@misc{BriegerHerzschuhPestryakovaetal.2019, author = {Brieger, Frederic and Herzschuh, Ulrike and Pestryakova, Luidmila Agafyevna and Bookhagen, Bodo and Zakharov, Evgenii S. and Kruse, Stefan}, title = {Advances in the derivation of Northeast Siberian forest metrics using high-resolution UAV-based photogrammetric point clouds}, 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 = {1337}, issn = {1866-8372}, doi = {10.25932/publishup-47331}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-473318}, pages = {24}, year = {2019}, abstract = {Forest structure is a crucial component in the assessment of whether a forest is likely to act as a carbon sink under changing climate. Detailed 3D structural information about the tundra-taiga ecotone of Siberia is mostly missing and still underrepresented in current research due to the remoteness and restricted accessibility. Field based, high-resolution remote sensing can provide important knowledge for the understanding of vegetation properties and dynamics. In this study, we test the applicability of consumer-grade Unmanned Aerial Vehicles (UAVs) for rapid calculation of stand metrics in treeline forests. We reconstructed high-resolution photogrammetric point clouds and derived canopy height models for 10 study sites from NE Chukotka and SW Yakutia. Subsequently, we detected individual tree tops using a variable-window size local maximum filter and applied a marker-controlled watershed segmentation for the delineation of tree crowns. With this, we successfully detected 67.1\% of the validation individuals. Simple linear regressions of observed and detected metrics show a better correlation (R2) and lower relative root mean square percentage error (RMSE\%) for tree heights (mean R2 = 0.77, mean RMSE\% = 18.46\%) than for crown diameters (mean R2 = 0.46, mean RMSE\% = 24.9\%). The comparison between detected and observed tree height distributions revealed that our tree detection method was unable to representatively identify trees <2 m. Our results show that plot sizes for vegetation surveys in the tundra-taiga ecotone should be adapted to the forest structure and have a radius of >15-20 m to capture homogeneous and representative forest stands. Additionally, we identify sources of omission and commission errors and give recommendations for their mitigation. In summary, the efficiency of the used method depends on the complexity of the forest's stand structure.}, language = {en} } @article{ZornLeCorvecVarleyetal.2019, author = {Zorn, Edgar Ulrich and Le Corvec, Nicolas and Varley, Nick R. and Salzer, Jacqueline T. and Walter, Thomas R. and Navarro-Ochoa, Carlos and Vargas-Bracamontes, Dulce M. and Thiele, Samuel T. and Ar{\´a}mbula Mendoza, Ra{\´u}l}, title = {Load stress controls on directional lava dome growth at Volcan de Colima, Mexico}, series = {Frontiers in Earth Science}, volume = {7}, journal = {Frontiers in Earth Science}, publisher = {Frontiers Media}, address = {Lausanne}, issn = {2296-6463}, doi = {10.3389/feart.2019.00084}, pages = {18}, year = {2019}, abstract = {During eruptive activity of andesitic stratovolcanoes, the extrusion of lava domes, their collapse and intermittent explosions are common volcanic hazards. Many lava domes grow in a preferred direction, in turn affecting the direction of lava flows and pyroclastic density currents. Access to active lava domes is difficult and hazardous, so detailed data characterizing lava dome growth are typically limited, keeping the processes controlling the directionality of extrusions unclear. Here we combine TerraSAR-X satellite radar observations with high-resolution airborne photogrammetry to assess morphological changes, and perform finite element modeling to investigate the impact of loading stress on shallow magma ascent directions associated with lava dome extrusion and crater formation at Volcan de Colima, Mexico. The TerraSAR-X data, acquired in similar to 1-m resolution spotlight mode, enable us to derive a chronology of the eruptive processes from intensity-based time-lapse observations of the general crater and dome evolution. The satellite images are complemented by close-range airborne photos, processed by the Structure-from-Motion workflow. This allows the derivation of high-resolution digital elevation models, providing insight into detailed loading and unloading features. During the observation period from Jan-2013 to Feb-2016, we identify a dominantly W-directed dome growth and lava flow production until Jan-2015. In Feb-2015, following the removal of the active summit dome, the surface crater widened and elongated along a NE-SW axis. Later in May-2015, a new dome grew toward the SW of the crater while a separate vent developed in the NE of the crater, reflecting a change in the direction of magma ascent and possible conduit bifurcation. Finite element models show a significant stress change in agreement with the observed magma ascent direction changes in response to the changing surface loads, both for loading (dome growth) and unloading (crater forming excavation) cases. These results allow insight into shallow dome growth dynamics and the migration of magma ascent in response to changing volcano summit morphology. They further highlight the importance of detailed volcano summit morphology surveillance, as changes in direction or location of dome extrusion may have major implications regarding the directions of potential volcanic hazards, such as pyroclastic density currents generated by dome collapse.}, language = {en} } @article{BriegerHerzschuhPestryakovaetal.2019, author = {Brieger, Frederic and Herzschuh, Ulrike and Pestryakova, Luidmila Agafyevna and Bookhagen, Bodo and Zakharov, Evgenii S. and Kruse, Stefan}, title = {Advances in the Derivation of Northeast Siberian Forest Metrics Using High-Resolution UAV-Based Photogrammetric Point Clouds}, series = {Remote sensing}, volume = {11}, journal = {Remote sensing}, number = {12}, publisher = {MDPI}, address = {Basel}, issn = {2072-4292}, doi = {10.3390/rs11121447}, pages = {24}, year = {2019}, abstract = {Forest structure is a crucial component in the assessment of whether a forest is likely to act as a carbon sink under changing climate. Detailed 3D structural information about the tundra-taiga ecotone of Siberia is mostly missing and still underrepresented in current research due to the remoteness and restricted accessibility. Field based, high-resolution remote sensing can provide important knowledge for the understanding of vegetation properties and dynamics. In this study, we test the applicability of consumer-grade Unmanned Aerial Vehicles (UAVs) for rapid calculation of stand metrics in treeline forests. We reconstructed high-resolution photogrammetric point clouds and derived canopy height models for 10 study sites from NE Chukotka and SW Yakutia. Subsequently, we detected individual tree tops using a variable-window size local maximum filter and applied a marker-controlled watershed segmentation for the delineation of tree crowns. With this, we successfully detected 67.1\% of the validation individuals. Simple linear regressions of observed and detected metrics show a better correlation (R2) and lower relative root mean square percentage error (RMSE\%) for tree heights (mean R2 = 0.77, mean RMSE\% = 18.46\%) than for crown diameters (mean R2 = 0.46, mean RMSE\% = 24.9\%). The comparison between detected and observed tree height distributions revealed that our tree detection method was unable to representatively identify trees <2 m. Our results show that plot sizes for vegetation surveys in the tundra-taiga ecotone should be adapted to the forest structure and have a radius of >15-20 m to capture homogeneous and representative forest stands. Additionally, we identify sources of omission and commission errors and give recommendations for their mitigation. In summary, the efficiency of the used method depends on the complexity of the forest's stand structure.}, 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} }