@article{RungeNitzeGrosse2021, author = {Runge, Alexandra and Nitze, Ingmar and Grosse, Guido}, title = {Remote sensing annual dynamics of rapid permafrost thaw disturbances with LandTrendr}, series = {Remote sensing of environment : an interdisciplinary journal}, volume = {268}, journal = {Remote sensing of environment : an interdisciplinary journal}, publisher = {Elsevier}, address = {New York}, issn = {0034-4257}, doi = {10.1016/j.rse.2021.112752}, pages = {18}, year = {2021}, abstract = {Permafrost is warming globally which leads to widespread permafrost thaw. Particularly ice-rich permafrost is vulnerable to rapid thaw and erosion, impacting whole landscapes and ecosystems. Retrogressive thaw slumps (RTS) are abrupt permafrost disturbances that expand by several meters each year and lead to an increased soil organic carbon release. Local Remote Sensing studies identified increasing RTS activity in the last two decades by increasing number of RTS or heightened RTS growth rates. However, a large-scale assessment across diverse permafrost regions and at high temporal resolution allowing to further determine RTS thaw dynamics and its main drivers is still lacking. In this study we apply the disturbance detection algorithm LandTrendr for automated large-scale RTS mapping and high temporal thaw dynamic assessment to North Siberia (8.1 x 106km2). We adapted and parametrised the temporal segmentation algorithm for abrupt disturbance detection to incorporate Landsat+Sentinel-2 mosaics, conducted spectral filtering, spatial masking and filtering, and a binary machine-learning object classification of the disturbance output to separate between RTS and false positives (F1 score: 0.609). Ground truth data for calibration and validation of the workflow was collected from 9 known RTS cluster sites using very highresolution RapidEye and PlanetScope imagery. Our study presents the first automated detection and assessment of RTS and their temporal dynamics at largescale for 2001-2019. We identified 50,895 RTS and a steady increase in RTS-affected area from 2001 to 2019 across North Siberia, with a more abrupt increase from 2016 onward. Overall the RTS-affected area increased by 331\% compared to 2000 (2000: 20,158 ha, 2001-2019: 66,699 ha). Contrary to this, 5 focus sites show spatiotemporal variability in their annual RTS dynamics, with alternating periods of increased and decreased RTS development, indicating a close relationship to thaw drivers. The majority of identified RTS was active from 2000 onward and only a small proportion initiated during the assessment period, indicating that the increase in RTS-affected area was mainly caused by enlarging existing RTS and not by new RTS. The detected increase in RTS dynamics suggests advancing permafrost thaw and underlines the importance of assessing abrupt permafrost disturbances with high spatial and temporal resolution at large-scales. Obtaining such consistent disturbance products will help to parametrise regional and global climate change models.}, language = {en} } @misc{WagnerOswaldFrick2018, author = {Wagner, Kathrin and Oswald, Sascha Eric and Frick, Annett}, title = {Multitemporal soil moisture monitoring by use of optical remote sensing data in a dike relocation area}, series = {Remote Sensing for Agriculture, Ecosystems, and Hydrology XX}, volume = {10783}, journal = {Remote Sensing for Agriculture, Ecosystems, and Hydrology XX}, publisher = {SPIE-INT Soc Optical Engineering}, address = {Bellingham}, isbn = {978-1-5106-2150-3}, issn = {0277-786X}, doi = {10.1117/12.2325319}, pages = {5}, year = {2018}, abstract = {The nature restoration project 'Lenzener Elbtalaue', realised from 2002 to 2011 at the river Elbe, included the first large scale dike relocation in Germany (420 ha). Its aim was to initiate the development of endangered natural wetland habitats and processes, accompanied by greater biodiversity in the former grassland dominated area. The monitoring of spatial and temporal variations of soil moisture in this dike relocation area is therefore particularly important for estimating the restoration success. The topsoil moisture monitoring from 1990 to 2017 is based on the Soil Moisture Index (SMI)1 derived with the triangle method2 by use of optical remotely sensed data: land surface temperature and Normalized Differnce Vegetation Index are calculated from Landsat 4/5/7/8 data and atmospheric corrected by use of MODIS data. Spatial and temporal soil moisture variations in the restored area of the dike relocation are compared to the agricultural and pasture area behind the new dike. Ground truth data in the dike relocation area was obtained from field measurements in October 2017 with a FDR device. Additionally, data from a TERENO soil moisture sensor network (SoilNet) and mobile cosmic ray neutron sensing (CRNS) rover measurements are compared to the results of the triangle method for a region in the Harz Mountains (Germany). The SMI time series illustrates, that the dike relocation area has become significantly wetter between 1990 and 2017, due to restructuring measurements. Whereas the SMI of the dike hinterland reflects constant and drier conditions. An influence of climate is unlikely. However, validation of the dimensionless index with ground truth measurements is very difficult, mostly due to large differences in scale.}, language = {en} } @phdthesis{Runge2021, author = {Runge, Alexandra}, title = {Multispectral time series analyses with Landsat and Sentinel-2 to assess permafrost disturbances in North Siberia}, doi = {10.25932/publishup-52206}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-522062}, school = {Universit{\"a}t Potsdam}, pages = {xxiv, 134}, year = {2021}, abstract = {Permafrost is warming globally, which leads to widespread permafrost thaw and impacts the surrounding landscapes, ecosystems and infrastructure. Especially ice-rich permafrost is vulnerable to rapid and abrupt thaw, resulting from the melting of excess ground ice. Local remote sensing studies have detected increasing rates of abrupt permafrost disturbances, such as thermokarst lake change and drainage, coastal erosion and RTS in the last two decades. All of which indicate an acceleration of permafrost degradation. In particular retrogressive thaw slumps (RTS) are abrupt disturbances that expand by up to several meters each year and impact local and regional topographic gradients, hydrological pathways, sediment and nutrient mobilisation into aquatic systems, and increased permafrost carbon mobilisation. The feedback between abrupt permafrost thaw and the carbon cycle is a crucial component of the Earth system and a relevant driver in global climate models. However, an assessment of RTS at high temporal resolution to determine the dynamic thaw processes and identify the main thaw drivers as well as a continental-scale assessment across diverse permafrost regions are still lacking. In northern high latitudes optical remote sensing is restricted by environmental factors and frequent cloud coverage. This decreases image availability and thus constrains the application of automated algorithms for time series disturbance detection for large-scale abrupt permafrost disturbances at high temporal resolution. Since models and observations suggest that abrupt permafrost disturbances will intensify, we require disturbance products at continental-scale, which allow for meaningful integration into Earth system models. The main aim of this dissertation therefore, is to enhance our knowledge on the spatial extent and temporal dynamics of abrupt permafrost disturbances in a large-scale assessment. To address this, three research objectives were posed: 1. Assess the comparability and compatibility of Landsat-8 and Sentinel-2 data for a combined use in multi-spectral analysis in northern high latitudes. 2. Adapt an image mosaicking method for Landsat and Sentinel-2 data to create combined mosaics of high quality as input for high temporal disturbance assessments in northern high latitudes. 3. Automatically map retrogressive thaw slumps on the landscape-scale and assess their high temporal thaw dynamics. We assessed the comparability of Landsat-8 and Sentinel-2 imagery by spectral comparison of corresponding bands. Based on overlapping same-day acquisitions of Landsat-8 and Sentinel-2 we derived spectral bandpass adjustment coefficients for North Siberia to adjust Sentinel-2 reflectance values to resemble Landsat-8 and harmonise the two data sets. Furthermore, we adapted a workflow to combine Landsat and Sentinel-2 images to create homogeneous and gap-free annual mosaics. We determined the number of images and cloud-free pixels, the spatial coverage and the quality of the mosaic with spectral comparisons to demonstrate the relevance of the Landsat+Sentinel-2 mosaics. Lastly, we adapted the automatic disturbance detection algorithm LandTrendr for large-scale RTS identification and mapping at high temporal resolution. For this, we modified the temporal segmentation algorithm for annual gradual and abrupt disturbance detection to incorporate the annual Landsat+Sentinel-2 mosaics. We further parametrised the temporal segmentation and spectral filtering for optimised RTS detection, conducted further spatial masking and filtering, and implemented a binary object classification algorithm with machine-learning to derive RTS from the LandTrendr disturbance output. We applied the algorithm to North Siberia, covering an area of 8.1 x 106 km2. The spectral band comparison between same-day Landsat-8 and Sentinel-2 acquisitions already showed an overall good fit between both satellite products. However, applying the acquired spectral bandpass coefficients for adjustment of Sentinel-2 reflectance values, resulted in a near-perfect alignment between the same-day images. It can therefore be concluded that the spectral band adjustment succeeds in adjusting Sentinel-2 spectral values to those of Landsat-8 in North Siberia. The number of available cloud-free images increased steadily between 1999 and 2019, especially intensified after 2016 with the addition of Sentinel-2 images. This signifies a highly improved input database for the mosaicking workflow. In a comparison of annual mosaics, the Landsat+Sentinel-2 mosaics always fully covered the study areas, while Landsat-only mosaics contained data-gaps for the same years. The spectral comparison of input images and Landsat+Sentinel-2 mosaic showed a high correlation between the input images and the mosaic bands, testifying mosaicking results of high quality. Our results show that especially the mosaic coverage for northern, coastal areas was substantially improved with the Landsat+Sentinel-2 mosaics. By combining data from both Landsat and Sentinel-2 sensors we reliably created input mosaics at high spatial resolution for comprehensive time series analyses. This research presents the first automatically derived assessment of RTS distribution and temporal dynamics at continental-scale. In total, we identified 50,895 RTS, primarily located in ice-rich permafrost regions, as well as a steady increase in RTS-affected areas between 2001 and 2019 across North Siberia. From 2016 onward the RTS area increased more abruptly, indicating heightened thaw slump dynamics in this period. Overall, the RTS-affected area increased by 331 \% within the observation period. Contrary to this, five focus sites show spatiotemporal variability in their annual RTS dynamics, alternating between periods of increased and decreased RTS development. This suggests a close relationship to varying thaw drivers. The majority of identified RTS was active from 2000 onward and only a small proportion initiated during the assessment period. This highlights that the increase in RTS-affected area was mainly caused by enlarging existing RTS and not by newly initiated RTS. Overall, this research showed the advantages of combining Landsat and Sentinel-2 data in northern high latitudes and the improvements in spatial and temporal coverage of combined annual mosaics. The mosaics build the database for automated disturbance detection to reliably map RTS and other abrupt permafrost disturbances at continental-scale. The assessment at high temporal resolution further testifies the increasing impact of abrupt permafrost disturbances and likewise emphasises the spatio-temporal variability of thaw dynamics across landscapes. Obtaining such consistent disturbance products is necessary to parametrise regional and global climate change models, for enabling an improved representation of the permafrost thaw feedback.}, language = {en} } @misc{RungeGrosse2019, author = {Runge, Alexandra and Grosse, Guido}, title = {Comparing Spectral Characteristics of Landsat-8 and Sentinel-2 Same-Day Data for Arctic-Boreal Regions}, series = {Postprints der Universit{\"a}t Potsdam Mathematisch-Naturwissenschaftliche Reihe}, journal = {Postprints der Universit{\"a}t Potsdam Mathematisch-Naturwissenschaftliche Reihe}, number = {767}, issn = {1866-8372}, doi = {10.25932/publishup-43866}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-438660}, pages = {29}, year = {2019}, abstract = {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.}, language = {en} } @article{RungeGrosse2019, author = {Runge, Alexandra and Grosse, Guido}, title = {Comparing Spectral Characteristics of Landsat-8 and Sentinel-2 Same-Day Data for Arctic-Boreal Regions}, series = {Remote Sensing}, volume = {11}, journal = {Remote Sensing}, publisher = {MDPI}, address = {Basel}, issn = {2072-4292}, doi = {10.3390/rs11141730}, pages = {29}, year = {2019}, abstract = {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.}, language = {en} } @article{WeithoffRochaGaedke2015, author = {Weithoff, Guntram and Rocha, Marcia R. and Gaedke, Ursula}, title = {Comparing seasonal dynamics of functional and taxonomic diversity reveals the driving forces underlying phytoplankton community structure}, series = {Freshwater biology}, volume = {60}, journal = {Freshwater biology}, number = {4}, publisher = {Wiley-Blackwell}, address = {Hoboken}, issn = {0046-5070}, doi = {10.1111/fwb.12527}, pages = {758 -- 767}, year = {2015}, abstract = {In most biodiversity studies, taxonomic diversity is the measure for the multiplicity of species and is often considered to represent functional diversity. However, trends in taxonomic diversity and functional diversity may differ, for example, when many functionally similar but taxonomically different species co-occur in a community. The differences between these diversity measures are of particular interest in diversity research for understanding diversity patterns and their underlying mechanisms. We analysed a temporally highly resolved 20-year time series of lake phytoplankton to determine whether taxonomic diversity and functional diversity exhibit similar or contrasting seasonal patterns. We also calculated the functional mean of the community in n-dimensional trait space for each sampling day to gain further insights into the seasonal dynamics of the functional properties of the community. We found an overall weak positive relationship between taxonomic diversity and functional diversity with a distinct seasonal pattern. The two diversity measures showed synchronous behaviour from early spring to mid-summer and a more complex and diverging relationship from autumn to late winter. The functional mean of the community exhibited a recurrent annual pattern with the most prominent changes before and after the clear-water phase. From late autumn to winter, the functional mean of the community and functional diversity were relatively constant while taxonomic diversity declined, suggesting competitive exclusion during this period. A further decline in taxonomic diversity concomitant with increasing functional diversity in late winter to early spring is seen as a result of niche diversification together with competitive exclusion. Under these conditions, several different sets of traits are suitable to thrive, but within one set of functional traits only one, or very few, morphotypes can persist. Taxonomic diversity alone is a weak descriptor of trait diversity in phytoplankton. However, the combined analysis of taxonomic diversity and functional diversity, along with the functional mean of the community, allows for deeper insights into temporal patterns of community assembly and niche diversification.}, language = {en} } @unpublished{GairingHoegeleKosenkova2016, author = {Gairing, Jan and H{\"o}gele, Michael and Kosenkova, Tetiana}, title = {Transportation distances and noise sensitivity of multiplicative L{\´e}vy SDE with applications}, volume = {5}, number = {2}, publisher = {Universit{\"a}tsverlag Potsdam}, address = {Potsdam}, issn = {2193-6943}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-86693}, pages = {24}, year = {2016}, abstract = {This article assesses the distance between the laws of stochastic differential equations with multiplicative L{\´e}vy noise on path space in terms of their characteristics. The notion of transportation distance on the set of L{\´e}vy kernels introduced by Kosenkova and Kulik yields a natural and statistically tractable upper bound on the noise sensitivity. This extends recent results for the additive case in terms of coupling distances to the multiplicative case. The strength of this notion is shown in a statistical implementation for simulations and the example of a benchmark time series in paleoclimate.}, language = {en} }