@article{MayrKuenzerGessneretal.2019,
  author    = {Mayr, Stefan and Kuenzer, Claudia and Gessner, Ursula and Klein, Igor and Rutzinger, Martin},
  title     = {Validation of earth observation time-series: a review for large-area and temporally dense land surface products},
  series = {Remote Sensing},
  volume    = {11},
  journal   = {Remote Sensing},
  number    = {22},
  issn      = {2072-4292},
  doi       = {10.3390/rs11222616},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-193202},
  year      = {2019},
  abstract  = {Large-area remote sensing time-series offer unique features for the extensive investigation of our environment. Since various error sources in the acquisition chain of datasets exist, only properly validated results can be of value for research and downstream decision processes. This review presents an overview of validation approaches concerning temporally dense time-series of land surface geo-information products that cover the continental to global scale. Categorization according to utilized validation data revealed that product intercomparisons and comparison to reference data are the conventional validation methods. The reviewed studies are mainly based on optical sensors and orientated towards global coverage, with vegetation-related variables as the focus. Trends indicate an increase in remote sensing-based studies that feature long-term datasets of land surface variables. The hereby corresponding validation efforts show only minor methodological diversification in the past two decades. To sustain comprehensive and standardized validation efforts, the provision of spatiotemporally dense validation data in order to estimate actual differences between measurement and the true state has to be maintained. The promotion of novel approaches can, on the other hand, prove beneficial for various downstream applications, although typically only theoretical uncertainties are provided.},
  language  = {en}
}
@article{RiyasSyedKumaretal.2021,
  author    = {Riyas, Moidu Jameela and Syed, Tajdarul Hassan and Kumar, Hrishikesh and Kuenzer, Claudia},
  title     = {Detecting and analyzing the evolution of subsidence due to coal fires in Jharia coalfield, India using Sentinel-1 SAR data},
  series = {Remote Sensing},
  volume    = {13},
  journal   = {Remote Sensing},
  number    = {8},
  issn      = {2072-4292},
  doi       = {10.3390/rs13081521},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-236703},
  year      = {2021},
  abstract  = {Public safety and socio-economic development of the Jharia coalfield (JCF) in India is critically dependent on precise monitoring and comprehensive understanding of coal fires, which have been burning underneath for more than a century. This study utilizes New-Small BAseline Subset (N-SBAS) technique to compute surface deformation time series for 2017-2020 to characterize the spatiotemporal dynamics of coal fires in JCF. The line-of-sight (LOS) surface deformation estimated from ascending and descending Sentinel-1 SAR data are subsequently decomposed to derive precise vertical subsidence estimates. The most prominent subsidence (~22 cm) is observed in Kusunda colliery. The subsidence regions also correspond well with the Landsat-8 based thermal anomaly map and field evidence. Subsequently, the vertical surface deformation time-series is analyzed to characterize temporal variations within the 9.5 km\(^2\) area of coal fires. Results reveal that nearly 10\% of the coal fire area is newly formed, while 73\% persisted throughout the study period. Vulnerability analyses performed in terms of the susceptibility of the population to land surface collapse demonstrate that Tisra, Chhatatanr, and Sijua are the most vulnerable towns. Our results provide critical information for developing early warning systems and remediation strategies.},
  language  = {en}
}
@article{DirscherlDietzKneiseletal.2020,
  author    = {Dirscherl, Mariel and Dietz, Andreas J. and Kneisel, Christof and Kuenzer, Claudia},
  title     = {Automated mapping of Antarctic supraglacial lakes using a Machine Learning approach},
  series = {Remote Sensing},
  volume    = {12},
  journal   = {Remote Sensing},
  number    = {7},
  issn      = {2072-4292},
  doi       = {10.3390/rs12071203},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-203735},
  year      = {2020},
  abstract  = {Supraglacial lakes can have considerable impact on ice sheet mass balance and global sea-level-rise through ice shelf fracturing and subsequent glacier speedup. In Antarctica, the distribution and temporal development of supraglacial lakes as well as their potential contribution to increased ice mass loss remains largely unknown, requiring a detailed mapping of the Antarctic surface hydrological network. In this study, we employ a Machine Learning algorithm trained on Sentinel-2 and auxiliary TanDEM-X topographic data for automated mapping of Antarctic supraglacial lakes. To ensure the spatio-temporal transferability of our method, a Random Forest was trained on 14 training regions and applied over eight spatially independent test regions distributed across the whole Antarctic continent. In addition, we employed our workflow for large-scale application over Amery Ice Shelf where we calculated interannual supraglacial lake dynamics between 2017 and 2020 at full ice shelf coverage. To validate our supraglacial lake detection algorithm, we randomly created point samples over our classification results and compared them to Sentinel-2 imagery. The point comparisons were evaluated using a confusion matrix for calculation of selected accuracy metrics. Our analysis revealed wide-spread supraglacial lake occurrence in all three Antarctic regions. For the first time, we identified supraglacial meltwater features on Abbott, Hull and Cosgrove Ice Shelves in West Antarctica as well as for the entire Amery Ice Shelf for years 2017-2020. Over Amery Ice Shelf, maximum lake extent varied strongly between the years with the 2019 melt season characterized by the largest areal coverage of supraglacial lakes (~763 km\(^2\)). The accuracy assessment over the test regions revealed an average Kappa coefficient of 0.86 where the largest value of Kappa reached 0.98 over George VI Ice Shelf. Future developments will involve the generation of circum-Antarctic supraglacial lake mapping products as well as their use for further methodological developments using Sentinel-1 SAR data in order to characterize intraannual supraglacial meltwater dynamics also during polar night and independent of meteorological conditions. In summary, the implementation of the Random Forest classifier enabled the development of the first automated mapping method applied to Sentinel-2 data distributed across all three Antarctic regions.},
  language  = {en}
}
@article{MayrKleinRutzingeretal.2021,
  author    = {Mayr, Stefan and Klein, Igor and Rutzinger, Martin and Kuenzer, Claudia},
  title     = {Systematic water fraction estimation for a global and daily surface water time-series},
  series = {Remote Sensing},
  volume    = {13},
  journal   = {Remote Sensing},
  number    = {14},
  issn      = {2072-4292},
  doi       = {10.3390/rs13142675},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-242586},
  year      = {2021},
  abstract  = {Fresh water is a vital natural resource. Earth observation time-series are well suited to monitor corresponding surface dynamics. The DLR-DFD Global WaterPack (GWP) provides daily information on globally distributed inland surface water based on MODIS (Moderate Resolution Imaging Spectroradiometer) images at 250 m spatial resolution. Operating on this spatiotemporal level comes with the drawback of moderate spatial resolution; only coarse pixel-based surface water quantification is possible. To enhance the quantitative capabilities of this dataset, we systematically access subpixel information on fractional water coverage. For this, a linear mixture model is employed, using classification probability and pure pixel reference information. Classification probability is derived from relative datapoint (pixel) locations in feature space. Pure water and non-water reference pixels are located by combining spatial and temporal information inherent to the time-series. Subsequently, the model is evaluated for different input sets to determine the optimal configuration for global processing and pixel coverage types. The performance of resulting water fraction estimates is evaluated on the pixel level in 32 regions of interest across the globe, by comparison to higher resolution reference data (Sentinel-2, Landsat 8). Results show that water fraction information is able to improve the product's performance regarding mixed water/non-water pixels by an average of 11.6\% (RMSE). With a Nash-Sutcliffe efficiency of 0.61, the model shows good overall performance. The approach enables the systematic provision of water fraction estimates on a global and daily scale, using only the reflectance and temporal information contained in the input time-series.},
  language  = {en}
}
@article{LatifiHeurich2019,
  author    = {Latifi, Hooman and Heurich, Marco},
  title     = {Multi-scale remote sensing-assisted forest inventory: a glimpse of the state-of-the-art and future prospects},
  series = {Remote Sensing},
  volume    = {11},
  journal   = {Remote Sensing},
  number    = {11},
  issn      = {2072-4292},
  doi       = {10.3390/rs11111260},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-197358},
  year      = {2019},
  abstract  = {Advances in remote inventory and analysis of forest resources during the last decade have reached a level to be now considered as a crucial complement, if not a surrogate, to the long-existing field-based methods. This is mostly reflected in not only the use of multiple-band new active and passive remote sensing data for forest inventory, but also in the methodic and algorithmic developments and/or adoptions that aim at maximizing the predictive or calibration performances, thereby minimizing both random and systematic errors, in particular for multi-scale spatial domains. With this in mind, this editorial note wraps up the recently-published Remote Sensing special issue "Remote Sensing-Based Forest Inventories from Landscape to Global Scale", which hosted a set of state-of-the-art experiments on remotely sensed inventory of forest resources conducted by a number of prominent researchers worldwide.},
  language  = {en}
}
@article{HolzwarthThonfeldAbdullahietal.2020,
  author    = {Holzwarth, Stefanie and Thonfeld, Frank and Abdullahi, Sahra and Asam, Sarah and Da Ponte Canova, Emmanuel and Gessner, Ursula and Huth, Juliane and Kraus, Tanja and Leutner, Benjamin and Kuenzer, Claudia},
  title     = {Earth Observation based monitoring of forests in Germany: a review},
  series = {Remote Sensing},
  volume    = {12},
  journal   = {Remote Sensing},
  number    = {21},
  issn      = {2072-4292},
  doi       = {10.3390/rs12213570},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-216334},
  year      = {2020},
  abstract  = {Forests in Germany cover around 11.4 million hectares and, thus, a share of 32\% of Germany's surface area. Therefore, forests shape the character of the country's cultural landscape. Germany's forests fulfil a variety of functions for nature and society, and also play an important role in the context of climate levelling. Climate change, manifested via rising temperatures and current weather extremes, has a negative impact on the health and development of forests. Within the last five years, severe storms, extreme drought, and heat waves, and the subsequent mass reproduction of bark beetles have all seriously affected Germany's forests. Facing the current dramatic extent of forest damage and the emerging long-term consequences, the effort to preserve forests in Germany, along with their diversity and productivity, is an indispensable task for the government. Several German ministries have and plan to initiate measures supporting forest health. Quantitative data is one means for sound decision-making to ensure the monitoring of the forest and to improve the monitoring of forest damage. In addition to existing forest monitoring systems, such as the federal forest inventory, the national crown condition survey, and the national forest soil inventory, systematic surveys of forest condition and vulnerability at the national scale can be expanded with the help of a satellite-based earth observation. In this review, we analysed and categorized all research studies published in the last 20 years that focus on the remote sensing of forests in Germany. For this study, 166 citation indexed research publications have been thoroughly analysed with respect to publication frequency, location of studies undertaken, spatial and temporal scale, coverage of the studies, satellite sensors employed, thematic foci of the studies, and overall outcomes, allowing us to identify major research and geoinformation product gaps.},
  language  = {en}
}
@article{DirscherlDietzKneiseletal.2021,
  author    = {Dirscherl, Mariel and Dietz, Andreas J. and Kneisel, Christof and Kuenzer, Claudia},
  title     = {A novel method for automated supraglacial lake mapping in Antarctica using Sentinel-1 SAR imagery and deep learning},
  series = {Remote Sensing},
  volume    = {13},
  journal   = {Remote Sensing},
  number    = {2},
  issn      = {2072-4292},
  doi       = {10.3390/rs13020197},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-222998},
  year      = {2021},
  abstract  = {Supraglacial meltwater accumulation on ice sheets can be a main driver for accelerated ice discharge, mass loss, and global sea-level-rise. With further increasing surface air temperatures, meltwater-induced hydrofracturing, basal sliding, or surface thinning will cumulate and most likely trigger unprecedented ice mass loss on the Greenland and Antarctic ice sheets. While the Greenland surface hydrological network as well as its impacts on ice dynamics and mass balance has been studied in much detail, Antarctic supraglacial lakes remain understudied with a circum-Antarctic record of their spatio-temporal development entirely lacking. This study provides the first automated supraglacial lake extent mapping method using Sentinel-1 synthetic aperture radar (SAR) imagery over Antarctica and complements the developed optical Sentinel-2 supraglacial lake detection algorithm presented in our companion paper. In detail, we propose the use of a modified U-Net for semantic segmentation of supraglacial lakes in single-polarized Sentinel-1 imagery. The convolutional neural network (CNN) is implemented with residual connections for optimized performance as well as an Atrous Spatial Pyramid Pooling (ASPP) module for multiscale feature extraction. The algorithm is trained on 21,200 Sentinel-1 image patches and evaluated in ten spatially or temporally independent test acquisitions. In addition, George VI Ice Shelf is analyzed for intra-annual lake dynamics throughout austral summer 2019/2020 and a decision-level fused Sentinel-1 and Sentinel-2 maximum lake extent mapping product is presented for January 2020 revealing a more complete supraglacial lake coverage (~770 km\(^2\)) than the individual single-sensor products. Classification results confirm the reliability of the proposed workflow with an average Kappa coefficient of 0.925 and a F\(_1\)-score of 93.0\% for the supraglacial water class across all test regions. Furthermore, the algorithm is applied in an additional test region covering supraglacial lakes on the Greenland ice sheet which further highlights the potential for spatio-temporal transferability. Future work involves the integration of more training data as well as intra-annual analyses of supraglacial lake occurrence across the whole continent and with focus on supraglacial lake development throughout a summer melt season and into Antarctic winter.},
  language  = {en}
}
@article{RoesslerWittIkonenetal.2021,
  author    = {R{\"o}ßler, Sebastian and Witt, Marius S. and Ikonen, Jaakko and Brown, Ian A. and Dietz, Andreas J.},
  title     = {Remote sensing of snow cover variability and its influence on the runoff of S{\´a}pmi's rivers},
  series = {Geosciences},
  volume    = {11},
  journal   = {Geosciences},
  number    = {3},
  issn      = {2076-3263},
  doi       = {10.3390/geosciences11030130},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-234261},
  year      = {2021},
  abstract  = {The boreal winter 2019/2020 was very irregular in Europe. While there was very little snow in Central Europe, the opposite was the case in northern Fenno-Scandia, particularly in the Arctic. The snow cover was more persistent here and its rapid melting led to flooding in many places. Since the last severe spring floods occurred in the region in 2018, this raises the question of whether more frequent occurrences can be expected in the future. To assess the variability of snowmelt related flooding we used snow cover maps (derived from the DLR's Global SnowPack MODIS snow product) and freely available data on runoff, precipitation, and air temperature in eight unregulated river catchment areas. A trend analysis (Mann-Kendall test) was carried out to assess the development of the parameters, and the interdependencies of the parameters were examined with a correlation analysis. Finally, a simple snowmelt runoff model was tested for its applicability to this region. We noticed an extraordinary variability in the duration of snow cover. If this extends well into spring, rapid air temperature increases leads to enhanced thawing. According to the last flood years 2005, 2010, 2018, and 2020, we were able to differentiate between four synoptic flood types based on their special hydrometeorological and snow situation and simulate them with the snowmelt runoff model (SRM).},
  language  = {en}
}
@article{RemelgadoSafiWegmann2020,
  author    = {Remelgado, Ruben and Safi, Kamran and Wegmann, Martin},
  title     = {From ecology to remote sensing: using animals to map land cover},
  series = {Remote Sensing in Ecology and Conservation},
  volume    = {6},
  journal   = {Remote Sensing in Ecology and Conservation},
  number    = {1},
  doi       = {10.1002/rse2.126},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-225200},
  pages     = {93-104},
  year      = {2020},
  abstract  = {Land cover is a key variable in monitoring applications and new processing technologies made deriving this information easier. Yet, classification algorithms remain dependent on samples collected on the field and field campaigns are limited by financial, infrastructural and political boundaries. Here, animal tracking data could be an asset. Looking at the land cover dependencies of animal behaviour, we can obtain land cover samples over places that are difficult to access. Following this premise, we evaluated the potential of animal movement data to map land cover. Specifically, we used 13 White Storks (Cicona cicona) individuals of the same population to map agriculture within three test regions distributed along their migratory track. The White Stork has adapted to foraging over agricultural lands, making it an ideal source of samples to map this land use. We applied a presence-absence modelling approach over a Normalized Difference Vegetation Index (NDVI) time series and validated our classifications, with high-resolution land cover information. Our results suggest White Stork movement is useful to map agriculture, however, we identified some limitations. We achieved high accuracies (F1-scores > 0.8) for two test regions, but observed poor results over one region. This can be explained by differences in land management practices. The animals preferred agriculture in every test region, but our data showed a biased distribution of training samples between irrigated and non-irrigated land. When both options occurred, the animals disregarded non-irrigated land leading to its misclassification as non-agriculture. Additionally, we found difference between the GPS observation dates and the harvest times for non-irrigated crops. Given the White Stork takes advantage of managed land to search for prey, the inactivity of these fields was the likely culprit of their underrepresentation. Including more species attracted to agriculture - with other land-use dependencies and observation times - can contribute to better results in similar applications.},
  language  = {en}
}
@article{SognoKleinKuenzer2022,
  author    = {Sogno, Patrick and Klein, Igor and Kuenzer, Claudia},
  title     = {Remote sensing of surface water dynamics in the context of global change — a review},
  series = {Remote Sensing},
  volume    = {14},
  journal   = {Remote Sensing},
  number    = {10},
  issn      = {2072-4292},
  doi       = {10.3390/rs14102475},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-275274},
  year      = {2022},
  abstract  = {Inland surface water is often the most accessible freshwater source. As opposed to groundwater, surface water is replenished in a comparatively quick cycle, which makes this vital resource — if not overexploited — sustainable. From a global perspective, freshwater is plentiful. Still, depending on the region, surface water availability is severely limited. Additionally, climate change and human interventions act as large-scale drivers and cause dramatic changes in established surface water dynamics. Actions have to be taken to secure sustainable water availability and usage. This requires informed decision making based on reliable environmental data. Monitoring inland surface water dynamics is therefore more important than ever. Remote sensing is able to delineate surface water in a number of ways by using optical as well as active and passive microwave sensors. In this review, we look at the proceedings within this discipline by reviewing 233 scientific works. We provide an extensive overview of used sensors, the spatial and temporal resolution of studies, their thematic foci, and their spatial distribution. We observe that a wide array of available sensors and datasets, along with increasing computing capacities, have shaped the field over the last years. Multiple global analysis-ready products are available for investigating surface water area dynamics, but so far none offer high spatial and temporal resolution.},
  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{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{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}
}