Snow depth mapping in high-alpine catchments using digital photogrammetry

Bühler, Yves; Marty, Mauro; Egli, Luca; Veitinger, Jochen; Jonas, Tobias; Thee, P.; Ginzler, Christian

doi:10.5194/tc-9-229-2015

Cited by 127 publications

(170 citation statements)

References 54 publications

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“…SOCET SET ATE and NGATE (all ADS100 point clouds depend on the same absolute orientation) reveal a larger median height difference of 10-17cm. In relation to the 15cm GSD the ADS100 results are definitely improved compared with the results from 2012 (Bühler 2015a Figures 6 and 7 show a rather consistent deviation pattern. SOCET SET ATE and NGATE show for points 01-04 differences 10-22cm, for PHS the difference varies between 4-8cm, which corresponds to the ADS100 GSD of 15cm and PHS GSD of 3cm.…”

Section: Figure 4 Extracted Point Clouds Around Reference Pointssupporting

confidence: 52%

“…Using operationally available aerial images acquired with the Leica ADS80 in 2012, it could be demonstrated that snow depth measurements with 0.30m RMSE can be achieved in high-alpine catchment areas (Bühler 2015a). Due to the high radiometric resolution of the images (12 bits) and the use of the near infrared band (NIR), images were not saturated over bright, snow-covered areas and required texture could be identified even in shadow areas.…”

Section: Introductionmentioning

confidence: 99%

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Comparison of Digital Surface Models for Snow Depth Mapping With Uav and Aerial Cameras

Boesch¹,

Bühler²,

Marty³

et al. 2016

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Commission VIII, WG 6 KEY WORDS: snow height, dense point cloud, digital surface model, aerial camera ABSTRACT:Photogrammetric workflows for aerial images have improved over the last years in a typically black-box fashion. Most parameters for building dense point cloud are either excessive or not explained and often the progress between software releases is poorly documented. On the other hand, development of better camera sensors and positional accuracy of image acquisition is significant by comparing product specifications. This study shows, that hardware evolutions over the last years have a much stronger impact on height measurements than photogrammetric software releases. Snow height measurements with airborne sensors like the ADS100 and UAV-based DSLR cameras can achieve accuracies close to GSD * 2 in comparison with ground-based GNSS reference measurements. Using a custom notch filter on the UAV camera sensor during image acquisition does not yield better height accuracies. UAV based digital surface models are very robust. Different workflow parameter variations for ADS100 and UAV camera workflows seem to have only random effects. INTRODUCTIONDeriving information on snow depth and its spatial distribution is essential for several applications in hydrology and foremost in snow and avalanche research. Using operationally available aerial images acquired with the Leica ADS80 in 2012, it could be demonstrated that snow depth measurements with 0.30m RMSE can be achieved in high-alpine catchment areas (Bühler 2015a). Due to the high radiometric resolution of the images (12 bits) and the use of the near infrared band (NIR), images were not saturated over bright, snow-covered areas and required texture could be identified even in shadow areas. The NIR band was also used for snow type mapping catchments (Bühler et al. 2015b). Using 2 different photogrammetric software suites for ADS80-data, SOCET SET ATE (automatic terrain extraction) and SOCET SET NGATE (next geneneration automatic terrain extraction), accuracy results were partially comparable. In recent years Leica has renewed its product line with the ADS100 camera system and sensor head SH 100 and offers significant enhancements such as an improved radiometric resolution from 12 to 14 bits, an increased spatial resolution from 25cm to 15cm at the same flight height and a triple stereo capability for all spectral bands. A major restriction for the frequent use of professional aerial sensor systems to map snow height is the fact, that they are mostly operated by national or commercial agencies, though orders are expensive and require long term planning, which is often not feasible due to changing weather and snow conditions in winter time.In contrast many different types of unmanned aerial vehicles (UAV) with a large variety of camera sensors are meanwhile available, allowing for flexible and cost effective calculation of digital surface models (DSM). The user controlled definition of acquisition time and region of interest with UAV's is a clear advantage and a...

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Section: Figure 4 Extracted Point Clouds Around Reference Pointssupporting

confidence: 52%

Section: Introductionmentioning

confidence: 99%

Comparison of Digital Surface Models for Snow Depth Mapping With Uav and Aerial Cameras

Boesch¹,

Bühler²,

Marty³

et al. 2016

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

Self Cite

View full text Add to dashboard Cite

show abstract

“…Currently, snow depth is mainly estimated from optical measurements such as photogrammetry (Marti et al, 2016;Bühler et al, 2015) or lidar instruments (Deems et al, 2013); however, the applicability of high-frequency radar instruments is currently discussed (Evans and Kruse, 2014). Snow density could potentially be derived from measurements of the snow water equivalent (Leinss et al, 2015) if data about the snow depth are available.…”

Section: Discussionmentioning

confidence: 99%

Anisotropy of seasonal snow measured by polarimetric phase differences in radar time series

Leinß

Löwe²,

Proksch³

et al. 2016

The Cryosphere

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Abstract. The snow microstructure, i.e., the spatial distribution of ice and pores, generally shows an anisotropy which is driven by gravity and temperature gradients and commonly determined from stereology or computer tomography. This structural anisotropy induces anisotropic mechanical, thermal, and dielectric properties. We present a method based on radio-wave birefringence to determine the depth-averaged, dielectric anisotropy of seasonal snow with radar instruments from space, air, or ground. For known snow depth and density, the birefringence allows determination of the dielectric anisotropy by measuring the copolar phase difference (CPD) between linearly polarized microwaves propagating obliquely through the snowpack. The dielectric and structural anisotropy are linked by MaxwellGarnett-type mixing formulas. The anisotropy evolution of a natural snowpack in Northern Finland was observed over four winters (2009)(2010)(2011)(2012)(2013) with the ground-based radar instrument "SnowScat". The radar measurements indicate horizontal structures for fresh snow and vertical structures in old snow which is confirmed by computer tomographic in situ measurements. The temporal evolution of the CPD agreed in ground-based data compared to space-borne measurements from the satellite TerraSAR-X. The presented dataset provides a valuable basis for the development of new snow metamorphism models which include the anisotropy of the snow microstructure.

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“…For example, [48] used 37 measurements over 6900 m 2 (0.5 pt/100 m 2 ), [23] took between 0.04 pt/100 m 2 and 0.11 pt/100 m 2 , and [52] took between 0.04 pt/100 m 2 and 0.2 pt/100 m 2 .…”

Section: Discussionmentioning

confidence: 99%

“…Remote sensing captures the spatial and temporal patterns of snow and thus overcomes the potential undersampling of point measurements and the long surveys needed for snow courses [16,17]. Existing methods include Terrestrial Laser Scanner (TLS, [7,13,[18][19][20][21][22]), digital photogrammetry [23,24], tachymetry [20], Ground Penetrating Radar (GPR) [25], time-lapse photography [26,27], or satellite-based sensors [16]. Among these alternatives, TLS is the commonest choice in most applications [28].…”

Section: Introductionmentioning

confidence: 99%

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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Snow depth mapping in high-alpine catchments using digital photogrammetry

Cited by 127 publications

References 54 publications

Comparison of Digital Surface Models for Snow Depth Mapping With Uav and Aerial Cameras

Comparison of Digital Surface Models for Snow Depth Mapping With Uav and Aerial Cameras

Anisotropy of seasonal snow measured by polarimetric phase differences in radar time series

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

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