Portland State University PDXScholar Geology Faculty Publications and Presentations Geology 3-17-2017 Analyzing Glacier Surface Motion Using LiDAR Data Jennifer W. Tellig University of Houston Craig Glennie University of Houston Andrew G. Fountain Portland State University, [email protected] David C. Finnegan Cold Regions Research and Engineering Laboratory Remote Sensing/GIS Center of Excellence Follow this and additional works at: https://pdxscholar.library.pdx.edu/geology_fac Part of the Geology Commons, and the Glaciology Commons Let us know how access to this document benefits ou.y Citation Details Telling, J. W., Glennie, C., Fountain, A. G., & Finnegan, D. C. (2017). Analyzing Glacier Surface Motion Using LiDAR Data. Remote Sensing, 9(3), 1-12. doi:10.3390/rs9030283 This Article is brought to you for free and open access. It has been accepted for inclusion in Geology Faculty Publications and Presentations by an authorized administrator of PDXScholar. Please contact us if we can make this document more accessible: [email protected]. remote sensing Article Analyzing Glacier Surface Motion Using LiDAR Data Jennifer W. Telling 1,*, Craig Glennie 1, Andrew G. Fountain 2 and David C. Finnegan 3 1 National Center for Airborne Laser Mapping, University of Houston, 5000 Gulf Freeway, Building 4, Room 216, Houston, TX 77204-5059, USA; [email protected] 2 Department of Geology, Portland State University, P.O. Box 751, Portland, OR 97207-0751, USA; [email protected] 3 U.S. Army Engineer Research and Development Center Cold Regions Research and Engineering Laboratory Remote Sensing/GIS Center of Excellence, ATTN: CEERD-PA-H, 72 Lyme Road, Hanover, NH 03755-1290, USA; [email protected] * Correspondence: [email protected]; Tel.: +1-201-835-8478 Academic Editors: Guoqing Zhou, Xiaofeng Li and Prasad S. Thenkabail Received: 31 January 2017; Accepted: 14 March 2017; Published: 17 March 2017 Abstract: Understanding glacier motion is key to understanding how glaciers are growing, shrinking, and responding to changing environmental conditions. In situ observations are often difficult to collect and offer an analysis of glacier surface motion only at a few discrete points. Using light detection and ranging (LiDAR) data collected from surveys over six glaciers in Greenland and Antarctica, particle image velocimetry (PIV) was applied to temporally-spaced point clouds to detect and measure surface motion. The type and distribution of surface features, surface roughness, and spatial and temporal resolution of the data were all found to be important factors, which limited the use of PIV to four of the original six glaciers. The PIV results were found to be in good agreement with other, widely accepted, measurement techniques, including manual tracking and GPS, and offered a comprehensive distribution of velocity data points across glacier surfaces. For three glaciers in Taylor Valley, Antarctica, average velocities ranged from 0.8–2.1 m/year. For one glacier in Greenland, the average velocity was 22.1 m/day (8067 m/year). Keywords: terrestrial laser scanning; airborne laser scanning; LiDAR; morphology; glacier surface velocity 1. Introduction Glaciers are dynamic and in constant flux, and understanding glacial motion provides important information about their growth and retreat [1]. However, the size and extent of glaciers, along with challenging environmental conditions, can severely limit data collection in the field or, in particularly hazardous conditions, field work may be precluded entirely [2–4]. As remote sensing data becomes more widely available, it is becoming a primary data collection technique in the cryosphere, particularly in areas that are inaccessible to traditional field methods [2]. Traditional methods of collecting glacier velocity data rely on stakes drilled into the ice and/or GPS deployed on the glacier surface [5–8]. The optical and geodetic surveys require little post-processing but only provide data at discrete points, usually covering a fraction of the entire glacier. Remote sensing techniques, including synthetic aperture radar (SAR) and multispectral imagery, are increasingly being used to detect and monitor changes in the cryosphere [4,9–12]. In difficult to reach areas of the Himalayas, SAR has been used to measure flow velocities at the Kangshung and Khumbu Glaciers [11]. SAR was also used to measure flow velocities at the Shirase Glacier, Antarctica, and at Helheim Glacier, in Greenland [9,10,12]. Often, these methods rely on satellite based platforms, which offer regular, long duration coverage, but they can be inhibited by severe topography and viewing Remote Sens. 2017, 9, 283; doi:10.3390/rs9030283 www.mdpi.com/journal/remotesensing Remote Sens. 2017, 9, 283 2 of 12 Remote Sens. 2017, 9, 283 2 of 13 offerangle regular, [11,12]. Inlong addition, duration SAR coverage, tends to but have they low ca backscatteringn be inhibited intensity by severe over topography dry snow, and reducing viewing the volumeangle [11,12]. of data In collected addition, in SAR some tends environments to have low [11 backscattering,12]. intensity over dry snow, reducing the volumeTerrestrial of data and collected airborne laserin some scanning environments (TLS and [11,12]. ALS, respectively) are two active remote sensing techniquesTerrestrial that useand lightairborne detection laser and scanning ranging (TLS (LiDAR), and ALS, typically respectively) in the near-infrared are two active wavelengths, remote sensingto provide techniques precise 3Dthat elevation use light models detection of surfaces. and ranging TLS and(LiDAR), ALS have typically both beenin the used near-infrared to survey wavelengths,glaciers to create to provide highly precise detailed 3D digital elevation elevation model modelss of surfaces. (DEMs) TLS for and the ALS purpose have ofboth determining been used tointerannual survey glaciers variability to create in surface highly elevation detailed in di ordergital toelevation estimate models mass balance (DEMs) or for long-term the purpose volume of determiningchange [2,3,13 interannual,14]. variability in surface elevation in order to estimate mass balance or long- term Involume this study, change we [2,3,13,14]. use LiDAR DEMs, collected over time, to calculate glacier surface velocity using two differentIn this study, methods—particle we use LiDAR DEMs, image collected velocimetry over (PIV) time, andto calculate manual glacier tracking. surface Applying velocity PIV, using an twoimage different processing methods—particle technique [15–18 image], to LiDAR velocimetry data provides (PIV) and an opportunity manual tracking. to measure Applying velocity PIV, across an imageentire glaciersprocessing rapidly technique and the [15–18], ability to measureLiDAR data high provides resolution an nuances opportunity in the to flow measure field thatvelocity may acrossbe missed entire using glaciers other rapidly techniques. and the When ability compared to measure to other high methodsresolution such nuances as feature in the extraction, flow field that and mayother be image missed correlation using other techniques, techniques. PIV When offers compar an approached to other that methods is sensitive such to as small feature scale, extraction, locally variableand other changes. image correlation This is especially techniques, important PIV offers on glacier an approach surfaces that where is sensitive common to features small scale, may deformlocally variableslightly betweenchanges. the This collection is especially of repeat important data. Successful on glacier application surfaces of where this method common will features make another may deformtechnique slightly available between for assessing the collection velocity of ofrepeat glaciers data. and Successful other slow application moving landforms. of this method The PIV will results make anotherare compared technique to manual available tracking for assessing results and, velocity where of available,glaciers and in situother data. slow The moving new method landforms. is tested The PIVon ALS results and are TLS compared point clouds to ofmanual six glaciers tracking in Antarcticaresults and, and where Greenland, available, with in data situ covering data. The a range new methodof spatial is andtested temporal on ALS resolutions. and TLS point clouds of six glaciers in Antarctica and Greenland, with data covering a range of spatial and temporal resolutions. 2. Study Sites 2. Study Sites The horizontal surface motion for six glaciers—five from Taylor Valley, Antarctica (Canada, Commonwealth,The horizontal Rhone, surface Suess, motion Taylor) for and six one glaciers—five from Greenland from (Helheim)—was Taylor Valley, analyzedAntarctica in the(Canada, study. TaylorCommonwealth, Valley (Figure Rhone,1) is oneSuess, of theTaylor) McMurdo and one Dry from Valleys Greenland (MDV) located(Helheim)—was in East Antarctica analyzed (77.5 in the◦S, 163study.◦E). Taylor The valley Valley landscape (Figure 1) is composedis one of the of McMurdo sandy, gravelly Dry Valleys valleybottoms (MDV) located with expanses in East ofAntarctica exposed (77.5°S,bedrock 163°E). and is populatedThe valley withlandscape perennially is composed ice-covered of sandy, lakes gravelly and ephemeral valley bo streamsttoms with originating expanses from of exposedglaciers thatbedrock flow intoand theis populated valleys from with the surroundingperennially ice-covered mountains [lakes19]. Air and temperatures ephemeral averagestreams originatingabout −17 ◦ Cfrom and glaciers summer that temperatures flow