Analyzing Glacier Surface Motion Using LiDAR Data

Telling, J. W.; Glennie, C. L.; Fountain, Andrew G.; Finnegan, D. C.

doi:10.3390/rs9030283

Cited by 26 publications

(18 citation statements)

References 24 publications

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“…A subregion of the study basin ( Figure 1) was measured using a long-range terrestrial LiDAR scanner (Reigl VZ®-6000) that operates at a near-infrared wavelength (1064 nm), with a range exceeding 6,000 m for a good diffusively reflective target and an angular step resolution of up to 0.002°, making it highly suitable for monitoring changes of snow and ice in mountain environments (Fischer et al, 2016;Telling et al, 2017). We obtained a 0.74 km 2 scan for the dates 13 September (with snow, hereafter LID_Snow ON ) and 12 December 2017 (without snow, hereafter LID_Snow OFF ) for comparison of Pléiades vertical differences ("apparent snow depth"-section 3.6.2).…”

Section: Lidarmentioning

confidence: 99%

Snow Depth Patterns in a High Mountain Andean Catchment from Satellite Optical Tristereoscopic Remote Sensing

Shaw

Gascoin

Mendoza

et al. 2020

Water Resources Research

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Obtaining detailed information about high mountain snowpacks is often limited by insufficient ground‐based observations and uncertainty in the (re)distribution of solid precipitation. We utilize high‐resolution optical images from Pléiades satellites to generate a snow depth map, at a spatial resolution of 4 m, for a high mountain catchment of central Chile. Results are negatively biased (median difference of −0.22 m) when compared against observations from a terrestrial Light Detection And Ranging scan, though replicate general snow depth variability well. Additionally, the Pléiades dataset is subject to data gaps (17% of total pixels), negative values for shallow snow (12%), and noise on slopes >40–50° (2%). We correct and filter the Pléiades snow depths using surface classification techniques of snow‐free areas and a random forest model for data gap filling. Snow depths (with an estimated error of ~0.36 m) average 1.66 m and relate well to topographical parameters such as elevation and northness in a similar way to previous studies. However, estimations of snow depth based upon topography (TOPO) or physically based modeling (DBSM) cannot resolve localized processes (i.e., avalanching or wind scouring) that are detected by Pléiades, even when forced with locally calibrated data. Comparing these alternative model approaches to corrected Pléiades snow depths reveals total snow volume differences between −28% (DBSM) and +54% (TOPO) for the catchment and large differences across most elevation bands. Pléiades represents an important contribution to understanding snow accumulation at sparsely monitored catchments, though ideally requires a careful systematic validation procedure to identify catchment‐scale biases and errors in the snow depth derivation.

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Section: Lidarmentioning

confidence: 99%

Snow Depth Patterns in a High Mountain Andean Catchment from Satellite Optical Tristereoscopic Remote Sensing

Shaw

Gascoin

Mendoza

et al. 2020

Water Resources Research

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“…Changes to snow cover are thought to be small in these locations, as lidar points are in the glacier ablation zones where snow cover is ephemeral and patchy. Instead, glacier surface change is dominated by the translation of the surficial ridge and swale topography (Telling et al, 2017). Glacier ablation zones have largely thinned over the 14-year period with mean elevation changes ranging from −2.1 m at Miers Glacier in Miers Valley to −0.28 m at Victoria Lower glacier.…”

Section: Glaciersmentioning

confidence: 99%

Decadal topographic change in the McMurdo Dry Valleys of Antarctica: Thermokarst subsidence, glacier thinning, and transfer of water storage from the cryosphere to the hydrosphere

et al. 2018

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Recent local-scale observations of glaciers, streams, and soil surfaces in the McMurdo Dry Valleys of Antarctica (MDV) have documented evidence for rapid ice loss, glacial thinning, and ground surface subsidence associated with melting of ground ice. To evaluate the extent, magnitude, and location of decadal-scale landscape change in the MDV, we collected airborne lidar elevation data in 2014-2015 and compared these data to a 2001-2002 airborne lidar campaign. This regional assessment of elevation change spans the recent acceleration of warming and melting observed by long-term meteorological and ecosystem response experiments, allowing us to assess the response of MDV surfaces to warming and potential thawing feedbacks. We find that locations of thermokarst subsidence are strongly associated with the presence of excess ground ice and with proximity to surface or shallow subsurface (active layer) water. Subsidence occurs across soil types and landforms, in low-lying, low-slope areas with impeded drainage and also high on steep valley walls. Glacier thinning is widespread and is associated with the growth of fine-scale roughness. Pond levels are rising in most closed-basin lakes in the MDV, across all microclimate zones. These observations highlight the continued importance of insolation-driven melting in the MDV. The regional melt pattern is consistent with an overall transition of water storage from the local cryosphere (glaciers, permafrost) to the hydrosphere (closed basin lakes and ponds as well as the Ross Sea). We interpret this regional melting pattern to reflect a transition to Arctic and alpine-style, hydrologically mediated permafrost and glacial melt.

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“…Laser scanning uses LiDAR measurements to record vast numbers of points in a geographic scene that represent the geometry and textural information of objects (Otepka et al, 2013). It can even capture (micro-)topography of the surface that might be occluded by vegetation and is a powerful remote sensing technique that has been used, for example, in generating 3D models of cities (Rebecca et al, 2008), for flood and glacier modelling (Telling et al, 2017), sand dune modelling (Dong, 2015), and extraction of vegetation parameters (Rosette et al, 2009).…”

Section: Introductionmentioning

confidence: 99%

Feature Relevance Analysis for 3d Point Cloud Classification Using Deep Learning

Kumar

Anders

Winiwarter

et al. 2019

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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<p><strong>Abstract.</strong> 3D point clouds acquired by laser scanning and other techniques are difficult to interpret because of their irregular structure. To make sense of this data and to allow for the derivation of useful information, a segmentation of the points in groups, units, or classes fit for the specific use case is required. In this paper, we present a non-end-to-end deep learning classifier for 3D point clouds using multiple sets of input features and compare it with an implementation of the state-of-the-art deep learning framework PointNet++. We first start by extracting features derived from the local normal vector (normal vectors, eigenvalues, and eigenvectors) from the point cloud, and study the result of classification for different local search radii. We extract additional features related to spatial point distribution and use them together with the normal vector-based features. We find that the classification accuracy improves by up to 33% as we include normal vector features with multiple search radii and features related to spatial point distribution. Our method achieves a mean Intersection over Union (mIoU) of 94% outperforming PointNet++’s Multi Scale Grouping by up to 12%. The study presents the importance of multiple search radii for different point cloud features for classification in an urban 3D point cloud scene acquired by terrestrial laser scanning.</p>

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Analyzing Glacier Surface Motion Using LiDAR Data

Cited by 26 publications

References 24 publications

Snow Depth Patterns in a High Mountain Andean Catchment from Satellite Optical Tristereoscopic Remote Sensing

Snow Depth Patterns in a High Mountain Andean Catchment from Satellite Optical Tristereoscopic Remote Sensing

Decadal topographic change in the McMurdo Dry Valleys of Antarctica: Thermokarst subsidence, glacier thinning, and transfer of water storage from the cryosphere to the hydrosphere

Feature Relevance Analysis for 3d Point Cloud Classification Using Deep Learning

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