Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | erence DEMs ff 20cm). The ability < and had ground sample distances of 6 2 8cm (both 95 % confidence) for our meth- 333 334 2 ± , and M. Sturm 2 30cm and a precision of ± , C. F. Larsen 1 This discussion paper is/has beenPlease under refer review to for the the corresponding journal final The paper Cryosphere in (TC). TC if available. Institute of Northern Engineering, University of Alaska Fairbanks, 306Geophysical Tanana Institute, Loop, University of Alaska Fairbanks, 903 Koyukuk Drive, Fairbanks, Abstract Airborne photogrammetry is undergoingpowerful software, a and renaissance: simplified lower-costentry methods equipment, and have more now significantlyand allow lowered temporal repeat-mapping the frequencies barriers-to- that of wereply cryospheric previously too these dynamics expensive techniques at to consider. tomain spatial Here the airborne we resolutions ap- measurement hardware ofdual-frequency consists snow GPS. of The depth photogrammetric a from processingavailable consumer-grade manned is implementation digital aircraft. done of using The camera thehardware a coupled commercially- and Structure to software, from a exclusivenique Motion of (SfM) creates aircraft, algorithm. directly-georeferenced costscosts. maps The less To system without than map ground USD snow 30free 000. control, depth, and The further we snow-covered tech- made reducing conditions, digital then elevation subtracted models these (DEMs) to during create di snow- (dDEMs). We assessed the accuracyDEMs (geolocation) through and precision comparisons (repeatability)DEMs. to of We our ground validated control theseborne assessments points through lidar and comparisons and to toaccuracy by time-series DEMs another of made of by photogrammetric our air- system.ods. own We We then empirically validated determined ourmeasurements dDEMs at an against 3 more test thantypes. areas 6000 These in hand-probed areas snow Alaska ranged depth to covering from 20 a cm. 5 We wide-variety to found of thata 40 km depths terrain 10 produced cm and from standard snow theat dDEMs deviation, 95 matched % and probe confidence. these depths Due with ground depth to such distributions the as precision were frost ofcome statistically heave, this sources identical vegetative technique, of compaction other error by real in snow, changes and the on even measurement the footprints of be- thin snow packs ( to directly measure suchto extrapolate small isolated changes field over measurements. entire The fact landscapes that eliminates this the mapping can need be done at Mapping snow-depth from manned-aircraft on landscape scales at centimeter resolution using Structure-from-Motion photogrammetry M. Nolan The Cryosphere Discuss., 9, 333–381,www.the-cryosphere-discuss.net/9/333/2015/ 2015 doi:10.5194/tcd-9-333-2015 © Author(s) 2015. CC Attribution 3.0 License. 1 Fairbanks, AK 99775,2 USA AK 99775, USA Received: 30 October 2014 – Accepted: 6Correspondence December to: 2014 M. – Nolan Published: ([email protected]) 15 JanuaryPublished 2015 by Copernicus Publications on behalf of the European Geosciences Union. 5 10 15 20 25 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | enbacher and ff erence interpreted ff become covered by 10 2 ord, 2010; Rittger et al., 2013; Rott et al., ff 335 336 ects how much work grazing animals such as ff cient vertical accuracy to measure snow depth (Mc- ffi ciently reflecting sunlight because of its high albedo (Goodrich, 1982; ffi Despite its importance, our current abilities to measure snow depth are limited. The Airborne and terrestrial photogrammetry for determining snow depth were seri- 2008). Similarly, it is possible tobut measure again the the SWE accuracy using an and airborne spatial gamma resolution detector, of the method is low (O inversions in order to infer the depth (Cli Colbeck, 1991). A techniqueis that has to received measure considerableand the attention subtract in elevation from recent of this years the the snow-free snow surface surface elevation, with by the airborne di or ground-based lidar Curdy et al., 1944). Theseestablishing maps their required elevation identifying and control position,field from points and another on the using the paper process or ground ofand mylar and subtracting expense maps was of one challenging. this elevation The overallto method complication largely in be the abandoned pre-digitalused in era the was for study enough glacier of to volume seasonal cause change snow, the though detection technique it and has for continued other to large-scale be deformation pro- son, 2004). Automated pointhave measurements also such been as employed snow successfullyments, pillows require for and modeling many to sonic years, move rangers button from et like discrete al., hand point 2007; probe data ListonRemote measure- to and sensing the Sturm, landscape-scale of 2002; (Lis- Serreze snowsensing et coverage al., of using 1999; snow Slater optical depth andity sensors or Clark, is or snow 2006). fairly water radar equivalent routine, scattering is but properties based remote of on the the microwave snow emissiv- and requires complex and problematic used for the studyMiller, of 1960; glaciers Post, 1995, (Brandenberger,photogrammetry 1969). 1959; to However Hamilton, several snow 1965; issues cover. The Hitchcock hamperedficulties low and of applying dynamic changing classical range exposures ofsnowfields, mid-flight making film often it combined produced impossible with for over-exposed the thewhen images photogrammetrist dif- the of to the snow determine elevation. images Even and had skill suitable to contrast, produce it a took map an of extraordinary su amount of time simplest and oldest technique ishas to severe probe limitations or core withcountry the respect snow (Conway and to by hand, Abrahamson, areal 1984; but coverage, McKay, this 1968; and technique Sturm, can 2010; be Sturm and risky Ben- in avalanche ously investigated startingable in (McKay, 1968). the At 1960s, thatthe though time, landscape lacking little scale, any published it other was method information an of is mapping obvious avail- snow technique depth to at consider as it was already being caribou will need to donivean in animals order like to voles feed and1993). and it weasels controls (Pauli the et quality al., of the 2013; habitat Pruitt, for 1959; sub- Russell et al., as snow depth (Deems2004; et Prokop, 2008). al., Operating 2013; on30 Fassnacht the years, and photogrammetry same Deems, principle, has 2006;et but also al., Hopkinson pre-dating been 2014; et lidar used Cline, studies al., 1994; toand König by produce Arabsheibani, and snow 2013; Sturm, Otake, depth 1998; 1980; Lee maps Rawls et (Bühler et al., al., 2008; 1980; McKay, 1968; Yan and Najibi Cheng, 2008). Warren, 1982). The depth of the snow a substantially lower costs thanstudying current change in methods the may cryosphere. transform the way we approach 1 Introduction There are many reasons whyable. being In able to the map Northern snowto depth Hemisphere 400 over alone cm a of about landscape snow is 30change each desir- million on winter, km making the seasonal planet1993). snow the (Déry Billions largest of and annual topographic people Brown,crop rely 2007; irrigation, on Lemke or snow et electricityducing in avalanches al., (Barnett or some 2007; et floods capacity, Robinson (Castebrunet al.,Snow whether et plays et 2005). al., for a al., Snow 2014; key drinking role Jamieson canthe water, in and also soil the Stethem, while surface be 2002). e energy a balance hazard, of the pro- planet, thermally insulating 5 5 25 25 20 20 15 15 10 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | interchangeably map 10cm with ground sam- ± 338 337 , yaw, pitch, and roll (that is, position and tilt Z , values defining the measured surface. This point Y , Z , X Y , X orts of photogrammetrists and snow scientists beginning in the ff As we report here, recent advances in digital photogrammetric technology have now cloud can then becorrected gridded image into mosaic a digital (Maune, elevation 2001); model here (DEM) we or use an the orthometrically- term 2 Recent enhancements to airborne photogrammetricOur methods approach relies on threerecent components years. that These have are undergonedigital the much cameras improvement photogrammetric used in to software take usedthat the geolocate to aerial the photographs, maps create and within the the thements. real maps, airborne Our world. GPS chief the We techniques were contribution not has involvedand been with low-cost to these system. develop- integrate these components into a simplified 2.1 Software We used Agisoft’s PhotoscanMotion software (SfM) for processing, algorithmet which al., at uses 2012). a itslizing There Structure core this are from algorithm, at (Koenderinktional though least photogrammetric-processing and software we 7 Van triangulate other haveground the Doorn, that not software positions have of packages 1991; tested been points“point currently imaged all Westoby on cloud” multiple available – the of times a uti- in these. collection overlapping of Both photographs to SfM create and a tradi- of the camera). Interiorcal orientations length, refer sensor to dimensions, pixel thepoint. pitch specifics These of of result the the sensor,Where in camera lens the about distortions, and and modern lens: 10 principle fo- points, software unknowns, no has depending tilt an on information, and advantageif the no the is lens a remaining that priori distortion variables lens itrequired model. are calibrations, as requires provided as input, with no these there adequate can ground is be accuracy. control Because no calculated tilts need are for an not inertial measurement unit (IMU) on the air- with DEM. As partfore the of maps this can process,photos be two and made. include types Exterior 6 of orientations unknowns: unknowns refer must to be the determined position be- and tilt of the all of earth sciences,(UAVs). These being systems primarily are deployed being used onlogical to sites, low-cost map forest unmanned glaciers, canopies, river aerial urban beds,2012; vehicles coastlines, development, Eisenbeiß archeo- and and more Zürich, (d’Oleire-Oltmanns 2009;holtz et Fonstad al., et et al., al., 2013;2014; Gauthier 2013; Rinaudo et Irschara et al., al., 2014; et Hugen- 2012;2012; al., Ryan Whitehead et 2010; et al., Lucieer al., 2014;for 2013; et manned Vanderjagt et Woodget aircraft, al., al., which et 2013; 2013; can al.,without Westoby measure the Nex 2015). et regulatory larger Our restrictions and al., spatial currently techniques scales Remondino, imposedment with on were package UAVs. better Using designed costing an accuracy airborne and less equip- here than that USD 30 we 000 can (excludingpling produce the distances maps aircraft), (GSD) of we as snow demonstrate to low depth show as accurate that 6 to cm. the Wedepth results present distribution produced results heretofore using rarely from this available 3of for technique many field study. of (Fig. The sites the technique 1) in technological takes revealon Alaska developments advantage details of the the of pioneering past snow ten e years, but in principle builds made it possible to nottogrammetry, only but produce accurate to snow do depthaccuracy maps so compared through at to airborne most larger pho- otherin spatial-scales, techniques. at consumer These lower advances camera include cost,power, improvements sensors, and and especially without GPS photogrammetric loss processing software.need of for This techniques, purpose-built software photogrammetric desktop largely cameras anding eliminates computational inertial hundreds the motion of units thousands (IMUs), of sav- dollars. These techniques are gaining popularity across 1940s. cesses such as landslides2003; Krimmel, (Bauder 1989; et Miller al., et al., 2007; 2009). Bitelli et al., 2004; Cox and March, 5 5 15 10 25 20 15 20 25 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | 7920 sets of × ff (about 100 knots) will 1 − 340 339 set by the reduced purchase price, high image quality, and ff 4912 pixels (36 Mpix), compared to the Vexcel Ultracam with 11 704 × 2.3 GPS While the GPS techniquesprocessing we software and use hardware havesubstantially. integration been When have available maps streamlined forcontrol), are the some the user-experience directly time, accuracy georeferenced advances ofpositions. in (that To the achieve is, our georeferencing results, without a isthat modern using dependent can multi-frequency GPS ground on track system aircraft the must position be accuracy to used of within photo centimeters. The three dimensional o the GPS antenna relative tomust the also camera be image determined plane, often forlever referred each arms to aircraft as installation. are “lever In used arms”, camera processing in the position. GPS a Without data, an coordinate the the IMU, transformation aircraft this from frame transformation of the relies reference istion antenna upon aligned is position the with assumption to often the tangent that the violatedaircraft of in its yaw trajectory. can the This be assump- presenceFinally, mitigated the of by time crosswinds, placing that but the thethe photo GPS such post-processed was antenna errors taken GPS directly must associated record. above be An with the used aircraft camera. to traveling determine at its 50 ms position within (92 Mpix), resulting in flight linesof that need overlap. to be In about 60a our % closer DSLR for applications the is same the more amount ease increased than of use o cost of ofmand the and extra consumer competition, camera. flight and DSLRsranked time the are when D800E due driven it image by to was quality enormoussumer using lens specifications consumer released selection. were de- ). (www.dxomark.com The the wide Similarresponsible dynamic highest advantages for range and our exist low ability in noisein to of con- the the capture same D800E image, texture arepast. problems in largely that both plagued bright film-based snow photogrammetry and of snow shadowed in rock the travel 5 cm in aquires a millisecond. timing Thus connection to betweenbelow achieve camera the and a millisecond GPS 5 level. cm with Thereuses signal accuracy are the latencies in a flash reduced variety camera trigger to of positionevent output ways marker re- from this in the can the camera be GPS record. to done; our generate method a TTL pulse that3 places an Methods 3.1 Photo acquisition We pre-planned flight lines and shutterlap, intervals such to that provide most 60 of % the sidelap ground and within 80 % the end- map was imaged more than 9 times. Flight craft. Because the softwarefor a performs purpose-built a aerial photography camera/lensremoved, camera allowing calibration use with of on strong consumer-grade camera-lens the cameras.ware stability To is create fly, is able the the also to point need access cloud,the the the graphics soft- full card. computational resources available, including the GPU of 2.2 Camera For this work wewhich used a costs digital aboutcamera single lens USD 3300. such reflex camera InUSD 1 (DSLR), as 000 000. contrast, the A Nikon the amount, D800E, primary modern, but attribute Vexcel as of high-end Ultracammounts we photogrammetric on photogrammetric- show, cameras DSLRs. might Photogrammetric the is camerasthe SfM their also cost cross-track have software stable direction a in lens adequately greater between comparisonhas number accounts 7360 with of USD a pixels for 300 in 000 DSLR. the For example, less and the stable D800E sensor 5 5 10 15 15 20 20 25 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | sets determined with standard ff erence DEM (dDEM) by subtracting a snow- 342 341 ff ne co-registrations (Nuth and Kääb, 2011). ffi erencing, the two maps were first co-registered horizontally ff erential GPS and has a nominal accuracy of about 5 m. The probes ff cult to assess, but most metrics indicate that 95 % of the points are erencing ffi ff 10cm on most projects. ± erential GNSS method for projects near a CORS base station and using the PPP ff to minimize errors in geolocation using simple 2-D o free DEM from afor snow-covered each DEM pixel to (James et determine al.,2010). the 2012; To vertical Maune, optimize 2001; change the Nuth between di and them Kääb, 2011; Wheaton et al., 3.4 DEM di To measure snow depth, we created a di sub-pixel image correlation techniquessnow-free using locations Matlab. in both Vertical maps alignmentdescribed (e.g., was later, a we done wind-blown found outcrop at thatdetermine or misfits we a or did plowed non-a runway). not As need to employ3.5 sophisticated techniques to Snow probing We tested the resulting snowmeasurements. depth maps We by used collecting about severalHolmgren, 6000 GPS-enabled 1999). hand-probed In depth depth most probesthat cases to cut these through depth do obvious data this snowwe were features (Sturm probed collected (drifts, on along and shallow a traverse areas, griddepth lines etc.), or map. but on in Probe a some spacingthe spiral cases in varied time a depending available way that forprobes on would is the the allow not work, a the length di production but of of was the a typically snow traverse about line 1 and m. The GPS used on the have an inherentof error about due 2 to cm.usually penetration In had of our a the remotewhich surface probe field can roughness tip areas introduce on the into spatial20 a cm substrate the aliasing resolution. wavelength of when snow shorter compared tussocks substrate than to and the airborne ice probe maps wedges that spacing, have 6– lines were uploaded intosurvey-GPS a was Garmin set to aircraft-GPS recordmounted for vertically at pilot in 5 Hz. the display aircraft’s The and camera5 Nikon port. s) navigation. D800E The was The shooting with controlled interval Nikkor by ratefor 24 (typically a mm 2 details), lens custom-built to was intervalometer which (contact scribed alsowww.fairbanksfodar.com above. Typically provided photos were preciseprocessing acquired to as maximize shutter-timing available raw contrast. NEF to Aused files Cessna the to 170B to flown acquire facilitate by survey the later the photos. first post- GPS author was as3.2 de- GPS processing GPS data were processedDi with GrafNav GNSS Post-Processing(Precise Software Point using Positioning) their methodSoler, in 2008). remote Positions areas were (GaoGPS automatically and solution interpolated Shen, using within 2002; theintervalometer GrafNav Snay to event and from TTL markers pulse the created converter. 5 Eachassociated Hz by photo with position the image was filenames camera exported to and flashinto create manually an port Photoscan exterior through orientation Pro file the positions that along was is imported with di the photos themselves. The true accuracy of photo We used Photoscan runningand on a a high end dualtypically GPU Xeon initiated for within eight-core map Photoscan computer construction. toconstruct align with To the make the 192 geometry, photos, individual GB build optimize maps, a Ram the mesh, aprocessing bundle and times adjustment, batch export ranged file a from was DEM 2–24 h, andresolution. depending orthophoto on product. size Total of the project and processing within 3.3 Map construction 5 5 15 10 20 10 15 25 20 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | er- ff 1km and erent pho- × ff 16cm. The photogram- ± 5km and encompasses the full range × 343 344 30cm and precision of ± erent design to validate our accuracy and precision ff ers from the one described above in that it used a 28 mm lens and ff erent thickness, and which change shape and depth frequently. Due to security and The Minto Flats site was selected because of its undisturbed snow cover and het- The Fairbanks International Airport was selected due to its convenience and snow The Hulahula River valley was selected for our snow research due to its history of ff 3cm. The airborne imagery was acquired in a variety of lighting conditions, including modate aircraft operations. Theblowing, runways are grading, kept and clear removal,di of all snow, of which which requires create snow berms adjacent to the runways of 1995). The airborne study areaof was about these 2km terrain elements.the Our largest snow-probe lake measurements in wereair). made the Using three at area separate the and GPS-enabled probes, edgemade cover 2432 on of about snow 2 depth 9 April measurements ha 2014, were and (about largely in cross-track 1 % a separation grid of of pattern0.1–0.6 the about m. with area 6 along-track We m. separation mapped made of Measured by about(Table six snow 1 2); m depths airborne we largely maps ranged also of from made this two area other processed maps to about on 15 3 cm April GSD using lidar and a di other issues, snow probingpoints at and the we airport dositions was not over limited the statistically to airport analyzeusing collection the these (Table snow-free 1) of data. runway mostly a as We control. for few made The assessments hundred six maps of made airborne were accuracy acqui- roughly and 5km precision, erogeneous terrain. It isusing located a about ski-plane 50 to km land westswamps, on of areas its of Fairbanks many shrubs, and frozen spruce lakes. can The and be area birch accessed is forests, and characterized taiga by tundra, snow cover (Sturm et al., processed to 6 orground 12 control cm points GSD. (GCPs); We all± used GCPs a used in GPS thislow-angle to paper mid-winter measure sun have 29 an and beneath taxiway accuracy a of markings thick about as overcast. ways. Near the runways and taxiways the snow gets extensively reworked to accom- characteristics. It is locatedbanks only and the a plane few we2014, kilometers used about from for 43 this the cm work of University is snow of located fell there. Alaska and During Fair- remained the winter undisturbed of in 2013– the infields between run- metric system di derived from the fully coupledences GPS/IMU in processing, and processing there workflow werea as other 12 cm minor well. ground di This sample photogrammetric distance (GSD). DEM was processed to 4 Study areas and measurements We collected data from three studyMinto areas Flats, in and Alaska: the the Fairbanks Hulahula International River Airport, watershed. togrammetric system for validation, ason described 2 above. April We also usingorthoimagery measured spray as 21 there paint GCPs was to no create intervening snow markers; fall these orhydrological remained melt. studies, visible its in relationship theproject, to 3 and the its April nearby, long-term relevance to McCall ecological Glacier research research in the Arctic National Wildlife Refuge routed its photo event markersthe through GPS/IMU the data, IMU the associatedbetween with software the is the lidar able GPS system. to antenna With directly and calculate camera. the Thus full image lever positions arm from solution this aircraft were 3.6 Validation DEMs On the same day(3 we April acquired 2014, a described photogrammetric below),DEM DEM we from at also a acquired the system a Minto lidar of Flats DEM slightly study and di area a photogrammetric assessments. This lidar and second photogrammetric180 system flown were carried by in the ais second Cessna based author and upon were aIceBridge operated Riegl flights simultaneously. Q240i This in and lidaris is Alaska. system the particularly The principal well system system characterizedgram has used of with been for boresight dozens angle NASA’s in of determination Operation confidence extensive calibration and it use monitoring flights has (Johnson since and et an 2009 a al., accuracy and careful 2013). of At pro- 95 % 5 5 20 15 10 25 20 25 15 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | sets ff erence ff . The GCPs are sets erence between two ff ff 8cm, as we described ± . A longer term project seeks ff . Using this method, the millions geolocation o 2.5km. No GCPs were acquired. sets × ff sets to one of our maps, which we defined ff 345 346 3cm). We determined horizontal co-registration ± , about 6 % of which is covered by glaciers (Nolan 50% of the full range. 2 ± 47.5% about the mean for non-normal distributions; range to indicate the level of accuracy or precision at ± co-registration o ± with snow depth from 0–3 m. Airborne mapping was done on erences ff ff , and then compared this map to the other maps (Maune, 2001); cient to produce maps with excellent agreement to our snow probing data, ffi reference map sets using standard image correlation. We calculated vertical co-registration o We used two methods to assess accuracy. In the first, we assessed the di We report our precision as 95 % of the RMSE elevation di ff our assessments we usethe the 95 % confidence intervalsimply for cite normal the values distributions of (following points Maune, 2001) and we between the maps and GCPs, calling the results proprietary and essentially black-box, weysis could so not we conduct empirically a first-principle assessed error map anal- errors, largely following Maune (2001). In all of with 7 or less data points, we use accurate to about 3 cm,well-distributed throughout but the the study most area, weond making method, this have we a for applied weak these any test geolocation oneas spatially. o In site a the is sec- 29we and term they these are map not of di pixels of the entire referencecontrolled map by become the pseudo-GCPs, with precision theirbelow) of accuracy rather the largely than reference map theo itself GPS-GCPs (about ( DEMs after they have beenof optimally both co-registered. spatially Using correlated this andmetric. method, uncorrelated the Given errors magnitude that are our capturedthis in precision was the is su same on precision thewe centimeter-level did and not that distinguish we theTechnically later this amount show of RMSE that spatial-correlation measures within thewhen this precision computed 95 of % from a RMSE dDEM, two further. no not maps gridding an where artifacts individual are no DEM, present changes but the (both in described maps later), are the the and surface metric therefore have defines the how occurred level identical of and change-detection possible in the dDEMs. Our goal in thisalign section with the is real-world to withoutcation answer using errors, ground two control?” how questions and identical “How “Afteroccurred?” are correcting well These for our do geolo- questions maps our addresscause assuming airborne map both no maps accuracy changes the to and photogrammetric the precision, and surface respectively. GPS have Be- software we used to make our maps is 5 Assessment and validation of map accuracy and precision at snow-free areas. The plowedwe runway could in do the this airport statistically; at datapixels other was for sites the spot we only used measurements the location only. orthoimages where to locate snow-free to understand current ratesthrough the and photogrammetric volumes techniques we oftions describe snowmelt, here; will these glacier be environmental addressed ques- melt,of in and snow subsequent aufeis in papers. melt the Thethe Arctic snow-probing Hulahula National was River Wildlife part valley Refuge ofa were (Sturm a collected flat et study in river al., three terrace 1995,the terrain with river 2015). types a with The on snow thin data 18 depths (15–20cutting in varying cm), March into from uniform 2014: a 0.2–0.6 (1) snow m, 40 m and cover,20 (3) blu (2) April a (snow-covered) a series and set 15 ofthe of June drifted-in snow-covered 2014 gullies islands map (mostly in snow-free was exceptlittle made in change 31 drifts). had days Though occurred after into snow the about depths probing, 20 over cm this our GSD period. results and The indicate covered DEMs an that were area processed 14km (Nolan et al., 2005, 2011;the Weller valley et extends al., from 2007). the Located continentalwith 500 divide km a of north-east watershed the of of Brooks Fairbanks, about Rangeet 1800 to km the al., Arctic 2011). Ocean, not Unlike the most major watersheds hydrologicala in lesser event the extent of Alaskan aufeis. theto As Arctic, year disappear the the and due climate allow snowmelt to warms, snowmelt however, pulse the to these dominate influence is ice the of reservoirs run-o are glaciers likely and to 5 5 10 20 25 25 20 15 10 15 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | set set sets ff ff ff 30cm or 30cm as set could ± ff ± sets through ff set was 23 cm 10cm remains ff erences in relief ± 0.05m. Because ff ± sets (Table 1, Column 3) ff set is about ff set of 30 cm and a vertical o ff sets of the other maps made at the ff erence may relate to di 0.07m, with 5 of the 7 maps clustered ff ± sets ff 8cm, as described in the sections below. In sets 348 347 ± ff 2000m) surrounding the centerline of the run- × sets of the other 5 maps from the airport time- sets in Table 1, we define this October map as set was applied given that a subpixel o ff ff ff 30cm. The di 30cm when photo position accuracy is ± ± set was applied to our 3 April photogrammetric DEM to create erences in scale. In any case, overall we conclude that our ff ff erent systems and found that they confirmed our results. ff 30cm, noting that is likely conservative. The underlying causes for why set was less than 15 cm (one pixel). The mean vertical o ± 15 compared to ff ± 30cm and precision better than ± Overall the Minto Flats data showed better co-registration accuracy than the airport We repeated this same analysis for the Minto Flats time-series (Table 2). As shown Our overall assessment is that our maps (at 6 to 15 cm GSD) have accuracy better We measured 21 GCPs at the Minto Flats site. These targets were circles on the The results of these two GCP tests indicate a geolocation accuracy of data, about accuracy was using a block of pixels (roughly 20m by making spot measurementsorthoimage. of These the showwithin dDEMs a half in scatter that. snow-free of areas only located using the of the terrain – theweaker airport is due nearly to flat and fewer thus di perhaps making the solutionmap geometry geolocation accuracy is unclear but likely is related to the photogrammetric bundle5.3 adjustment. Precision The primary challenge inon determining the map precision ground isexample, at that surface change the many at real centimeter this changesconsolidation level level occur or of higher that the can ground, confound be or(Esch, caused the by 1995; by compression precision frost Ménard of heave et vegetation assessment. and under al., thaw For the 2014; weight Sturm of et snow al., 2005; Taber, 1929). Thus the design of ter (Table 1, Columns 1–2). We calculated mean vertical o way, which was largely snow-free throughout the winter (Fig. 2). The range of o there was no large snow-free surface like the runway, we determined vertical o in Table 2, the full range of horizontal co-registration o (highest minus lowest, last row(2nd Table 1) to is a last better row, indicatorreference Table of DEM. 1), accuracy As than as the discussed thenot mean in mean completely more could snow-free, depth be sochanges in due to the the Sect. to surface. range 5.3, Nonetheless, a of botha this systematic the reasonable vertical “snow-free” mean issue co-registration and accuracy. error area with the was has the range indicate been impacted by real image correlation of the snow-free runway markings, rounding to the nearest centime- than this paper we dobut not note address that whether thisassessments, accuracy remains we or to compared precision be onesame vary day explored. of with using To our larger di validate reference GSDs, these DEMs accuracy to and two precision DEMs made on the We measured 29 GCPscated at within the 300 airport. m Thesequisition of and were each found made other. a at We mean taxiway compared horizontal markings, geolocation these all o to lo- the October snow-free ac- (Table 2). This vertical o the reference map; no horizontal o better. 5.2 Accuracy from co-registration o We assessed theseries co-registration relative o to October reference map. We calculated the horizontal o 5.1 Accuracy based on geolocation o of 13 cm (Tablethe 1). reference Applying map the to determine o co-registration o ment within the orthomosaic, butgeolocation they o were suitable for determining that the horizontal snow surface made with orange spray paint. They were too small for sub-pixel align- airport. not be reliably determined. 5 5 15 25 20 10 25 10 15 20 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | er- ff erence 6cm is erential ff ± ff set we found sets in Table 1 ff ff cient to measure sets described in ) and found 95 % ffi ff 8 10 × 6 6cm). Whether these di n > ± , 2 15km 6cm (all subpixel). The mean value of ± ∼ 6m), we found that 95 % of the RMSE × 10cm, which is excellent. 349 350 ± erence between the runway blocks were less ff 44cm. This distribution was non-Gaussian, with ± 3cm (95 % confidence). ± 6cm or better when the confounding influence of real ± erently than we did at Minto Flats. At the airport, we ff ers slightly. We measured the scatter of these centerline 100 pixels (6m ff × erence DEM, with Fig. 2b showing the corresponding snow-covered erences had a range less than 12 cm ( ff ff 10cm (about twice the standard deviation shown in Column 4). Visual inspection ± To assess the horizontal precision, we used custom feature tracking software (Mark Next we examined the scatter about the mean co-registration o First, we examined the data graphically as is shown in Fig. 2a–c. Figure 2a shows an Finally, we extracted elevation profiles down the centerline of that block where plow- Thus our overall assessment of the airport time-series is that is that both vertical pixel displacement about the meandisplacement was was within also within a few centimeters of the co-registration o of the vertical variation to be within tracking software Imcorr (Scambosmeasure et al., velocity 1992). fields Such ofet software glaciers al., is 2005; from commonly Huang optical usedrunway and markings to and Li, and radar 2011). many satellite other Inbetween surface our imagery them features case, detected are (Berthier because not by moving, we thisthe any software know maps. relative indicates Using that motion a the the lack twoand position of snow-free of search horizontal orthoimages chips precision (6 within October of and 100 30 September 2013) surface changes is removed. 5.3.2 Minto Flats precision assessment Here we compare twointervening snow DEMs fall or of snow melt thethe (6 Minto dDEM and 8 Flats of November). Once area the co-registered made entire we created two area days at apart 15 cm with GSD no ( Fahnestock, personal communication, 2014) using a python version of the feature- ences are due tomisalignments) is frost not heave known, oran but outstanding spatially-coherent the result noise fact and, that (perhapssnow as 95 depth caused % we variations describe of by at in the photo centimeter Sect. variation resolution. 6, is more within than su through whole-image correlation (Table 1), as expected. and horizontal map precision is transects as function of distancetransect along points the was runway. Here 21 the cm,length maximum the these range mean between di range was 9 cm, and over 95 % of the transect topography, though each di example of a di scene for reference. Figure 2csnow-free shows runway. Over transects the from crest all of 6the the maps elevations centerline compared that where to extend plowing across within is the best, we found that ing is best toshows further that eliminate each of the these influence transects of captured snow the same (green 10 line to in 50 cm Fig. variations 2a). in runway Figure 2d our tests is largely aboutthe controlling precision for such at confounding influences theused and the airport we same time-series assessed di ofassessments. the At snow-free Minto runway Flats, sections we that comparedchanges we the were used 6 negligible. for and accuracy 8 November maps as intervening 5.3.1 Airport precision assessment We tried toReal assess changes vertical in the precision surfacemagnitude), in elevation yet were several the present precision ways in was using these still tests the excellent. (though runway of unknown time-series. Sect. 5.2. We did this over awinter. Column block of 4 the of runway Table 1 that indicates was that kept once largely co-registered snow-free using through the o (Columns 1–3), 95 % of the vertical di frost heave and settling. Despite theseelevation), confounding we influences still (real found changes only in a surface range of than of the orthophotos (e.g.,clear Fig. of 2b) snow shows and that changed this between block maps. of Further, pixels our was inspection not of completely the di maps indicates that spatially-correlatedsegments variations separated by of expansion joints 5–10 across cm all in of the elevation tarmac, occur suggesting di over 5 5 15 25 10 20 10 25 15 20 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | - ) 6 ff and 10 2 10cm, − ± n > , causing 1 − set of 21 cm, ff 8cm is a reasonable ± set from our reference DEM was only 2 cm 352 351 ff 8cm. These distributions are shown graphically in ± 15m. We cropped the dDEM to include only a large lake ( ± 8cm (95 %) over the largest lake in the area. While we don’t have any ± erence in scatter is largely caused by spatial aliasing of trees. Minto Flats’ ff erent imaging physics between lidar and photogrammetry and by aliasing arti- erences observed above that precision level were due to trees, likely caused by ff ff The results of all these tests indicate that our reference DEM is essentially identical We co-registered the validation photogrammetry with our reference DEM using the Comparisons with the lidar DEM similarly validated our results. We created a 100 cm Based on our results at the airport and Minto Flats, we believe This di 51cm (95 %), but over just the largest lake in the area the variation was only sets. Once co-registered, theover the vertical entire o domain. Visualerrors, inspection such of as the warps dDEMall or showed di tilts, no greater spatially-correlated than the lidar’s precision level of 16 cm. Nearly to the validation data and thus validate our specifications to the best of their abilities. the di with the latter beingidentical a to those better in test Fig. 3a. inthat We terms statistically performed the a of Kolmogorov–Smirnov two validation; test samples andconfidence these are determined from level. distributions the This are same is nearly continuoustween shown distribution the at graphically hypsometries the of in 95 the % Fig. lidar 3b, and which the reference shows DEM. the similarity be- facts caused by the 100 cm± GSD. Over the entire domain we found a dDEM variation of with variation of same methods previously described and found a vertical co-registration o formal accuracy or precision specificationsto for the the validation system system, given thathave its similar created similarity specifications. the reference DEM it seems reasonableGSD that DEM from they should the lidar point cloud, which had a point density of 2 points m comparing our reference DEM forlidar Minto and Flats (3 a April) 2nd to photogrammetric DEMs system on (Sect. the 3.5). same day using a footprint of aboutcause 100 cm. we We have then noDEMs resampled orthoimage and the then reference for used DEM the these to lidar, for this we sub-pixel GSD. created image Be- correlation shaded to relief calculate images horizontal of o the tree-tops to change position atthat this within resolution. Visual clearings inspectionThus of between any the mapping the dDEM system confirms trees creatingaliasing a the DEM issues, at precision and this is GSD ourartifacts would the are precision have not these is same present. same therefore spatial as best on determined the wherevalue lakes. such for the gridding precision of ourrors method. exist If any in warps, our tilts,should or data, be other spatially-correlated repeatable they er- to arefacts. 8 largely cm, confined exclusive of to any within spatial this aliasing5.4 level. or Thus other our gridding Comparison DEMs to arti- validation DEMs Here we seek to validatetion our “How accuracy well and do precision our numbers DEMs by compare answering to the those ques- made by other systems?” We do this by and found the variation dropped to tails extending to Fig. 3a. trees are skinny spruceeach other and by leaf-free a tree birch,15 length cm up or GSD, more, to our like 20 DEMs a mand forest are thus of tall, not most widely typically able trees scattered to are flag separatedof represented resolve poles. from tree by Even these height several at spike-shaped pixels with targets that surroundingin each adequately ground average the height. some DEM, The fraction with result isthat cone tree. that height Because trees dependent these appear on cones asor how are cones origin so the coordinates narrow, DEM slight can mesh cause errorsof happened dDEM in these errors horizontal to approaching maps co-registration lie the was over heights also of made the when trees; winds one at ground level were over 15 ms 5 5 15 25 20 10 15 20 25 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | er- ff erent footwear ff erence between probe and map values, ff 353 354 set for those measurements was 10 cm. For ff 30cm. But when suitable ground control points can be ± sets greater than 15 cm were located in areas where the erences is directly related to how well we can co-register ff ff ectively improved to the level of the precision of our maps, or ff erence maps. Here we found such snow-vegetation dynamics ff 8cm. Here we describe the accuracy our photogrammetric snow depth mea- ± Figure 4d presents a comparison of about 500 probe measurements typical of the vegetation was compressible, such as inmapped the summer tall surface grasses near in the thesethis edge areas canopy of is the becomes lake. the compressed The snow top to of depths the the in point vegetative the canopy.were where In di causing it winter, up can to even 30 produce cm “negative” of error. That is, the maps we produced here were no less data set. The standard deviation of o entiate these individual tracks based6 on to their 10 indentations cm (Fig. deep 4c), andeach which about have 10 ranged an times from as independent wide.better nominal The depth GPS accuracy precision units of than embedded about into theof the maps 5 the m, probes but probe thus a measurements coarser thethem together horizontal ground properly precision. suggested data relative Analysis there to has of was the all independently no footprints, likely varying. single because Short shift each that of probe’s GPSdependently aligned manually accuracy shifting to was each the ofpossible. corresponding the This 2432 footprints, meant measurements that there in- footprints’aerial disturbance was to mapping no the of snow simple snow depthwithout spatial was depth, included exact alignment but in co-registration not the results the in conservative, the depth as ground we comparisons show probe were next. data. satisfactory Nevertheless, and even thus our the full 2432errors), measurements, the including standard those deviationreveals made that was nearly 26 within all cm, of the but the o careful forests visual (with examination aliasing of imagery 6.2 Minto Flats snow depth analysis Before statistically comparing ourminus probe 28 September measurements 2013), to wetimally the assessed co-registered whether dDEM to the (03 the proberesolved maps April measurements in using were 2014 the our op- DEM footprints and(ski, in orthophoto snowshoe, the or (Fig. snow. boots), 4a These and and were the b). clearly resolution We of each the wore map di was such that we could di found, this accuracy is e about the two DEMs used toto produce finding the dDEM. snow-free Thissuch areas co-registration as error, that vegetative in compression, are turn, frostsnow-free is not heave, ground related aufeis control confounded melt, points, by the orgeolocation accuracy real erosion. accuracy, of or Without changes our about suitable snow to depth maps the is surface limited to our surements by the standard deviationas of the the di mean iscies a independent function of of systemreal ground precision. changes control occurring As and on before,map-probe co-registration, the analysis our which at ground, assessment three have which sites: is accura- Fairbanks, the we and confounded Fairbanks describe the International by Hulahula Airport, below. River We Minto valley, Flats as conducted near described this in6.1 Sect. 4. Airport snow depth analysis Due to security and other issuesof we snow were depth. only able We toand found collect groomed the a ramp area few undisturbed snow spot snow depth measurements than to depth be 1 m. to 10–15 cm, Comparison be and of the about thesein plowed drifts 43 values the cm, to to caption be the Fig. of greater 2a packed Fig. shows 2. close agreement, as described 6 Snow depth mapping accuracy Here we address the questionsnow “How depths?” well To do do our this photogrammetricments. techniques we The measure compared mean of our these maps di to over 6000 snow probe measure- 5 5 25 10 15 20 10 15 20 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | - ff sets ff set here was ff erences over liquid wa- ff erence map with snow depths of only 10 ff 356 355 8cm), but the fundamental assumption that the ± set needed to be removed to reduce the map-probe mean ff set of 55 cm. Subsequent analysis of the probe data indicated ff erence maps in the active river bed is complicated by the fact that our pho- ff erence map as a change in snow depth. In winter, the river bed surrounding the erences between maps were caused only by snow accumulation had been violated set beyond 10 cm is likely attributed to (1) uncorrected probe positions resulting in set. Using several snow free areas identified using the orthoimages, we determined set to zero over the snow-covered points that had the least likelihood of there being Map values along the terrace (orthogonal transects in Fig. 5) showed even better The island transects (Fig. 7) revealed a similarly strong correspondence between Figure 6 highlights some results from the gullies. Here a series of ice wedges have ff ff ff ff ff set of all 1111 sampleonly points 10 spanning cm, a compared transect to ofbe 1.6 15–20 km cm explained had for by a the the standard otherized deviation relatively terrain of by homogenous types. a terrain This low low of shrub variance wide, cover could shallow where slopes sprigs character- and branches poked through the consistently correspondence with probe values than they did at gully and island sites. Here, the o were caused by eitherthe the probe edges being of stopped theinfluence by island the river correspondence. were ice Nevertheless, obscured eroded, theand by or agreement the snow both. between probe or values the On that is map the still depths island excellent. itself, numerous shrubs island was completely snow coveredisland’s and summer the boundaries transects (Fig. extendedcates 7a). over changes In the up most edge to of a ofof meter the these our larger edge than di locations, revealed by thetogrammetric the technique map probe does indi- not (Fig. work 7b). as Interpretationoutside accurately the over scope water, for of a this variety paper.pub. Further, of our data) reasons stream show gauging that measurements (Nolan, thehere. un- water Thus height extra care in in springter interpretation can bodies. needs be Given to over our be a taken meter map of higher precision, di than it in is fall therefore likely that remaining edge-o map and probe datadi as well as new sources of confounding error in interpreting the 20 cm, not including points whereo the probes did notmisalignment reach between the probes bottom. and The maps, bulkspatial which of depth matters this heterogeneity more in is steeperterrain’s larger, terrain (2) surface where a roughness spatial ofprobe sample spacing, 15 bias cm and caused (3) on real byheave. surface spatial the Considering changes tussock such wavelengths these as potential below vegetative sources compressibility GSD of or error, and frost the below agreement is remarkable. to 20 cm. Figureabout 6d 20 cm reveals to a theimage snow left in depth of Fig. it. of 6b, These near whereComparison values exposed zero can of tussocks be to about can qualitatively the beprobe 200 confirmed depths seen right by probe and to the of the points the winter features the rightthat in delineated exceed gully but by Fig. the probing, not and 120 including 6e to cm those range the reveals parts of left. that of the the probes. the gully The standard maps deviation match of o the di with snow depths ofbe 100 seen to in 200 cm. both To the the summer right image of and the di gully a polygonal network can where there is compressible vegetation. 6.3 Hulahula River snow depths Similar to the otherthe sites, same we image correlation begano technique this we analysis used by in co-registering Minto the Flats, we DEMs. found Using no horizontal thermally eroded to form a connectedis drainage completely system. drifted In over winter, this by drainage snow, as network can be seen by comparison of Fig. 6a–c with 6d, there was a verticalthat o 20 cm of thato vertical o vegetation compression. Considering the surfaceis amplitude about of 15 the cm, tussock these tundra shiftsNevertheless, here are this small and process within highlights theare that noise co-registration, of the and other primary confounding that factors. overcome errors determining using in co-registration ground snow below control points. the deptha Once 30 accuracy cm the dDEM maps level and were can co-registered, compareda be we large it created river to terrace. the probe values in the gullies, on the islands, and on precise than described in Sect. 5 ( 5 5 25 20 15 10 25 20 10 15 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | ne ffi set was ff erential ablation ff set as being real. ff set could be eliminated ff cult to overcome in the future. ffi ciently accurate to measure snow ffi erent GSDs on results by using a 40 cm ff set is only 10 cm despite these confounding 358 357 ff erences there can be reduced to zero, a single a set for the terrace points than used at the islands or ff ff erence map are being caused by snow depth. Because our ff value of 0.35, indicating that the two sample distributions are the D erent co-registration o ff set between map and probe for all 3382 points measured at the Hulahula site ff set, but it did change the individual pointwise comparisons. That is, comparing ff The issues of contrast and lighting that plagued the early pioneers of film photogram- The o metry to map snow deptheras have and largely been in-flight overcome. exposure Withthey evaluation, do the not flat advent prevent of measurement. lighting Two digital types conditionstrast of cam- map in are errors deep still are shadows produced challenging or bycontrast lack flat but of features lighting. con- are In reduced, the resulting worstIn of in this these the cases, case, point the either cloudwill spatial density the result. density also of This resolution being does of reduced. occur,such the but areas, rarely. the DEM Depending sensor must on noiseware camera be itself as settings can reduced real (and be contrast or misinterpreted camera) features. a in by Because the the void photogrammetric location of of soft- this no sensor data noise changes from translation appears to be enoughference to maps. co-register A an key entire lesson mapsnow-free, learned and but create here also excellent is that dif- they that(as be it at free is the of not airport) confounding enoughnon-photogrammetric or real that challenge vegetative changes for these compression such mapping points (as of as are attation thin frost Minto that snow heave changes Flats). packs in relates Similarly, the the to di the primary interpre- ten a function of snow coverrate itself, such of as thermally-driven the amount frost ofwere heave. vegetative still However, compression given or good the that without oursuch accounting map-probe errors for can comparisons these be ignored. errors, it is clear that for many purposes technique can measure changeat at that the centimeter level becomes todepth. decimeter noise These level, when confounding any interpreting changes real in the change surface results elevation as are purely all changes site dependent in and snow of- GPS data and how itwealth is of used within airborne-GPS the research photogrammetric10 from bundle cm lidar adjustment. is Given studies, currently the it a is hard likely that limit a and map one accuracy that of will be di had a standard deviation of 16above. cm, We without briefly filtering explored for the any influence of of the di sources of error noted However, as we have demonstrated,peatability geolocation (precision). (accuracy) is As not longcan as as be important stable, found as such snow-free that re- points the map within di the mapped domain influences. GSD compared to a 20 cmof GSD; this o did not appreciably changemap the data standard to deviation map datadeviation, (20 to which 40 is cm GSD) onhalf at the the of probe order the locations of 16 ledreal cm to the change a variation on precision 7 we cm the we standard ground. foundconfirmed found The between by similarity in map between a Sect. and mapbelow 4. Kolmogorov–Smirnov and probe the probe Thus test, may critical data perhaps be which setssame attributable is gives at further to a the value 95 %photogrammetric of confidence maps level. 0.06; are That just this is, asdepth, to is accurate despite the as well the the best probe many ofinto data confounding our the for influences ability maps. characterizing to besides snow determine, depth the that are incorporated 7 Discussion The photogrammetric method described herepacks is of su nearly anynological thickness, challenge though for improvements the are future possible. The is primary improving geolocation tech- accuracy, which relates to Indeed, it is again remarkable that the o 18 cm deep snow. However, despite the better standard deviation, the mean o 10 cm, as opposedusing to a zero di atgullies, the but compression other of sites. the relativelyor This uniform drifting vegetative mean of canopy, di the o prober’scise snow machine geolocation track of over the the intervening snow month, probe or data the impre- could easily explain the o 5 5 25 20 10 15 10 15 20 25 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | ers sev- ff the road sys- ff erence maps, these summer and ff er the advantage of mapping large ff 360 359 ort cannot be avoided unless one uses a UAV that can ff ered from them. These contrast issues can be avoided completely by waiting ff Photogrammetry from manned-aircraft thus fills an important gap between ground- While lidar is also typically flown from manned-aircraft, photogrammetry o While there is currently a lot of interest in using low-cost UAVs as platforms for SfM quality, though they operate onmay larger spatial-scales not where be such required, resolutionniques such and that quality as can to measuresistent detect ice Scatter change sheets techniques, at require dynamics.of the substantial Those limitations expertise centimeter satellite (look-angles, to tech- level, shadowing/layover, implement,nence, such phase have etc.), as decorrelation, a and scatterer InSAR variety haveNolan perma- and and high its Fatland, data 2003). Per- Given costs thefrom limitations (Delacourt manned of et aircraft, satellites and al., mostto the cryospheric 2007; cost extrapolation of of scientists Ferretti repeat ground-based et studying lidar with measurements landscape al., the using change 2001; GPS hope resort and thatstudy increasingly their of sUASs, measurements snow-depths areaircraft has fills representative demonstrated a of that niche theof that using broader ground-based approaches photogrammetry area. measurements, the from Our erosion, spatial-scales for thermokarst, manned- of about aufeis satellites dynamics, the with and price landslides the are of accuracy all either. examples of Glacier topographic melt, coastal based and satellitechange methods, in not topography. No just satellite for methods snow can depth produce DEMs but of for our measuring resolution nearly and any spatial-scales, but the photogrammetric methodthat allows is creation perfectly co-registered of with aunambiguously the color identify DEM. orthoimage what For snow ispacks studies, snow or this those and image covering what allows aufeis, is aslike us barchans well to not, as and especially useful sastrugi. for useful Whenwinter recognizing interpreting in structures images the thin in allow di the snow- us snow described to related to investigate vegetation changes or thatric sediment erosion. seem system We suspect, found had that such about our16 as cm photogrammet- twice respectively) those the and we precision abouttem the as can same measure the accuracy, thinner and lidar snowpacks thusalso system more the substantially accurately. we The photogrammetric less photogrammetric compared sys- expensive system than tofor is most research (8 lidar groups vs. units, wanting to reducing operate the their cost own of systems. ownership required without use ofcosts multi-frequency well GPS above and USD 10 high-quality 000Our optics, and goal which increases is increases payloads outside alsothousands the to of limits square measure of kilometers, snow and most this sUAS. depthtally simply a of is sUAS not entire is feasible a with watersheds, field sUASsFor covering tool – example, requiring fundamen- hundreds-to- we the same flew logistics ouraway as Hulahula ground-based – measurements. missions to as do day similarattendant trips work logistical from with support Fairbanks, over a and 500Fairbanks, sUAS km costs; would would require even require overcoming our similar a worktem, challenges. multi-day at Thus an field for Minto expedition expeditionary use Flats, with field o truly only e replace 50 km a manned-aircraft, from suchet as al., a 2011; Predator, Schreiber Global et Hawk,expensive al., or 2002; than Sierra Whitlock, (Fladeland the 2014). Such mannedfly UAVs aircraft are than considerably we more small used, UAVs,undefined are and in considerably have the more a US.out complicated commercial Thus most to manned-aircraft regulatory of are Alaska, componentground-based the where that fieldwork most our is is reasonable research not currently is choice planned. based, through- to cover largeeral areas advantages or for when mapping other snow depth. Both o photogrammetry (also known assearch small requires Umanned manned aircraft Aerial forfuture Systems, several to reasons. or adapt our Though sUASs), methods it our onto may a re- be sUAS, we possible could in not the achieve the precision our needs image to image, topographicbut noise in steep results. mountainous This terrain noise canerrors reach in is 10–20 this typically m. paper We on because didpaper they the not su occur formally 1–2 rarely, m address and such none level, offor the better study lighting, areas used andDEM in when and this confirmed these by errors the do orthoimage. occur they are easily identifiable in the 5 5 20 25 15 10 15 25 10 20 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | ord it. ff 8cm at a spatial resolu- ± erenced to detect changes ff -the-shelf software that is reasonably ff 10cm when confounding influences of ± 362 361 30cm and a precision of ± We would like to thank members of the SnowStar 2014 team for data col- for field support, and Mark Fahnestock for feature tracking assistance. 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Glaciol.,Legresy, 46, B.: 145–149, Surface 2007. motion ofmote mountain Sens. glaciers Environ., 95, derived 14–28, from 2005. satellite optical imagery, Re- techniques to monitor landslideSensing bodies, and International Spatial Information Archives Sciences, of 35, Photogrammetry, 246–251, Remote 2004. cal Society, New York, AGS Report, 11 pp., 1959. tinuous mapping of snow depthCryosphere in Discuss., high 8, alpine 3297–3333, catchments using doi:10.5194/tcd-8-3297-2014, digital 2014. photogrammetry, The conditions and avalanche2050 activity and in 2070–2100 periods, a The2014. Cryosphere, warming 8, climate: 1673–1697,, doi:10.5194/tc-8-1673-2014 the French Alps over the 2020– Cline, D. W.: Digital photogrammetric determination of alpineConway, snowpack H. distribution and Abrahamson, for J.: hydro- Cox, Snow stability L. index, and J. 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Geophys., 20, 67–89, 1982. 5 5 10 15 20 10 20 15 25 30 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | - ff set are co- ff 0.08 ± sets of that DEM minus the reference ff sets to 29 GCPs. All other o ff 0.16 0.02 0.14 snow melting 0.29 5.20.37 6 Peak snow 0.04 4.2 12 Snow covered ± − − ± − set (m) set (m) Dev (cm) (cm) ff ff 370 369 05 . 0.510.480.18 0.45 0.240.34 5.30.21 5.8 0.13 6 6 Snow free Snow covered 5.1 0.09 0.46 0.31 5.0 14 Mostly melted 0 0.22 0.07 − − − − ± − − ± set (m) o set (m) o ff ff 02 0.15 0.11 0.13 0.13 0.18 0.25 . − − − ± − − 0 0.05 0.010.07 0.250.06 0.23 0.03 0.22 0.15 snow free 0.30 0.15 Frozen, snow dusting set (m) o − ± − − − ff set (m) o o ff o sets of that DEM minus the reference DEM for the snow-free area of the runway. ff Fairbanks International Airport accuracy and precision assessment. Values for the Minto Flats accuracy assessment. Values for the reference DEM are geolocation o (Range/2): Means: ± Date3 Apr 201428 Sep 2013 Easting Northing Elevation 0 GSD (m) Notes 0 0.23 0.15 Reference Map 27 Jan 201419 Apr 2014 6 Nov 20148 Nov 2014 0.02 0.01 0.26 0.15 0.03 0.02 0.15 snow covered 0.15 Frozen, snow dusting (Range/2): Date6 October 201330 September 2013 21 January 2014 18 February 2014 Easting 0 NorthingMeans: 0.02 Elevation± Elev. St. 0.30 GSD Notes 0.13 1.7 6 Reference, snow free 3 April 2014 20 April 2014 sets to 21 GCPs. All other values are co-registration o Table 2. DEM. Statistics at bottom do not include the reference DEM. registration o The group statistics at bottom dorepresent not accuracy include the while reference the DEM. 4th The first represents 3 precision. columns of numbers Table 1. reference DEM (6 October 2013) are geolocation o Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | (red) and ff ) respectively. Com- c and b 372 371 . This map was created by subtracting a summer DEM (b) An oblique view of snow depth in the Hulahula River valley in Arctic Alaska pro- 16cm standard deviation. Figure 2. duced by the photogrammetricand technique small described gullies a in few the metersters in in text. width depth In are (light the filled blue). with foreground At snow ice ranging left, wedges from drifts centimeters over to 2 decime- m thick have formed in the lee of a blu Figure 1. (a) have not completely melted by June (13 June 2014) from a winter DEM (20 April 2014), as shown in ( parison of our airborne± measurements to 3382 direct measurements here show agreement to Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | a we have pro- (c) 2m. In ± , which extends the length of the runway) (a) 373 374 6cm. erences between five maps and the reference map of ± ff . The UTM 6N graticule is overlaid at 100 m spacing in ( We examined elevations from each map along a transect (b) (e) 10cm. The excursions from normal distribution are likely caused by ± . Over most of the plowed runway, the scatter between measurements is erence map (21 Jan 2014 minus 6 October 2013) produced by our tech- Histograms of the di (a) ff (d) A di ). The natural accumulation level of about 43 cm (green) can be seen in all undisturbed b 3cm, allowing us to clearly resolve accumulations on the edges of the runway of centimeters Figure 2. (a) nique shows centimeter-scale detailInternational in Airport the in complex mid-winter snow depth fields found at the Fairbanks with 95 % of the range of scatter within areas, such as the grassythe infields trucks near have the not runways plowed.to and The no in main change between runway (dark the isto blue) small plowed allow but airplanes throughout ski the where the plane parking winter access.way ramp and Plowing (yellow holds shows has and packed little piled red). snow upDEM; Subtle 10–12 snow cm it striping to deep is over seen (light the 1 in mairplanes blue) pattern around that the left have the infields moved in edges saturate is the the of real color grass the scale and by run- with mowing. comes changes Not up from to all the indicated summer changes are due to snow: remnant snow/ice and themapping dates fact from that frost the heave. runways experience real elevation changes between Figure 2. and duced cross-runway profiles from atransect time-series shown of in six maps± made in 2013–2014 alongto the decimeters. red 6 October 2013 for onlywith the 95 % snow-free of runway points pixels show with a roughly normal distribution, each down the centerline of the runway (green line in Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | The (b) Distributions of eleva- (a) 0.08m (95 %). The histograms have been nor- ± 376 375 15m, caused by spatial aliasing of trees. The black curve ± Assessment and validation of precision at Minto Flats. erences between 6 and 8 November DEMs to assess precision. The red curve is non- ff Figure 4. hypsometries of the reference DEM and validation lidar DEM are nearly identical qualitatively. Figure 3. tion di is a better indicationmalized of by system their precision maximum at value and the means removed for comparison purposes. Gaussian with tails extending to Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | ) indicate the c the right side of ff and b the section indicates our (c) the section clearly shows the s of the river. The river, flowing ff (b) . In (c) erence in addition to the snow thickness ff erences in foot wear and snow compaction; ff and ); these transects continue o Four back-and-forth transects of probing have a 378 377 (b) (d) . , thus probe #’s 0–50, 200–300, and 450–550 are not (b) (a) ). Here taller shrubs and grasses were compressed the a erence map of the Hulahula River valley (20 April 2014 minus 13 s and gullies at lower left, and are likely caused by the compression ff ff The subset of the winter orthoimage of Minto Flats airborne map area where . The coincidence between probe and photogrammetric snow depths is remarkable. This top-view di (a) June 2014) contains the obliquewith region areas shown free in of Fig. snow, snowwith 1. filling snow, It individual as reveals ice a well wedges complex infrom as snow polygonal large left distribution, ground, and drifts to gullies over filled right,described 3 m in northbound thick the to along text,snow the the because probing. blu Inset Arctic the boxes summer show Ocean the surfacepoints locations (green is of at areas) Fig. water. left). 6 introduces The (gully UTM transects redappear larger 6N at at points right) graticule errors, the and overlaid is edges as Fig. are overlaid 7 ofof (island from blu at shrubs 500 by m the spacing. snow The pack majority there. of negative values most by winter snowchange itself. cover, causing a real map di Figure 5. Missing map values occurfound near where the there forest was edge open (at left water in in summer. The deepest snow was shown in been unfolded here (as indicatedthe image by about arrows twice in as far as seen in footsteps caused by an operator wearing snowshoes (arrows). In note the change in vertical scale from start of the depth cross-sections shownphotogrammetric in mapping can actually resolve di Figure 4. (a) ground measurements were made.Snow The probe GPS UTM locations 6N (dots coloredseen graticule by on probe with user) the 50 were m always orthoimage within spacing 3 acquired provides m of the scale. the next footprints day. The transects labeled ( Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | . As in Fig. 4, we (b) , which again shows a remarkable (e) as a black line. The profiles show that the (a) 379 380 Drifting fills gullies with snow in the Hulahula region (inset at right in Fig. 5). erence between measurements). The probes have a maximum reach of 1.2 m and so ff are 80 m long and their location is shown in Figure 6. (a–c) We probed across this gullysnow in depths. 4 The transects snow (red and(d) dots) ground to test surface our profiles technique (summer over and a winter wide elevations) range shown of in main gully, here about 1.5 mzero deep, to was the completely right filled with as snow, confirmed but by snow depths bare were ground near showing in the orthoimage did not penetrate through the deeper parts of the gully. have unfolded the 4 probe transectsagreement into between a the continuous ground line probingdepths and reproduced the all photogrammetric of map. the The featuresdespite revealed map-derived by the snow the several probes, with confounding a influencesmonth standard di described deviation of in 20 cm, the text (e.g., footprints, vegetation, 1 Figure 6. Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | . (b) sets occurred where the probes ff sets between probe and map depths ff 381 . In some locations at the edge of the island, the probe (b) sets that are accurate but not related to snow depth. Note: the match those used for the lines in clearly show that the largest o ff (a) (a) Snow depth measurements made across one of the islands in the Hulahula River were caused by changes other than snow depth itself. Superposition of probe locations Figure 7. (a) (see island inset in Fig. 1) reveal that that the primary o (b) on the summer orthoimage extended over the active river channel points were not overproduced large open (0.2 water, to but 1.0 m) riverine o erosion, along with snow-compressed shrubs, colors of the probe points in