Using a fixed-wing UAS to map snow depth distribution: an evaluation at peak accumulation

Michele, Carlo De; Avanzi, Francesco; Passoni, D.; Barzaghi, Riccardo; Pinto, Livio; Dosso, Paolo; Ghezzi, Antonio; Gianatti, Roberto; Vedova, Giacomo Della

doi:10.5194/tc-10-511-2016

Cited by 103 publications

(109 citation statements)

References 66 publications

(97 reference statements)

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“…The comparison between UAS flights and manual sampling show higher RMSEs, in agreement with what reported before by e.g., [49] (0.14 m), [50] (less than 0.15 on rocks and less than 0.3 m on grass), [52] This finding expands existing comparisons between UAS flights and other techniques on snow, which have focused on either manual sampling (see previous paragraph) or terrestrial laser scanners, capable of centrimetric accuracy [28,54]. A MS was used here on snow for the first time, to our knowledge.…”

Section: Discussionsupporting

confidence: 82%

“…Larger errors are attributed to vegetated areas [28,50,52]. However, the performances of UAS on snow have mostly been quantified using datasets at low density [48][49][50][51][52] or mixed fresh-old snow and bare-ice surface textures [55], whereas only [54] and [28] present comparisons with a TLS under different illumination conditions. Snow tends to form homogeneous surfaces and therefore the identification of homologous points on different images of the photogrammetric block can be highly uncertain [55][56][57][58], especially in case of high-resolution images where each frame covers only a small area.…”

Section: Introductionmentioning

confidence: 99%

“…Existing works employing UAS on snow or glaciers report an expected Root Mean Square Error (RMSE) for HS below 30 cm (e.g., see [28,[48][49][50][51][52][53][54][55]). Larger errors are attributed to vegetated areas [28,50,52].…”

Section: Introductionmentioning

confidence: 99%

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Centimetric Accuracy in Snow Depth Using Unmanned Aerial System Photogrammetry and a MultiStation

et al. 2018

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Performing two independent surveys in 2016 and 2017 over a flat sample plot (6700 m 2 ), we compare snow-depth measurements from Unmanned-Aerial-System (UAS) photogrammetry and from a new high-resolution laser-scanning device (MultiStation) with manual probing, the standard technique used by operational services around the world. While previous comparisons already used laser scanners, we tested for the first time a MultiStation, which has a different measurement principle and is thus capable of millimetric accuracy. Both remote-sensing techniques measured point clouds with centimetric resolution, while we manually collected a relatively dense amount of manual data (135 pt in 2016 and 115 pt in 2017). UAS photogrammetry and the MultiStation showed repeatable, centimetric agreement in measuring the spatial distribution of seasonal, dense snowpack under optimal illumination and topographic conditions (maximum RMSE of 0.036 m between point clouds on snow). A large fraction of this difference could be due to simultaneous snowmelt, as the RMSE between UAS photogrammetry and the MultiStation on bare soil is equal to 0.02 m. The RMSE between UAS data and manual probing is in the order of 0.20-0.30 m, but decreases to 0.06-0.17 m when areas of potential outliers like vegetation or river beds are excluded. Compact and portable remote-sensing devices like UASs or a MultiStation can thus be successfully deployed during operational manual snow courses to capture spatial snapshots of snow-depth distribution with a repeatable, vertical centimetric accuracy.

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Section: Discussionsupporting

confidence: 82%

Section: Introductionmentioning

confidence: 99%

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Centimetric Accuracy in Snow Depth Using Unmanned Aerial System Photogrammetry and a MultiStation

et al. 2018

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“…Therefore, the use of methods applying one or more independent variables leads to applicable results as shown in our study. Promising results have been also reported using remote sensing approaches such as the use of MODIS satellite data (Duchacek, 2014;He et al, 2014;Krajčí et al, 2016;Parajka et al, 2012), aerial or terrestrial laser scanning (Grünewald et al, 2013;López-Moreno et al, 2015) and unmanned aerial systems (UAV) (De Michele et al, 2016;Lendzioch et al, 2016). We are now testing camera placed on UAV to monitor the snow depth (Lendzioch et al, 2016).…”

Section: Snow Sampling Designmentioning

confidence: 97%

Canopy structure and topography effects on snow distribution at a catchment scale: Application of multivariate approaches

Jeníček

Pevná

Matějka

2017

Journal of Hydrology and Hydromechanics

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Abstract:The knowledge of snowpack distribution at a catchment scale is important to predict the snowmelt runoff. The objective of this study is to select and quantify the most important factors governing the snowpack distribution, with special interest in the role of different canopy structure. We applied a simple distributed sampling design with measurement of snow depth and snow water equivalent (SWE) at a catchment scale. We selected eleven predictors related to character of specific localities (such as elevation, slope orientation and leaf area index) and to winter meteorological conditions (such as irradiance, sum of positive air temperature and sum of new snow depth). The forest canopy structure was described using parameters calculated from hemispherical photographs. A degree-day approach was used to calculate melt factors. Principal component analysis, cluster analysis and Spearman rank correlation were applied to reduce the number of predictors and to analyze measured data. The SWE in forest sites was by 40% lower than in open areas, but this value depended on the canopy structure. The snow ablation in large openings was on average almost two times faster compared to forest sites. The snow ablation in the forest was by 18% faster after forest defoliation (due to the bark beetle). The results from multivariate analyses showed that the leaf area index was a better predictor to explain the SWE distribution during accumulation period, while irradiance was better predictor during snowmelt period. Despite some uncertainty, parameters derived from hemispherical photographs may replace measured incoming solar radiation if this meteorological variable is not available.

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“…Most of these methods are expensive and their deployment is time demanding. UAS was tested by De Michele et al (2016). They concluded that UAS provides data with very high accuracy.…”

Section: Introductionmentioning

confidence: 99%

Experimental measurements for improved understanding and simulation of snowmelt events in the Western Tatra Mountains

Krajčí

Danko

Hlavčo

et al. 2016

Journal of Hydrology and Hydromechanics

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Snow accumulation and melt are highly variable. Therefore, correct modeling of spatial variability of the snowmelt, timing and magnitude of catchment runoff still represents a challenge in mountain catchments for flood forecasting. The article presents the setup and results of detailed field measurements of snow related characteristics in a mountain microcatchment (area 59 000 m 2 , mean altitude 1509 m a. s. l.) in the Western Tatra Mountains, Slovakia obtained in winter 2015. Snow water equivalent (SWE) measurements at 27 points documented a very large spatial variability through the entire winter. For instance, range of the SWE values exceeded 500 mm at the end of the accumulation period (March 2015). Simple snow lysimeters indicated that variability of snowmelt and discharge measured at the catchment outlet corresponded well with the rise of air temperature above 0°C. Temperature measurements at soil surface were used to identify the snow cover duration at particular points. Snow melt duration was related to spatial distribution of snow cover and spatial patterns of snow radiation. Obtained data together with standard climatic data (precipitation and air temperature) were used to calibrate and validate the spatially distributed hydrological model MIKE-SHE. The spatial redistribution of input precipitation seems to be important for modeling even on such a small scale. Acceptable simulation of snow water equivalents and snow duration does not guarantee correct simulation of peakflow at shorttime (hourly) scale required for example in flood forecasting. Temporal variability of the stream discharge during the snowmelt period was simulated correctly, but the simulated discharge was overestimated.

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Using a fixed-wing UAS to map snow depth distribution: an evaluation at peak accumulation

Cited by 103 publications

References 66 publications

Centimetric Accuracy in Snow Depth Using Unmanned Aerial System Photogrammetry and a MultiStation

Centimetric Accuracy in Snow Depth Using Unmanned Aerial System Photogrammetry and a MultiStation

Canopy structure and topography effects on snow distribution at a catchment scale: Application of multivariate approaches

Experimental measurements for improved understanding and simulation of snowmelt events in the Western Tatra Mountains

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