Measuring the spatiotemporal variability of snow depth in subarctic environments using unmanned aircraft systems (UAS) – Part 2: Snow processes and snow-canopy interactions
Abstract. Detailed information on seasonal snow cover and depth is essential to the understanding of snow processes, operational forecasting, and as input for hydrological models. Recent advances in unmanned aircraft systems (UASs) and structure from motion (SfM) techniques have enabled low-cost monitoring of spatial snow depth distribution in resolutions up to a few centimeters. Here, we study the spatiotemporal variability of snow depth and interactions between snow and vegetation in different subarctic landscapes consisting of a mosaic of conifer forest, mixed forest, transitional woodland/shrub, and peatland areas. To determine the spatiotemporal variability of snow depth, we used high-resolution (50 cm) snow depth maps generated from repeated UAS-SfM surveys in the winter of 2018/2019 and a snow-free bare ground survey after snowmelt. Due to poor sub-canopy penetration with the UAS-SfM method, tree masks were utilized to remove canopy areas and the area (36 cm) immediately next to the canopy before analysis. Snow depth maps were compared to the in-situ snow course and a single-point continuous ultrasonic snow depth measurement. Based on the results, the difference between the UAS-SfM survey median snow depth and single-point measurement increased for all land cover types during the snow season, from +5 cm at the beginning of the accumulation to -16 cm in coniferous forests and -32 cm in peatland during the melt period. This highlights the poor representation of point measurements even on the sub-catchment scale. The high-resolution snow depth maps agreed well with the snow course measurement, but the spatial extent and resolution of maps were substantially higher. The snow depth variability (5–95 percentiles) within different land cover types increased from 17 cm to 42 cm in peatlands and from 33 cm to 49 cm in the coniferous forest from the beginning of the snow accumulation to the melt period. Both the median snow depth and its variability were found to increase with canopy density; this increase was greatest in the conifer forest area, followed by mixed forest, transitional woodland/shrub, and open peatlands. Using the high spatial resolution data, we found a systematic increase (2–20 cm), then a decline of snow depth near the canopy with increasing distance (from 1 m to 2.5 m) of the peak value through the snow season. This study highlights the potential of the UAS-SfM in high-resolution monitoring of snow depth in multiple land cover types and snow-vegetation interactions in subarctic and remote areas where field data is not available.
Leo-Juhani Meriö et al.
Status: final response (author comments only)
RC1: 'Comment on tc-2022-242', Anonymous Referee #1, 11 Feb 2023
- AC1: 'Reply on RC1', Leo-Juhani Merio, 13 Apr 2023
RC2: 'Comment on tc-2022-242', Anonymous Referee #2, 16 Feb 2023
- AC2: 'Reply on RC2', Leo-Juhani Merio, 13 Apr 2023
Leo-Juhani Meriö et al.
Unmanned aircraft system (UAS) snow depth mapping at the Pallas Atmosphere-Ecosystem Supersite https://doi.org/10.23729/43d37797-e8cf-4190-80f1-ff567ec62836
Leo-Juhani Meriö et al.
Viewed (geographical distribution)
Authors analysed the spatial and temporal variability of snow depth and interactions between snow and vegetation in a subarctic landscape with coniferous forest, mixed forest, and peatland areas. To determine the variability of snow depth, they used high-resolution maps acquired from four UAS surveys in a one winter season verified by manual snow course measurements and one automatic snow depth station. Authors used interesting approach of creating a tree-mask to remove canopy areas from analysis due to poor penetration of the UAS camera. Authors found that both snow depth and its variability increased with the canopy density. Authors also described the snow depth increase and then decline with a distance from canopy, as well as the increase of the peak value distance from tree as the season progressed.
In my opinion, authors did an interesting work which certainly has a scientific relevance. I think this is an important pure and thorough field study. Therefore, the study has clear potential to be published. However, I do not see much novelty in the study both in terms improving our knowledge or methodological approaches. I see some methodological novelty in using the tree mask to limit the data, but the question is whether it is really novel. I am not saying that the study lacks novelty at all, but I think the authors should better define what is new in the study and how it goes beyond the previous studies. Besides, I have a few other comments listed below, which should be addressed before I can recommend the manuscript for publication.
I did not fully understand why authors used CORINE land cover data since they worked with precise UAV based data describing the specific pixel distance from the canopy/trees (which were used to create canopy masks). Maybe I just did not understand it correctly from the text, but why they did not use accurate canopy structure data for the whole analysis? Or was CORINE data used only for general description of the land cover types in individual plots? Please explain it in more detail (probably in methods).
Result section 4.3 contains three figures; however, the related text doesn’t contain detailed explanation and interpretation (it consists only in three short paragraphs). Please extent the related text substantially to provide the reader with detailed description and interpretation of the related figures.
In my opinion, conclusion section is too brief and general. I would suggest including more details (including numbers) about individual conclusions. As it is now, it looks like a summary describing what authors did rather than main study conclusions.
L 21: One of the study conclusion is that differences between UAS and ground measurements highlights “the poor representation of point measurements even on the sub-catchment scale”. This might be certainly true; however, this may also show that point measurement location is not fully representative for the wider area. Could you please add some more discussion related to this issue?
L 29-31: Authors stated that “This study highlights the potential of the UAS-SfM in high-resolution monitoring of snow depth in multiple land cover types …”. UAS is nowadays standardly applied and well-established method for snow depth monitoring (even in diverse vegetations). Therefore, the statement that “it has a potential” might be relevant perhaps 5-7 years ago, but not nowadays. Please consider reformulation.
L 44-45: Although I agree that individual factors control snow depth at different spatial scales, I do not think that such distinct limit (100 m) can be defined. Maybe consider reformulation.
L 79-87: Here authors explain the novelty of their study. Besides others, authors see the novelty in applying UAS imaging in boreal regions. Why this is specifically novel? How the UAS imaging in boreal regions differs from imaging in other areas? I think that application of UAS in boreal regions just because it was never used there before, doesn’t mean novelty per se. Please consider more specific explanation.
L 85: One of the research questions is how UAS can be used for snow depth imaging during poor light conditions (probably because the study plots are far beyond the artic cycle). Authors addressed this question rather marginally in Section 5.4, but maybe this might be one of the novel issues which might deserve more attention (see also my general comment and the previous comment).
Fig. 2: Lines with min/max snow depth means snow depth evolution of the one winter season with highest/lowest snow depth or each date on x-axis means maximum/minimum value for this date from all winters at the study period? Please clarify.
L 123-124: Could you be a bit more specific why two of the surveys were discarded?
Table 1 (and maybe also Table 2 and 3): Consider adding also absolute values of snow depth and not only differences between point measurements and UAS data.
Fig. 6: What is the physical explanation of increasing differences of the snow depth near canopy with progressing season? How important is the longwave radiation emitted by trees which increases the snowmelt rates near tree trunks? Please discuss shortly.
L 338: While I generally agree with provided explanation of highest snow depth in forested areas, do you have any data to support this interpretation?
L 345: While this is rather trivial conclusion, I think it might be beneficial for end users (e.g., operational services) and thus it may appear also in the conclusion section.
L 357-361: I see the point, however, why it should be interesting? Could you explain it in more detail?
L 65: Instead of “submitted to the same journal”, I would specify its name.
Fig. 2: Please consider change of individual line colours/types to increase readability.
L 362: “For open peatland landcover, this peak may be explained …” It is not clear what “this” refers to. Please consider reformulation.