@article{KorzeniowskaPfeiferLandtwing2018,
  author    = {Korzeniowska, Karolina and Pfeifer, Norbert and Landtwing, Stephan},
  title     = {Mapping gullies, dunes, lava fields, and landslides via surface roughness},
  series = {Geomorphology : an international journal on pure and applied geomorphology},
  volume    = {301},
  journal   = {Geomorphology : an international journal on pure and applied geomorphology},
  publisher = {Elsevier Science},
  address   = {Amsterdam},
  issn      = {0169-555X},
  doi       = {10.1016/j.geomorph.2017.10.011},
  pages     = {53 -- 67},
  year      = {2018},
  abstract  = {Gully erosion is a widespread and significant process involved in soil and land degradation. Mapping gullies helps to quantify past, and anticipate future, soil losses. Digital terrain models offer promising data for automatically detecting and mapping gullies especially in vegetated areas, although methods vary widely measures of local terrain roughness are the most varied and debated among these methods. Rarely do studies test the performance of roughness metrics for mapping gullies, limiting their applicability to small training areas. To this end, we systematically explored how local terrain roughness derived from high-resolution Light Detection And Ranging (LiDAR) data can aid in the unsupervised detection of gullies over a large area. We also tested expanding this method for other landforms diagnostic of similarly abrupt land-surface changes, including lava fields, dunes, and landslides, as well as investigating the influence of different roughness thresholds, resolutions of kernels, and input data resolution, and comparing our method with previously published roughness algorithms. Our results show that total curvature is a suitable metric for recognising analysed gullies and lava fields from LiDAR data, with comparable success to that of more sophisticated roughness metrics. Tested dunes or landslides remain difficult to distinguish from the surrounding landscape, partly because they are not easily defined in terms of their topographic signature.},
  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}
}
@phdthesis{Korzeniowska2017,
  author    = {Korzeniowska, Karolina},
  title     = {Object-based image analysis for detecting landforms diagnostic of natural hazards},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-402240},
  school      = {Universit{\"a}t Potsdam},
  pages     = {XV, 139},
  year      = {2017},
  abstract  = {Natural and potentially hazardous events occur on the Earth's surface every day. The most destructive of these processes must be monitored, because they may cause loss of lives, infrastructure, and natural resources, or have a negative effect on the environment. A variety of remote sensing technologies allow the recoding of data to detect these processes in the first place, partly based on the diagnostic landforms that they form. To perform this effectively, automatic methods are desirable. Universal detection of natural hazards is challenging due to their differences in spatial impacts, timing and longevity of consequences, and the spatial resolution of remote-sensing data. Previous studies have reported that topographic metrics such as roughness, which can be captured from digital elevation data, can reveal landforms diagnostic of natural hazards, such as gullies, dunes, lava fields, landslides and snow avalanches, as these landforms tend to be more heterogeneous than the surrounding landscape. A single roughness metric is often limited in such detections; however, a more complex approach that exploits the spatial relation and the location of objects, such as object-based image analysis (OBIA), is desirable. In this thesis, I propose a topographic roughness measure derived from an airborne laser scanning (ALS) digital terrain model (DTM) and discuss its performance in detecting landforms principally diagnostic of natural hazards. I further develop OBIA-based algorithms for the detection of snow avalanches using near-infrared (NIR) aerial images, and the size (changes) of mountain lakes using LANDSAT satellite images. I quantitatively test and document how the level of difficulty in detecting these very challenging landforms depends on the input data resolution, the derivatives that could be evaluated from images and DTMs, the size, shape and complexity of landforms, and the capabilities of obtaining the information in the data. I demonstrate that surface roughness is a promising metric for detecting different landforms in diverse environments, and that OBIA assists significantly in detecting parts of lakes and snow avalanches that may not be correctly assigned by applying only the thresholding of spectral properties of data and their derivatives. The curvature-based surface roughness parameter allows the detection of gullies, dunes, lava fields and landslides with a user's accuracy of 0.63, 0.21, 0.53, and 0.45, respectively. The OBIA algorithms for detecting lakes and snow avalanches obtained user's accuracy of 0.98, and 0.78, respectively. Most of the analysed landforms constituted only a small part of the entire dataset, and therefore the user's accuracy is the most appropriate performance measure that should be given in a such classification, because it tells how many automatically-extracted pixels in fact represent the object that one wants to classify, and its calculation does not take the second (background) class into account. One advantage of the proposed roughness parameter is that it allows the extraction of the heterogeneity of the surface without the need for data detrending. The OBIA approach is novel in that it allows the classification of lakes regardless of the physical state of their water, and also allows the separation of frozen lakes from glaciers that have very similar water indices used in purely optical remote sensing applications. The algorithm proposed for snow avalanches allows the detection of release zones, tracks, and deposition zones by verifying the snow heterogeneity based on a roughness metric evaluated from a water index, and by analysing the local relation of segments with their neighbouring objects. This algorithm contains few steps, which allows for the simultaneous classification of avalanches that occur on diverse mountain slopes and differ in size and shape. This thesis contributes to natural hazard research as it provides automatic solutions to tracking six different landforms that are diagnostic of natural hazards over large regions. This is a step toward delineating areas susceptible to the processes producing these landforms and the improvement of hazard maps.},
  language  = {en}
}
@article{KorzeniowskaBuehlerMartyetal.2017,
  author    = {Korzeniowska, Karolina and Buehler, Yves and Marty, Mauro and Korup, Oliver},
  title     = {Regional snow-avalanche detection using object-based image analysis of near-infrared aerial imagery},
  series = {Natural hazards and earth system sciences},
  volume    = {17},
  journal   = {Natural hazards and earth system sciences},
  publisher = {Copernicus},
  address   = {G{\"o}ttingen},
  issn      = {1561-8633},
  doi       = {10.5194/nhess-17-1823-2017},
  pages     = {1823 -- 1836},
  year      = {2017},
  abstract  = {Snow avalanches are destructive mass movements in mountain regions that continue to claim lives and cause infrastructural damage and traffic detours. Given that avalanches often occur in remote and poorly accessible steep terrain, their detection and mapping is extensive and time consuming. Nonetheless, systematic avalanche detection over large areas could help to generate more complete and up-to-date inventories (cadastres) necessary for validating avalanche forecasting and hazard mapping. In this study, we focused on automatically detecting avalanches and classifying them into release zones, tracks, and run-out zones based on 0.25m near-infrared (NIR) ADS80-SH92 aerial imagery using an object-based image analysis (OBIA) approach. Our algorithm takes into account the brightness, the normalised difference vegetation index (NDVI), the normalised difference water index (NDWI), and its standard deviation (SDNDWI) to distinguish avalanches from other land-surface elements. Using normalised parameters allows applying this method across large areas. We trained the method by analysing the properties of snow avalanches at three 4km\&\#8722;2 areas near Davos, Switzerland. We compared the results with manually mapped avalanche polygons and obtained a user's accuracy of >0.9 and a Cohen's kappa of 0.79-0.85. Testing the method for a larger area of 226.3km\&\#8722;2, we estimated producer's and user's accuracies of 0.61 and 0.78, respectively, with a Cohen's kappa of 0.67. Detected avalanches that overlapped with reference data by >80\% occurred randomly throughout the testing area, showing that our method avoids overfitting. Our method has potential for large-scale avalanche mapping, although further investigations into other regions are desirable to verify the robustness of our selected thresholds and the transferability of the method.},
  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}
}
@misc{KorzeniowskaBuehlerMauroetal.2017,
  author    = {Korzeniowska, Karolina and B{\"u}hler, Yves and Mauro, Marty and Korup, Oliver},
  title     = {Regional snow-avalanche detection using object-based image analysis of near-infrared aerial imagery},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-403942},
  pages     = {14},
  year      = {2017},
  abstract  = {Snow avalanches are destructive mass movements in mountain regions that continue to claim lives and cause infrastructural damage and traffic detours. Given that avalanches often occur in remote and poorly accessible steep terrain, their detection and mapping is extensive and time consuming. Nonetheless, systematic avalanche detection over large areas could help to generate more complete and up-to-date inventories (cadastres) necessary for validating avalanche forecasting and hazard mapping. In this study, we focused on automatically detecting avalanches and classifying them into release zones, tracks, and run-out zones based on 0.25 m near-infrared (NIR) ADS80-SH92 aerial imagery using an object-based image analysis (OBIA) approach. Our algorithm takes into account the brightness, the normalised difference vegetation index (NDVI), the normalised difference water index (NDWI), and its standard deviation (SDNDWI) to distinguish avalanches from other land-surface elements. Using normalised parameters allows applying this method across large areas. We trained the method by analysing the properties of snow avalanches at three 4 km-2 areas near Davos, Switzerland. We compared the results with manually mapped avalanche polygons and obtained a user's accuracy of > 0.9 and a Cohen's kappa of 0.79-0.85. Testing the method for a larger area of 226.3 km-2, we estimated producer's and user's accuracies of 0.61 and 0.78, respectively, with a Cohen's kappa of 0.67. Detected avalanches that overlapped with reference data by > 80 \% occurred randomly throughout the testing area, showing that our method avoids overfitting. Our method has potential for large-scale avalanche mapping, although further investigations into other regions are desirable to verify the robustness of our selected thresholds and the transferability of the method.},
  language  = {en}
}
@article{KorzeniowskaBuehlerMartyetal.2017,
  author    = {Korzeniowska, Karolina and B{\"u}hler, Yves and Marty, Mauro and Korup, Oliver},
  title     = {Regional snow-avalanche detection using object-based image analysis of near-infrared aerial imagery},
  series = {Natural hazards and earth system sciences : NHESS},
  volume    = {17},
  journal   = {Natural hazards and earth system sciences : NHESS},
  publisher = {Copernicus},
  address   = {G{\"o}ttingen},
  issn      = {1561-8633},
  doi       = {10.5194/nhess-17-1823-2017},
  pages     = {1823 -- 1836},
  year      = {2017},
  abstract  = {Snow avalanches are destructive mass movements in mountain regions that continue to claim lives and cause infrastructural damage and traffic detours. Given that avalanches often occur in remote and poorly accessible steep terrain, their detection and mapping is extensive and time consuming. Nonetheless, systematic avalanche detection over large areas could help to generate more complete and up-to-date inventories (cadastres) necessary for validating avalanche forecasting and hazard mapping. In this study, we focused on automatically detecting avalanches and classifying them into release zones, tracks, and run-out zones based on 0.25 m near-infrared (NIR) ADS80-SH92 aerial imagery using an object-based image analysis (OBIA) approach. Our algorithm takes into account the brightness, the normalised difference vegetation index (NDVI), the normalised difference water index (NDWI), and its standard deviation (SDNDWI) to distinguish avalanches from other land-surface elements. Using normalised parameters allows applying this method across large areas. We trained the method by analysing the properties of snow avalanches at three 4 km-2 areas near Davos, Switzerland. We compared the results with manually mapped avalanche polygons and obtained a user's accuracy of > 0.9 and a Cohen's kappa of 0.79-0.85. Testing the method for a larger area of 226.3 km-2, we estimated producer's and user's accuracies of 0.61 and 0.78, respectively, with a Cohen's kappa of 0.67. Detected avalanches that overlapped with reference data by > 80 \% occurred randomly throughout the testing area, showing that our method avoids overfitting. Our method has potential for large-scale avalanche mapping, although further investigations into other regions are desirable to verify the robustness of our selected thresholds and the transferability of the method.},
  language  = {en}
}