Snow depth mapping with unpiloted aerial system lidar observations: a case study in Durham, New Hampshire, United States

Jacobs, Jennifer M.; Hunsaker, Adam G.; Sullivan, Franklin B.; Palace, Michael; Burakowski, Elizabeth A.; Herrick, Christina; Cho, Eunsang

doi:https://doi.org/10.5194/tc-15-1485-2021

Articles | Volume 15, issue 3

https://doi.org/10.5194/tc-15-1485-2021

© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/tc-15-1485-2021

© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 15, issue 3

Research article

|

24 Mar 2021

Research article |

| 24 Mar 2021

Snow depth mapping with unpiloted aerial system lidar observations: a case study in Durham, New Hampshire, United States

Jennifer M. Jacobs, Adam G. Hunsaker, Franklin B. Sullivan, Michael Palace, Elizabeth A. Burakowski, Christina Herrick, and Eunsang Cho

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Final revised paper (published on 24 Mar 2021)
Supplement to the final revised paper
Preprint (discussion started on 18 Feb 2020)

Interactive discussion

Status: closed

AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment

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- Supplement

RC1: 'Review for The Cryosphere Discussion tc-2020-37', Anonymous Referee #1, 04 May 2020
- AC1: 'Author Response to Comments on tc-2020-37', Jennifer Jacobs, 22 Jun 2020
RC2: 'Review of Shallow snow depth mapping with unmanned aerial systems lidar observations: A case study in Durham, New Hampshire, United States By Jennifer M. Jacobs and six others', Anonymous Referee #2, 05 May 2020
- AC1: 'Author Response to Comments on tc-2020-37', Jennifer Jacobs, 22 Jun 2020

Peer-review completion

AR: Author's response | RR: Referee report | ED: Editor decision

ED: Publish subject to revisions (further review by editor and referees) (30 Jun 2020) by Philip Marsh

AR by Jennifer Jacobs on behalf of the Authors (28 Jul 2020)

ED: Referee Nomination & Report Request started (28 Sep 2020) by Philip Marsh

RR by Anonymous Referee #1 (09 Oct 2020)

RR by Anonymous Referee #3 (26 Nov 2020)

Suggestions for revision or reasons for rejection

This paper presents a snow depth map methodology for a UAV and LIDAR combination to measure thin snowpack of ephemeral snow. The study site contains a low vegetated field with a mixed forest which is useful to evaluate vegetation interaction. The main question focuses on the capability of LIDAR for snow depth mapping mounted on UAV because the sensor combination of LIDAR and UAV had not been extensively published yet. Most of the paper evaluates the accuracy of the map with respect to point cloud density (link to DTM resolution and LIDAR returns), vegetation cover and slope. A substantial amount of methodology and flight experience makes this paper focus on technical and methodological issues rather than informing us on snow processes of thin and ephemeral snow with vegetation interaction.
I do not think another methodological paper on snow depth mapping would be beneficial to the Cryosphere community. Differential snow depth mapping from LIDAR dates to the beginning of the century (Deems et al., 2006; Hopkinson et al., 2004) with airborne data from plane quickly became a more efficient tool to map large areas. A numerous numbers of article have used airborne LIDAR data for snow depth mapping (Currier et al., 2019; Grünewald et al., 2013; Hopkinson et al., 2012, 2004; Mazzotti et al., 2019; Nolan et al., 2015; Painter et al., 2016) with development on data processing (see review from Deems et al., 2013) and topographic and vegetation induced errors on LIDAR DEM (Spaete et al., 2011). Since the UAV platform only change the altitude and the coverage area, I do not see the point of having another methodological article. Regarding flight experience with UAV, a substantial amount of paper regarding snow mapping has already been published for smaller area with multi-rotor systems (Buhler et al., 2016; Cimoli et al., 2017; Fernandes et al., 2018) and larger area with fixed wing systems (De Michele et al., 2016; Harder et al., 2016; Redpath et al., 2018).
The high error in forested environment clearly need to be more investigated. It is stated that the magnaprobe measurements overestimate the snow depth in forested environment by penetrating low lying vegetation and soil. So, is the LIDAR mean depth of 6cm in more representative than magnaprobe (15 cm) and federal sampling tube (13 cm) which both showed larger depth for forested than field? Some forested areas can have deeper snowpack (Trujillo et al., 2009) due to wind reduction but snow interception by canopy will decrease snow accumulation on the ground as forest cover increases (Varhola et al., 2010). This needs to be sorted out especially when it is know that differential snow depth mapping underestimate snow for small vegetation as the snow off DEM is higher than the bare ground but when snow is measured for truth scene, the same vegetation is compressed by the weight of the snow (Buhler et al., 2016; Nolan et al., 2015).
The paper lacks a novel or in depth analysis of relations or processes regarding snow derived from a snow map that would be beneficial to the community like for e.g. a comparison with SfM photogrammetry in forested environments (Harder et al., 2020), statistical analysis of snow depth (Grünewald et al., 2013; Wainwright et al., 2017) or spatial analysis with variogram or fractal analysis between open field and forested environments (Deems et al., 2006; Redpath et al., 2018; Trujillo et al., 2009). To do this, I would suggest trying to improve with additional dataset over wider areas, only one site is not enough or perhaps different seasons to explore temporality and resubmit to this journal after next field season or go with this version towards a technical journal regarding UAV.

References
Buhler, Y., Adams, M.S., Bosch, R., Stoffel, A., 2016. Mapping snow depth in alpine terrain with unmanned aerial systems (UASs): Potential and limitations. Cryosphere 10, 1075–1088. https://doi.org/10.5194/tc-10-1075-2016
Cimoli, E., Marcer, M., Vandecrux, B., Bøggild, C.E., Williams, G., Simonsen, S.B., 2017. Application of low-cost uass and digital photogrammetry for high-resolution snow depth mapping in the Arctic. Remote Sens. 9, 1–29. https://doi.org/10.3390/rs9111144
Currier, W.R., Pflug, J., Mazzotti, G., Jonas, T., Deems, J.S., Bormann, K.J., Painter, T.H., Hiemstra, C.A., Gelvin, A., Uhlmann, Z., Spaete, L., Glenn, N.F., Lundquist, J.D., 2019. Comparing Aerial Lidar Observations With Terrestrial Lidar and Snow-Probe Transects From NASA’s 2017 SnowEx Campaign. Water Resour. Res. 55, 6285–6294. https://doi.org/10.1029/2018WR024533
De Michele, C., Avanzi, F., Passoni, D., Barzaghi, R., Pinto, L., Dosso, P., Ghezzi, A., Gianatti, R., Vedova, G. Della, 2016. Using a fixed-wing UAS to map snow depth distribution: An evaluation at peak accumulation. Cryosphere 10, 511–522. https://doi.org/10.5194/tc-10-511-2016
Deems, J.S., Fassnacht, S.R., Elder, K.J., 2006. Fractal Distribution of Snow Depth from Lidar Data. J. Hydrometeorol. 7, 285–297. https://doi.org/10.1175/JHM487.1
Deems, J.S., Painter, T.H., Finnegan, D.C., 2013. Lidar measurement of snow depth: A review. J. Glaciol. 59, 467–479. https://doi.org/10.3189/2013JoG12J154
Fernandes, R., Prevost, C., Canisius, F., Leblanc, S.G., Maloley, M., Oakes, S., Holman, K., Knudby, A., 2018. Monitoring snow depth change across a range of landscapes with ephemeral snowpacks using structure from motion applied to lightweight unmanned aerial vehicle videos. Cryosphere 12, 3535–3550. https://doi.org/10.5194/tc-12-3535-2018
Grünewald, T., Stötter, J., Pomeroy, J.W., Dadic, R., Moreno Baños, I., Marturià, J., Spross, M., Hopkinson, C., Burlando, P., Lehning, M., 2013. Statistical modelling of the snow depth distribution in open alpine terrain. Hydrol. Earth Syst. Sci. 17, 3005–3021. https://doi.org/10.5194/hess-17-3005-2013
Harder, P., Pomeroy, J.W., Helgason, W.D., Helgason, W.D., 2020. Improving sub-canopy snow depth mapping with unmanned aerial vehicles: Lidar versus structure-from-motion techniques. Cryosphere 14, 1919–1935. https://doi.org/10.5194/tc-14-1919-2020
Harder, P., Schirmer, M., Pomeroy, J., Helgason, W., 2016. Accuracy of snow depth estimation in mountain and prairie environments by an unmanned aerial vehicle. Cryosphere. https://doi.org/10.5194/tc-10-2559-2016
Hopkinson, C., Collins, T., Anderson, A., Pomeroy, J., Spooner, I., 2012. Spatial snow depth assessment using LiDAR transect samples and public GIS data layers in the Elbow River Watershed, Alberta. Can. Water Resour. J. 37, 69–87. https://doi.org/10.4296/cwrj3702893
Hopkinson, C., Sitar, M., Chasmer, L., Treitz, P., 2004. Mapping Snowpack Depth beneath Forest Canopies Using Airborne Lidar. Photogramm. Eng. Remote Sens. 70, 323–330. https://doi.org/10.14358/PERS.70.3.323
Mazzotti, G., Currier, W.R., Deems, J.S., Pflug, J.M., Lundquist, J.D., Jonas, T., 2019. Revisiting Snow Cover Variability and Canopy Structure Within Forest Stands: Insights From Airborne Lidar Data. Water Resour. Res. 55, 6198–6216. https://doi.org/10.1029/2019WR024898
Nolan, M., Larsen, C., Sturm, M., 2015. Mapping snow depth from manned aircraft on landscape scales at centimeter resolution using structure-from-motion photogrammetry. Cryosphere 9, 1445–1463. https://doi.org/10.5194/tc-9-1445-2015
Painter, T.H., Berisford, D.F., Boardman, J.W., Bormann, K.J., Deems, J.S., Gehrke, F., Hedrick, A., Joyce, M., Laidlaw, R., Marks, D., Mattmann, C., McGurk, B., Ramirez, P., Richardson, M., Skiles, S.M.K., Seidel, F.C., Winstral, A., 2016. The Airborne Snow Observatory: Fusion of scanning lidar, imaging spectrometer, and physically-based modeling for mapping snow water equivalent and snow albedo. Remote Sens. Environ. https://doi.org/10.1016/j.rse.2016.06.018
Redpath, T.A.N., Sirguey, P., Cullen, N.J., 2018. Repeat mapping of snow depth across an alpine catchment with RPAS photogrammetry 3477–3497.
Spaete, L.P., Glenn, N.F., Derryberry, D.R., Sankey, T.T., Mitchell, J.J., Hardegree, S.P., 2011. Vegetation and slope effects on accuracy of a LiDAR-derivedDEMin the sagebrush steppe. Remote Sens. Lett. https://doi.org/10.1080/01431161.2010.515267
Trujillo, E., Ramírez, J.A., Elder, K.J., 2009. Scaling properties and spatial organization of snow depth fields in sub-alpine forest and alpine tundra. Hydrol. Process. https://doi.org/10.1002/hyp.7270
Varhola, A., Coops, N.C., Weiler, M., Moore, R.D., 2010. Forest canopy effects on snow accumulation and ablation: An integrative review of empirical results. J. Hydrol. 392, 219–233. https://doi.org/10.1016/j.jhydrol.2010.08.009
Wainwright, H.M., Liljedahl, A.K., Dafflon, B., Ulrich, C., Peterson, J.E., Gusmeroli, A., Hubbard, S.S., 2017. Mapping snow depth within a tundra ecosystem using multiscale observations and Bayesian methods. Cryosph. 11, 857–875. https://doi.org/10.5194/tc-11-857-2017

Referee Report: PDF

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RR by Anonymous Referee #2 (05 Dec 2020)

Suggestions for revision or reasons for rejection

Re-review of TC-2020-37: Snow depth mapping with unpiloted aerial system lidar observations: A case study in Durham, New Hampshire, United States
by Jennifer M. Jacobs and others.

In my original review, I commented on the limited field data used to produce the paper. I appreciate the effort the authors went to place their study in context (their Table R1), and while I would like to have seen their analysis encompass far more results, I grant that the paper as now re-written has a distinct and useful purpose related to pushing new technology ahead. I particularly found the discussion of UAS/lidar use and limitations quite helpful. The discussion of forest and slope errors is also far more mature and comprehensive. I recommend publication with some minor revisions.

• GENERAL: There are some inconsistencies between how things are discussed in the text and how they are labeled in the figures. The authors should go through and try to make these consistent. For example, cell size vs. DTM resolution, and I think terrain slope vs. terrain variability.
• In the abstract the authors say that in the forest there was “ ….modestly reduced performance”, but the degradation is 4x. Delete “Modest”.
• Perhaps it is that I have been doing research and spent 9 years as a sub- and chief editor of a journal, but I still found the introduction longer than need be. Anyone reading this paper is likely to be deeply involved in snow research and knows snow changes a lot, that our model are good but drift without real data to guide them, etc. I suggest just getting to it in the introduction: delete the first 3 paragraphs of the introduction….or at least make the point we need high quality depth data more concisely.
• Line 78-80: This sentence is run-on and awkward.
• Line 87: My understanding is that now DGPS level positioning on UAS's removes the need for ground control points. If that is true, perhaps moderatr this statement.
• Figure 1: Much better and very useful and handsome.
• Figure 2: Last word (bottom). Not sure what this means.
• Section 3.2: Well done.
• Line 309: Compare this to the statement in the Abstract.
• Figure 5: Great figure now even better.
• Line 351: Insert “the”

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RR by Anonymous Referee #4 (04 Jan 2021)

Suggestions for revision or reasons for rejection

Review of Jacobs et al., Snow depth mapping with unpiloted aerial system lidar observations: A case study in Durham, New Hampshire, United States

This is an interesting paper on using UAS based lidar map snow depth. This is an emerging tool that will undoubtedly advance observation of fine scale snow distributions and hopefully understanding of small –scale snow processes ultimately. Overall this paper provides a detailed description of a campaign in 2018/2019 which is important to understand the accuracy of this instrument and operational, considerations. I do have some concerns in terms of its placement in the context of the current literature, the communication of some of the accuracy results, and some of the more technical aspects of the writing. I would consider these minor revisions and once resolved look forward to its publication!
I will summaries some main points that need addressing followed by some technical notes.
Literature context.
The introduction could be made more punchy and to the point. Large scale context of snow is important but we’re only looking at how to measure it at a fine scale in this paper so making it more concise on the progression in instrument techniques that have been employed would be an improvement. The Harder et al 2020 paper is cited as the first example of a UAS-lidar snow depth study. This is a clear comparable study but besides being mentioned nothing is else is mentioned about it in the intro. What are the common comparison points that can be used to relate the two studies? What differentiates the two (ie. Sensor quality/cost to get comparable results as this is labeled as a modest cost system (define?) versus Harder et al who use a higher cost Riegl system)?

Accuracy results:
There are many ways to present the results of this works but a common approach in much of the UAS-SFM, (and lidar), ALS and TLS is to consider the RMSE and Bias metrics prominently and these are reported in the literature review. This paper does present these values in Figure 3 and the first paragraph of section 3.2. The metrics reported RSMD of 1.22cm and 10.5 cm in open field and forest respectively are quite comparable to other work but these connections are not made. Rather only the 1.22 cm open field RMSE is reported in the abstract – no mention of the larger forest error or anything in the conclusion. In lieu of direct comparison with insitu data there is a focus on the one sided confidence intervals that are reported at < 4cm. Confidence intervals can be important to isolating areas of uncertainty but are not fully suitable to assess the skill/accuracy of the product. If the results could be clarified to emphasize the RMSD and MAD more clearly and the meaning/role of the confidence intervals to be clarified more strongly would make this better.

Technical writing
Writing is good throughout but there are some sections and word choices that need work – specific examples given below but not exhaustive. Specificity of word section and consistency throughout will make things much clearer.

Technical edits

Line 33:”differentially” – word choice. Awkward with “differences” earlier in sentence

Line 48: “High vertical resolution snow mapping” word choice. Perhaps “Mapping snow with high spatial resolutions and vertical precisions….”

Line 51: “Despite importance” -> “Despite the importance”

Line 54-56: “ Using traditional, precise point measurements with a limited sample size, the experimental design requires a balance between the sampling extent and sample spacing (Clark et al. 2011). - >: “ Using a traditional experimental design with precise point measurements of a limited sample size requires a balance between sampling extent and sample spacing”

Throughout: Are we using UAV or UAS? Title and intro start with UAV and then it switches to UAS at line 82.

Line 140-146: I see there were batteries changes in this mapping mission. How long does it take to survey this area? Important information to present and perhaps bring into discussion at end on how scalable this technology is with actual times/areas to benchmark a discussion.

Line 151: “antiparallel” = perpendicular?
Line 152-154: The boreshighting and roll-pitch offsets need to be done for each flight? Or are these done once and they are stable thereafter?
Line 155-156: What and how are points filtered?
Line 174-175: (Figure 1) at end of sentence implies that it contains a methodology which it does not and is a little confusing.
Line 173-184: This section could be tightened up. Landscape classes where derived from CHM and simply > 15m was coniferous, deciduous was < 15m and field was what? 0m?
Line 211-217: Is this section necessary? At this stage of snow depth accuracy evaluation do we even need to know that the soil was frozen?
Line 227-233: I found this section/ equation/term definitions to be confusing and incomplete.
Figure 7: seems low res in my pdf.
Line 380: surveying in locations prior to snow-on (and presumable mark with a stake?) risks modifying snow processes at that locations and poetically biasing the snowpack which may have relevance for certain processes being studied, and risks destructive sampling impacts if same location is being repeatedly visited over a season.
Line 411-412: Confidence intervals for snow depth are high on slopes? What slope angles are we seeing this relationship? In the literature snow depth errors tend to be higher on slopes – see terrain induced errors section in Deems 2013
Deems, Jeffrey S., Thomas H. Painter, and David C. Finnegan. "Lidar measurement of snow depth: a review." Journal of Glaciology 59.215 (2013): 467-479.
Line 416: “higher” word choice- “larger” may be better
Line 453: “antiparallel” = “perpendicular”

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ED: Publish subject to minor revisions (review by editor) (14 Jan 2021) by Philip Marsh

AR by Jennifer Jacobs on behalf of the Authors (25 Jan 2021) Author's response Author's tracked changes Manuscript

ED: Publish as is (25 Jan 2021) by Philip Marsh

AR by Jennifer Jacobs on behalf of the Authors (05 Feb 2021)

Short summary

This pilot study describes a proof of concept for using lidar on an unpiloted aerial vehicle to map shallow snowpack (< 20 cm) depth in open terrain and forests. The 1 m² resolution snow depth map, generated by subtracting snow-off from snow-on lidar-derived digital terrain models, consistently had 0.5 to 1 cm precision in the field, with a considerable reduction in accuracy in the forest. Performance depends on the point cloud density and the ground surface variability and vegetation.