Terrestrial laser scanning for quantifying small-scale vertical
movements of the ground surface in Artic permafrost regions

Marx, Sabrina; Anders, Katharina; Antonova, Sofia; Beck, Inga; Boike, Julia; Marsh, Philip; Langer, Moritz; Höfle, Bernhard

doi:10.5194/esurf-2017-49

Cited by 8 publications

(15 citation statements)

References 36 publications

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“…Warming of permafrost over the last few decades has been reported from many circum-Arctic boreholes (Biskaborn et al, 2019;Romanovsky et al, 2010). Warming and thawing of permafrost and an overall reduction in the area that it covers have been predicted under future climate change scenarios by the CMIP5 climate models, but at widely varying rates (Koven et al, 2012;McGuire et al, 2018). Continued observations, not only of the thermal state of permafrost but also of the multiple other types of data required to understand the changes to permafrost, are therefore of great importance.…”

Section: Introductionmentioning

confidence: 99%

A 16-year record (2002–2017) of permafrost, active-layer, and meteorological conditions at the Samoylov Island Arctic permafrost research site, Lena River delta, northern Siberia: an opportunity to validate remote-sensing data and land surface, snow, and permafrost models

Boike

Nitzbon

Anders

et al. 2019

Earth Syst. Sci. Data

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Abstract. Most of the world's permafrost is located in the Arctic, where its frozen organic carbon content makes it a potentially important influence on the global climate system. The Arctic climate appears to be changing more rapidly than the lower latitudes, but observational data density in the region is low. Permafrost thaw and carbon release into the atmosphere, as well as snow cover changes, are positive feedback mechanisms that have the potential for climate warming. It is therefore particularly important to understand the links between the energy balance, which can vary rapidly over hourly to annual timescales, and permafrost conditions, which changes slowly on decadal to centennial timescales. This requires long-term observational data such as that available from the Samoylov research site in northern Siberia, where meteorological parameters, energy balance, and subsurface observations have been recorded since 1998. This paper presents the temporal data set produced between 2002 and 2017, explaining the instrumentation, calibration, processing, and data quality control. Furthermore, we present a merged data set of the parameters, which were measured from 1998 onwards. Additional data include a high-resolution digital terrain model (DTM) obtained from terrestrial lidar laser scanning. Since the data provide observations of temporally variable parameters that influence energy fluxes between permafrost, active-layer soils, and the atmosphere (such as snow depth and soil moisture content), they are suitable for calibrating and quantifying the dynamics of permafrost as a component in earth system models. The data also include soil properties beneath different microtopographic features (a polygon centre, a rim, a slope, and a trough), yielding much-needed information on landscape heterogeneity for use in land surface modelling. For the record from 1998 to 2017, the average mean annual air temperature was −12.3 ∘C, with mean monthly temperature of the warmest month (July) recorded as 9.5 ∘C and for the coldest month (February) −32.7 ∘C. The average annual rainfall was 169 mm. The depth of zero annual amplitude is at 20.75 m. At this depth, the temperature has increased from −9.1 ∘C in 2006 to −7.7 ∘C in 2017. The presented data are freely available through the PANGAEA (https://doi.org/10.1594/PANGAEA.891142) and Zenodo (https://zenodo.org/record/2223709, last access: 6 February 2019) websites.

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Section: Introductionmentioning

confidence: 99%

A 16-year record (2002–2017) of permafrost, active-layer, and meteorological conditions at the Samoylov Island Arctic permafrost research site, Lena River delta, northern Siberia: an opportunity to validate remote-sensing data and land surface, snow, and permafrost models

Boike

Nitzbon

Anders

et al. 2019

Earth Syst. Sci. Data

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“…Sci. Data Discuss., https://doi.org/10.5194/essd-2018-82 from the scan positions as the incidence angle from the TLS instrument has a direct effect on 860 the penetration depth of the laser beam (Marx et al, 2017).…”

mentioning

confidence: 99%

A 16-year record (2002–2017) of permafrost, active layer, and meteorological conditions at the Samoylov Island Arctic permafrost research site, Lena River Delta, northern Siberia: an opportunity to validate remote sensing data and land surface, snow, and permafrost models

Boike¹,

Nitzbon²,

Anders³

et al. 2018

Preprint

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Abstract. Most of the world's permafrost is located in the Arctic, where its frozen organic carbon con-tent makes it a potentially important influence on the global climate system. The Arctic climate appears to be changing more rapidly than the lower latitudes, but observational data density in the region is low. Permafrost thaw and carbon release into the atmosphere is a positive feed-back mechanism that has the potential for climate warming. It is therefore particularly im-portant to understand the links between the energy balance, which can vary rapidly over hour-ly to annual time scales, and permafrost condition, which changes slowly on decadal to cen-tennial timescales. This requires long-term observational data such as that available from the Samoylov research site in northern Siberia, where meteorological parameters, energy balance, and subsurface observations have been recorded since 1998. This paper presents the temporal data set produced between 2002 and 2017, explaining the instrumentation, calibration, pro-cessing and data quality control. Additional data include a high-resolution digital terrain mod-el (DTM) obtained from terrestrial LiDAR laser scanning. Since the data provide observations of temporally variable parameters that influence energy fluxes between permafrost, active lay-er soils, and the atmosphere (such as snow depth and soil moisture content), they are suitable for calibrating and quantifying the dynamics of permafrost as a component in earth system models. The data also include soil properties beneath different microtopographic features (a polygon center, a rim, a slope, and a trough), yielding much-needed information on landscape heterogeneity for use in land surface modeling. For the record from 1998 to 2017, the average mean annual air temperature was − 12.3 °C, with mean monthly temperature of the warmest month (July) recorded as 9.5 °C and for the coldest month (February) − 32.7 °C. The average annual rainfall was 169 mm. The depth of zero annual amplitude niveau is at 20.8 m, and has warmed from − 9.1 °C in 2006 to − 7.7 °C in 2017. The presented data are available in the supplementary material of this paper and through the PANGAEA website (https://doi.org/10.1594/PANGAEA.891142).

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“…Eltner and Baumgart (2015) reported that small elevation changes as a result of erosion and deposition can be detected by multitemporal TLS. The smallest changes detected reliably in their study were 1.5 10 2 m. With the exception of Marx et al (2017), no studies of TLS measurements of surface elevation changes smaller than a centimetre have been found for on-field experiments.…”

Section: Introductionmentioning

confidence: 90%

“…The smallest changes detected reliably in their study were 1.5×10 −2 m. With the exception of Marx et al. (), no studies of TLS measurements of surface elevation changes smaller than a centimetre have been found for on‐field experiments.…”

Section: Introductionmentioning

confidence: 99%

Quantifying the dynamics of microtopography during a snowmelt event

Barneveld

Zee

Stolte

2019

Earth Surf Processes Landf

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Knowledge of soil microtopography and its changes in space and over time is important to the understanding of how tillage influences infiltration, runoff generation and erosion. In this study, the use of a terrestrial laser scanner (TLS) is assessed for its ability to quantify small changes in the soil surface at high spatial resolutions for a relatively large surface area (100 m2). Changes in soil surface morphology during snow cover and melt are driven by frost heave, slaking, pressure exertion by the snowpack and overland flow (erosion and deposition). An attempt is undertaken to link these processes to observed changes at the soil surface. A new algorithm for soil surface roughness is introduced to make optimal use of the raw point cloud. This algorithm is less scale dependent than several commonly used roughness calculations. The results of this study show that TLSs can be used for multitemporal scanning of large surfaces and that small changes in surface elevation and roughness can be detected. Statistical analysis of the observed changes against terrain indices did not yield significant evidence for process differentiation. © 2019 The Authors. Earth Surface Processes and Landforms Published by John Wiley & Sons Ltd. © 2019 The Authors. Earth Surface Processes and Landforms Published by John Wiley & Sons Ltd.

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Terrestrial laser scanning for quantifying small-scale vertical movements of the ground surface in Artic permafrost regions

Cited by 8 publications

References 36 publications

A 16-year record (2002–2017) of permafrost, active-layer, and meteorological conditions at the Samoylov Island Arctic permafrost research site, Lena River delta, northern Siberia: an opportunity to validate remote-sensing data and land surface, snow, and permafrost models

A 16-year record (2002–2017) of permafrost, active-layer, and meteorological conditions at the Samoylov Island Arctic permafrost research site, Lena River delta, northern Siberia: an opportunity to validate remote-sensing data and land surface, snow, and permafrost models

A 16-year record (2002–2017) of permafrost, active layer, and meteorological conditions at the Samoylov Island Arctic permafrost research site, Lena River Delta, northern Siberia: an opportunity to validate remote sensing data and land surface, snow, and permafrost models

Quantifying the dynamics of microtopography during a snowmelt event

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