Rapid melting dynamics of an alpine glacier with repeated UAV photogrammetry

Rossini, Micol; Mauro, Biagio Di; Garzonio, Roberto; Baccolo, Giovanni; Cavallini, Giuseppe; Mattavelli, M; Amicis, M De; Colombo, R.

doi:10.1016/j.geomorph.2017.12.039

Cited by 121 publications

(124 citation statements)

References 53 publications

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“…This study illustrates how CloudCompare provides a straightforward toolkit for quantifying change, both at the scale of individual boulders and for deposits as a whole. This potential for producing four-dimensional data using SfM has already been realized in a wide variety of fields (e.g., Bryson et al, 2012Bryson et al, , 2013Eltner et al, 2015Eltner et al, , 2017Gillan et al, 2017;Rossini et al, 2018), and the methodology described here could be widely adopted in disciplines other than geomorphology (e.g., ecology, land-use surveying). The ease of use and minimal training required mean that this methodology can be adopted by both expert and non-expert users, opening the door to rapid data acquisition, effective use of datasets collected with different hardware, and short-term monitoring of changing sites by researchers, citizen scientists, and other stakeholders.…”

Section: Discussionmentioning

confidence: 88%

“…The workflow implemented in this study is ideal for efficient repeat observations. This potential for producing four-dimensional data has already been realized in a range of studies [19,20,41,46,153,154]. Much of the procedure in Agisoft can be automated via custom scripts or the Batch Process option [105].…”

Section: Effective Efficient Methodology For Quantitative Repeat Phomentioning

confidence: 99%

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Measuring Change Using Quantitative Differencing of Repeat Structure-From-Motion Photogrammetry: The Effect of Storms on Coastal Boulder Deposits

Nagle-McNaughton

Cox

2019

Remote Sensing

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Repeat photogrammetry is increasingly the go-too tool for long-term geomorphic monitoring, but quantifying the differences between structure-from-motion (SfM) models is a developing field. Volumetric differencing software (such as the open-source package CloudCompare) provides an efficient mechanism for quantifying change in landscapes. In this case study, we apply this methodology to coastal boulder deposits on Inishmore, Ireland. Storm waves are known to move these rocks, but boulder transportation and evolution of the deposits are not well documented. We used two disparate SfM data sets for this analysis. The first model was built from imagery captured in 2015 using a GoPro Hero 3+ camera (fisheye lens) and the second used 2017 imagery from a DJI FC300X camera (standard digital single-lens reflex (DSLR) camera); and we used CloudCompare to measure the differences between them. This study produced two noteworthy findings: First, volumetric differencing reveals that short-term changes in boulder deposits can be larger than expected, and that frequent monitoring can reveal not only the scale but the complexities of boulder transport in this setting. This is a valuable addition to our growing understanding of coastal boulder deposits. Second, SfM models generated by different imaging hardware can be successfully compared at sub-decimeter resolution, even when one of the camera systems has substantial lens distortion. This means that older image sets, which might not otherwise be considered of appropriate quality for co-analysis with more recent data, should not be ignored as data sources in long-term monitoring studies.Despite these improvements in protocols for data collection and analysis, quantifying change via repeat photogrammetry remains a challenge, generally requiring manipulation of digital terrain models (DTMs) using Geographic Information Systems (GIS) [45,46]. But recent innovations in 3D differencing software, as implemented in the open-source package CloudCompare (www.danielgm.net/cc/) enable rapid, objective, and replicable quantitative differencing [47]. CloudCompare is a cross-platform 3D point cloud and mesh processing package, which provides a suite of features for direct comparison of dense point clouds (>10 million points) and meshes on standard consumer computer hardware [48,49]. Accessibility and ease of use make CloudCompare an attractive tool, and it is applied increasingly in a variety of fields, including mapping [50,51], archaeology [52,53], and geomorphology [54][55][56][57].Another issue relevant to long term monitoring studies is how to deal with repeat imagery captured using different kinds of hardware, with different resolutions and/or distortion levels. The ultra-wide-angle (fisheye) lenses mounted on the popular consumer camera brand GoPro capture a~120 • field of view, but have barrel distortion due to their short focal length. Despite their limitations, GoPro cameras are popular tools for acquiring UAV imagery [24,31,[58][59][60], they are relatively inexpensive, are light enou...

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Section: Discussionmentioning

confidence: 88%

Section: Effective Efficient Methodology For Quantitative Repeat Phomentioning

confidence: 99%

Measuring Change Using Quantitative Differencing of Repeat Structure-From-Motion Photogrammetry: The Effect of Storms on Coastal Boulder Deposits

Nagle-McNaughton

Cox

2019

Remote Sensing

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“…The SfM photogrammetry, with imagery typically obtained using small UAVs, is now commonplace for low-level aerial image acquisition (e.g., Tonkin et al, 2014;Rippin et al, 2015;Ely et al, 2017;Rossini et al, 2018). At Midtre Lovénbreen, a DJI S800 UAV was used to acquire aerial images on 20 July 2014.…”

Section: Surface Characterisation: 2014mentioning

confidence: 99%

Evolution of high-Arctic glacial landforms during deglaciation

et al. 2018

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Glacial landsystems in the high-Arctic have been reported to undergo geomorphological transformation during deglaciation. This research evaluates moraine evolution over a decadal timescale at Midtre Lovénbreen, Svalbard. This work is of interest because glacial landforms developed in Svalbard have been used as an analogue for landforms developed during Pleistocene mid-latitude glaciation. Ground penetrating radar was used to investigate the subsurface characteristics of moraines. To determine surface change, a LiDAR topographic data set (obtained 2003) and a UAV-derived (obtained 2014) digital surface model processed using structure-from-motion (SfM) are also compared. Evaluation of these data sets together enables subsurface character and landform response to climatic amelioration to be linked. Ground penetrating radar evidence shows that the moraine substrate at Midtre Lovénbreen includes ice-rich (radar velocities of 0.17 m ns (mean thresholded rate of −4.39 m over the 11-year observation period). However, the debris-rich zones show a relatively low rate of surface change (mean thresholded rate of −0.98 m over the 11-year observation period), and the morphology of the debris-rich landforms appear stable over the observation period. A complex response of proglacial landforms to climatic warming is shown to occur within and between glacier forelands as indicated by spatially variable surface lowering rates. Landform response is controlled by the ice-debris balance of the moraine substrate, along with the topographic context (such as the influence of meltwater). Site-specific characteristics such as surface debris thickness and glaciofluvial drainage are, therefore, argued to be a highly important control on surface evolution in ice-cored terrain, resulting in a diverse response of high-Arctic glacial landsystems to climatic amelioration. These results highlight that care is needed when assessing the long-term preservation potential of contemporary landforms at high-Arctic glaciers. A better understanding of ice-cored terrain facilitates the development of appropriate age and climatic interpretations that can be obtained from palaeo ice-marginal landsystems.

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“…Given the changing climate can reduce the water availability in these snow-dominated regions (Barnett et al, 2005;Lemke et al, 2007;Middelkoop et al, 2001), accurate monitoring and forecasting of seasonal changes in snow distribution remain crucial for effective water resources management. Accurate knowledge of snow accumulation can also help us improve our understanding of environmental processes in mountain areas including changes in ground temperatures (Apaloo et al, 2012;Luetschg & Haeberli, 2007), permafrost creep (Delaloye et al, 2010;Ikeda et al, 2008), avalanches (Bühler et al, 2011), rockfalls (Haberkorn et al, 2016), landslides (Matsuura et al, 2003;Okamoto et al, 2018), glacier dynamics (Immerzeel et al, 2014;Rossini et al, 2018), and vegetation growth (Jonas et al, 2008). Additionally, high-resolution snow pack data can be used as reference data to test the accuracy of satellite remote-sensing products (Marti et al, 2016;Tinkham et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

Quantifying Uncertainties in Snow Depth Mapping From Structure From Motion Photogrammetry in an Alpine Area

Goetz

Brenning

2019

Water Resources Research

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Mapping snow conditions in alpine areas is crucial for monitoring local hydrology to support water resource management decisions. Recently, the use of structure‐from‐motion multiview stereo 3‐D reconstruction (or SFM photogrammetry) to derive high‐resolution digital elevation models (DEMs) has become popular for mapping snow depth in alpine areas. In this study, methods for communicating spatial uncertainties in snow depth calculated from SFM‐derived DEMs are presented using a case study in the French Alps. A spatially varying snow depth precision estimate was determined using an error propagation model based on the precision of the acquired SFM DEMs, which was obtained from repeated unmanned aerial vehicle flights. Spatially varying snow depth detection limits were determined using Student's t distribution. It was found that snow depths as shallow as 1 to 5 cm could be detected with high confidence for most of the study area. Areas of high uncertainties were generally related to where the extent of the ground control coverage did not match in the snow‐on and snow‐off surveys and in areas with higher surface roughness. A map of the snow depth detection threshold was found to be useful for identifying areas with high uncertainties and potential biases in the SFM snow depths, such as errors due to changes in topography between DEM acquisition dates and poor SFM reconstruction.

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Rapid melting dynamics of an alpine glacier with repeated UAV photogrammetry

Cited by 121 publications

References 53 publications

Measuring Change Using Quantitative Differencing of Repeat Structure-From-Motion Photogrammetry: The Effect of Storms on Coastal Boulder Deposits

Measuring Change Using Quantitative Differencing of Repeat Structure-From-Motion Photogrammetry: The Effect of Storms on Coastal Boulder Deposits

Evolution of high-Arctic glacial landforms during deglaciation

Quantifying Uncertainties in Snow Depth Mapping From Structure From Motion Photogrammetry in an Alpine Area

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