@article{LappeUllmannBachofer2022, author = {Lappe, Ronja and Ullmann, Tobias and Bachofer, Felix}, title = {State of the Vietnamese coast — assessing three decades (1986 to 2021) of coastline dynamics using the Landsat archive}, series = {Remote Sensing}, volume = {14}, journal = {Remote Sensing}, number = {10}, issn = {2072-4292}, doi = {10.3390/rs14102476}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-275281}, year = {2022}, abstract = {Vietnam's 3260 km coastline is densely populated, experiences rapid urban and economic growth, and faces at the same time a high risk of coastal hazards. Satellite archives provide a free and powerful opportunity for long-term area-wide monitoring of the coastal zone. This paper presents an automated analysis of coastline dynamics from 1986 to 2021 for Vietnam's entire coastal zone using the Landsat archive. The proposed method is implemented within the cloud-computing platform Google Earth Engine to only involve publicly and globally available datasets and tools. We generated annual coastline composites representing the mean-high water level and extracted sub-pixel coastlines. We further quantified coastline change rates along shore-perpendicular transects, revealing that half of Vietnam's coast did not experience significant change, while the remaining half is classified as erosional (27.7\%) and accretional (27.1\%). A hotspot analysis shows that coastal segments with the highest change rates are concentrated in the low-lying deltas of the Mekong River in the south and the Red River in the north. Hotspots with the highest accretion rates of up to +47 m/year are mainly associated with the construction of artificial coastlines, while hotspots with the highest erosion rates of -28 m/year may be related to natural sediment redistribution and human activity.}, language = {en} } @article{YangYaoLietal.2022, author = {Yang, Xuting and Yao, Wanqiang and Li, Pengfei and Hu, Jinfei and Latifi, Hooman and Kang, Li and Wang, Ningjing and Zhang, Dingming}, title = {Changes of SOC content in China's Shendong coal mining area during 1990-2020 investigated using remote sensing techniques}, series = {Sustainability}, volume = {14}, journal = {Sustainability}, number = {12}, issn = {2071-1050}, doi = {10.3390/su14127374}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-278939}, year = {2022}, abstract = {Coal mining, an important human activity, disturbs soil organic carbon (SOC) accumulation and decomposition, eventually affecting terrestrial carbon cycling and the sustainability of human society. However, changes of SOC content and their relation with influential factors in coal mining areas remained unclear. In the study, predictive models of SOC content were developed based on field sampling and Landsat images for different land-use types (grassland, forest, farmland, and bare land) of the largest coal mining area in China (i.e., Shendong). The established models were employed to estimate SOC content across the Shendong mining area during 1990-2020, followed by an investigation into the impacts of climate change and human disturbance on SOC content by a Geo-detector. Results showed that the models produced satisfactory results (R\(^2\) > 0.69, p < 0.05), demonstrating that SOC content over a large coal mining area can be effectively assessed using remote sensing techniques. Results revealed that average SOC content in the study area rose from 5.67 gC·kg\(^{-1}\) in 1990 to 9.23 gC·kg\(^{-1}\) in 2010 and then declined to 5.31 gC·Kg\(^{-1}\) in 2020. This could be attributed to the interaction between the disturbance of soil caused by coal mining and the improvement of eco-environment by land reclamation. Spatially, the SOC content of farmland was the highest, followed by grassland, and that of bare land was the lowest. SOC accumulation was inhibited by coal mining activities, with the effect of high-intensity mining being lower than that of moderate- and low-intensity mining activities. Land use was found to be the strongest individual influencing factor for SOC content changes, while the interaction between vegetation coverage and precipitation exerted the most significant influence on the variability of SOC content. Furthermore, the influence of mining intensity combined with precipitation was 10 times higher than that of mining intensity alone.}, language = {en} } @article{Hardaker2022, author = {Hardaker, Sina}, title = {More Than Infrastructure Providers - Digital Platforms' Role and Power in Retail Digitalisation in Germany}, series = {Tijdschrift voor Economische en Sociale Geografie}, volume = {113}, journal = {Tijdschrift voor Economische en Sociale Geografie}, number = {3}, doi = {10.1111/tesg.12511}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-287297}, pages = {310 -- 328}, year = {2022}, abstract = {Digital platforms, such as Amazon, represent the major beneficiaries of the Covid-19 crisis. This study examines the role of digital platforms and their engagement in digitalisation initiatives targeting (small) brick-and-mortar retailers in Germany, thereby contributing to a better understanding of how digital platforms augment, substitute or reorganise physical retail spaces. This study applies a mixed-method approach based on qualitative interviews, participant observation as well as media analysis. First, the study illustrates the controversial role of digital platforms by positioning themselves as supporting partners of the (offline) retailers, while simultaneously shifting power towards the platforms themselves. Second, digital platforms have established themselves not only as infrastructure providers but also as actors within these infrastructures, framing digital as well as physical retail spaces, inter alia due to their role as publicly legitimised retail advisers. Third, while institutions want to help retailers to survive, they simultaneously enhance retailers' dependency on digital platforms.}, language = {en} } @article{KacicKuenzer2022, author = {Kacic, Patrick and Kuenzer, Claudia}, title = {Forest biodiversity monitoring based on remotely sensed spectral diversity — a review}, series = {Remote Sensing}, volume = {14}, journal = {Remote Sensing}, number = {21}, issn = {2072-4292}, doi = {10.3390/rs14215363}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-290535}, year = {2022}, abstract = {Forests are essential for global environmental well-being because of their rich provision of ecosystem services and regulating factors. Global forests are under increasing pressure from climate change, resource extraction, and anthropologically-driven disturbances. The results are dramatic losses of habitats accompanied with the reduction of species diversity. There is the urgent need for forest biodiversity monitoring comprising analysis on α, β, and γ scale to identify hotspots of biodiversity. Remote sensing enables large-scale monitoring at multiple spatial and temporal resolutions. Concepts of remotely sensed spectral diversity have been identified as promising methodologies for the consistent and multi-temporal analysis of forest biodiversity. This review provides a first time focus on the three spectral diversity concepts "vegetation indices", "spectral information content", and "spectral species" for forest biodiversity monitoring based on airborne and spaceborne remote sensing. In addition, the reviewed articles are analyzed regarding the spatiotemporal distribution, remote sensing sensors, temporal scales and thematic foci. We identify multispectral sensors as primary data source which underlines the focus on optical diversity as a proxy for forest biodiversity. Moreover, there is a general conceptual focus on the analysis of spectral information content. In recent years, the spectral species concept has raised attention and has been applied to Sentinel-2 and MODIS data for the analysis from local spectral species to global spectral communities. Novel remote sensing processing capacities and the provision of complementary remote sensing data sets offer great potentials for large-scale biodiversity monitoring in the future.}, language = {en} } @article{AyalaCarrilloFarfanCardenasNielsenetal.2022, author = {Ayala-Carrillo, Mariana and Farf{\´a}n, Michelle and C{\´a}rdenas-Nielsen, Anah{\´i} and Lemoine-Rodr{\´i}guez, Richard}, title = {Are wildfires in the wildland-urban interface increasing temperatures? A land surface temperature assessment in a semi-arid Mexican city}, series = {Land}, volume = {11}, journal = {Land}, number = {12}, issn = {2073-445X}, doi = {10.3390/land11122105}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-297308}, year = {2022}, abstract = {High rates of land conversion due to urbanization are causing fragmented and dispersed spatial patterns in the wildland-urban interface (WUI) worldwide. The occurrence of anthropogenic fires in the WUI represents an important environmental and social issue, threatening not only vegetated areas but also periurban inhabitants, as is the case in many Latin American cities. However, research has not focused on the dynamics of the local climate in the WUI. This study analyzes whether wildfires contribute to the increase in land surface temperature (LST) in the WUI of the metropolitan area of the city of Guanajuato (MACG), a semi-arid Mexican city. We estimated the pre- and post-fire LST for 2018-2021. Spatial clusters of high LST were detected using hot spot analysis and examined using ANOVA and Tukey's post-hoc statistical tests to assess whether LST is related to the spatial distribution of wildfires during our study period. Our results indicate that the areas where the wildfires occurred, and their surroundings, show higher LST. This has negative implications for the local ecosystem and human population, which lacks adequate infrastructure and services to cope with the effects of rising temperatures. This is the first study assessing the increase in LST caused by wildfires in a WUI zone in Mexico.}, language = {en} } @article{AnsahAbuKleemannetal.2022, author = {Ansah, Christabel Edena and Abu, Itohan-Osa and Kleemann, Janina and Mahmoud, Mahmoud Ibrahim and Thiel, Michael}, title = {Environmental contamination of a biodiversity hotspot — action needed for nature conservation in the Niger Delta, Nigeria}, series = {Sustainability}, volume = {14}, journal = {Sustainability}, number = {21}, issn = {2071-1050}, doi = {10.3390/su142114256}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-297214}, year = {2022}, abstract = {The Niger Delta belongs to the largest swamp and mangrove forests in the world hosting many endemic and endangered species. Therefore, its conservation should be of highest priority. However, the Niger Delta is confronted with overexploitation, deforestation and pollution to a large extent. In particular, oil spills threaten the biodiversity, ecosystem services, and local people. Remote sensing can support the detection of spills and their potential impact when accessibility on site is difficult. We tested different vegetation indices to assess the impact of oil spills on the land cover as well as to detect accumulations (hotspots) of oil spills. We further identified which species, land cover types, and protected areas could be threatened in the Niger Delta due to oil spills. The results showed that the Enhanced Vegetation Index, the Normalized Difference Vegetation Index, and the Soil Adjusted Vegetation Index were more sensitive to the effects of oil spills on different vegetation cover than other tested vegetation indices. Forest cover was the most affected land-cover type and oil spills also occurred in protected areas. Threatened species are inhabiting the Niger Delta Swamp Forest and the Central African Mangroves that were mainly affected by oil spills and, therefore, strong conservation measures are needed even though security issues hamper the monitoring and control.}, language = {en} } @article{HaHuthBachoferetal.2022, author = {Ha, Tuyen V. and Huth, Juliane and Bachofer, Felix and Kuenzer, Claudia}, title = {A review of Earth observation-based drought studies in Southeast Asia}, series = {Remote Sensing}, volume = {14}, journal = {Remote Sensing}, number = {15}, issn = {2072-4292}, doi = {10.3390/rs14153763}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-286258}, year = {2022}, abstract = {Drought is a recurring natural climatic hazard event over terrestrial land; it poses devastating threats to human health, the economy, and the environment. Given the increasing climate crisis, it is likely that extreme drought phenomena will become more frequent, and their impacts will probably be more devastating. Drought observations from space, therefore, play a key role in dissimilating timely and accurate information to support early warning drought management and mitigation planning, particularly in sparse in-situ data regions. In this paper, we reviewed drought-related studies based on Earth observation (EO) products in Southeast Asia between 2000 and 2021. The results of this review indicated that drought publications in the region are on the increase, with a majority (70\%) of the studies being undertaken in Vietnam, Thailand, Malaysia and Indonesia. These countries also accounted for nearly 97\% of the economic losses due to drought extremes. Vegetation indices from multispectral optical remote sensing sensors remained a primary source of data for drought monitoring in the region. Many studies (~21\%) did not provide accuracy assessment on drought mapping products, while precipitation was the main data source for validation. We observed a positive association between spatial extent and spatial resolution, suggesting that nearly 81\% of the articles focused on the local and national scales. Although there was an increase in drought research interest in the region, challenges remain regarding large-area and long time-series drought measurements, the combined drought approach, machine learning-based drought prediction, and the integration of multi-sensor remote sensing products (e.g., Landsat and Sentinel-2). Satellite EO data could be a substantial part of the future efforts that are necessary for mitigating drought-related challenges, ensuring food security, establishing a more sustainable economy, and the preservation of the natural environment in the region.}, language = {en} } @article{KoehlerBauerDietzetal.2022, author = {Koehler, Jonas and Bauer, Andr{\´e} and Dietz, Andreas J. and Kuenzer, Claudia}, title = {Towards forecasting future snow cover dynamics in the European Alps — the potential of long optical remote-sensing time series}, series = {Remote Sensing}, volume = {14}, journal = {Remote Sensing}, number = {18}, issn = {2072-4292}, doi = {10.3390/rs14184461}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-288338}, year = {2022}, abstract = {Snow is a vital environmental parameter and dynamically responsive to climate change, particularly in mountainous regions. Snow cover can be monitored at variable spatial scales using Earth Observation (EO) data. Long-lasting remote sensing missions enable the generation of multi-decadal time series and thus the detection of long-term trends. However, there have been few attempts to use these to model future snow cover dynamics. In this study, we, therefore, explore the potential of such time series to forecast the Snow Line Elevation (SLE) in the European Alps. We generate monthly SLE time series from the entire Landsat archive (1985-2021) in 43 Alpine catchments. Positive long-term SLE change rates are detected, with the highest rates (5-8 m/y) in the Western and Central Alps. We utilize this SLE dataset to implement and evaluate seven uni-variate time series modeling and forecasting approaches. The best results were achieved by Random Forests, with a Nash-Sutcliffe efficiency (NSE) of 0.79 and a Mean Absolute Error (MAE) of 258 m, Telescope (0.76, 268 m), and seasonal ARIMA (0.75, 270 m). Since the model performance varies strongly with the input data, we developed a combined forecast based on the best-performing methods in each catchment. This approach was then used to forecast the SLE for the years 2022-2029. In the majority of the catchments, the shift of the forecast median SLE level retained the sign of the long-term trend. In cases where a deviating SLE dynamic is forecast, a discussion based on the unique properties of the catchment and past SLE dynamics is required. In the future, we expect major improvements in our SLE forecasting efforts by including external predictor variables in a multi-variate modeling approach.}, language = {en} } @article{DongWurmTaubenboeck2022, author = {Dong, Ruirui and Wurm, Michael and Taubenb{\"o}ck, Hannes}, title = {Seasonal and diurnal variation of land surface temperature distribution and its relation to land use/land cover patterns}, series = {International Journal of Environmental Research and Public Health}, volume = {19}, journal = {International Journal of Environmental Research and Public Health}, number = {19}, issn = {1660-4601}, doi = {10.3390/ijerph191912738}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-290393}, year = {2022}, abstract = {The surface urban heat island (SUHI) affects the quality of urban life. Because varying urban structures have varying impacts on SUHI, it is crucial to understand the impact of land use/land cover characteristics for improving the quality of life in cities and urban health. Satellite-based data on land surface temperatures (LST) and derived land use/cover pattern (LUCP) indicators provide an efficient opportunity to derive the required data at a large scale. This study explores the seasonal and diurnal variation of spatial associations from LUCP and LST employing Pearson correlation and ordinary least squares regression analysis. Specifically, Landsat-8 images were utilized to derive LSTs in four seasons, taking Berlin as a case study. The results indicate that: (1) in terms of land cover, hot spots are mainly distributed over transportation, commercial and industrial land in the daytime, while wetlands were identified as hot spots during nighttime; (2) from the land composition indicators, the normalized difference built-up index (NDBI) showed the strongest influence in summer, while the normalized difference vegetation index (NDVI) exhibited the biggest impact in winter; (3) from urban morphological parameters, the building density showed an especially significant positive association with LST and the strongest effect during daytime.}, language = {en} } @article{LibandaPaeth2023, author = {Libanda, Brigadier and Paeth, Heiko}, title = {Modelling wind speed across Zambia: Implications for wind energy}, series = {International Journal of Climatology}, volume = {43}, journal = {International Journal of Climatology}, number = {2}, doi = {10.1002/joc.7826}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-312134}, pages = {772 -- 786}, year = {2023}, abstract = {Wind energy is a key option in global dialogues about climate change mitigation. Here, we combined observations from surface wind stations, reanalysis datasets, and state-of-the-art regional climate models from the Coordinated Regional Climate Downscaling Experiment (CORDEX Africa) to study the current and future wind energy potential in Zambia. We found that winds are dominated by southeasterlies and are rarely strong with an average speed of 2.8 m·s\(^{-1}\). When we converted the observed surface wind speed to a turbine hub height of 100 m, we found a ~38\% increase in mean wind speed for the period 1981-2000. Further, both simulated and observed wind speed data show statistically significant increments across much of the country. The only areas that divert from this upward trend of wind speeds are the low land terrains of the Eastern Province bordering Malawi. Examining projections of wind power density (WPD), we found that although wind speed is increasing, it is still generally too weak to support large-scale wind power generation. We found a meagre projected annual average WPD of 46.6 W·m\(^{-2}\). The highest WPDs of ~80 W·m\(^{-2}\) are projected in the northern and central parts of the country while the lowest are to be expected along the Luangwa valley in agreement with wind speed simulations. On average, Zambia is expected to experience minor WPD increments of 0.004 W·m\(^{-2}\) per year from 2031 to 2050. We conclude that small-scale wind turbines that accommodate cut-in wind speeds of 3.8 m·s\(^{-1}\) are the most suitable for power generation in Zambia. Further, given the limitations of small wind turbines, they are best suited for rural and suburban areas of the country where obstructions are few, thus making them ideal for complementing the government of the Republic of Zambia's rural electrification efforts.}, language = {en} } @article{Ibebuchi2023, author = {Ibebuchi, Chibuike Chiedozie}, title = {On the representation of atmospheric circulation modes in regional climate models over Western Europe}, series = {International Journal of Climatology}, volume = {43}, journal = {International Journal of Climatology}, number = {1}, doi = {10.1002/joc.7807}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-312424}, pages = {668 -- 682}, year = {2023}, abstract = {Atmospheric circulation is a key driver of climate variability, and the representation of atmospheric circulation modes in regional climate models (RCMs) can enhance the credibility of regional climate projections. This study examines the representation of large-scale atmospheric circulation modes in Coupled Model Inter-comparison Project phase 5 RCMs once driven by ERA-Interim, and by two general circulation models (GCMs). The study region is Western Europe and the circulation modes are classified using the Promax rotated T-mode principal component analysis. The results indicate that the RCMs can replicate the classified atmospheric modes as obtained from ERA5 reanalysis, though with biases dependent on the data providing the lateral boundary condition and the choice of RCM. When the boundary condition is provided by ERA-Interim that is more consistent with observations, the simulated map types and the associating time series match well with their counterparts from ERA5. Further, on average, the multi-model ensemble mean of the analysed RCMs, driven by ERA-Interim, indicated a slight improvement in the representation of the modes obtained from ERA5. Conversely, when the RCMs are driven by the GCMs that are models without assimilation of observational data, the representation of the atmospheric modes, as obtained from ERA5, is relatively less accurate compared to when the RCMs are driven by ERA-Interim. This suggests that the biases stem from the GCMs. On average, the representation of the modes was not improved in the multi-model ensemble mean of the five analysed RCMs driven by either of the GCMs. However, when the best-performed RCMs were selected on average the ensemble mean indicated a slight improvement. Moreover, the presence of the North Atlantic Oscillation (NAO) in the simulated modes depends also on the lateral boundary conditions. The relationship between the modes and the NAO was replicated only when the RCMs were driven by reanalysis. The results indicate that the forcing model is the main factor in reproducing the atmospheric circulation.}, language = {en} } @article{PhilippDietzUllmannetal.2023, author = {Philipp, Marius and Dietz, Andreas and Ullmann, Tobias and Kuenzer, Claudia}, title = {A circum-Arctic monitoring framework for quantifying annual erosion rates of permafrost coasts}, series = {Remote Sensing}, volume = {15}, journal = {Remote Sensing}, number = {3}, issn = {2072-4292}, doi = {10.3390/rs15030818}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-304447}, year = {2023}, abstract = {This study demonstrates a circum-Arctic monitoring framework for quantifying annual change of permafrost-affected coasts at a spatial resolution of 10 m. Frequent cloud coverage and challenging lighting conditions, including polar night, limit the usability of optical data in Arctic regions. For this reason, Synthetic Aperture RADAR (SAR) data in the form of annual median and standard deviation (sd) Sentinel-1 (S1) backscatter images covering the months June-September for the years 2017-2021 were computed. Annual composites for the year 2020 were hereby utilized as input for the generation of a high-quality coastline product via a Deep Learning (DL) workflow, covering 161,600 km of the Arctic coastline. The previously computed annual S1 composites for the years 2017 and 2021 were employed as input data for the Change Vector Analysis (CVA)-based coastal change investigation. The generated DL coastline product served hereby as a reference. Maximum erosion rates of up to 67 m per year could be observed based on 400 m coastline segments. Overall highest average annual erosion can be reported for the United States (Alaska) with 0.75 m per year, followed by Russia with 0.62 m per year. Out of all seas covered in this study, the Beaufort Sea featured the overall strongest average annual coastal erosion of 1.12 m. Several quality layers are provided for both the DL coastline product and the CVA-based coastal change analysis to assess the applicability and accuracy of the output products. The predicted coastal change rates show good agreement with findings published in previous literature. The proposed methods and data may act as a valuable tool for future analysis of permafrost loss and carbon emissions in Arctic coastal environments.}, language = {en} } @article{KacicThonfeldGessneretal.2023, author = {Kacic, Patrick and Thonfeld, Frank and Gessner, Ursula and Kuenzer, Claudia}, title = {Forest structure characterization in Germany: novel products and analysis based on GEDI, Sentinel-1 and Sentinel-2 data}, series = {Remote Sensing}, volume = {15}, journal = {Remote Sensing}, number = {8}, issn = {2072-4292}, doi = {10.3390/rs15081969}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-313727}, year = {2023}, abstract = {Monitoring forest conditions is an essential task in the context of global climate change to preserve biodiversity, protect carbon sinks and foster future forest resilience. Severe impacts of heatwaves and droughts triggering cascading effects such as insect infestation are challenging the semi-natural forests in Germany. As a consequence of repeated drought years since 2018, large-scale canopy cover loss has occurred calling for an improved disturbance monitoring and assessment of forest structure conditions. The present study demonstrates the potential of complementary remote sensing sensors to generate wall-to-wall products of forest structure for Germany. The combination of high spatial and temporal resolution imagery from Sentinel-1 (Synthetic Aperture Radar, SAR) and Sentinel-2 (multispectral) with novel samples on forest structure from the Global Ecosystem Dynamics Investigation (GEDI, LiDAR, Light detection and ranging) enables the analysis of forest structure dynamics. Modeling the three-dimensional structure of forests from GEDI samples in machine learning models reveals the recent changes in German forests due to disturbances (e.g., canopy cover degradation, salvage logging). This first consistent data set on forest structure for Germany from 2017 to 2022 provides information of forest canopy height, forest canopy cover and forest biomass and allows estimating recent forest conditions at 10 m spatial resolution. The wall-to-wall maps of the forest structure support a better understanding of post-disturbance forest structure and forest resilience.}, language = {en} } @article{ReinersSobrinoKuenzer2023, author = {Reiners, Philipp and Sobrino, Jos{\´e} and Kuenzer, Claudia}, title = {Satellite-derived land surface temperature dynamics in the context of global change — a review}, series = {Remote Sensing}, volume = {15}, journal = {Remote Sensing}, number = {7}, issn = {2072-4292}, doi = {10.3390/rs15071857}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-311120}, year = {2023}, abstract = {Satellite-derived Land Surface Temperature (LST) dynamics have been increasingly used to study various geophysical processes. This review provides an extensive overview of the applications of LST in the context of global change. By filtering a selection of relevant keywords, a total of 164 articles from 14 international journals published during the last two decades were analyzed based on study location, research topic, applied sensor, spatio-temporal resolution and scale and employed analysis methods. It was revealed that China and the USA were the most studied countries and those that had the most first author affiliations. The most prominent research topic was the Surface Urban Heat Island (SUHI), while the research topics related to climate change were underrepresented. MODIS was by far the most used sensor system, followed by Landsat. A relatively small number of studies analyzed LST dynamics on a global or continental scale. The extensive use of MODIS highly determined the study periods: A majority of the studies started around the year 2000 and thus had a study period shorter than 25 years. The following suggestions were made to increase the utilization of LST time series in climate research: The prolongation of the time series by, e.g., using AVHRR LST, the better representation of LST under clouds, the comparison of LST to traditional climate change measures, such as air temperature and reanalysis variables, and the extension of the validation to heterogenous sites.}, language = {en} } @article{KunzUllmannKneiseletal.2023, author = {Kunz, Julius and Ullmann, T. and Kneisel, C. and Baumhauer, R.}, title = {Three-dimensional subsurface architecture and its influence on the spatiotemporal development of a retrogressive thaw slump in the Richardson Mountains, Northwest Territories, Canada}, series = {Arctic, Antarctic, and Alpine Research}, volume = {55}, journal = {Arctic, Antarctic, and Alpine Research}, number = {1}, issn = {1523-0430}, doi = {10.1080/15230430.2023.2167358}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-350147}, year = {2023}, abstract = {The development of retrogressive thaw slumps (RTS) is known to be strongly influenced by relief-related parameters, permafrost characteristics, and climatic triggers. To deepen the understanding of RTS, this study examines the subsurface characteristics in the vicinity of an active thaw slump, located in the Richardson Mountains (Western Canadian Arctic). The investigations aim to identify relationships between the spatiotemporal slump development and the influence of subsurface structures. Information on these were gained by means of electrical resistivity tomography (ERT) and ground-penetrating radar (GPR). The spatiotemporal development of the slump was revealed by high-resolution satellite imagery and unmanned aerial vehicle-based digital elevation models (DEMs). The analysis indicated an acceleration of slump expansion, especially since 2018. The comparison of the DEMs enabled the detailed balancing of erosion and accumulation within the slump area between August 2018 and August 2019. In addition, manual frost probing and GPR revealed a strong relationship between the active layer thickness, surface morphology, and hydrology. Detected furrows in permafrost table topography seem to affect the active layer hydrology and cause a canalization of runoff toward the slump. The three-dimensional ERT data revealed a partly unfrozen layer underlying a heterogeneous permafrost body. This may influence the local hydrology and affect the development of the RTS. The results highlight the complex relationships between slump development, subsurface structure, and hydrology and indicate a distinct research need for other RTSs.}, language = {en} } @article{MeistervonSuchodoletzZeeden2023, author = {Meister, Julia and von Suchodoletz, Hans and Zeeden, Christian}, title = {Preface: Quaternary research from and inspired by the first virtual DEUQUA conference}, series = {E\&G Quaternary Science Journal}, volume = {72}, journal = {E\&G Quaternary Science Journal}, number = {2}, doi = {10.5194/egqsj-72-185-2023}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-350157}, pages = {185-187}, year = {2023}, abstract = {No abstract available.}, language = {en} } @article{SchaeferFaethKneiseletal.2023, author = {Sch{\"a}fer, Christian and F{\"a}th, Julian and Kneisel, Christof and Baumhauer, Roland and Ullmann, Tobias}, title = {Multidimensional hydrological modeling of a forested catchment in a German low mountain range using a modular runoff and water balance model}, series = {Frontiers in Forests and Global Change}, volume = {6}, journal = {Frontiers in Forests and Global Change}, doi = {10.3389/ffgc.2023.1186304}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-357358}, year = {2023}, abstract = {Sufficient plant-available water is one of the most important requirements for vital, stable, and well-growing forest stands. In the face of climate change, there are various approaches to derive recommendations considering tree species selection based on plant-available water provided by measurements or simulations. Owing to the small-parcel management of Central European forests as well as small-spatial variation of soil and stand properties, in situ data collection for individual forest stands of large areas is not feasible, considering time and cost effort. This problem can be addressed using physically based modeling, aiming to numerically simulate the water balance. In this study, we parameterized, calibrated, and verified the hydrological multidimensional WaSiM-ETH model to assess the water balance at a spatial resolution of 30 m in a German forested catchment area (136.4 km2) for the period 2000-2021 using selected in situ data, remote sensing products, and total runoff. Based on the model output, drought-sensitive parameters, such as the difference between potential and effective stand transpiration (Tdiff) and the water balance, were deduced from the model, analyzed, and evaluated. Results show that the modeled evapotranspiration (ET) correlated significantly (R2 = 0.80) with the estimated ET using MODIS data (MOD16A2GFv006). Compared with observed daily, monthly, and annual runoff data, the model shows a good performance (R2: 0.70|0.77|0.73; Kling-Gupta efficiency: 0.59|0.62|0.83; volumetric efficiency: 0.52|0.60|0.83). The comparison with in situ data from a forest monitoring plot, established at the end of 2020, indicated good agreement between observed and simulated interception and soil water content. According to our results, WaSiM-ETH is a potential supplement for forest management, owing to its multidimensionality and the ability to model soil water balance for large areas at comparable high spatial resolution. The outputs offer, compared to non-distributed models (like LWF-Brook90), spatial differentiability, which is important for small-scale parceled forests, regarding stand structure and soil properties. Due to the spatial component offered, additional verification possibilities are feasible allowing a reliable and profound verification of the model and its parameterization.}, language = {en} } @article{BucheltBloetheKuenzeretal.2023, author = {Buchelt, Sebastian and Bl{\"o}the, Jan Henrik and Kuenzer, Claudia and Schmitt, Andreas and Ullmann, Tobias and Philipp, Marius and Kneisel, Christof}, title = {Deciphering small-scale seasonal surface dynamics of rock glaciers in the Central European Alps using DInSAR time series}, series = {Remote Sensing}, volume = {15}, journal = {Remote Sensing}, number = {12}, issn = {2072-4292}, doi = {10.3390/rs15122982}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-362939}, year = {2023}, abstract = {The Essential Climate Variable (ECV) Permafrost is currently undergoing strong changes due to rising ground and air temperatures. Surface movement, forming characteristic landforms such as rock glaciers, is one key indicator for mountain permafrost. Monitoring this movement can indicate ongoing changes in permafrost; therefore, rock glacier velocity (RGV) has recently been added as an ECV product. Despite the increased understanding of rock glacier dynamics in recent years, most observations are either limited in terms of the spatial coverage or temporal resolution. According to recent studies, Sentinel-1 (C-band) Differential SAR Interferometry (DInSAR) has potential for monitoring RGVs at high spatial and temporal resolutions. However, the suitability of DInSAR for the detection of heterogeneous small-scale spatial patterns of rock glacier velocities was never at the center of these studies. We address this shortcoming by generating and analyzing Sentinel-1 DInSAR time series over five years to detect small-scale displacement patterns of five high alpine permafrost environments located in the Central European Alps on a weekly basis at a range of a few millimeters. Our approach is based on a semi-automated procedure using open-source programs (SNAP, pyrate) and provides East-West displacement and elevation change with a ground sampling distance of 5 m. Comparison with annual movement derived from orthophotos and unpiloted aerial vehicle (UAV) data shows that DInSAR covers about one third of the total movement, which represents the proportion of the year suited for DInSAR, and shows good spatial agreement (Pearson R: 0.42-0.74, RMSE: 4.7-11.6 cm/a) except for areas with phase unwrapping errors. Moreover, the DInSAR time series unveils spatio-temporal variations and distinct seasonal movement dynamics related to different drivers and processes as well as internal structures. Combining our approach with in situ observations could help to achieve a more holistic understanding of rock glacier dynamics and to assess the future evolution of permafrost under changing climatic conditions.}, language = {en} }