Peters, L., and Greeley, R., 1987, The role of lava tubes in Hawaiian vol- lower fl ow fi eld on Etna: Bulletin of Volcanology, Dunbar, N., 2008, History of Quaternary volcanism canoes, in Decker, R.W., et al., eds., Volcanism in v. 72, p. 641–656, doi: 10.1007/s00445-010-0351-1. and lava dams in western Grand Canyon based on Hawaii: Papers to commemorate the 75th anniversary Ball, M., and Pinkerton, H., 2006, Factors affecting the lidar analysis, 40Ar/39Ar dating, and field studies: of the founding of the Hawaiian Volcano Observa- accuracy of thermal imaging cameras in volcanology: Implications for fl ow stratigraphy, timing of volcanic tory: U.S. Geological Survey Professional Paper 1350, Journal of Geophysical Research, v. 111, B11203, doi: events, and lava dams: Geosphere, v. 4, p. 183–206, p. 1589–1605. 10.1029/2005JB003829. doi: 10.1130/GES00133.1. Greeley, R., Fagents, S.A., Harris, R.S., Kadel, S.D., Wil- Behncke, B., Neri, M., and Nagay, A., 2005, Lava fl ow haz- Dana, J.D., 1891, Characteristics of volcanoes: With con- liams, D.A., and Guest, J.E., 1998, Erosion by fl owing ard at Mount Etna (Italy): New data from a GIS-based tributions of facts and principles from the Hawaiian lava: Field evidence: Journal of Geophysical Research, study, in Manga, M., and Ventura, G., eds., Kinemat- Islands: New York, Dodd, Mead, and Company, 399 p. v. 103, p. 27325–27345. ics and dynamics of lava fl ows: Geological Society of Deardorff, N.D., and Cashman, K.V., 2012, Emplace- Gregg, T.K.P., Fink, J.H., and Griffi ths, R.W., 1998, For- America Special Paper 396, p. 189–209, doi: 10.1130 ment conditions of the c. 1600 ybp Collier Cone lava mation of multiple fold generations on lava fl ow sur- /2005.2396(13). fl ow, Oregon: A lidar investigation: Bulletin of Vol- faces: Infl uence of strain rate, cooling rate, and lava Besl, P.J., and McKay, N.D., 1992, A method for registration of canology, v. 74, p. 2051–2066, doi:10.1007/s00445 composition: Journal of Volcanology and Geothermal 3-D shapes: IEEE Transactions on Pattern Analysis and -012-0650-9. Research, v. 80, p. 281–292, doi: 10.1016/S0377-0273 Machine Intelligence, v. 14, p. 239–256, doi: 10.1109 Deligne, N.I., Cashman, K.V., and Roering, J.J., 2012, After (97)00048-6. /34.121791. the lava fl ow: The importance of external soil sources Griffi ths, R.W., 2000, The dynamics of lava fl ows: Annual Bisson, M., Behncke, B., Fornaciai, A., and Neri, M., for plant colonization of recent lava fl ows in the cen- Review of Fluid Mechanics, v. 32, p. 477–518, 2009, LiDAR-based digital terrain analysis of an area tral Oregon Cascades, USA: Geomorphology, doi: doi: 10.1146/annurev.fl uid.32.1.477. exposed to the risk of lava fl ow invasion: The Zafferana 10.1016/j.geomorph.2012.12.009. Griffi ths, R.W., Kerr, R.C., and Cashman, K.V., 2003, Pat- Etnea territory, Mt. Etna (Italy): Natural Hazards, v. 50, Dietterich, H.R., Poland, M.P., Schmidt, D.A., Cashman, terns of solidifi cation in channel fl ows with surface p. 321–334, doi: 10.1007/s11069-009-9346-7. K.V., Sherrod, D.R., and Espinosa, A.T., 2012, Track- cooling: Journal of Fluid Mechanics, v. 496, p. 33–62, Bretar, F., Chauve, A., Mallet, C., and Jutzi, B., 2008, Man- ing lava fl ow emplacement on the east rift zone of doi: 10.1017/S0022112003006517. aging full waveform lidar data: A challenging task for Kiluaea, Hawai’i, with synthetic paerture radar (SAR) Guest, J.E., Kilburn, C.R.J., Pinkerton, H., and Duncan, the forthcoming years: The International Archives of coherence: Geochemistry, Geophysics, Geosystems, A.M., 1987, The evolution of lava fl ow-fi elds: Obser- Photogrammetry: Remote Sensing and Spatial Infor- v. 13, doi:10.1029/2011GC004016. vations of the 1981 and 1983 eruptions of Mount Etna, mation Sciences, v. 37, p. 415–420. Dragoni, M., Piombo, A., and Tallarico, A., 1995, A model Sicily: Bulletin of Volcanology, v. 49, p. 527–540, Brossy, C.C., House, P.K., Ely, L.L., O’Connor, J.E., Saf- for the formation of lava tubes by roofi ng over a chan- doi: 10.1007/BF01080447. ran, E.B., Bondre, N., Champion, D.E., and Grant, G., nel: Journal of Geophysical Research, v. 100, p. 8435– Harris, A.J., and Rowland, S., 2001, FLOWGO: A kine- 2008, The role of late Cenozoic lava fl ows in the evolu- 8447, doi: 10.1029/94JB03263. matic thermo-rheological model for lava fl owing in tion of the Owyhee River Canyon, Oregon: American Evans, D.L., Farr, T.G., Zebker, H.A., van Zyl, J.J., and a channel: Bulletin of Volcanology, v. 63, p. 20–44, Geophysical Union Fall Meeting, abs. H53B-1035. Mouginis-Mark, P.J., 1992, Radar interferometric doi: 10.1007/s004450000120. Buckley, S.J., Howell, J.A., Enge, H.D., and Kurz, T.H., studies of the Earth’s topography: Eos (Transactions, Harris, A., Dehn, J., Patrick, M., Calvari, S., Ripepe, M., 2008, Terrestrial laser scanning in geology: Data acqui- American Geophysical Union), v. 73, p. 553, 557–558, and Lodato, L., 2005, Lava effusion rates from hand- sition, processing and accuracy considerations: Geo- doi: 10.1029/91eo00408. held thermal infrared imagery: An example from the logical Society of London Journal, v. 165, p. 625–638, Favalli, M., Fornaciai, A., and Pareschi, M.T., 2009a, June 2003 effusive activity at Stromboli: Bulletin of doi: 10.1144/0016-76492007-100. LIDAR strip adjustment: Application to volcanic areas: Vol canology, v. 68, p. 107–117, doi: 10.1007/s00445 Calvari, S., and Pinkerton, H., 1998, Formation of lava tubes Geomorphology, v. 111, p. 123–135, doi: 10.1016 -005-0425-7. and extensive fl ow fi eld during the 1991–1993 eruption /j.geomorph.2009.04.010. Harris, A., Favalli, M., Mazzarini, F., and Pareschi, M.T., 2007, of Mount Etna: Journal of Geophysical Research, v. 103, Favalli, M., Tarquini, S., Fornaciai, A., and Boschi, E., Best-fi t results from application of a thermo-rheological no. B11, p. 27291–27301, doi: 10.1029/97JB03388. 2009b, A new approach to risk assessment of lava model for channelized lava fl ow to high spatial resolu- Calvari, S., Coltelli, M., Neri, M., Pompilio, M., and Scribano , fl ow at Mount Etna: Geology, v. 37, p. 1111–1114, tion morphological data: Geophysical Research Letters, V., 1994, The 1991–1993 Etna eruption: Chronology doi: 10.1130/G30187A.1. v. 34, L01301, doi: 10.1029/2006GL028126. and lava fl ow-fi eld evolution: Acta Vulcanologica, v. 4, Favalli, M., Mazzarini, F., Pareschi, M.T., and Boschi, E., Harris, A.J.L., Favalli, M., Wright, R., and Garbeil, H., p. 1–14. 2009c, Topographic controls on lava flow paths at 2011, Hazard assessment at Mount Etna using a hybrid Cann, J.R., Blackman, D.K., Smith, D.K., McAllister, E., Mount Etna, Italy: Implications for hazard assessment: lava fl ow inundation model and satellite-based land Janssen, B., Mello, S., and Avgerinos, E., 1997, Cor- Journal of Geophysical Research, v. 114, F01019, classifi cation: Natural Hazards, v. 58, p. 1001–1027, rugated slip surfaces formed at ridge-transform inter- doi: 10.1029/2007JF000918. doi: 10.1007/s11069-010-9709-0. sections on the Mid-Atlantic Ridge: Nature, v. 385, Favalli, M., Harris, A.J.L., Fornaciai, A., Pareschi, M.T., Heliker, C., and Wright, T.L., 1992, The Pu’u ’O’o-Kupaia- p. 329–332, doi: 10.1038/385329a0. and Mazzarini, F., 2010a, The distal segment of Etna’s naha eruption of Hawaii’s Kilauea volcano: Earth in Cashman, K.V., Thornber, C., and Kauahikaua, J.P., 1999, 2001 basaltic lava fl ow: Bulletin of Volcanology, v. 72, Space, v. 4, p. 5–7. Cooling and crystallization of lava in open chan- p. 119–127, doi: 10.1007/s00445-009-0300-z. Helz, R.T., Banks, N.G., Heliker, C., Neal, C.A., and Wolfe, nels, and the transition of pāhoehoe lava to ‘ā‘a: Bul- Favalli, M., Fornaciai, A., Mazzarini, F., Harris, A., Neri, M., E.W., 1995, Comparative geothermometry of recent letin of Volcanology, v. 61, p. 306–323, doi: 10.1007 Behncke, B., and Pareschi, M.T., 2010b, Evolution of Hawaiian eruptions: Journal of Geophysical Research, /s004450050299. an active lava fl ow fi eld using a multitemporal LIDAR v. 100, p. 17637–17,657, doi: 10.1029/95JB01309. Cashman, K.V., Kerr, R.C., and Griffi ths, R.W., 2006, A acquisition: Journal of Geophysical Research, v. 115, Helz, R.T., Heliker, C., Hon, K., and Mangan, M., 2003, laboratory model of surface crust formation and disrup- B11203, doi: 10.1029/2010JB007463. Thermal effi ciency of lava tubes in the Pu‘u ‘Ō‘ō- tion on lava fl ows through non-uniform channels: Bul- Felpeto, A., Arana, V., Ortiz, R., Astiz, M., and Garcia, A., Kūpaianaha eruption, in Heliker, C., et al., eds., The letin of Volcanology, v. 68, p. 753–770, doi: 10.1007 2001, Assessment and modeling of lava fl ow hazard on Pu‘u ‘Ō‘ō-Kūpaianaha eruption of Kilauea Volcano, /s00445-005-0048-z. Lanzarote (Canary Islands): Natural Hazards, v. 23, Hawaii: The fi rst 20 years: U.S. Geological Survey Coltelli, M., Proietti, C., Branca, S., Marsella, M., Andronico, p. 247–257, doi: 10.1023/A:1011112330766. Professional Paper 1676, p. 105–120. D., and Lodato, L., 2007, Analysis of the 2001 lava Fink, J.H., and Fletcher, R.C., 1978, Ropy pahoehoe: Sur- Hiesinger, H., Head, J.W., and Neukum, G., 2007, Young flow eruption of Mt. Etna from three-dimensional face folding of a viscous fl uid: Journal of Volcanol- lava fl ows on the eastern fl ank of Ascraeus Mons: Rheo- mapping: Journal of Geophysical Research, v. 112, ogy and Geothermal Research, v. 4, p. 151–170, logical properties derived from High Resolution Stereo F02029, doi: 10.1029/2006JF000598. doi: 10.1016/0377-0273(78)90034-3. Camera (HRSC) images and Mars Orbiter Laser Altim- Corsini, A., Borgatti, L., Cervi, F., Dahne, A., Ronchetti, F., Fink, J.H., and Zimbelman, J.R., 1986, Rheology of the 1983 eter (MOLA) data: Journal of Geophysical Research, and Sterzai, P., 2009, Estimating mass-wasting processes Royal Gardens basalt fl ows, Kilauea volcano, Hawaii: v. 112, E05011, doi: 10.1029/2006JE002717. in active earth slides–earth fl ows with time-series of Bulletin of Volcanology, v. 48, p. 87–96, doi: 10.1007 Hofton, M.A., Malavassi, E., and Blair, J.B., 2006, Quan- high-resolution DEMs from photogrammetry and air- /BF01046544. tifying recent pyroclastic and lava fl ows at Arenal borne LiDAR: Natural Hazards and Earth System Sci- Glaze, L.S., Baloga, S.M., and Stofan, E.R., 2003, A method- Volcano, Costa Rica, using medium-footprint ences, v. 9, p. 433–439, doi: 10.5194/nhess-9-433-2009. ology for constraining lava fl ow rheologies with MOLA: lidar: Geophysical Research Letters, v. 33, L21306, Crisci, G.M., Avolio, M.V., Behncke, B., D’Ambrosio, D., Icarus, v. 165, p. 26–33, doi: 10.1016/S0019-1035 doi: 10.1029/2006GL027822. Di Gregorio, S., Lupiano, V., Neri, M., Rongo, R., and (03)00171-4. Huggel, C., Schneider, D., Julio Miranda, P., and Delgado Spataro, W., 2010, Predicting the impact of lava fl ows Glaze, L.S., Baloga, S.M., Garry, W.B., Fagents, S.A., and Granados, H., 2008, Evaluation of ASTER and SRTM at Mount Etna, Italy: Journal of Geophysical Research, Parcheta, C., 2009, A hybrid model for leveed lava DEM data for lahar modeling. A case study on lahars v. 115, B04203, doi: 10.1029/2009JB006431. fl ows: Implications for eruption styles on Mars: Journal from Popocatépetl Volcano, Mexico: Journal of Vol- Cronin, S.J., and Cashman, K.V., 2007, Volcanic oral tradi- of Geophysical Research, v. 114, E07001, doi: 10.1029 canology and Geothermal Research, v. 170, p. 99–110, tions in hazard assessment and mitigation, in Grattan, /2008JE003278. doi: 10.1016/j.jvolgeores.2007.09.005.

1678 Geosphere, December 2013

Downloaded from http://pubs.geoscienceworld.org/gsa/geosphere/article-pdf/9/6/1664/3346138/1664.pdf by guest on 26 September 2021 How lava ﬂ ows

Hulme, G., 1974, The interpretation of lava fl ow morphol- for surveying a shallow, braided, sand-bedded river: airborne lidar data: Remote Sensing of Environment, ogy: Royal Astronomical Society Geophysical Journal, Journal of Hydraulic Engineering, v. 133, 838, v. 96, p. 149–164, doi: 10.1016/j.rse.2005.01.017. v. 39, p. 361–383, doi: 10.1111/j.1365-246X.1974 doi: 10.1061/(ASCE)0733-9429(2007)133:7(838). Naranjo, J.A., Sparks, R.S.J., Stasiuk, M.V., Moreno, H., .tb05460.x. Kumari, P., Carter, W.E., and Shrestha, R.L., 2011, Adjust- and Ablay, G.J., 1992, Morphological, structural and Jaboyedoff, M., Oppiker, T., Abellan, A., Derron, M.H., ment of systematic errors in ALS data through surface textural variations in the 1988–1990 andesite lava of Loye, A., Metzger, R., and Pedrazzini, A., 2010, Use of matching: Advances in Space Research, v. 47, p. 1851– Lonquimay Volcano, Chile: Geological Magazine, LIDAR in landslide investigations: A review: Natural 1864, doi:10.1016/j.asr.2010.12.015. v. 129, p. 657–678, doi: 10.1017/S0016756800008426. Hazards, doi: 10.1007/s11069-010-9634-2. Latypov, D., 2002, Estimating relative lidar accuracy infor- Neri, M., Mazzarini, F., Tarquini, F., Bisson, M., Isola, I., James, M.R., Pinkerton, H., and Robson, S., 2007, Image- mation from overlapping fl ight lines: ISPRS Journal of Behncke, B., and Pareschi, M.T., 2008, The changing based measurement of fl ux variation in distal regions Photogrammetry and Remote Sensing, v. 56, p. 236– face of Mount Etna’s summit area documented with of active lava fl ows: Geochemistry Geophysics Geo- 245, doi: 10.1016/S0924-2716(02)00047-3. Lidar technology: Geophysical Research Letters, v. 35, systems, v. 8, Q03006, doi: 10.1029/2006GC001448. Lipman, P.W., and Banks, N.G., 1987, Aa fl ow dynamics, L09305, doi: 10.1029/2008GL033740. James, M.R., Pinkerton, H., and Ripepe, M., 2010, Imag- Mauna Loa 1984, in Decker, R.W., et al., eds., Vol- Oppenheimer, C., 1998, Volcanological applications of ing short period variations in lava fl ux: Bulletin of Vol- canism in Hawaii: Papers to commemorate the 75th meteorological satellites: International Journal of canology, v. 72, p. 671–676, doi: 10.1007/s00445-010 anniversary of the founding of the Hawaiian Volcano Remote Sensing, v. 19, p. 2829–2864, doi: 10.1080 -0354-y. Observatory: U.S. Geological Survey Professional /014311698214307. Johnson, A.M., 1970, Physical processes in geology: San Paper 1350, p. 1527–1567. Peterson, D.W., and Tilling, R.I., 1980, Transition of basal- Francisco, California, Freeman, Cooper & Company, Lockwood, J.P., and Lipman, P.W., 1987, Holocene eruptive tic lava from pahoehoe to aa, Kilauea Volcano, Hawaii: 577 p., history of Mauna Loa volcano, in Decker, R.W., et al., Field observations and key factors: Journal of Vol- Katz, M.G., and Cashman, K.V., 2003, Hawaiian lava fl ows eds., Volcanism in Hawaii: Papers to commemorate canology and Geothermal Research, v. 7, p. 271–293, in the third dimension: Identifi cation and interpreta- the 75th anniversary of the founding of the Hawaiian doi: 10.1016/0377-0273(80)90033-5. tion of pahoehoe and ‘ā‘a distribution in the KP-1 and Volcano Observatory: U.S. Geological Survey Profes- Peterson, D.W., Holcomb, R.T., Tilling, R.I., and Chris- SOH-4 cores: Geochemistry Geophysics Geosystems, sional Paper 1350, p. 509–535. tiansen, R.L., 1994, Development of lava tubes in the v. 4, 8705, doi: 10.1029/2001GC000209. Lu, Z., Fielding, E., Patrick, M.R., and Trautwein, C.M., light of observations at Mauna Ulu, Kilauea Volcano, Kauahikaua, J.P., 2007, Lava fl ow hazard assessment, as of 2003, Estimating lava volume by precision combina- Hawaii: Bulletin of Volcanology, v. 56, p. 343–360, August 2007, for Kilauea East Rift Zone Eruptions, tion of multiple baseline spaceborne and airborne inter- doi: 10.1007/BF00326461. Hawaii Island: U.S. Geological Survey Open-File ferometric synthetic aperture radar: The 1997 eruption Petrie, G., and Toth, C.K., 2008a, Introduction to laser Report 2007-1264, 9 p. of Okmok volcano, Alaska: Geoscience and Remote ranging, profi ling and scanning, in Shan, J., and Toth, Kauahikaua, J., Margriter, S., Lockwood, J., and Trusdell, Sensing: IEEE Transactions, v. 41, p. 1428–1436. C.K., eds., Topographic laser ranging and scanning: F., 1995, Applications of GIS to the estimation of Lyell, C., 1830, Principles of geology (facsimile reprint of Principles and processing: Boca Raton, Florida, CRC lava fl ow hazards on Mauna Loa Volcano, Hawaii, in fi rst edition, 3 volumes, 1990–1991): Chicago, Illinois, Press, p. 1–28. Rhodes, J.M., and Lockwood, J.P., eds., Mauna Loa University of Chicago Press, 330 p. Petrie, G., and Toth, C.K., 2008b, Airbone and spaceborne revealed: Structure, composition, history, and hazards: Macfarlane, D.G., Wadge, G., Robertson, D.A., James, laser profi les and scanners, in Shan, J., and Toth, C.K., American Geophysical Union Geophysical Mono- M.R., and Pinkerton, H., 2006, Use of a portable topo- eds., Topographic laser ranging and scanning: Princi- graph 92, p. 315–326, doi: 10.1029/GM092p0315. graphic mapping millimetre wave radar at an active lava ples and processing: Boca Raton, Florida, CRC Press, Kauahikaua, J., Mangan, M., Heliker, C., and Mattox, T., fl ow: Geophysical Research Letters, v. 33, L03301, p. 29–86. 1996, A quantitative look at the demise of a basaltic doi: 10.1029/2005GL025005. Petrie, G., and Toth, C.K., 2008c, Terrestrial laser scanners, vent: The death of Kupaianaha, Kilauea Volcano, Mallet, C., and Bretar, F., 2009, Full-waveform topographic in Shan, J., and Toth, C.K., eds., Topographic laser Hawai’i: Bulletin of Volcanology, v. 57, p. 641–648, lidar: State-of-the-art: Journal of Photogrammetry ranging and scanning: Principles and processing: Boca doi: 10.1007/s004450050117. and Remote Sensing, v. 64, p. 1–16, doi: 10.1016 Raton, Florida, CRC Press, p. 87–128. Kauahikaua, J., Cashman, K.V., Mattox, T.N., Heliker, /j.isprsjprs.2008.09.007. Pieri, D.C., and Baloga, S.M., 1986, Eruption rate, area, C.C., Hon, K.A., Mangan, M.T., and Thornber, Marsella, M., Proietti, C., Sonnessa, A., Coltelli, M., Tommasi, and length relationships for some Hawaiian lava fl ows: C.R., 1998, Observations on basaltic lava streams P., and Bernardo, E., 2009, The evolution of the Sciara Journal of Volcanology and Geothermal Research, in tubes from Kilauea Volcano, island of Hawaii: del Fuoco subaerial slope during the 2007 Stromboli v. 30, p. 29–45, doi: 10.1016/0377-0273(86)90066-1. Journal of Geophysical Research, v.103, p. 27303– eruption: Relation between deformation processes and Pyle, D.M., 2000, Sizes of volcanic eruptions, in Sigurdsson, 27,323, doi: http://adsabs.harvard.edu/cgi-bin/nph-abs effusive activity: Journal of Volcanology and Geo- H., ed., Encyclopedia of volcanoes, San Diego, Cali- _connect?fforward=http://dx.doi.org/10.1029 thermal Research, v. 182, p. 201–213, doi: 10.1016 fornia, Academic Press, p. 263–270. /97JB03576. /j.jvolgeores.2009.02.002. Pyle, D.M., and Elliott, J.R., 2006, Quantitative morphol- Kauahikaua, J., Cashman, K., Clague, D., Champion, D., Mazzarini, F., Pareschi, M.T., Favalli, M., Isola, I., Tarquini, ogy, recent evolution, and future activity of the Kameni and Hagstrum, J., 2002, Emplacement of the most S., and Boschi, E., 2005, Morphology of basaltic lava Islands volcano, Santorini, Greece: Geosphere, v. 2, recent lava fl ows on Hualalai Volcano, Hawai’i: Bul- channels during the Mt. Etna September 2004 erup- p. 253-268, doi: 10.1130/GES00028.1. letin of Volcanology, v. 64, p. 229–253, doi: 10.1007 tion from airborne laser altimeter data: Geophysi- Realmuto, V.J., Hon, K., Kahle, A.B., Abbott, E.A., and /s00445-001-0196-8. cal Research Letters, v. 32, L04305, doi: 10.1029 Pieri, D.C., 1992, Multispectral thermal infrared map- Kauahikaua, J., Sherrod, D.R., Cashman, K.V., Heliker, /2004GL021815. ping of the 1 October 1988 Kupaianaha fl ow fi eld, C., Hon, K., Mattox, T.N., and Johnson, J.A., 2003, Mazzarini, F., Pareschi, M.T., Favalli, M., Isola, I., Tarquini, Kilauea volcano, Hawaii: Bulletin of Volcanology, Hawaiian lava-flow dynamics during the Pu‘u S., and Boschi, E., 2007, Lava fl ow identifi cation and v. 55, p. 33–44, doi: 10.1007/BF00301118. ‘Ō‘ō-Kūpaianaha eruption: A tale of two decades, in aging by means of lidar intensity: Mount Etna case: Riker, J.M., Cashman, K.V., Kauahikaua, J.P., and Mon- Heliker, C., et al., eds., The Pu‘u ‘Ō‘ō-Kūpaianaha Journal of Geophysical Research, v. 112, B02201, tierth, C.M., 2009, The length of channelized lava eruption of Kilauea Volcano, Hawaii: The fi rst 20 doi: 10.1029/2005JB004166. fl ows: Insight from the 1859 eruption of Mauna Loa years: U.S. Geological Survey Professional Paper 167, McKean, J., and Roering, J., 2004, Objective landslide detec- Volcano, Hawaii: Journal of Volcanology and Geo- p. 63–87. tion and surface morphology mapping using high-reso- thermal Research, v. 183, p. 139–156, doi: 10.1016 Kerr, R.C., 2001, Thermal erosion by laminar lava fl ows: lution airborne laser altimetry: Geomorphology, v. 57, /j.jvolgeores.2009.03.002. Journal of Geophysical Research, v. 106, p. 26453– p. 331–351, doi: 10.1016/S0169-555X(03)00164-8. Roering, J.J., Mackey, B.H., Marshall, J.A., Sweeney, K.E., 26465, doi: 10.1029/2001JB000227. McKean, J.A., Isaak, D.J., and Wright, C.W., 2008, Geomor- Deligne, N.I., Booth, A.M., Handwerger, A.L., and Kerr, R.C., Griffi ths, R.W., and Cashman, K.V., 2006, phic controls on salmon nesting patterns described by Cerovski-Darriau, C., 2013, “You are HERE”: Con- Formation of channelized lava fl ows on an uncon- a new, narrow-beam terrestrial-aquatic lidar: Frontiers necting the dots with airborne lidar for geomorphic fi ned slope: Journal of Geophysical Research, v. 111, in Ecology and the Environment, v. 6, p. 125–130, fi eldwork: Geomorphology, doi:10/1016/j.geomorph B10206, doi: 10.1029/2005JB004225. doi: 10.1890/070109. .2013.04.009. Keszthelyi, L., 1995, A preliminary thermal budget for lava McKean, J., Nagel, D., Tonina, D., Bailey, P., Wright, C.W., Rowland, S.K., and Walker, G.P.L., 1990, Pahoehoe and aa tubes on the Earth and planets: Journal of Geophysical Bohn, C., and Nayegandhi, A., 2009, Remote sensing of in Hawaii: Volumetric fl ow rate controls the lava struc- Research, v. 100, p. 20411–20420. channels and riparian zones with a narrow-beam aquatic- ture: Bulletin of Volcanology, v. 52, p. 615–628, doi: Kilburn, C.R.J., 1996, Patterns and predictability in the terrestrial LIDAR: Remote Sensing, v. 1, p. 1065–1096, 10.1007/BF00301212. emplacement of subaerial lava fl ows and fl ow fi elds, doi: 10.3390/rs1041065. Rowland, S.K., Garbeil, H., and Harris, A.J.L., 2005, Lengths in Scarpa, C., and Tilling, R.I., eds., Monitoring and Moore, H.J., 1987, Preliminary estimates of the rheologi- and hazards from channel-fed lava fl ows on Mauna mitigation of volcano hazards: Berlin, Springer-Verlag, cal properties of 1984 Mauna Loa lava, in Decker, Loa, Hawaii, determined from thermal and downslope p. 491–537. R.W., et al., eds., Volcanism in Hawaii: Papers to com- modeling with FLOWGO: Bulletin of Volcanology, Kilburn, C.R.J., 2004, Fracturing as a quantitative indi- memorate the 75th anniversary of the founding of the v. 67, p. 634–647, doi: 10.1007/s00445-004-0399-x. cator of lava fl ow dynamics: Journal of Volcanol- Hawaiian Volcano Observatory: U.S. Geological Sur- Rudwick, M.J.S., 2008, Worlds before Adam: The recon- ogy and Geothermal Research, v. 132, p. 209–224, vey Professional Paper 1350, p. 1569–1588. struction of geohistory in the age of reform: Chicago, doi: 10.1016/S0377-0273(03)00346-9. Mouginis-Mark, P.J., and Garbeil, H., 2005, Quality of Illinois, University of Chicago Press, 648 p. Kinzel, P.J., Wayne Wright, C., Nelson, J.M., and Burman , TOPSAR topographic data for volcanology studies Scarth, A., 2009, Vesuvius: A biography: Princeton, New A.R., 2007, Evaluation of an experimental LiDAR at Kīlauea Volcano, Hawaii: An assessment using Jersey, Princeton University Press, 352 p.

Geosphere, December 2013 1679

Downloaded from http://pubs.geoscienceworld.org/gsa/geosphere/article-pdf/9/6/1664/3346138/1664.pdf by guest on 26 September 2021 Cashman et al.

Scrope, G.P., and Poulett, G., 1825, Considerations on vol- Theilig, E., and Greeley, R., 1986, Lava fl ows on Mars: Wolfe, E.W., Garcia, M.O., Jackson, D.B., Koyanagi, R.Y., canos: London, Phillips, 270 p. Analy sis of small surface features and comparisons with Neal, C.A., and Okamura, A.T., 1987, The Pu’u’O’o Shan, J., and Toth, K., eds., 2008, Topographic laser ranging terrestrial analogs: Journal of Geophysical Research, eruption of Kilauea volcano, episodes 1–20, January 3, and scanning: Principles and processing: Boca Raton, v. 91, p. E193–E206, doi: 10.1029/JB091iB13p0E193. 1983, to June 8, 1984, in Decker, R.W., et al., eds., Florida, CRC Press, 590 p. Trusdell, F.A., 1995, Lava fl ow hazards and risk assessment Volcanism in Hawaii: Papers to commemorate the 75th Soule, S.A., and Cashman, K.V., 2005, Shear rate depen- on Mauna Loa Volcano, Hawaii, in Rhodes, J.M., and anniversary of the founding of the Hawaiian Volcano dence of the pahoehoe-to-’a’a transition: Analog Lockwood, J.P., eds., Mauna Loa revealed: Structure, Observatory: U.S. Geological Survey Professional experiments: Geology, v. 33, p. 361–364, doi: 10.1130 composition, history, and hazards: American Geo- Paper 1350, p. 471–508. /G21269.1. physical Union Geophysical Monograph 92, p. 327– Wright, C.W., Hoge, F.E., Swift, R.N., Yungle, J.K., and Soule, S.A., Cashman, K.V., and Kauahikaua, J.P., 2004, 336, doi: 10.1029/GM092p0327. Schirtzinger, C.R., 2001, Next-generation NASA air- Examining fl ow emplacement through the surface Tucholke, B.E., Lin, J., and Kleinrock, M.C., 1998, Mega- borne oceanographic Lidar systems: Applied Optics, morphology of three rapidly emplaced, solidified mullions and mullion structure defi ning oceanic meta- v. 40, p. 336–342, doi: 10.1364/AO.40.000336. lava flows, Kilauea Volcano, Hawai’i: Bulletin of morphic core complexes on the Mid-Atlantic Ridge: Wright, R., Blake, S., Harris, A.J.L., and Rothery, D.A., Volcanology, v. 66, p. 1–14, doi: 10.1007/s00445-003 Journal of Geophysical Research, v. 103, p. 9857– 2001, A simple explanation for the space-based calcu- -0291-0. 9866, doi: 10.1029/98JB00167. lation of lava eruption rates: Earth and Planetary Sci- Soule, S.A., Fornari, D.J., Perfi t, M.R., Tivey, M.A., Ridley, Vaughan, K.L., McDaniel, P.A., and Phillips, W.M., 2011, ence Letters, v. 192, p. 223–233, doi: 10.1016/S0012 W.I., and Schouten, H., 2005, Channelized lava fl ows at Episodic soil succession on basaltic lava fi elds in a cool, -821X(01)00443-5. the East Pacifi c Rise crest 9°–10°N: The importance of dry environment: Soil Science Society of America Jour- Wright, R., Flynn, L.P., Garbeil, H., Harris, A.J.L., and Pilger, off-axis lava transport in developing the architecture of nal, v. 75, p. 1462–1470, doi: 10.2136/sssaj2010.0341. E., 2004, MODVOLC: Near-real-time thermal moni- young oceanic crust: Geochemistry Geophysics Geo- Ventura, G., and Vilardo, G., 2008, Emplacement mechanism toring of global volcanism: Journal of Volcanology and systems, v. 6, Q08005, doi: 10.1029/2005GC000912. of gravity fl ows inferred from high resolution Lidar Geothermal Research, v. 135, p. 29–49, doi: 10.1016 Sparks, R.S.J., Pinkerton, H., and Hulme, G., 1976, Clas- data: The 1944 Somma-Vesuvius lava flow (Italy): /j.jvolgeores.2003.12.008. sifi cation and formation of lava levees on Mount Etna, Geomorphology, v. 95, p. 223–235, doi: 10.1016 Wright, R., Garbeil, H., Baloga, S.M., and Mouginis-Mark, Sicily : Geology, v. 4, p. 269–271, doi: 10.1130/0091 /j.geomorph.2007.06.005. P.J., 2006, An assessment of shuttle radar topography -7613(1976)4<269:CAFOLL>2.0.CO;2. Wadge, G., Young, P.A.V., and McKendrick, I.J., 1994, mission digital elevation data for studies of volcano Stevens, N.F., Murray, J.B., and Wadge, G., 1997, The vol- Mapping lava fl ow hazards using computer simulation: morphology: Remote Sensing of Environment, v. 105, ume and shape of the 1991–1993 lava fl ow fi eld at Journal of Geophysical Research, v. 99, p. 489–504, p. 41–53, doi: 10.1016/j.rse.2006.06.002. Mount Etna, Sicily: Bulletin of Volcanology, v. 58, doi: 10.1029/93JB01561. Wright, T.L., Chun, J.Y.F., Exposo, J., Heliker, C., Hodge, p. 449–454, doi: 10.1007/s004450050153. Wadge, G., Macfarlane, D.G., Odbert, H.M., James, M.R., J., Lockwood, J.P., and Vogt, S.M., 1992, Map showing Stevens, N.F., Wadge, G., and Williams, C.A., 2001, Post- Hole, J.K., Ryan, G., and Bass, V., 2008, Time series lava-fl ow hazard zones, Island of Hawaii: US Geologi- emplacement lava subsidence and the accuracy of ERS radar observations of a growing lava dome: Journal of cal Survey Miscellaneous Field Studies Map MF-2193, InSAR digital elevation models of volcanoes: Interna- Geophysical Research, v. 113, B08210, doi: 10.1029 scale: 1:250,000. tional Journal of Remote Sensing, v. 22, p. 819–828, /2007JB005466. Zebker, H.A., Madsen, S.N., Martin, J., Wheeler, K.B., doi: 10.1080/01431160051060246. Walker, G.P.L., 1973, Lengths of lava fl ows: Royal Society Miller, T., Lou, Y., Alberti, G., Vetrella, S., and Cucci, Stolper, E.M., DePaolo, D.J., and Thomas, D.M., 2009, London Philosophical Transactions, ser. A, v. 274, A., 1992, The TOPSAR interferometric radar topo- Deep drilling into a mantle plume volcano: The Hawaii p. 107–118, doi: 10.1098/rsta.1973.0030. graphic mapping instrument: IEEE Transactions on Scientifi c Drilling Project: Scientifi c Drilling, no. 7, Warner, N.H., and Gregg, T.K.P., 2003, Evolved lavas Geoscience and Remote Sensing, v. 30, p. 933–940, p. 4–14, doi: 10.2204/iodp.sd.7.02.2009. on Mars? Observations from southwest Arsia Mons doi: 10.1109/36.175328. Swanson, D.A., 2008, Hawaiian oral tradition describes 400 and Sabancaya volcano, Peru: Journal of Geophysi- Zebker, H.A., Rosen, P., Hensley, S., and Mouginis- years of volcanic activity at Kīlauea: Journal of Vol- cal Research, v. 108, no. E10, 5112, doi: 10.1029 Mark, P.J., 1996, Analysis of active lava fl ows on canology and Geothermal Research, v. 176, p. 427– /2002JE001969. Kilauea volcano, Hawaii, using SIR-C radar cor- 431, doi: 10.1016/j.jvolgeores.2008.01.033. Williams, D., Kadel, S.D., Greeley, R., Lesher, C.M., and relation measurements: Geology, v. 24, p. 495–498, Tarquini, S., and Favalli, M., 2010, Changes of the suscep- Clynne, M.A., 2004, Erosion by fl owing lava: Geo- doi: 10.1130/0091-7613(1996)024<0495:AOALFO tibility to lava fl ow invasion induced by morphologi- chemical evidence in the Cave Basalt, Mount St. Helens, >2.3.CO;2. cal modifi cations of an active volcano: The case of Washington: Bulletin of Volcanology, v. 66, p. 168– Zimbelman, J.R., Garry, W.B., Johnston, A.K., and Wil- Mount Etna, Italy: Natural Hazards, v. 54, p. 537–546, 181, doi: 10.1007/s00445-003-0301-2. liams, S.H., 2008, Emplacement of the 1907 Mauna doi: 10.1007/s11069-009-9484-y. Wolfe, E.W., ed., 1988, The Puu Oo Eruption of Kilauea Loa basalt fl ow as derived from precision topography Tazieff, H., 1977, An exceptional eruption: Mt. Nyiragongo, Volcano, Hawaii: Episodes 1 through 20, January 3, and satellite imaging: Journal of Volcanology and Geo- January 10th, 1977: Bulletin Volcanologique, v. 40, 1983 through June 8, 1984: US Geological Survey thermal Research, v. 177, p. 837–847, doi: 10.1016 no. 3, p. 1–12. Professional Paper 1463, 251 p. /j.jvolgeores.2008.01.042.

1680 Geosphere, December 2013

Downloaded from http://pubs.geoscienceworld.org/gsa/geosphere/article-pdf/9/6/1664/3346138/1664.pdf by guest on 26 September 2021