Strong degradation of palsas and peat plateaus in northern Norway during the last 60 years

Abstract. Palsas and peat plateaus are permafrost landforms occurring in subarctic mires which constitute sensitive ecosystems with strong significance for vegetation, wildlife, hydrology and carbon cycle. We have systematically mapped the occurrence of palsas and peat plateaus in the northernmost county of Norway (Finnmark, ~ 50 000 km2) by manual interpretation of aerial images from 2005–2014 at a spatial resolution of 250 m2. At this resolution, mires and wetlands with palsas or peat plateaus occur in about 850 km2 of Finnmark, with the actual palsas and peat plateaus underlain by permafrost covering a surface area of approximately 110 km2. Secondly, we have quantified the lateral changes of the extent of palsas and peat plateaus for four study areas located along a NW–SE transect through Finnmark by utilizing repeat aerial imagery from the 1950s to the 2010s. The results of the lateral changes reveal a total decrease of 33–71 % in the areal extent of palsas and peat plateaus during the study period, with the largest lateral change rates observed in the last decade. However, the results indicate that degradation of palsas and peat plateaus in northern Norway has been a consistent process during the second half of the 20th century and possibly even earlier. Significant rates of degradation are observed in all investigated time periods since the 1950s, and thermokarst landforms observed on aerial images from the 1950s suggest that lateral degradation was already an ongoing process at this time. The results of this study show that lateral erosion of palsas and peat plateaus is an important pathway for permafrost degradation in the sporadic permafrost zone in northern Scandinavia. While the environmental factors governing the rate of erosion are not yet fully understood, we note a moderate increase in both air temperature and precipitation during the last few decades in the region.

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Strong degradation of palsas and peat plateaus in northern Norway during the last 60 years

The Cryosphere ◽

10.5194/tc-11-1-2017 ◽

2017 ◽

Vol 11 (1) ◽

pp. 1-16 ◽

Cited By ~ 23

Author(s):

Amund F. Borge ◽

Sebastian Westermann ◽

Ingvild Solheim ◽

Bernd Etzelmüller

Keyword(s):

Snow Depth ◽

Aerial Imagery ◽

Aerial Images ◽

Moderate Increase ◽

Permafrost Degradation ◽

Northern Norway ◽

Important Pathway ◽

The 1950S ◽

Lateral Erosion ◽

Peat Plateaus

Abstract. Palsas and peat plateaus are permafrost landforms occurring in subarctic mires which constitute sensitive ecosystems with strong significance for vegetation, wildlife, hydrology and carbon cycle. Firstly, we have systematically mapped the occurrence of palsas and peat plateaus in the northernmost county of Norway (Finnmark, ∼ 50 000 km2) by manual interpretation of aerial images from 2005 to 2014 at a spatial resolution of 250 m. At this resolution, mires and wetlands with palsas or peat plateaus occur in about 850 km2 of Finnmark, with the actual palsas and peat plateaus underlain by permafrost covering a surface area of approximately 110 km2. Secondly, we have quantified the lateral changes of the extent of palsas and peat plateaus for four study areas located along a NW–SE transect through Finnmark by utilizing repeat aerial imagery from the 1950s to the 2010s. The results of the lateral changes reveal a total decrease of 33–71 % in the areal extent of palsas and peat plateaus during the study period, with the largest lateral change rates observed in the last decade. However, the results indicate that degradation of palsas and peat plateaus in northern Norway has been a consistent process during the second half of the 20th century and possibly even earlier. Significant rates of areal change are observed in all investigated time periods since the 1950s, and thermokarst landforms observed on aerial images from the 1950s suggest that lateral degradation was already an ongoing process at this time. The results of this study show that lateral erosion of palsas and peat plateaus is an important pathway for permafrost degradation in the sporadic permafrost zone in northern Scandinavia. While the environmental factors governing the rate of erosion are not yet fully understood, we note a moderate increase in air temperature, precipitation and snow depth during the last few decades in the region.

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Thermal erosion patterns of permafrost peat plateaus in northern Norway

10.5194/tc-2020-338 ◽

2020 ◽

Author(s):

Léo C. P. Martin ◽

Jan Nitzbon ◽

Johanna Scheer ◽

Kjetil S. Aas ◽

Trond Eiken ◽

...

Keyword(s):

Thermal Regime ◽

Snow Depth ◽

Subsurface Water ◽

Small Scale ◽

Surface Model ◽

Permafrost Degradation ◽

Modeling Framework ◽

Northern Norway ◽

Thermal Erosion ◽

Peat Plateaus

Abstract. Subarctic peatlands underlain by permafrost contain significant amounts of organic carbon and our ability to quantify the evolution of such permafrost landscapes in numerical models is critical to provide robust predictions of the environmental and climatic changes to come. Yet, the accuracy of large-scale predictions is so far hampered by small-scale physical processes that create a high spatial variability of surface ground thermal regime and thus of permafrost degradation patterns. In this regard, a better understanding of the small-scale interplay between microtopography and lateral fluxes of heat, water and snow can be achieved by field monitoring and process-based numerical modeling. Here, we quantify the topographic changes of the Šuoššjávri peat plateau (Northern Norway) over a three-years period using repeated drone-based high-resolution photogrammetry. Our results show that edge degradation is the main process through which thermal erosion occurs and represents about 80 % of measured subsidence, while most of the inner plateau surface exhibits no detectable subsidence. Based on detailed investigation of eight zones of the plateau edge, we show that this edge degradation corresponds to a volumetric loss of 0.13 ± 0.07 m3 yr−1 m−1 (cubic meter per year and per meter of plateau circumference). Using the CryoGrid land surface model, we show that these degradation patterns can be reproduced in a modeling framework that implements lateral redistribution of snow, subsurface water and heat, as well as ground subsidence due to melting of excess ice. We reproduce prolonged climate-driven edge degradation that is consistent with field observations and present a sensitivity test of the plateau degradation on snow depth over the plateau. Small snow depth variations (from 0 to 30 cm) result in highly different degradation behavior, from stability to fast degradation. These results represent a new step in the modeling of climate-driven landscape development and permafrost degradation in highly heterogeneous landscapes such as peat plateaus. Our approach provides a physically based quantification of permafrost thaw with a new level of realism, notably, regarding feedback mechanisms between the dynamical topography and the lateral fluxes through which a small modification of the snow depth result in dramatic modifications of the permafrost degradation intensity. In this regard, these results also highlight the major control of snow pack characteristics on the ground thermal regime and the potential improvement that accurate snow representation and prediction could bring to projections of permafrost degradation.

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Carbon degradation in Subarctic organic permafrost (peat plateaus) after thawing – what constraints CO2 and CH4 production?

10.5194/egusphere-egu21-11121 ◽

2021 ◽

Author(s):

Sigrid Trier Kjær ◽

Nora Nedkvitne ◽

Sebastian Westermann ◽

Inge Althuizen ◽

Peter Dörsch

Keyword(s):

Organic Matter ◽

Active Layer ◽

Degradation Kinetics ◽

Soil Depth ◽

The Arctic ◽

Permafrost Degradation ◽

Northern Norway ◽

Degradation Rates ◽

Peat Plateaus ◽

Carbon Degradation

Rapid warming in Subarctic areas releases large amounts of frozen carbon which can potentially result in large CO2 and CH4 emissions to the atmosphere. In Northern Norway vast amount of carbon are stored in peat plateaus, but these landscape elements have been found to decrease laterally since at least the 1950s. Peat plateaus are very sensitive to climate change as the permafrost is relatively warm compared to permafrost found in the arctic. So far, only limited information is available about potential degradation kinetics of organic carbon in these ecosystems. We sampled organic matter from depth profiles along a well-documented chronosequence of permafrost degradation in Northern Norway. After thawing over-night, we incubated permafrost and active layer for up to 3 months at 10&#176;C. To determine factors constraining degradation, we measured gas kinetics (O2, CO2, CH4) under different conditions (oxic/anoxic, loosely packed/stirred suspensions in water, with altered DOC content and nutrient amendments) and related them to pH, DOC, element (C, N, P, S) and &#948;13C and &#948;15N signatures of the peat. Organic matter degradation was strongly inhibited in the absence of oxygen. By contrast, CH4 production or release seemed to be related to soil depth rather than incubation conditions and was found to be highest in samples from the transition zone between active layer and permafrost. Degradation rates and their dependencies on peat characteristics will be compared with permafrost characteristics along the chronosequence and additional experiments exploring the role of O2, DOC and other nutrients for carbon degradation will be discussed.

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Lateral thermokarst patterns in permafrost peat plateaus in northern Norway

The Cryosphere ◽

10.5194/tc-15-3423-2021 ◽

2021 ◽

Vol 15 (7) ◽

pp. 3423-3442

Author(s):

Léo C. P. Martin ◽

Jan Nitzbon ◽

Johanna Scheer ◽

Kjetil S. Aas ◽

Trond Eiken ◽

...

Keyword(s):

Numerical Models ◽

Ground Subsidence ◽

Subsurface Water ◽

Small Scale ◽

Surface Model ◽

Permafrost Degradation ◽

Modeling Framework ◽

Northern Norway ◽

Annual Volume ◽

Peat Plateaus

Abstract. Subarctic peatlands underlain by permafrost contain significant amounts of organic carbon. Our ability to quantify the evolution of such permafrost landscapes in numerical models is critical for providing robust predictions of the environmental and climatic changes to come. Yet, the accuracy of large-scale predictions has so far been hampered by small-scale physical processes that create a high spatial variability of thermal surface conditions, affecting the ground thermal regime and thus permafrost degradation patterns. In this regard, a better understanding of the small-scale interplay between microtopography and lateral fluxes of heat, water and snow can be achieved by field monitoring and process-based numerical modeling. Here, we quantify the topographic changes of the Šuoššjávri peat plateau (northern Norway) over a three-year period using drone-based repeat high-resolution photogrammetry. Our results show thermokarst degradation is concentrated on the edges of the plateau, representing 77 % of observed subsidence, while most of the inner plateau surface exhibits no detectable subsidence. Based on detailed investigation of eight zones of the plateau edge, we show that this edge degradation corresponds to an annual volume change of 0.13±0.07 m3 yr−1 per meter of retreating edge (orthogonal to the retreat direction). Using the CryoGrid3 land surface model, we show that these degradation patterns can be reproduced in a modeling framework that implements lateral redistribution of snow, subsurface water and heat, as well as ground subsidence due to melting of excess ice. By performing a sensitivity test for snow depths on the plateau under steady-state climate forcing, we obtain a threshold behavior for the start of edge degradation. Small snow depth variations (from 0 to 30 cm) result in highly different degradation behavior, from stability to fast degradation. For plateau snow depths in the range of field measurements, the simulated annual volume changes are broadly in agreement with the results of the drone survey. As snow depths are clearly correlated with ground surface temperatures, our results indicate that the approach can potentially be used to simulate climate-driven dynamics of edge degradation observed at our study site and other peat plateaus worldwide. Thus, the model approach represents a first step towards simulating climate-driven landscape development through thermokarst in permafrost peatlands.

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IMG2nDSM: Height Estimation from Single Airborne RGB Images with Deep Learning

Remote Sensing ◽

10.3390/rs13122417 ◽

2021 ◽

Vol 13 (12) ◽

pp. 2417

Author(s):

Savvas Karatsiolis ◽

Andreas Kamilaris ◽

Ian Cole

Keyword(s):

Deep Learning ◽

State Of The Art ◽

Aerial Imagery ◽

Aerial Images ◽

Surface Model ◽

Large Area ◽

Digital Terrain ◽

Terrain Models ◽

Architectural Features ◽

Rgb Images

Estimating the height of buildings and vegetation in single aerial images is a challenging problem. A task-focused Deep Learning (DL) model that combines architectural features from successful DL models (U-NET and Residual Networks) and learns the mapping from a single aerial imagery to a normalized Digital Surface Model (nDSM) was proposed. The model was trained on aerial images whose corresponding DSM and Digital Terrain Models (DTM) were available and was then used to infer the nDSM of images with no elevation information. The model was evaluated with a dataset covering a large area of Manchester, UK, as well as the 2018 IEEE GRSS Data Fusion Contest LiDAR dataset. The results suggest that the proposed DL architecture is suitable for the task and surpasses other state-of-the-art DL approaches by a large margin.

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Sensitivities of glacier mass balance and runoff to climate perturbations in Norway

Annals of Glaciology ◽

10.3189/2015aog70a004 ◽

2015 ◽

Vol 56 (70) ◽

pp. 79-88 ◽

Cited By ~ 16

Author(s):

Markus Engelhardt ◽

Thomas V. Schuler ◽

Liss M. Andreassen

Keyword(s):

Mass Balance ◽

Temperature Change ◽

Air Temperature ◽

Annual Runoff ◽

Precipitation Change ◽

Glacier Mass Balance ◽

Northern Norway ◽

Temperature Range ◽

Temperature And Precipitation ◽

Southern Norway

AbstractThis study evaluates sensitivities of glacier mass balance and runoff to both annual and monthly perturbations in air temperature and precipitation at four highly glacierized catchments: Engabreen in northern Norway and Ålfotbreen, Nigardsbreen and Storbreen, which are aligned along a west–east profile in southern Norway. The glacier mass-balance sensitivities to changes in annual air temperature range from 1.74 m w.e. K−1 for Ålfotbreen to 0.55 m w.e. K−1 for Storbreen, the most maritime and the most continental glaciers in this study, respectively. The runoff sensitivities of all catchments are 20–25% per degree temperature change and 6–18% for a 30% precipitation change. A seasonality of the sensitivities becomes apparent. With increasing continentality, the sensitivity of mass balance and runoff to temperature perturbations during summer increases, and the sensitivity of annual runoff to both temperature and precipitation perturbations is constricted towards changes during the ablation period. Comparing sensitivities in northern and southern Norway, as well as the variability across southern Norway, reveals that continentality influences sensitivities more than latitude does.

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Mapping the Unseen: Exploiting Super-Resolution for Semantic Segmentation in Low-Resolution Images

10.5753/sibgrapi.est.2020.12987 ◽

2020 ◽

Author(s):

Matheus B. Pereira ◽

Jefersson Alex Dos Santos

Keyword(s):

Remote Sensing ◽

Pattern Recognition ◽

Super Resolution ◽

Remote Sensing Data ◽

Semantic Segmentation ◽

The Other ◽

Aerial Imagery ◽

Aerial Images ◽

Remote Sensing Images ◽

Low Resolution

High-resolution aerial images are usually not accessible or affordable. On the other hand, low-resolution remote sensing data is easily found in public open repositories. The problem is that the low-resolution representation can compromise pattern recognition algorithms, especially semantic segmentation. In this M.Sc. dissertation1 , we design two frameworks in order to evaluate the effectiveness of super-resolution in the semantic segmentation of low-resolution remote sensing images. We carried out an extensive set of experiments on different remote sensing datasets. The results show that super-resolution is effective to improve semantic segmentation performance on low-resolution aerial imagery, outperforming unsupervised interpolation and achieving semantic segmentation results comparable to highresolution data.

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Using historical aerial imagery to assess multidecadal kinematics and elevation changes. Application to mountain permafrost in the French Alps.

10.5194/egusphere-egu21-16371 ◽

2021 ◽

Author(s):

Diego Cusicanqui ◽

Antoine Rabatel ◽

Xavier Bodin ◽

Christian Vincent ◽

Emmanuel Thibert ◽

...

Keyword(s):

Ice Core ◽

Horizontal Displacement ◽

Vertical Surface ◽

Aerial Images ◽

Surface Velocity ◽

High Mountain ◽

European Alps ◽

Permafrost Degradation ◽

Mountain Areas ◽

Mountain Permafrost

Glacial and periglacial environments are highly sensitive to climate change, even more in mountain areas where warming is faster and, as a consequence, perennial features of the cryosphere like glaciers and permafrost have been fast evolving in the last decades. In the European Alps, glaciers retreat and permafrost thawing have led to the destabilization of mountain slopes, threatening human infrastructures and inhabitants. The observation of such changes at decadal scales is often limited to sparse in situ observations.Here, we present three study cases of mountain permafrost sites based on a multidisciplinary approach over almost seven decades. The goal is to investigate and quantify morphodynamic changes and understand the causes of these evolutions. We used stereo-photogrammetry techniques to generate orthophotos and (DEMs) from historical aerial images (available, in France since 1940s). From this, we produced diachronic comparison of DEMs to quantify vertical surface changes, as well as feature tracking techniques of multi-temporal digital orthophotos for estimating horizontal displacement rates. Locally, high-resolution datasets (i.e. LiDAR surveys, UAV acquisitions and Pl&#233;iades stereo imagery) were also exploited to improve the quality of photogrammetric products. In addition, we combine these results with geophysics (ERT and GPR) to estimate the ice content, geomorphological surveys to describe the complex environments and the relationship with climatic forcing.The first study case is the Laurichard rock glacier, where we were able to quantify changes of emergence velocities, fluxes, and volume. Together with an acceleration of surface velocity, important surface lowering have been found over the period 1952-2019, with a striking spatiotemporal reversal of volume balance.The second study site is the Tignes glacial and periglacial complex, where the changes of thermokarstic lakes surface were quantified. The results suggest that drainage probably affects the presence and the evolution of the largest thermorkarst. Here too, a significant ice loss was found on the central channel concomitant to an increase in surface velocities.The third study site is the Chauvet glacial and periglacial complex where several historical outburst floods are recorded during the 20th century, likely related to the permafrost degradation, the presence of thermokarstic lakes, and an intra-glacial channel. The lateral convergence of ice flow, due to the terrain subsidence caused by the intense melting, may cause the closure of the channel with a subsequent refill of the thermokarstic depression and finally a new catastrophic event.Our results highlight the important value of historical aerial photography for having a longer perspective on the evolution of the high mountain cryosphere, thanks to accurate quantification of pluri-annual changes of volume and surface velocity. For instance, we could evidence : (1) a speed-up of the horizontal displacements since the 1990s in comparison with the previous decades; (2) an important surface lowering related to various melting processes (ice-core, thermokarst) for the three study sites; (3) relationships between the observed evolution and the contemporaneous climate warming, with a long-term evolution controlled by the warming of the ground and short-term changes that may relate to snow or precipitation or to the activity of the glacial-periglacial landforms.

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Deep Learning Segmentation and 3D Reconstruction of Road Markings Using Multiview Aerial Imagery

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi8010047 ◽

2019 ◽

Vol 8 (1) ◽

pp. 47 ◽

Cited By ~ 1

Author(s):

Franz Kurz ◽

Seyed Azimi ◽

Chun-Yu Sheu ◽

Pablo d’Angelo

Keyword(s):

3D Reconstruction ◽

Autonomous Vehicles ◽

Automatic Segmentation ◽

Image Data ◽

Autonomous Driving ◽

Aerial Imagery ◽

Aerial Images ◽

Aerial Image ◽

Matching Problem ◽

Convolutional Network

The 3D information of road infrastructures is growing in importance with the development of autonomous driving. In this context, the exact 2D position of road markings as well as height information play an important role in, e.g., lane-accurate self-localization of autonomous vehicles. In this paper, the overall task is divided into an automatic segmentation followed by a refined 3D reconstruction. For the segmentation task, we applied a wavelet-enhanced fully convolutional network on multiview high-resolution aerial imagery. Based on the resulting 2D segments in the original images, we propose a successive workflow for the 3D reconstruction of road markings based on a least-squares line-fitting in multiview imagery. The 3D reconstruction exploits the line character of road markings with the aim to optimize the best 3D line location by minimizing the distance from its back projection to the detected 2D line in all the covering images. Results showed an improved IoU of the automatic road marking segmentation by exploiting the multiview character of the aerial images and a more accurate 3D reconstruction of the road surface compared to the semiglobal matching (SGM) algorithm. Further, the approach avoids the matching problem in non-textured image parts and is not limited to lines of finite length. In this paper, the approach is presented and validated on several aerial image data sets covering different scenarios like motorways and urban regions.

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