06 Fakultät Luft- und Raumfahrttechnik und Geodäsie
Permanent URI for this collectionhttps://elib.uni-stuttgart.de/handle/11682/7
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Item Open Access A method for evaluating population and infrastructure exposed to natural hazards : tests and results for two recent Tonga tsunamis(2023) Thomas, Bruce Enki Oscar; Roger, Jean; Gunnell, Yanni; Ashraf, SalmanBackground: Coastal communities are highly exposed to ocean- and -related hazards but often lack an accurate population and infrastructure database. On January 15, 2022 and for many days thereafter, the Kingdom of Tonga was cut off from the rest of the world by a destructive tsunami associated with the Hunga Tonga Hunga Ha’apai volcanic eruption. This situation was made worse by COVID-19-related lockdowns and no precise idea of the magnitude and pattern of destruction incurred, confirming Tonga’s position as second out of 172 countries ranked by the World Risk Index 2018. The occurrence of such events in remote island communities highlights the need for (1) precisely knowing the distribution of buildings, and (2) evaluating what proportion of those would be vulnerable to a tsunami.
Methods and Results: A GIS-based dasymetric mapping method, previously tested in New Caledonia for assessing and calibrating population distribution at high resolution, is improved and implemented in less than a day to jointly map population clusters and critical elevation contours based on runup scenarios, and is tested against destruction patterns independently recorded in Tonga after the two recent tsunamis of 2009 and 2022. Results show that ~ 62% of the population of Tonga lives in well-defined clusters between sea level and the 15 m elevation contour. The patterns of vulnerability thus obtained for each island of the archipelago allow exposure and potential for cumulative damage to be ranked as a function of tsunami magnitude and source area.
Conclusions: By relying on low-cost tools and incomplete datasets for rapid implementation in the context of natural disasters, this approach works for all types of natural hazards, is easily transferable to other insular settings, can assist in guiding emergency rescue targets, and can help to elaborate future land-use planning priorities for disaster risk reduction purposes.Item Open Access The potential of EO data for enhanced flood monitoring and forecasting : a consortium assessment(2026) Tarpanelli, Angelica; Massari, Christian; Revilla-Romero, Beatriz; Tourian, Mohammad J.; Saemian, Peyman; Elmi, Omid; Scherer, Daniel; Pedinotti, Vanessa; Kittel, Cecile; Benveniste, Jérôme; Bauer-Gottwein, Peter; Ciabatta, Luca; Chewning, Connor; Barbetta, Silvia; Filippucci, Paolo; Cantoni, Èlia; Dettmering, Denise; Andersson, Jafet; Gal, Laetitia; Gustafsson, David; Hundecha, Yeshewatesfa; Larnicol, Gilles; Larnier, Kevin; Nielsen, Karina; Paris, Adrien; Sadki, Malak; Schwatke, Christian; Tamagnone, Paolo; Vrettou, Artemis; Douch, Karim; Volden, Espen; Schumann, GuyThe monitoring and modeling of riverine floods have been covered extensively in the scientific literature with a substantial number of scientific contributions related to calibration/validation of hydraulic and hydrological models and assimilation of Earth Observation (EO) data into them. These models, when used for flood forecasting purposes, rely heavily on ground-based hydrological networks along with numerical weather models which, particularly in data-scarce regions, are often challenged by data sparsity. In these situations, EO data offer a viable solution to enhance the skill of these flood forecasting systems by providing global-scale observations of key hydrological variables such as precipitation, soil moisture, river discharge, water levels, and flood extent. This manuscript reviews and discusses the capability of these EO data in enhancing flood forecasting systems, by analyzing their accuracy, lead time, and reliability, while at the same time highlighting key challenges such as data latency, spatial–temporal resolution trade-offs, and model assimilation constraints. By leveraging recent advancements in remote sensing, data assimilation techniques, and artificial intelligence, EO-based flood forecasting has the potential to bridge existing observational gaps, particularly in vulnerable regions. The paper also outlines future research directions and technological developments needed to maximize the impact of satellite data in operational flood forecasting systems.Item Open Access A new magnetic anomaly map for Greenland based on a combination of equivalent source modeling and spherical harmonic expansion(2026) Heincke, Björn H.; Szwillus, Wolfgang; Freienstein, Judith; Ebbing, Jörg; Gaina, Carmen; Ruppel, Antonia; Dilixiati, Yixiati; Wansing, AgnesThe Greenland Magnetic Map (GREENMAG) is a new compilation of magnetic anomaly data that covers the inland ice, ice-free coastal areas, and adjacent shelf regions of Greenland ( 10.22008/FK2/LQN5YJ , Heincke and Szwillus, 2025). GREENMAG is based on all accessible modern regional aeromagnetic surveys from Greenland and vintage datasets without GPS positioning in areas where modern data are lacking. The magnetic anomaly map is generated by a combination of equivalent source (ES) modeling and spherical harmonic expansion. Hereby, the data points are used at their actual measurement location as input data for the inversion of the ES modeling. The equivalent sources are represented by magnetic dipoles that are arranged in three uniform grids with different source spacing and depths (coarsest spacing: 10 ×10 km; medium spacing: 2 ×2 km; finest spacing: 0.7 ×0.7 km). Regularization in the inversion for the different equivalent source grids are chosen such that the resulting resolution is adapted to the largely varying magnetic data coverage in Greenland. Since long wavelength components in aeromagnetic data are considered unreliable, they are replaced by the LCS-1 satellite model based on magnetic gradient measurements of the Swarm and CHAMP missions. For merging, the responses from the individual equivalent dipole sources are transferred to spherical harmonics and replaced for degree n=13-133 by the Gaussian coefficients of the LCS-1 model. The final magnetic anomaly map is calculated from the combined model at a constant height of 2000 m a.s.l. (WGS84) and with a grid spacing of 400 ×400 m. The comparison between the GREENMAG and the earlier compilation from the Circum-Arctic Mapping Project (CAMP-M) highlights the enhanced level of detail now available across many regions of Greenland.Item Open Access An improved tree crown delineation method based on a gradient feature-driven expansion process using airborne LiDAR data(2025) Jia, Jiaxuan; Zhang, Lei; Yin, Kai; Sörgel, UweAccurate individual tree crown delineation (ITCD), which can be used to estimate various forest parameters such as biomass, stem density, and carbon storage, stands as an essential component of precision forestry. Currently, raster data such as the canopy height model derived from airborne light detection and ranging (LiDAR) data have been widely used in large-scale ITCD. However, the accuracy of current existing algorithms is limited due to the influence of understory vegetation and variations in tree crown geometry (e.g., the delineated crown boundaries consistently extend beyond their actual boundaries). In this study, we achieved more accurate crown delineation results based on an expansion process. First, the initial crown boundaries were extracted through watershed segmentation. Then, a “from the inside out” expansion process was guided by a novel gradient feature to obtain accurate crown delineation results across different forest conditions. Results show that our method produced much better performance (~75% matched on average) than other commonly used methods across all test forest plots. The erroneous situation of “match but over-grow” is significantly reduced, regardless of forest conditions. Compared to other methods, our method demonstrates a notable increase in the precisely matched rate across different plot types, with an average increase of 25% in broadleaf plots, 18% in coniferous plots, 23% in mixed plots, 15% in high-density plots, and 32% in medium-density plots, without increasing over- and under- segmentation errors. Our method demonstrates potential applicability across various forest conditions, facilitating future large-scale ITCD tasks and precision forestry applications.Item Open Access Using GRACE data to study the impact of snow and rainfall on terrestrial water storage in Northeast China(2020) Qian, An; Yi, Shuang; Chang, Le; Sun, Guangtong; Liu, XiaoyangWater resources are important for agricultural, industrial, and urban development. In this paper, we analyzed the influence of rainfall and snowfall on variations in terrestrial water storage (TWS) in Northeast China from Gravity Recovery and Climate Experiment (GRACE) gravity satellite data, GlobSnow snow water equivalent product, and ERA5-land monthly total precipitation, snowfall, and snow depth data. This study revealed the main composition and variation characteristics of TWS in Northeast China. We found that GRACE provided an effective method for monitoring large areas of stable seasonal snow cover and variations in TWS in Northeast China at both seasonal and interannual scales. On the seasonal scale, although summer rainfall was 10 times greater than winter snowfall, the terrestrial water storage in Northeast China peaked in winter, and summer rainfall brought about only a sub-peak, 1 month later than the maximum rainfall. On the interannual scale, TWS in Northeast China was controlled by rainfall. The correlation analysis results revealed that the annual fluctuations of TWS and rainfall in Northeast China appear to be influenced by ENSO (EI Niño-Southern Oscillation) events with a lag of 2-3 years. In addition, this study proposed a reconstruction model for the interannual variation in TWS in Northeast China from 2003 to 2016 on the basis of the contemporary terrestrial water storage and rainfall data.Item Open Access Geospatial information research : state of the art, case studies and future perspectives(2022) Bill, Ralf; Blankenbach, Jörg; Breunig, Martin; Haunert, Jan-Henrik; Heipke, Christian; Herle, Stefan; Maas, Hans-Gerd; Mayer, Helmut; Meng, Liqui; Rottensteiner, Franz; Schiewe, Jochen; Sester, Monika; Sörgel, Uwe; Werner, MartinGeospatial information science (GI science) is concerned with the development and application of geodetic and information science methods for modeling, acquiring, sharing, managing, exploring, analyzing, synthesizing, visualizing, and evaluating data on spatio-temporal phenomena related to the Earth. As an interdisciplinary scientific discipline, it focuses on developing and adapting information technologies to understand processes on the Earth and human-place interactions, to detect and predict trends and patterns in the observed data, and to support decision making. The authors - members of DGK, the Geoinformatics division, as part of the Committee on Geodesy of the Bavarian Academy of Sciences and Humanities, representing geodetic research and university teaching in Germany - have prepared this paper as a means to point out future research questions and directions in geospatial information science. For the different facets of geospatial information science, the state of art is presented and underlined with mostly own case studies. The paper thus illustrates which contributions the German GI community makes and which research perspectives arise in geospatial information science. The paper further demonstrates that GI science, with its expertise in data acquisition and interpretation, information modeling and management, integration, decision support, visualization, and dissemination, can help solve many of the grand challenges facing society today and in the future.Item Open Access Spaceborne river discharge from a nonparametric stochastic quantile mapping function(2021) Elmi, Omid; Tourian, Mohammad J.; Bárdossy, András; Sneeuw, NicoThe number of active gauges with open‐data policy for discharge monitoring along rivers has decreased over the last decades. Therefore, spaceborne measurements are investigated as alternatives. Among different techniques for estimating river discharge from space, developing a rating curve between the ground‐based discharge and spaceborne river water level or width is the most straightforward one. However, this does not always lead to successful results, since the river section morphology often cannot simply be modeled by a limited number of parameters. Moreover, such methods do not deliver a proper estimation of the discharge's uncertainty as a result of the mismodeling and also the coarse assumptions made for the uncertainty of inputs. Here, we propose a nonparametric model for estimating river discharge and its uncertainty from spaceborne river width measurements. The model employs a stochastic quantile mapping scheme by, iteratively: (a) generating realizations of river discharge and width time series using Monte Carlo simulation, (b) obtaining a collection of quantile mapping functions by matching all possible permutations of simulated river discharge and width quantile functions, and (c) adjusting the measurement uncertainties according to the point cloud scatter. We validate our method over 14 different river reaches along the Niger, Congo, Po Rivers, and several river reaches in the Mississippi river basin. Our results show that the proposed algorithm can mitigate the effect of measurement noise and also possible mismodeling. Moreover, the proposed algorithm delivers a meaningful uncertainty for the estimated discharge and allows us to calibrate the error bars of in situ discharge measurements.Item Open Access Geospatial AI for heritage risk assessment : a machine learning approach to safeguarding cultural landmarks(2026) El-Deeb, Sara; Hussien, Osama; Fritsch, Dieter; Baraka, Moustafa Ahmed; Anwar, Mona B.Traditional heritage risk assessments rely on manual surveys and field inspections, which are often time-consuming and may fail to capture evolving risks comprehensively. In contrast, the emergence of geospatial big data presents new opportunities for leveraging artificial intelligence (AI) in heritage conservation. This study introduces an innovative methodology that integrates remote sensing data and machine learning to assess risks to heritage sites. Cairo, Egypt, with its vast and historically significant urban heritage, serves as the focal point of this analysis, as its cultural heritage (CH) faces increasing threats from rapid urbanization and development pressures. The proposed framework utilizes high-resolution satellite imagery and advanced geospatial analytics to systematically evaluate and prioritize vulnerable heritage sites. The analysis encompasses 9 districts, covering 1476 heritage sites, using a novel risk assessment framework that incorporates four key components: (1) urban development pressure measured through building density at multiple radii (50 m, 100 m, 200 m, 500 m); (2) heritage vulnerability assessment based on site age, area, and cultural significance; (3) isolation risk determined by surrounding building counts; and (4) environmental risk factors including material vulnerability, natural hazard exposure, emergency response limitations, and environmental pollution. Five machine learning models were evaluated using rigorous spatial cross-validation, with building density metrics at the 50 m radius emerging as the strongest independent predictors (r = 0.160, p < 0.001). By combining OpenStreetMap data, satellite imagery, and custom algorithms with methodologically rigorous feature selection to prevent data leakage, the study generates risk scores that facilitate data-driven decision-making for heritage preservation and sustainable tourism development. This study demonstrates how geospatial AI (GeoAI) can support CH preservation, and not just disaster prediction or urban analysis. The findings reveal that heritage risk is primarily driven by immediate urban context rather than site-specific characteristics, offering guidance for policymakers, UNESCO, NGOs, and developers in resource allocation.Item Open Access Editorial for PFG issue 5/2023(2023) Gerke, Markus; Cramer, MichaelItem Open Access Building a fully-automatized active learning framework for the semantic segmentation of geospatial 3D point clouds(2024) Kölle, Michael; Walter, Volker; Sörgel, UweIn recent years, significant progress has been made in developing supervised Machine Learning (ML) systems like Convolutional Neural Networks. However, it’s crucial to recognize that the performance of these systems heavily relies on the quality of labeled training data. To address this, we propose a shift in focus towards developing sustainable methods of acquiring such data instead of solely building new classifiers in the ever-evolving ML field. Specifically, in the geospatial domain, the process of generating training data for ML systems has been largely neglected in research. Traditionally, experts have been burdened with the laborious task of labeling, which is not only time-consuming but also inefficient. In our system for the semantic interpretation of Airborne Laser Scanning point clouds, we break with this convention and completely remove labeling obligations from domain experts who have completed special training in geosciences and instead adopt a hybrid intelligence approach. This involves active and iterative collaboration between the ML model and humans through Active Learning, which identifies the most critical samples justifying manual inspection. Only these samples (typically ≪1%of Passive Learning training points) are subject to human annotation. To carry out this annotation, we choose to outsource the task to a large group of non-specialists, referred to as the crowd, which comes with the inherent challenge of guiding those inexperienced annotators (i.e., “short-term employees”) to still produce labels of sufficient quality. However, we acknowledge that attracting enough volunteers for crowdsourcing campaigns can be challenging due to the tedious nature of labeling tasks. To address this, we propose employing paid crowdsourcing and providing monetary incentives to crowdworkers. This approach ensures access to a vast pool of prospective workers through respective platforms, ensuring timely completion of jobs. Effectively, crowdworkers become human processing units in our hybrid intelligence system mirroring the functionality of electronic processing units .