scholarly journals A Quantitative Graph-Based Approach to Monitoring Ice-Wedge Trough Dynamics in Polygonal Permafrost Landscapes

2021 ◽  
Vol 13 (16) ◽  
pp. 3098
Author(s):  
Tabea Rettelbach ◽  
Moritz Langer ◽  
Ingmar Nitze ◽  
Benjamin Jones ◽  
Veit Helm ◽  
...  
Keyword(s):  
Computer Vision ◽  
Considerable Increase ◽  
Graph Analysis ◽  
Average Depth ◽  
Surface Hydrology ◽  
Fire Scar ◽  
Digital Elevation ◽  
Rapid Changes ◽  
Ice Wedge ◽  
Northern Alaska

In response to increasing Arctic temperatures, ice-rich permafrost landscapes are undergoing rapid changes. In permafrost lowlands, polygonal ice wedges are especially prone to degradation. Melting of ice wedges results in deepening troughs and the transition from low-centered to high-centered ice-wedge polygons. This process has important implications for surface hydrology, as the connectivity of such troughs determines the rate of drainage for these lowland landscapes. In this study, we present a comprehensive, modular, and highly automated workflow to extract, to represent, and to analyze remotely sensed ice-wedge polygonal trough networks as a graph (i.e., network structure). With computer vision methods, we efficiently extract the trough locations as well as their geomorphometric information on trough depth and width from high-resolution digital elevation models and link these data within the graph. Further, we present and discuss the benefits of graph analysis algorithms for characterizing the erosional development of such thaw-affected landscapes. Based on our graph analysis, we show how thaw subsidence has progressed between 2009 and 2019 following burning at the Anaktuvuk River fire scar in northern Alaska, USA. We observed a considerable increase in the number of discernible troughs within the study area, while simultaneously the number of disconnected networks decreased from 54 small networks in 2009 to only six considerably larger disconnected networks in 2019. On average, the width of the troughs has increased by 13.86%, while the average depth has slightly decreased by 10.31%. Overall, our new automated approach allows for monitoring ice-wedge dynamics in unprecedented spatial detail, while simultaneously reducing the data to quantifiable geometric measures and spatial relationships.

scholarly journals Mapping snow depth within a tundra ecosystem using multiscale observations and Bayesian methods

2016 ◽  
Author(s):  
Haruko M. Wainwright ◽  
Anna K. Liljedahl ◽  
Baptiste Dafflon ◽  
Craig Ulrich ◽  
John E. Peterson ◽  
...  
Keyword(s):  
High Precision ◽  
Snow Depth ◽  
Spatial Sampling ◽  
Multi Scale ◽  
Arctic Alaska ◽  
Winter Snow ◽  
Digital Elevation ◽  
Tundra Ecosystem ◽  
Ice Wedge ◽  
Ground Penetrating

Abstract. This paper compares and integrates different strategies to characterize the variability of end-of-winter snow depth and its relationship to topography in ice-wedge polygon tundra of Arctic Alaska. Snow depth was measured using in situ snow depth probes, and estimated using ground penetrating radar (GPR) surveys and the Photogrammetric Detection and Ranging (PhoDAR) technique with an unmanned aerial system (UAS). We found that GPR data provided high-precision estimates of snow depth (RMSE = 2.9 cm), with a spatial sampling of 10 cm along transects. UAS-based approaches provided snow depth estimates in a less laborious manner compared to GPR and probing while yielding a high precision (RMSE = 6.0 cm) and a fine spatial sampling (4 cm by 4 cm). We then investigated the spatial variability of snow depth and its correlation to micro- and macrotopography using the snow-free LiDAR digital elevation map (DEM) and the wavelet approach. We found that the end-of-winter snow depth was highly variable over short (several meter) distances, and the variability was correlated with microtopography. Microtopographic lows (i.e., troughs and centers of low-centered polygons) were filled in with snow, which resulted in a smooth and even snow surface following macrotopography. We developed and implemented a Bayesian approach to integrate the snow-free LiDAR DEM and multi-scale measurements (probe and GPR) as well as the topographic correlation for estimating snow depth over the landscape. Our approach led to high precision estimates of snow depth (RMSE = 6.0 cm), at 0.5-meter resolution and over the LiDAR domain (750 m by 700 m).

scholarly journals Seasonal and Interannual Ground-Surface Displacement in Intact and Disturbed Tundra along the Dalton Highway on the North Slope, Alaska

Land ◽  
2020 ◽  
Vol 10 (1) ◽  
pp. 22
Author(s):  
Go Iwahana ◽  
Robert C. Busey ◽  
Kazuyuki Saito
Keyword(s):  
Ground Surface ◽  
Surface Displacement ◽  
Satellite System ◽  
The North ◽  
Surface Displacements ◽  
Standard Deviation Range ◽  
Thaw Depth ◽  
Ice Wedge ◽  
Global Navigation Satellite ◽  
Northern Alaska

Spatiotemporal variation in ground-surface displacement caused by ground freeze–thaw and thermokarst is critical information to understand changes in the permafrost ecosystem. Measurement of ground displacement, especially in the disturbed ground underlain by ice-rich permafrost, is important to estimate the rate of permafrost and carbon loss. We conducted high-precision global navigation satellite system (GNSS) positioning surveys to measure the surface displacements of tundra in northern Alaska, together with maximum thaw depth (TD) and surface moisture measurements from 2017 to 2019. The measurements were performed along two to three 60–200 m transects per site with 1–5 m intervals at the three areas. The average seasonal thaw settlement (STS) at intact tundra sites ranged 5.8–14.3 cm with a standard deviation range of 2.1–3.3 cm. At the disturbed locations, averages and variations in STS and the maximum thaw depth were largest in all observed years and among all sites. The largest seasonal and interannual subsidence (44 and 56 cm/year, respectively) were recorded at points near troughs of degraded ice-wedge polygons or thermokarst lakes. Weak or moderate correlation between STS and TD found at the intact sites became obscure as the thermokarst disturbance progressed, leading to higher uncertainty in the prediction of TD from STS.

scholarly journals Brief communication: Rapid machine learning-based extraction and measurement of ice wedge polygons in airborne lidar data

2018 ◽  
Author(s):  
Charles J. Abolt ◽  
Michael H. Young ◽  
Adam A. Atchley ◽  
Cathy J. Wilson
Keyword(s):  
Airborne Lidar ◽  
Robust Performance ◽  
Remotely Sensed Imagery ◽  
Network Geometry ◽  
Digital Elevation ◽  
Processing Procedure ◽  
Ice Wedge ◽  
Airborne Lidar Data ◽  
Prudhoe Bay

Abstract. We present a workflow for rapid delineation and microtopographic characterization of ice wedge polygons within high-resolution digital elevation models. The workflow, which is extensible to other forms of remotely sensed imagery, incorporates a convolutional neural network to detect pixels representing troughs. A watershed transformation is then used to segment imagery into discrete polygons. Regions of non-polygonal terrain are partitioned out using a simple post-processing procedure. Results from training and validation sites at Barrow and Prudhoe Bay, Alaska demonstrate robust performance in diverse tundra landscapes. The methodology permits fast, spatially extensive measurements of polygonal microtopography and trough network geometry.

scholarly journals An automatic and modular stereo pipeline for pushbroom images

2014 ◽  
Vol II-3 ◽  
pp. 49-56 ◽  
Author(s):  
C. de Franchis ◽  
E. Meinhardt-Llopis ◽  
J. Michel ◽  
J.-M. Morel ◽  
G. Facciolo
Keyword(s):  
Computer Vision ◽  
Satellite Images ◽  
Stereo Matching ◽  
Ad Hoc ◽  
Digital Elevation Models ◽  
Stereo Image ◽  
Satellite Imaging ◽  
Digital Elevation ◽  
Pointing Accuracy ◽  
Earth Observation Satellites

The increasing availability of high resolution stereo images from Earth observation satellites has boosted the development of tools for producing 3D elevation models. The objective of these tools is to produce digital elevation models of very large areas with minimal human intervention. The development of these tools has been shaped by the constraints of the remote sensing acquisition, for example, using ad hoc stereo matching tools to deal with the pushbroom image geometry. However, this specialization has also created a gap with respect to the fields of computer vision and image processing, where these constraints are usually factored out. In this work we propose a fully automatic and modular stereo pipeline to produce digital elevation models from satellite images. The aim of this new pipeline, called <i>Satellite Stereo Pipeline</i> and abbreviated as <i>s2p</i>, is to use (and test) off-the-shelf computer vision tools while abstracting from the complexity associated to satellite imaging. To this aim, images are cut in small tiles for which we proved that the pushbroom geometry is very accurately approximated by the pinhole model. These tiles are then processed with standard stereo image rectification and stereo matching tools. The specifics of satellite imaging such as pointing accuracy refinement, estimation of the initial elevation from SRTM data, and geodetic coordinate systems are handled transparently by s2p. We demonstrate the robustness of our approach on a large database of satellite images and by providing an online demo of s2p.

Numerical simulation of the paleohydrology of glacial Lake Oshkosh, eastern Wisconsin, USA

2008 ◽  
Vol 69 (1) ◽  
pp. 117-129 ◽  
Author(s):  
James A. Clark ◽  
Kevin M. Befus ◽  
Thomas S. Hooyer ◽  
Peter W. Stewart ◽  
Taylor D. Shipman ◽  
...  
Keyword(s):  
Ice Sheet ◽  
Laurentide Ice Sheet ◽  
Surface Hydrology ◽  
Isostatic Adjustment ◽  
Outburst Floods ◽  
Model Flow ◽  
Digital Elevation ◽  
Hydrologic System ◽  
Gis Methods ◽  
Analysis System

Proglacial lakes, formed during retreat of the Laurentide ice sheet, evolved quickly as outlets became ice-free and the earth deformed through glacial isostatic adjustment. With high-resolution digital elevation models (DEMs) and GIS methods, it is possible to reconstruct the evolution of surface hydrology. When a DEM deforms through time as predicted by our model of viscoelastic earth relaxation, the entire surface hydrologic system with its lakes, outlets, shorelines and rivers also evolves without requiring assumptions of outlet position. The method is applied to proglacial Lake Oshkosh in Wisconsin (13,600 to 12,900 cal yr BP). Comparison of predicted to observed shoreline tilt indicates the ice sheet was about 400 m thick over the Great Lakes region. During ice sheet recession, each of the five outlets are predicted to uplift more than 100 m and then subside approximately 30 m. At its maximum extent, Lake Oshkosh covered 6600 km2 with a volume of 111 km3. Using the Hydrologic Engineering Center-River Analysis System model, flow velocities during glacial outburst floods up to 9 m/s and peak discharge of 140,000 m3/s are predicted, which could drain 33.5 km3 of lake water in 10 days and transport boulders up to 3 m in diameter.

scholarly journals Mapping snow depth within a tundra ecosystem using multiscale observations and Bayesian methods

2017 ◽  
Vol 11 (2) ◽  
pp. 857-875 ◽  
Author(s):  
Haruko M. Wainwright ◽  
Anna K. Liljedahl ◽  
Baptiste Dafflon ◽  
Craig Ulrich ◽  
John E. Peterson ◽  
...  
Keyword(s):  
High Precision ◽  
Snow Depth ◽  
Spatial Sampling ◽  
Arctic Alaska ◽  
Winter Snow ◽  
Digital Elevation ◽  
Tundra Ecosystem ◽  
Ice Wedge ◽  
Ground Penetrating

Abstract. This paper compares and integrates different strategies to characterize the variability of end-of-winter snow depth and its relationship to topography in ice-wedge polygon tundra of Arctic Alaska. Snow depth was measured using in situ snow depth probes and estimated using ground-penetrating radar (GPR) surveys and the photogrammetric detection and ranging (phodar) technique with an unmanned aerial system (UAS). We found that GPR data provided high-precision estimates of snow depth (RMSE  =  2.9 cm), with a spatial sampling of 10 cm along transects. Phodar-based approaches provided snow depth estimates in a less laborious manner compared to GPR and probing, while yielding a high precision (RMSE  =  6.0 cm) and a fine spatial sampling (4 cm × 4 cm). We then investigated the spatial variability of snow depth and its correlation to micro- and macrotopography using the snow-free lidar digital elevation map (DEM) and the wavelet approach. We found that the end-of-winter snow depth was highly variable over short (several meter) distances, and the variability was correlated with microtopography. Microtopographic lows (i.e., troughs and centers of low-centered polygons) were filled in with snow, which resulted in a smooth and even snow surface following macrotopography. We developed and implemented a Bayesian approach to integrate the snow-free lidar DEM and multiscale measurements (probe and GPR) as well as the topographic correlation for estimating snow depth over the landscape. Our approach led to high-precision estimates of snow depth (RMSE  =  6.0 cm), at 0.5 m resolution and over the lidar domain (750 m × 700 m).

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