@misc{LaraNitzeGrosseetal.2018,
  author    = {Lara, Mark J. and Nitze, Ingmar and Große, Guido and McGuire, David},
  title     = {Tundra landform and vegetation productivity trend maps for the Arctic Coastal Plain of northern Alaska},
  series = {Postprints der Universit{\"a}t Potsdam : Mathematisch-Naturwissenschaftliche Reihe},
  journal   = {Postprints der Universit{\"a}t Potsdam : Mathematisch-Naturwissenschaftliche Reihe},
  number    = {1035},
  issn      = {1866-8372},
  doi       = {10.25932/publishup-45987},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-459875},
  pages     = {12},
  year      = {2018},
  abstract  = {Arctic tundra landscapes are composed of a complex mosaic of patterned ground features, varying in soil moisture, vegetation composition, and surface hydrology over small spatial scales (10-100 m). The importance of microtopography and associated geomorphic landforms in influencing ecosystem structure and function is well founded, however, spatial data products describing local to regional scale distribution of patterned ground or polygonal tundra geomorphology are largely unavailable. Thus, our understanding of local impacts on regional scale processes (e.g., carbon dynamics) may be limited. We produced two key spatiotemporal datasets spanning the Arctic Coastal Plain of northern Alaska (similar to 60,000 km(2)) to evaluate climate-geomorphological controls on arctic tundra productivity change, using (1) a novel 30m classification of polygonal tundra geomorphology and (2) decadal-trends in surface greenness using the Landsat archive (1999-2014). These datasets can be easily integrated and adapted in an array of local to regional applications such as (1) upscaling plot-level measurements (e.g., carbon/energy fluxes), (2) mapping of soils, vegetation, or permafrost, and/or (3) initializing ecosystem biogeochemistry, hydrology, and/or habitat modeling.},
  language  = {en}
}
@article{LaraNitzeGrosseetal.2018,
  author    = {Lara, Mark J. and Nitze, Ingmar and Große, Guido and McGuire, A. David},
  title     = {Tundra landform and vegetation productivity trend maps for the Arctic Coastal Plain of northern Alaska},
  series = {Scientific Data},
  volume    = {5},
  journal   = {Scientific Data},
  publisher = {Nature Publ. Group},
  address   = {London},
  issn      = {2052-4463},
  doi       = {10.1038/sdata.2018.58},
  pages     = {10},
  year      = {2018},
  abstract  = {Arctic tundra landscapes are composed of a complex mosaic of patterned ground features, varying in soil moisture, vegetation composition, and surface hydrology over small spatial scales (10-100 m). The importance of microtopography and associated geomorphic landforms in influencing ecosystem structure and function is well founded, however, spatial data products describing local to regional scale distribution of patterned ground or polygonal tundra geomorphology are largely unavailable. Thus, our understanding of local impacts on regional scale processes (e.g., carbon dynamics) may be limited. We produced two key spatiotemporal datasets spanning the Arctic Coastal Plain of northern Alaska (similar to 60,000 km(2)) to evaluate climate-geomorphological controls on arctic tundra productivity change, using (1) a novel 30m classification of polygonal tundra geomorphology and (2) decadal-trends in surface greenness using the Landsat archive (1999-2014). These datasets can be easily integrated and adapted in an array of local to regional applications such as (1) upscaling plot-level measurements (e.g., carbon/energy fluxes), (2) mapping of soils, vegetation, or permafrost, and/or (3) initializing ecosystem biogeochemistry, hydrology, and/or habitat modeling.},
  language  = {en}
}
@article{TapeJonesArpetal.2018,
  author    = {Tape, Ken D. and Jones, Benjamin M. and Arp, Christopher D. and Nitze, Ingmar and Grosse, Guido},
  title     = {Tundra be dammed},
  series = {Global change biology},
  volume    = {24},
  journal   = {Global change biology},
  number    = {10},
  publisher = {Wiley},
  address   = {Hoboken},
  issn      = {1354-1013},
  doi       = {10.1111/gcb.14332},
  pages     = {4478 -- 4488},
  year      = {2018},
  abstract  = {Increasing air temperatures are changing the arctic tundra biome. Permafrost is thawing, snow duration is decreasing, shrub vegetation is proliferating, and boreal wildlife is encroaching. Here we present evidence of the recent range expansion of North American beaver (Castor canadensis) into the Arctic, and consider how this ecosystem engineer might reshape the landscape, biodiversity, and ecosystem processes. We developed a remote sensing approach that maps formation and disappearance of ponds associated with beaver activity. Since 1999, 56 new beaver pond complexes were identified, indicating that beavers are colonizing a predominantly tundra region (18,293km(2)) of northwest Alaska. It is unclear how improved tundra stream habitat, population rebound following overtrapping for furs, or other factors are contributing to beaver range expansion. We discuss rates and likely routes of tundra beaver colonization, as well as effects on permafrost, stream ice regimes, and freshwater and riparian habitat. Beaver ponds and associated hydrologic changes are thawing permafrost. Pond formation increases winter water temperatures in the pond and downstream, likely creating new and more varied aquatic habitat, but specific biological implications are unknown. Beavers create dynamic wetlands and are agents of disturbance that may enhance ecosystem responses to warming in the Arctic.},
  language  = {en}
}
@article{KleinLantuitHeimetal.2021,
  author    = {Klein, Konstantin P. and Lantuit, Hugues and Heim, Birgit and Doxaran, David and Juhls, Bennet and Nitze, Ingmar and Walch, Daniela and Poste, Amanda and S{\o}reide, Janne E.},
  title     = {The Arctic Nearshore Turbidity Algorithm (ANTA)},
  series = {Science of remote sensing},
  volume    = {4},
  journal   = {Science of remote sensing},
  publisher = {Elsevier},
  address   = {Amsterdam},
  issn      = {2666-0172},
  doi       = {10.1016/j.srs.2021.100036},
  pages     = {11},
  year      = {2021},
  abstract  = {The Arctic is greatly impacted by climate change. The increase in air temperature drives the thawing of permafrost and an increase in coastal erosion and river discharge. This leads to a greater input of sediment and organic matter into coastal waters, which substantially impacts the ecosystems by reducing light transmission through the water column and altering the biogeochemistry, but also the subsistence economy of local people, and changes in climate because of the transformation of organic matter into greenhouse gases. Yet, the quantification of suspended sediment in Arctic coastal and nearshore waters remains unsatisfactory due to the absence of dedicated algorithms to resolve the high loads occurring in the close vicinity of the shoreline. In this study we present the Arctic Nearshore Turbidity Algorithm (ANTA), the first reflectance-turbidity relationship specifically targeted towards Arctic nearshore waters that is tuned with in-situ measurements from the nearshore waters of Herschel Island Qikiqtaruk in the western Canadian Arctic. A semi-empirical model was calibrated for several relevant sensors in ocean color remote sensing, including MODIS, Sentinel 3 (OLCI), Landsat 8 (OLI), and Sentinel 2 (MSI), as well as the older Landsat sensors TM and ETM+. The ANTA performed better with Landsat 8 than with Sentinel 2 and Sentinel 3. The application of the ANTA to Sentinel 2 imagery that matches in-situ turbidity samples taken in Adventfjorden, Svalbard, shows transferability to nearshore areas beyond Herschel Island Qikiqtaruk.},
  language  = {en}
}
@misc{KleinLantuitHeimetal.2021,
  author    = {Klein, Konstantin P. and Lantuit, Hugues and Heim, Birgit and Doxaran, David and Juhls, Bennet and Nitze, Ingmar and Walch, Daniela and Poste, Amanda and S{\o}reide, Janne E.},
  title     = {The Arctic Nearshore Turbidity Algorithm (ANTA)},
  series = {Zweitver{\"o}ffentlichungen der Universit{\"a}t Potsdam : Mathematisch-Naturwissenschaftliche Reihe},
  journal   = {Zweitver{\"o}ffentlichungen der Universit{\"a}t Potsdam : Mathematisch-Naturwissenschaftliche Reihe},
  number    = {1250},
  issn      = {1866-8372},
  doi       = {10.25932/publishup-55369},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-553692},
  pages     = {11},
  year      = {2021},
  abstract  = {The Arctic is greatly impacted by climate change. The increase in air temperature drives the thawing of permafrost and an increase in coastal erosion and river discharge. This leads to a greater input of sediment and organic matter into coastal waters, which substantially impacts the ecosystems by reducing light transmission through the water column and altering the biogeochemistry, but also the subsistence economy of local people, and changes in climate because of the transformation of organic matter into greenhouse gases. Yet, the quantification of suspended sediment in Arctic coastal and nearshore waters remains unsatisfactory due to the absence of dedicated algorithms to resolve the high loads occurring in the close vicinity of the shoreline. In this study we present the Arctic Nearshore Turbidity Algorithm (ANTA), the first reflectance-turbidity relationship specifically targeted towards Arctic nearshore waters that is tuned with in-situ measurements from the nearshore waters of Herschel Island Qikiqtaruk in the western Canadian Arctic. A semi-empirical model was calibrated for several relevant sensors in ocean color remote sensing, including MODIS, Sentinel 3 (OLCI), Landsat 8 (OLI), and Sentinel 2 (MSI), as well as the older Landsat sensors TM and ETM+. The ANTA performed better with Landsat 8 than with Sentinel 2 and Sentinel 3. The application of the ANTA to Sentinel 2 imagery that matches in-situ turbidity samples taken in Adventfjorden, Svalbard, shows transferability to nearshore areas beyond Herschel Island Qikiqtaruk.},
  language  = {en}
}
@article{NitzeGrosseJonesetal.2018,
  author    = {Nitze, Ingmar and Grosse, Guido and Jones, Benjamin M. and Romanovsky, Vladimir E. and Boike, Julia},
  title     = {Remote sensing quantifies widespread abundance of permafrost region disturbances across the Arctic and Subarctic},
  series = {Nature Communications},
  volume    = {9},
  journal   = {Nature Communications},
  publisher = {Nature Publ. Group},
  address   = {London},
  issn      = {2041-1723},
  doi       = {10.1038/s41467-018-07663-3},
  pages     = {11},
  year      = {2018},
  abstract  = {Local observations indicate that climate change and shifting disturbance regimes are causing permafrost degradation. However, the occurrence and distribution of permafrost region disturbances (PRDs) remain poorly resolved across the Arctic and Subarctic. Here we quantify the abundance and distribution of three primary PRDs using time-series analysis of 30-m resolution Landsat imagery from 1999 to 2014. Our dataset spans four continental-scale transects in North America and Eurasia, covering similar to 10\% of the permafrost region. Lake area loss (-1.45\%) dominated the study domain with enhanced losses occurring at the boundary between discontinuous and continuous permafrost regions. Fires were the most extensive PRD across boreal regions (6.59\%), but in tundra regions (0.63\%) limited to Alaska. Retrogressive thaw slumps were abundant but highly localized (< 10(-5)\%). Our analysis synergizes the global-scale importance of PRDs. The findings highlight the need to include PRDs in next-generation land surface models to project the permafrost carbon feedback.},
  language  = {en}
}
@misc{NitzeGrosseJonesetal.2019,
  author    = {Nitze, Ingmar and Grosse, Guido and Jones, Benjamin M. and Romanovsky, Vladimir E. and Boike, Julia},
  title     = {Remote sensing quantifies widespread abundance of permafrost region disturbances across the Arctic and Subarctic},
  series = {Postprints der Universit{\"a}t Potsdam Mathematisch-Naturwissenschaftliche Reihe},
  journal   = {Postprints der Universit{\"a}t Potsdam Mathematisch-Naturwissenschaftliche Reihe},
  number    = {799},
  issn      = {1866-8372},
  doi       = {10.25932/publishup-42617},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-426171},
  pages     = {11},
  year      = {2019},
  abstract  = {Local observations indicate that climate change and shifting disturbance regimes are causing permafrost degradation. However, the occurrence and distribution of permafrost region disturbances (PRDs) remain poorly resolved across the Arctic and Subarctic. Here we quantify the abundance and distribution of three primary PRDs using time-series analysis of 30-m resolution Landsat imagery from 1999 to 2014. Our dataset spans four continental-scale transects in North America and Eurasia, covering ~10\% of the permafrost region. Lake area loss (-1.45\%) dominated the study domain with enhanced losses occurring at the boundary between discontinuous and continuous permafrost regions. Fires were the most extensive PRD across boreal regions (6.59\%), but in tundra regions (0.63\%) limited to Alaska. Retrogressive thaw slumps were abundant but highly localized (<10-5\%). Our analysis synergizes the global-scale importance of PRDs. The findings highlight the need to include PRDs in next-generation land surface models to project the permafrost carbon feedback.},
  language  = {en}
}
@phdthesis{Nitze2017,
  author    = {Nitze, Ingmar},
  title     = {Remote sensing of rapid permafrost landscape dynamics},
  school      = {Universit{\"a}t Potsdam},
  pages     = {151},
  year      = {2017},
  language  = {en}
}
@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}
}
@article{LaraNitzeGrosseetal.2018,
  author    = {Lara, Mark J. and Nitze, Ingmar and Grosse, Guido and Martin, Philip and McGuire, A. David},
  title     = {Reduced arctic tundra productivity linked with landform and climate change interactions},
  series = {Scientific reports},
  volume    = {8},
  journal   = {Scientific reports},
  publisher = {Nature Publ. Group},
  address   = {London},
  issn      = {2045-2322},
  doi       = {10.1038/s41598-018-20692-8},
  pages     = {10},
  year      = {2018},
  abstract  = {Arctic tundra ecosystems have experienced unprecedented change associated with climate warming over recent decades. Across the Pan-Arctic, vegetation productivity and surface greenness have trended positively over the period of satellite observation. However, since 2011 these trends have slowed considerably, showing signs of browning in many regions. It is unclear what factors are driving this change and which regions/landforms will be most sensitive to future browning. Here we provide evidence linking decadal patterns in arctic greening and browning with regional climate change and local permafrost-driven landscape heterogeneity. We analyzed the spatial variability of decadal-scale trends in surface greenness across the Arctic Coastal Plain of northern Alaska (similar to 60,000 km(2)) using the Landsat archive (1999-2014), in combination with novel 30 m classifications of polygonal tundra and regional watersheds, finding landscape heterogeneity and regional climate change to be the most important factors controlling historical greenness trends. Browning was linked to increased temperature and precipitation, with the exception of young landforms (developed following lake drainage), which will likely continue to green. Spatiotemporal model forecasting suggests carbon uptake potential to be reduced in response to warmer and/or wetter climatic conditions, potentially increasing the net loss of carbon to the atmosphere, at a greater degree than previously expected.},
  language  = {en}
}