Die Rolle des Permafrost im Klimasystem Erde

Permafrost, der dauergefrorene Boden der Polarregionen, ist von der schnellen Erwärmung der Arktis direkt betroffen. Beobachtungen innerhalb des globalen Permafrost-Temperatur Messnetzwerks zeigen eine Erwärmung an den meisten Messtandorten mit besonders starken Trends in der hohen Arktis. Das damit einhergehende Tauen führt zu einem Rückzug des Permafrosts in vielen Regionen und damit massiven Veränderungen von grundlegenden Ökosystembedingungen in den etwa 23 Millionen Quadratkilometern arktischer Landschaften mit Permafrost-Einfluss. Permafrostböden speichern etwa 1300-1600 Milliarden Tonnen organischen Kohlenstoff und damit etwa doppelt so viel Kohlenstoff wie in der Atmosphäre enthalten ist. Die Freisetzung von Permafrostkohlenstoff durch Auftauen ist bisher nicht in globalen Klimamodellen berücksichtigt und trägt dazu bei, unseren Handlungsspielraum in klimapolitischen Fragen einzuengen. Die Auswirkungen von tauendem Permafrost im Zuge des Klimawandels sind damit auch für uns in Mitteleuropa direkt relevant

Deep Learning for mapping retrogressive thaw slumps across the Arctic

Retrogressive thaw slumps (RTS) are typical landscape processes of thawing and degrading permafrost. To this point, their distribution and dynamics are almost completely undocumented across many regions in the permafrost domain, partially due to the lack of data and monitoring techniques in the past. We are tackling this shortcoming by creating a deep learning based semantic segmentation framework to detect RTS, using multi-spectral PlanetScope, derived topographic (ArcticDEM) and multi-temporal Landsat Trend data. We created a highly automated processing pipeline, which is designed to create reproducible results and to be flexible for multiple input features. The processing workflow is based on the pytorch deep-learning framework and includes a variety of different segmentation architectures (UNet, UNet++, DeepLabV3), backbones and includes common data transformation techniques such as augmentation or normalization. We tested (training, validation) our DL based model in six different regions of 100 to 300 km² size across Canada (Banks Island, Tuktoyaktuk, Horton, Herschel Is.), and Siberia (Kolguev, Lena). We performed a regional cross-validation (5 regions training, 1 region validation) to test the spatial robustness and transferability of the algorithm. Furthermore, we tested different architectures backbones and loss-function to identify the best performing and most robust parameter sets. For training the models we created a training database of manually digitized and validated RTS polygons. The resulting model performance varied strongly between different regions with maximum Intersection over Union (IoU) scores between 0.15 and 0.58. The strong regional variation emphasizes the need for sufficiently large training data, which is representative for the massive variety of RTS. However, the creation of good training data proved to be challenging due to the fuzzy definition and delineation of RTS, particularly on the lower part. We have recently expanded our analysis to several RTS-rich regions across the Arctic (Fig.X) for the year 2021 and annual analysis (2018-2021) for RTS hot-spots, e.g. Banks Island, Peel Plateau and others. First model inference runs are promising for detecting RTS, but are still strongly overestimating the number and area of RTS, due to an excessive number of false positives. Model performance however, varies strongly between regions. Due to the strong variability of landscapes with RTS, we expect an improvement in model performance with an increase in the number and spatial distribution of training datasets. The community driven formation of the IPA Action Group RTSIn, which aims to create standardized RTS digitization protocols and training datasets for deep/machine-learning purposes will be a great boost for our purpose. With our standardized processing pipeline (preprocessing, training, inference), which allows to add more features based on user interest and data availability,, we tested our workflow for surface water and pingos with a mixture of publically available (Jones et al) and digitized data (Grosse pingos, Nitze water). These tests produced very good results and showed that the designed workflow is transferrable beyond the segmentation of RTS only. In the near future, we are aiming to integrate the community based training data and further gradually improve our training database. Within the framework of the ML4Earth project, we will create a temporal and pan-arctic monitoring system for RTS based on our highly automated processing chain. This will enable us to better understand pan-arctic RTS dynamics, their influencing factors, and consequences. Combining these spatial-temporal datasets with volumetric change information and carbon stock information will enable us to better quantify the consequences of thaw slumping across the permafrost domain

Thermokarst lake monitoring on the Bykovsky Peninsula using high-resolution remote sensing data

Thermokarst lakes are a characteristic element of arctic permafrost regions and an indicator for their rapid landscape changes. Assessing their dynamics contributes to the understanding of driving processes of change, to the evaluation of impacts on landscape characteristics as well as to the estimation of the impact on the permafrost-related carbon budget. Monitoring thermokarst lake dynamics on the Bykovsky Peninsula, consisting of ice-rich Yedoma deposits, using high resolution remote sensing imagery from 1951 to 2016, revealed a long-term tendency towards lake drainage. Approximately 17% of the 1951 lake area was lost due to coastal erosion or the development of drainage networks. In parallel, coastal erosion driven land loss amounts to 2.3% of the peninsula. We find process interconnections between coastal erosion and lake change, as well as lake change dependency on land elevation in a developed alas-yedoma thermokarst relief

Continental-scale drivers of lake drainage in permafrost regions

Lakes are ubiquitous with high-latitude ecosystems, covering up to 60 percent of the land surface in some regions. Due to their influence on an array of key biogeophysical processes, the recent decline in lake area (via gradual and abrupt) observed across permafrost ecosystems may hold significant implications for shifting carbon and energy dynamics. Since lakes are often highly dynamic, understanding the main drivers of lake area change may ultimately enable the prediction of lake persistence in a warmer climate; key to anticipating future carbon-climate feedbacks from Arctic ecosystems. Here we conducted a data-driven analysis of >600k lakes across four continental-scale transects (Alaska, E Canada, W Siberia, E Siberia), combining remote sensing-derived lake shape parameters and spatial dynamics with other ecosystem datasets, such as ground temperatures, climate, elevation/geomorphology, and permafrost landscape parameters. We grouped our lake-change dataset into non-drained, partially and completely drained lakes (25-75 %, >75% loss) and used the RandomForest Feature Importance to calculate the relative importance of each parameter. Furthermore we predicted the probability of lake drainage under current environmental conditions and changing permafrost temperatures. Initial results suggest a strong importance of ground temperatures, lake shape, and local geomorphology on lake drainage. Spatially coarser datasets of permafrost and thermokarst properties did not reveal correlations with the result. Our drainage prediction results show distinct spatial patterns, which are matching regional lake drainage patterns. Our model estimated ground temperature as one of the main impact factors, with an increased drainage likelihood in permafrost regions from -5 to 0 °C. Going forward, we will further test for short term influences, such as extreme weather events and wildfire on widespread lake drainage. As this analysis is purely data-driven, a comparison or combination with physics-based models and predictions will help to better validate our analysis

Sentinel-1 InSAR measurements of surface elevation changes over yedoma uplands on Sobo-Sise Island, Lena Delta

Yedoma is vulnerable to thawing and degradation under climate warming, which can result in lowering of surface elevations due to thaw subsidence. Quantitative knowledge about elevation changes can help us better understand the freeze-thaw processes of the active layer and yedoma deposits. In this study, we utilize C-band Sentinel-1 InSAR measurements, characterized by frequent sampling, to study the elevation changes over ice-rich yedoma uplands on Sobo-Sise Island, Lena Delta. We observe significant seasonal thaw subsidence during summer months and inter-annual elevation changes from 2016 to 2017. Here, we demonstrate the capability of Sentinel-1 to estimate elevation changes over yedoma uplands. We observe interesting patterns of stronger seasonal thaw subsidence on elevated flat yedoma uplands when compared to surrounding yedoma slopes. Inter-annual analyses from 2016 to 2017 revealed mostly positive surface elevation changes that might be caused by delayed thaw seasonal progression associated with mean annual air temperature fluctuations

Tundra landform and vegetation productivity trend maps for the Arctic Coastal Plain of northern Alaska

Arctic tundra landscapes are composed of a complex mosaic of patterned ground features, varying in soil moisture, vegetation composition, and surface hydrology over small spatial scales (10–100 m). The importance of microtopography and associated geomorphic landforms in influencing ecosystem structure and function is well founded, however, spatial data products describing local to regional scale distribution of patterned ground or polygonal tundra geomorphology are largely unavailable. Thus, our understanding of local impacts on regional scale processes (e.g., carbon dynamics) may be limited. We produced two key spatiotemporal datasets spanning the Arctic Coastal Plain of northern Alaska (~60,000 km2) to evaluate climate-geomorphological controls on arctic tundra productivity change, using (1) a novel 30m classification of polygonal tundra geomorphology and (2) decadal-trends in surface greenness using the Landsat archive (1999–2014). These datasets can be easily integrated and adapted in an array of local to regional applications such as (1) upscaling plot-level measurements (e.g., carbon/energy fluxes), (2) mapping of soils, vegetation, or permafrost, and/or (3) initializing ecosystem biogeochemistry, hydrology, and/or habitat modeling

The use of CORONA images in remote sensing of periglacial geomorphology: an illustration from the NE Siberian coast

CORONA images have been used for the mapping of periglacial features on the Bykovsky Peninsula and adjacent Khorogor Valley in northeast Siberia. Features, mapped and analysed within a geographical information system, include thermokarst depressions, thermo-erosional valleys, thermo-erosional cirques, thermokarst lakes, thermokarst lagoons and pingos. More than 50% of the area is strongly influenced by thermally-induced subsidence. Thermokarst in the area is probably less active today than in the early-middle Holocene

Comparing Spectral Characteristics of Landsat-8 and Sentinel-2 Same-Day Data for Arctic-Boreal Regions

The Arctic-Boreal regions experience strong changes of air temperature and precipitation regimes, which affect the thermal state of the permafrost. This results in widespread permafrost-thaw disturbances, some unfolding slowly and over long periods, others occurring rapidly and abruptly. Despite optical remote sensing offering a variety of techniques to assess and monitor landscape changes, a persistent cloud cover decreases the amount of usable images considerably. However, combining data from multiple platforms promises to increase the number of images drastically. We therefore assess the comparability of Landsat-8 and Sentinel-2 imagery and the possibility to use both Landsat and Sentinel-2 images together in time series analyses, achieving a temporally-dense data coverage in Arctic-Boreal regions. We determined overlapping same-day acquisitions of Landsat-8 and Sentinel-2 images for three representative study sites in Eastern Siberia. We then compared the Landsat-8 and Sentinel-2 pixel-pairs, downscaled to 60 m, of corresponding bands and derived the ordinary least squares regression for every band combination. The acquired coefficients were used for spectral bandpass adjustment between the two sensors. The spectral band comparisons showed an overall good fit between Landsat-8 and Sentinel-2 images already. The ordinary least squares regression analyses underline the generally good spectral fit with intercept values between 0.0031 and 0.056 and slope values between 0.531 and 0.877. A spectral comparison after spectral bandpass adjustment of Sentinel-2 values to Landsat-8 shows a nearly perfect alignment between the same-day images. The spectral band adjustment succeeds in adjusting Sentinel-2 spectral values to Landsat-8 very well in Eastern Siberian Arctic-Boreal landscapes. After spectral adjustment, Landsat and Sentinel-2 data can be used to create temporally-dense time series and be applied to assess permafrost landscape changes in Eastern Siberia. Remaining differences between the sensors can be attributed to several factors including heterogeneous terrain, poor cloud and cloud shadow masking, and mixed pixels