@misc{SchwanghartScherler2017,
  author    = {Schwanghart, Wolfgang and Scherler, Dirk},
  title     = {Bumps in river profiles},
  series = {Postprints der Universit{\"a}t Potsdam : Mathematisch-Naturwissenschaftliche Reihe},
  journal   = {Postprints der Universit{\"a}t Potsdam : Mathematisch-Naturwissenschaftliche Reihe},
  number    = {624},
  issn      = {1866-8372},
  doi       = {10.25932/publishup-41907},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-419077},
  pages     = {19},
  year      = {2017},
  abstract  = {The analysis of longitudinal river profiles is an important tool for studying landscape evolution. However, characterizing river profiles based on digital elevation models (DEMs) suffers from errors and artifacts that particularly prevail along valley bottoms. The aim of this study is to characterize uncertainties that arise from the analysis of river profiles derived from different, near-globally available DEMs. We devised new algorithms quantile carving and the CRS algorithm - that rely on quantile regression to enable hydrological correction and the uncertainty quantification of river profiles. We find that globally available DEMs commonly overestimate river elevations in steep topography. The distributions of elevation errors become increasingly wider and right skewed if adjacent hillslope gradients are steep. Our analysis indicates that the AW3D DEM has the highest precision and lowest bias for the analysis of river profiles in mountainous topography. The new 12m resolution TanDEM-X DEM has a very low precision, most likely due to the combined effect of steep valley walls and the presence of water surfaces in valley bottoms. Compared to the conventional approaches of carving and filling, we find that our new approach is able to reduce the elevation bias and errors in longitudinal river profiles.},
  language  = {en}
}
@article{PurintonBookhagen2021,
  author    = {Purinton, Benjamin and Bookhagen, Bodo},
  title     = {Beyond Vertical Point Accuracy},
  series = {Frontiers in Earth Science},
  journal   = {Frontiers in Earth Science},
  publisher = {Frontiers Media},
  address   = {Lausanne, Schweiz},
  issn      = {2296-6463},
  doi       = {10.3389/feart.2021.758606},
  pages     = {1 -- 24},
  year      = {2021},
  abstract  = {Quantitative geomorphic research depends on accurate topographic data often collected via remote sensing. Lidar, and photogrammetric methods like structure-from-motion, provide the highest quality data for generating digital elevation models (DEMs). Unfortunately, these data are restricted to relatively small areas, and may be expensive or time-consuming to collect. Global and near-global DEMs with 1 arcsec (∼30 m) ground sampling from spaceborne radar and optical sensors offer an alternative gridded, continuous surface at the cost of resolution and accuracy. Accuracy is typically defined with respect to external datasets, often, but not always, in the form of point or profile measurements from sources like differential Global Navigation Satellite System (GNSS), spaceborne lidar (e.g., ICESat), and other geodetic measurements. Vertical point or profile accuracy metrics can miss the pixel-to-pixel variability (sometimes called DEM noise) that is unrelated to true topographic signal, but rather sensor-, orbital-, and/or processing-related artifacts. This is most concerning in selecting a DEM for geomorphic analysis, as this variability can affect derivatives of elevation (e.g., slope and curvature) and impact flow routing. We use (near) global DEMs at 1 arcsec resolution (SRTM, ASTER, ALOS, TanDEM-X, and the recently released Copernicus) and develop new internal accuracy metrics to assess inter-pixel variability without reference data. Our study area is in the arid, steep Central Andes, and is nearly vegetation-free, creating ideal conditions for remote sensing of the bare-earth surface. We use a novel hillshade-filtering approach to detrend long-wavelength topographic signals and accentuate short-wavelength variability. Fourier transformations of the spatial signal to the frequency domain allows us to quantify: 1) artifacts in the un-projected 1 arcsec DEMs at wavelengths greater than the Nyquist (twice the nominal resolution, so > 2 arcsec); and 2) the relative variance of adjacent pixels in DEMs resampled to 30-m resolution (UTM projected). We translate results into their impact on hillslope and channel slope calculations, and we highlight the quality of the five DEMs. We find that the Copernicus DEM, which is based on a carefully edited commercial version of the TanDEM-X, provides the highest quality landscape representation, and should become the preferred DEM for topographic analysis in areas without sufficient coverage of higher-quality local DEMs.},
  language  = {en}
}
@misc{PurintonBookhagen2021,
  author    = {Purinton, Benjamin and Bookhagen, Bodo},
  title     = {Beyond Vertical Point Accuracy},
  series = {Zweitver{\"o}ffentlichungen der Universit{\"a}t Potsdam : Mathematisch-Naturwissenschaftliche Reihe},
  journal   = {Zweitver{\"o}ffentlichungen der Universit{\"a}t Potsdam : Mathematisch-Naturwissenschaftliche Reihe},
  publisher = {Universit{\"a}tsverlag Potsdam},
  address   = {Potsdam},
  issn      = {1866-8372},
  doi       = {10.25932/publishup-54980},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-549805},
  pages     = {1 -- 24},
  year      = {2021},
  abstract  = {Quantitative geomorphic research depends on accurate topographic data often collected via remote sensing. Lidar, and photogrammetric methods like structure-from-motion, provide the highest quality data for generating digital elevation models (DEMs). Unfortunately, these data are restricted to relatively small areas, and may be expensive or time-consuming to collect. Global and near-global DEMs with 1 arcsec (∼30 m) ground sampling from spaceborne radar and optical sensors offer an alternative gridded, continuous surface at the cost of resolution and accuracy. Accuracy is typically defined with respect to external datasets, often, but not always, in the form of point or profile measurements from sources like differential Global Navigation Satellite System (GNSS), spaceborne lidar (e.g., ICESat), and other geodetic measurements. Vertical point or profile accuracy metrics can miss the pixel-to-pixel variability (sometimes called DEM noise) that is unrelated to true topographic signal, but rather sensor-, orbital-, and/or processing-related artifacts. This is most concerning in selecting a DEM for geomorphic analysis, as this variability can affect derivatives of elevation (e.g., slope and curvature) and impact flow routing. We use (near) global DEMs at 1 arcsec resolution (SRTM, ASTER, ALOS, TanDEM-X, and the recently released Copernicus) and develop new internal accuracy metrics to assess inter-pixel variability without reference data. Our study area is in the arid, steep Central Andes, and is nearly vegetation-free, creating ideal conditions for remote sensing of the bare-earth surface. We use a novel hillshade-filtering approach to detrend long-wavelength topographic signals and accentuate short-wavelength variability. Fourier transformations of the spatial signal to the frequency domain allows us to quantify: 1) artifacts in the un-projected 1 arcsec DEMs at wavelengths greater than the Nyquist (twice the nominal resolution, so > 2 arcsec); and 2) the relative variance of adjacent pixels in DEMs resampled to 30-m resolution (UTM projected). We translate results into their impact on hillslope and channel slope calculations, and we highlight the quality of the five DEMs. We find that the Copernicus DEM, which is based on a carefully edited commercial version of the TanDEM-X, provides the highest quality landscape representation, and should become the preferred DEM for topographic analysis in areas without sufficient coverage of higher-quality local DEMs.},
  language  = {en}
}
@misc{KorzeniowskaKorup2017,
  author    = {Korzeniowska, Karolina and Korup, Oliver},
  title     = {Object-based detection of lakes prone to seasonal ice cover on the Tibetan Plateau},
  series = {Postprints der Universit{\"a}t Potsdam : Mathematisch-Naturwissenschaftliche Reihe},
  journal   = {Postprints der Universit{\"a}t Potsdam : Mathematisch-Naturwissenschaftliche Reihe},
  number    = {1037},
  issn      = {1866-8372},
  doi       = {10.25932/publishup-47503},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-475037},
  pages     = {25},
  year      = {2017},
  abstract  = {The Tibetan Plateau, the world's largest orogenic plateau, hosts thousands of lakes that play prominent roles as water resources, environmental archives, and sources of natural hazards such as glacier lake outburst floods. Previous studies have reported that the size of lakes on the Tibetan Plateau has changed rapidly in recent years, possibly because of atmospheric warming. Tracking these changes systematically with remote sensing data is challenging given the different spectral signatures of water, the potential for confusing lakes with glaciers, and difficulties in classifying frozen or partly frozen lakes. Object-based image analysis (OBIA) offers new opportunities for automated classification in this context, and we have explored this method for mapping lakes from LANDSAT images and Shuttle Radar Topography Mission (SRTM) elevation data. We tested our algorithm for most of the Tibetan Plateau, where lakes in tectonic depressions or blocked by glaciers and sediments have different surface colours and seasonal ice cover in images obtained in 1995 and 2015. We combined a modified normalised difference water index (MNDWI) with OBIA and local topographic slope data in order to classify lakes with an area > 10 km(2). Our method derived 323 water bodies, with a total area of 31,258 km(2), or 2.6\% of the study area (in 2015). The same number of lakes had covered only 24,892 km(2) in 1995; lake area has increased by -26\% in the past two decades. The classification had estimated producer's and user's accuracies of 0.98, with a Cohen's kappa and F-score of 0.98, and may thus be a useful approximation for quantifying regional hydrological budgets. We have shown that our method is flexible and transferable to detecting lakes in diverse physical settings on several continents with similar success rates.},
  language  = {en}
}
@article{KorzeniowskaKorup2017,
  author    = {Korzeniowska, Karolina and Korup, Oliver},
  title     = {Object-Based Detection of Lakes Prone to Seasonal Ice Cover on the Tibetan Plateau},
  series = {Remote sensing},
  volume    = {9},
  journal   = {Remote sensing},
  publisher = {MDPI},
  address   = {Basel},
  issn      = {2072-4292},
  doi       = {10.3390/rs9040339},
  pages     = {23},
  year      = {2017},
  language  = {en}
}