@phdthesis{AlHalbouni2019,
  author    = {Al-Halbouni, Djamil},
  title     = {Photogrammetry and distinct element geomechanical modelling of sinkholes and large-scale karstic depressions},
  doi       = {10.25932/publishup-43215},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-432159},
  school      = {Universit{\"a}t Potsdam},
  pages     = {137},
  year      = {2019},
  abstract  = {Sinkholes and depressions are typical landforms of karst regions. They pose a considerable natural hazard to infrastructure, agriculture, economy and human life in affected areas worldwide. The physio-chemical processes of sinkholes and depression formation are manifold, ranging from dissolution and material erosion in the subsurface to mechanical subsidence/failure of the overburden. This thesis addresses the mechanisms leading to the development of sinkholes and depressions by using complementary methods: remote sensing, distinct element modelling and near-surface geophysics. In the first part, detailed information about the (hydro)-geological background, ground structures, morphologies and spatio-temporal development of sinkholes and depressions at a very active karst area at the Dead Sea are derived from satellite image analysis, photogrammetry and geologic field surveys. There, clusters of an increasing number of sinkholes have been developing since the 1980s within large-scale depressions and are distributed over different kinds of surface materials: clayey mud, sandy-gravel alluvium and lacustrine evaporites (salt). The morphology of sinkholes differs depending in which material they form: Sinkholes in sandy-gravel alluvium and salt are generally deeper and narrower than sinkholes in the interbedded evaporite and mud deposits. From repeated aerial surveys, collapse precursory features like small-scale subsidence, individual holes and cracks are identified in all materials. The analysis sheds light on the ongoing hazardous subsidence process, which is driven by the base-level fall of the Dead Sea and by the dynamic formation of subsurface water channels. In the second part of this thesis, a novel, 2D distinct element geomechanical modelling approach with the software PFC2D-V5 to simulating individual and multiple cavity growth and sinkhole and large-scale depression development is presented. The approach involves a stepwise material removal technique in void spaces of arbitrarily shaped geometries and is benchmarked by analytical and boundary element method solutions for circular cavities. Simulated compression and tension tests are used to calibrate model parameters with bulk rock properties for the materials of the field site. The simulations show that cavity and sinkhole evolution is controlled by material strength of both overburden and cavity host material, the depth and relative speed of the cavity growth and the developed stress pattern in the subsurface. Major findings are: (1) A progressively deepening differential subrosion with variable growth speed yields a more fragmented stress pattern with stress interaction between the cavities. It favours multiple sinkhole collapses and nesting within large-scale depressions. (2) Low-strength materials do not support large cavities in the material removal zone, and subsidence is mainly characterised by gradual sagging into the material removal zone with synclinal bending. (3) High-strength materials support large cavity formation, leading to sinkhole formation by sudden collapse of the overburden. (4) Large-scale depression formation happens either by coalescence of collapsing holes, block-wise brittle failure, or gradual sagging and lateral widening. The distinct element based approach is compared to results from remote sensing and geophysics at the field site. The numerical simulation outcomes are generally in good agreement with derived morphometrics, documented surface and subsurface structures as well as seismic velocities. Complementary findings on the subrosion process are provided from electric and seismic measurements in the area. Based on the novel combination of methods in this thesis, a generic model of karst landform evolution with focus on sinkhole and depression formation is developed. A deepening subrosion system related to preferential flow paths evolves and creates void spaces and subsurface conduits. This subsequently leads to hazardous subsidence, and the formation of sinkholes within large-scale depressions. Finally, a monitoring system for shallow natural hazard phenomena consisting of geodetic and geophysical observations is proposed for similarly affected areas.},
  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}
}