Relative humidity vertical profiling using lidar-based synergistic methods in the framework of the Hygra-CD campaign

Accurate continuous measurements of relative hu- midity (RH) vertical profiles in the lower troposphere have become a significant scientific challenge. In recent years a synergy of various ground-based remote sensing instru- ments have been successfully used for RH vertical profil- ing, which has resulted in the improvement of spatial reso- lution and, in some cases, of the accuracy of the measure- ment. Some studies have also suggested the use of high- resolution model simulations as input datasets into RH ver- tical profiling techniques. In this paper we apply two syn- ergetic methods for RH profiling, including the synergy of lidar with a microwave radiometer and high-resolution at- mospheric modeling. The two methods are employed for RH retrieval between 100 and 6000 m with increased spatial res- olution, based on datasets from the HygrA-CD (Hygroscopic Aerosols to Cloud Droplets) campaign conducted in Athens, Greece from May to June 2014. RH profiles from synergetic methods are then compared with those retrieved using single instruments or as simulated by high-resolution models. Our proposed technique for RH profiling provides improved sta- tistical agreement with reference to radiosoundings by 27 % when the lidar–radiometer (in comparison with radiometer measurements) approach is used and by 15 % when a lidar model is used (in comparison with WRF-model simulations). Mean uncertainty of RH due to temperature bias in RH pro- filing was ~ 4 . 34 % for the lidar–radiometer and ~ 1 . 22 % for the lidar–model methods. However, maximum uncer- tainty in RH retrievals due to temperature bias showed that lidar-model method is more reliable at heights greater than 2000 m. Overall, our results have demonstrated the capabil- ity of both combined methods for daytime measurements in heights between 100 and 6000 m when lidar–radiometer or lidar–WRF combined datasets are available.Peer ReviewedPostprint (author's final draft

Nearshore hydrodynamics and morphology derived from video imagery

Tese de doutoramento, Geologia (Geodinâmica Externa), Universidade de Lisboa, Faculdade de Ciências, 2018The coastal zone is the dynamic interface between the land and the ocean. Natural processes, including wave action, flooding and coastal erosion, often endanger human occupation and the use of the littoral. It is therefore essential to improve our understanding of the physical processes occurring at the coast, particularly those related with coastal morphodynamics. Due to the complexity of the coastal environment, littoral studies should be as comprehensive as possible, covering both hydrodynamic forcing and morphological response. However, conventional in-situ survey methods involve the use of instrumentation which, due to the logistical commitments, do not provide the required time-space scales. Remote sensing methods emerge in this context as an interesting alternative solution to yield simultaneous high temporal frequency and high spatial resolution observations of the nearshore processes. Among others, shore-based video remote sensing systems have been proved, over the last three decades, as a cost-efficient and high-quality tool to support coastal scientists and managers. Video monitoring installations offer excellent spatio-temporal resolutions, in combination with cost-efficient long-term data acquisition. This dissertation aims to present new conceptual models and video imagery tools to assess nearshore morphodynamics. This objective was accomplished through the development of a set of efficient computational tools to extract synoptic hydrodynamic and morphology information from video images. Data used in this work were acquired at five different study sites located worldwide. At three sites, video data were collected from dedicated video systems installed for scientific purpose. Two more additional video data sets were derived from the acquisition of online-streaming surfcams, which are camera infrastructures installed at the coast to provide remote visual information of sea state to surf users. A stand-alone set of algorithm was built to process and to geo-reference the acquired video sequence using already existing software. In addition, the automated processing is set to produce special images, namely Timex Variance and Timestack. A first video-based technique exploited the pixel intensity variation of Timestack images to characterize nearshore hydrodynamics. The standard deviation of pixel intensity was successfully related to the spatial distribution of wave transformation domains. Therefore, shoaling, surf and swash zones could be clearly identified in the nearshore profile covered by the image. This technique provides a new tool to study the nearshore dynamics, as the extent of wave domains can be related with distinctive morphodynamic behaviour. The method can be also directly applied to Variance images, hence it offers the possibility of extending such studies to the alongshore dimension. A second methodology developed in the scope of the present work exploited the use of pixel intensity average of Timestack images to estimate wave breaking height. Breakpoint locations and pixel intensity profiles were used to define the cross-shore breaking pattern length visible on a time-averaged image, here defined as the parameter. A first approach coupled to the available bathymetry to solve a simple conceptual model for finding breaker height. Wave breaking height estimates yield a Normalized Root Mean Square Error (NRMSE) of 14% when compared to numerical model results, for offshore wave heights ranging from 1.6 m to 3.5 m. A second approach proposed the relationship /24 to replace water depth parameter on the simplest wave height calculation formula, which multiplies water depth by the breaker index. The technique can be directly applied on Timex, therefore images from four different sites were used to test its validity, obtaining an NRMSE of about 22% for a wide range of wave heights. A third methodology aimed to investigate the possibility of combining two shorebased remote sensing techniques, 2D terrestrial LiDAR and video imagery to perform detailed beach intertidal topography. 2D LiDAR provided precise shoreline elevation along a cross-shore beach transect, while shoreline contour was detected on Timex images in the alongshore dimension. The dataset from both instruments were complemented to perform 3D beach intertidal topography mapping with a Root Mean Square Error (RMSE) of approximately 0.12 m. Finally, a method to assess nearshore bathymetry was developed. The method is based on a depth inversion technique, where wave celerity was estimated using wave trajectories visible on Timestacks. The procedure differentiates the waves in the shoaling and breaking zones and then estimates local depth from shallow or intermediate water equations. In the test case, bathymetry was mapped till a depth of 11 m with relative short time observations (5 hours), registering a RMSE of about 0.46 m when compared to ground truth data. The techniques herein developed allow to extract from video images some of the key drivers of nearshore morphodynamics, such as wave breaking height and wave period, as well as the main morphological features, namely subtidal bathymetry and intertidal beach topography. The combination of the methodologies presented in this thesis provides a comprehensive coverage of nearshore processes, enabling a synoptic representation of hydrodynamics and morphology. These methodologies may foster the implementation of new video-based operational systems and support the quasi-real time determination of coastal indicators and early warning systems for coastal hazards.Fundação para a Ciência e a Tecnologia (FCT), SFRH/BD/52558/201

Desarrollo de geotecnologías aplicadas a la inspección y monitorización de entornos industriales

Tesis por compendio de publicaciones[ES]El desarrollo tecnológico de las últimas dos décadas ha supuesto un cambio radical que está llevando a un nuevo paradigma en el que se entremezclan el mundo físico y el digital. Estos cambios han influido enormemente en la sociedad, modificando las formas de comunicación, acceso a información, ocio, trabajo, etc. Asimismo, la industria ha adoptado estas tecnologías disruptivas, las cuales están contribuyendo a lograr un mayor control y automatización del proceso productivo. En el ámbito industrial, las tareas de mantenimiento son críticas para garantizar el correcto funcionamiento de una planta o instalación, ya que influyen directamente en la productividad y pueden suponer un elevado costo adicional. Las nuevas tecnologías están posibilitando la monitorización continua y a la inspección automatizada, proporcionando herramientas auxiliares a los inspectores que mejoran la detección de fallos y permiten anticipar y optimizar la planificación de las tareas de mantenimiento. Con el objetivo de desarrollar herramientas que aporten mejoras en las tareas de mantenimiento en industria, la presente tesis doctoral se basa en el estudio de como las geotecnologías pueden aportar soluciones óptimas en la monitorización e inspección. Debido a la gran variedad de entornos industriales, las herramientas de apoyo al mantenimiento deben adaptarse a cada caso en concreto. En este aspecto, y con el fin de demostrar la adaptabilidad de la geomática y las geotecnologías, se han estudiado instalaciones industriales de ámbitos muy diversos, como una sala de máquinas (escenario interior), plantas fotovoltaicas (escenario exterior) y soldaduras (interior y exterior). La escala de los escenarios objeto de estudio ha sido muy variada, desde las escalas más pequeñas, para el estudio de las soldaduras y la sala de máquinas, a las escalas más grandes, en los estudios de evolución de la vegetación y presencia de masas de agua en plantas fotovoltaicas. Las geotecnologías demuestran su versatilidad para trabajar a distintas escalas, con soluciones que permiten un gran detalle y precisión, como la fotogrametría de rango cercano y el sistema de escaneado portátil (Portable Mobile Mapping System - PMMS), y otras que pueden abarcar zonas más amplias del territorio, como es el caso de la teledetección o la fotogrametría con drones. Según lo expuesto anteriormente, el enfoque de la tesis ha sido el estudio de elementos o instalaciones industriales a diferentes escalas. En el primer caso se desarrolló una herramienta para el control de calidad externo de soldaduras utilizando fotogrametría de rango cercano y algoritmos para la detección automática de defectos. En el segundo caso se propuso el uso de un PMMS para optimizar la toma de datos en las tareas de inspección en instalaciones fluidomecánicas. En el tercer caso se utilizó la fotogrametría con drones y la combinación de imágenes RGB y térmicas con algoritmos de visión computacional para la detección de patologías en paneles fotovoltaicos. Finalmente, para la monitorización de la vegetación y la detección de masas de agua en el entorno de plantas fotovoltaicas, se empleó la teledetección mediante el cálculo de índices espectrales. [EN]The technological development of the last two decades has brought about a radical change that is leading to a new paradigm in which the physical and digital worlds are intertwined. These changes have had a great impact on society, modifying communication methods, access to information, leisure, work, etc. In addition, the industry has adopted these disruptive technologies, which are contributing to achieving greater control and automation of the production process. In the industrial sector, maintenance tasks are critical to ensuring the proper operation of a plant or facility, as they directly influence productivity and can involve high additional costs. New technologies are making continuous monitoring and automated inspection possible, providing auxiliary tools to inspectors that improve fault detection and allow for the anticipation and optimization of maintenance task planning. With the aim of developing tools that provide improvements in maintenance tasks in industry, this doctoral thesis is based on the study of how geotechnologies can provide optimal solutions in monitoring and inspection. Due to the great variety of industrial environments, maintenance support tools must adapt to each specific case. In this regard, and in order to demonstrate the adaptability of geomatics and geotechnologies, industrial installations from very diverse areas have been studied, such as a machine room (indoor scenario), photovoltaic plants (outdoor scenario), and welding (indoor and outdoor scenarios). The scale of the studied scenarios has been very varied, ranging from smaller scales for the study of welds and machine rooms, to larger scales in the studies of vegetation evolution and the presence of bodies of water in photovoltaic plants. Geotechnologies demonstrate their versatility to work at different scales, with solutions that allow for great detail and precision, such as close-range photogrammetry and the Portable Mobile Mapping System (PMMS), as well as others that can cover larger areas of the territory, such as remote sensing or photogrammetry with drones. The focus of the thesis has been the study of industrial elements or installations at different scales. In the first case, a tool was developed for external quality control of welding, using close-range photogrammetry and algorithms for automatic defect detection. In the second case, the use of a PMMS is proposed to optimize data collection in fluid-mechanical installation inspection tasks. In the third case, drone photogrammetry and the combination of RGB and thermal images with computer vision algorithms were used for the detection of pathologies in photovoltaic panels. Finally, for the monitoring of vegetation and the detection of water masses in the environment of photovoltaic plants, remote sensing was employed through the calculation of spectral indices

Development of a novel data acquisition and processing methodology applied to the boresight alignment of marine mobile LiDAR systems

Le système LiDAR mobile (SLM) est une technologie d'acquisition de données de pointe qui permet de cartographier les scènes du monde réel en nuages de points 3D. Les applications du SLM sont très vastes, de la foresterie à la modélisation 3D des villes, en passant par l'évaluation de l'inventaire routier et la cartographie des infrastructures portuaires. Le SLM peut également être monté sur diverses plateformes, telles que des plateformes aériennes, terrestres, marines, etc. Indépendamment de l'application et de la plateforme, pour s'assurer que le SLM atteigne sa performance optimale et sa meilleure précision, il est essentiel de traiter correctement les erreurs systématiques du système, spécialement l'erreur des angles de visée à laquelle on s'intéresse particulièrement dans cette thèse. L'erreur des angles de visée est définie comme le désalignement rotationnel des deux parties principales du SLM, le système de positionnement et d'orientation et le scanneur LiDAR, introduit par trois angles de visée. En fait, de petites variations angulaires dans ces paramètres peuvent causer des problèmes importants d'incertitude géométrique dans le nuage de points final et il est vital d'employer une méthode d'alignement pour faire face à la problématique de l'erreur des angles de visée de ces systèmes. La plupart des méthodes existantes d'alignement des angles de visée qui ont été principalement développées pour les SLM aériens et terrestres, tirent profit d'éléments in-situ spécifiques et présents sur les sites de levés et adéquats pour ces méthodes. Par exemple, les éléments linéaires et planaires extraits des toits et des façades des maisons. Cependant, dans les environnements sans présence de ces éléments saillants comme la forêt, les zones rurales, les ports, où l'accès aux éléments appropriées pour l'alignement des angles de visée est presque impossible, les méthodes existantes fonctionnent mal, voire même pas du tout. Par conséquent, cette recherche porte sur l'alignement des angles de visée d'un SLM dans un environnement complexe. Nous souhaitons donc introduire une procédure d'acquisition et traitement pour une préparation adéquate des données, qui servira à la méthode d'alignement des angles de visée du SLM. Tout d'abord, nous explorons les différentes possibilités des éléments utilisés dans les méthodes existantes qui peuvent aider à l'identification de l'élément offrant le meilleur potentiel pour l'estimation des angles de visée d'un SLM. Ensuite, nous analysons, parmi un grand nombre de possibles configurations d'éléments (cibles) et patrons de lignes de balayage, celle qui nous apparaît la meilleure. Cette analyse est réalisée dans un environnement de simulation dans le but de générer différentes configurations de cibles et de lignes de balayage pour l'estimation des erreurs des angles de visée afin d'isoler la meilleure configuration possible. Enfin, nous validons la configuration proposée dans un scénario réel, soit l'étude de cas du port de Montréal. Le résultat de la validation révèle que la configuration proposée pour l'acquisition et le traitement des données mène à une méthode rigoureuse d'alignement des angles de visée qui est en même temps précise, robuste et répétable. Pour évaluer les résultats obtenus, nous avons également mis en œuvre une méthode d'évaluation de la précision relative, qui démontre l'amélioration de la précision du nuage de points après l'application de la procédure d'alignement des angles de visée.A Mobile LiDAR system (MLS) is a state-of-the-art data acquisition technology that maps real-world scenes in the form of 3D point clouds. The MLS's list of applications is vast, from forestry to 3D city modeling and road inventory assessment to port infrastructure mapping. The MLS can also be mounted on various platforms, such as aerial, terrestrial, marine, and so on. Regardless of the application and the platform, to ensure that the MLS achieves its optimal performance and best accuracy, it is essential to adequately address the systematic errors of the system, especially the boresight error. The boresight error is the rotational misalignment offset of the two main parts of the MLS, the positioning and orientation system (POS) and the LiDAR scanner. Minor angular parameter variations can cause important geometric accuracy issues in the final point cloud. Therefore, it is vital to employ an alignment method to cope with the boresight error problem of such systems. Most of the existing boresight alignment methods, which have been mainly developed for aerial and terrestrial MLS, take advantage of the in-situ tie-features in the environment that are adequate for these methods. For example, tie-line and tie-plane are extracted from building roofs and facades. However, in low-feature environments like forests, rural areas, ports, and harbors, where access to suitable tie-features for boresight alignment is nearly impossible, the existing methods malfunction or do not function. Therefore, this research addresses the boresight alignment of a marine MLS in a low-feature maritime environment. Thus, we aim to introduce an acquisition procedure for suitable data preparation, which will serve as input for the boresight alignment method of a marine MLS. First, we explore various tie-features introduced in the existing ways that eventually assist in the identification of the suitable tie-feature for the boresight alignment of a marine MLS. Second, we study the best configuration for the data acquisition procedure, i.e., tie-feature(s) characteristics and the necessary scanning line pattern. This study is done in a simulation environment to achieve the best visibility of the boresight errors on the selected suitable tie-feature. Finally, we validate the proposed configuration in a real-world scenario, which is the port of Montreal case study. The validation result reveals that the proposed data acquisition and processing configuration results in an accurate, robust, and repeatable rigorous boresight alignment method. We have also implemented a relative accuracy assessment to evaluate the obtained results, demonstrating an accuracy improvement of the point cloud after the boresight alignment procedure

A review of laser scanning for geological and geotechnical applications in underground mining

Laser scanning can provide timely assessments of mine sites despite adverse challenges in the operational environment. Although there are several published articles on laser scanning, there is a need to review them in the context of underground mining applications. To this end, a holistic review of laser scanning is presented including progress in 3D scanning systems, data capture/processing techniques and primary applications in underground mines. Laser scanning technology has advanced significantly in terms of mobility and mapping, but there are constraints in coherent and consistent data collection at certain mines due to feature deficiency, dynamics, and environmental influences such as dust and water. Studies suggest that laser scanning has matured over the years for change detection, clearance measurements and structure mapping applications. However, there is scope for improvements in lithology identification, surface parameter measurements, logistic tracking and autonomous navigation. Laser scanning has the potential to provide real-time solutions but the lack of infrastructure in underground mines for data transfer, geodetic networking and processing capacity remain limiting factors. Nevertheless, laser scanners are becoming an integral part of mine automation thanks to their affordability, accuracy and mobility, which should support their widespread usage in years to come

3D Reconstruction of Building Rooftop and Power Line Models in Right-of-Ways Using Airborne LiDAR Data

The research objectives aimed to achieve thorough the thesis are to develop methods for reconstructing models of building and PL objects of interest in the power line (PL) corridor area from airborne LiDAR data. For this, it is mainly concerned with the model selection problem for which model is more optimal in representing the given data set. This means that the parametric relations and geometry of object shapes are unknowns and optimally determined by the verification of hypothetical models. Therefore, the proposed method achieves high adaptability to the complex geometric forms of building and PL objects. For the building modeling, the method of implicit geometric regularization is proposed to rectify noisy building outline vectors which are due to noisy data. A cost function for the regularization process is designed based on Minimum Description Length (MDL) theory, which favours smaller deviation between a model and observation as well as orthogonal and parallel properties between polylines. Next, a new approach, called Piecewise Model Growing (PMG), is proposed for 3D PL model reconstruction using a catenary curve model. It piece-wisely grows to capture all PL points of interest and thus produces a full PL 3D model. However, the proposed method is limited to the PL scene complexity, which causes PL modeling errors such as partial, under- and over-modeling errors. To correct the incompletion of PL models, the inner and across span analysis are carried out, which leads to replace erroneous PL segments by precise PL models. The inner span analysis is performed based on the MDL theory to correct under- and over-modeling errors. The across span analysis is subsequently carried out to correct partial-modeling errors by finding start and end positions of PLs which denotes Point Of Attachment (POA). As a result, this thesis addresses not only geometrically describing building and PL objects but also dealing with noisy data which causes the incompletion of models. In the practical aspects, the results of building and PL modeling should be essential to effectively analyze a PL scene and quickly alleviate the potentially hazardous scenarios jeopardizing the PL system

UAV-LiCAM SYSTEM DEVELOPMENT: CALIBRATION AND GEO-REFERENCING

In the last decade, applications of unmanned aerial vehicles (UAVs), as remote-sensing platforms, have extensively been investigated for fine-scale mapping, modeling and monitoring of the environment. In few recent years, integration of 3D laser scanners and cameras onboard UAVs has also received considerable attention as these two sensors provide complementary spatial/spectral information of the environment. Since lidar performs range and bearing measurements in its body-frame, precise GNSS/INS data are required to directly geo-reference the lidar measurements in an object-fixed coordinate system. However, such data comes at the price of tactical-grade inertial navigation sensors enabled with dual-frequency RTK-GNSS receivers, which also necessitates having access to a base station and proper post-processing software. Therefore, such UAV systems equipped with lidar and camera (UAV-LiCam Systems) are too expensive to be accessible to a wide range of users. Hence, new solutions must be developed to eliminate the need for costly navigation sensors. In this paper, a two-fold solution is proposed based on an in-house developed, low-cost system: 1) a multi-sensor self-calibration approach for calibrating the Li-Cam system based on planar and cylindrical multi-directional features; 2) an integrated sensor orientation method for georeferencing based on unscented particle filtering which compensates for time-variant IMU errors and eliminates the need for GNSS measurements

On the use of smartphones as novel photogrammetric water gauging instruments: Developing tools for crowdsourcing water levels

The term global climate change is omnipresent since the beginning of the last decade. Changes in the global climate are associated with an increase in heavy rainfalls that can cause nearly unpredictable flash floods. Consequently, spatio-temporally high-resolution monitoring of rivers becomes increasingly important. Water gauging stations continuously and precisely measure water levels. However, they are rather expensive in purchase and maintenance and are preferably installed at water bodies relevant for water management. Small-scale catchments remain often ungauged. In order to increase the data density of hydrometric monitoring networks and thus to improve the prediction quality of flood events, new, flexible and cost-effective water level measurement technologies are required. They should be oriented towards the accuracy requirements of conventional measurement systems and facilitate the observation of water levels at virtually any time, even at the smallest rivers. A possible solution is the development of a photogrammetric smartphone application (app) for crowdsourcing water levels, which merely requires voluntary users to take pictures of a river section to determine the water level. Today’s smartphones integrate high-resolution cameras, a variety of sensors, powerful processors, and mass storage. However, they are designed for the mass market and use low-cost hardware that cannot comply with the quality of geodetic measurement technology. In order to investigate the potential for mobile measurement applications, research was conducted on the smartphone as a photogrammetric measurement instrument as part of the doctoral project. The studies deal with the geometric stability of smartphone cameras regarding device-internal temperature changes and with the accuracy potential of rotation parameters measured with smartphone sensors. The results show a high, temperature-related variability of the interior orientation parameters, which is why the calibration of the camera should be carried out during the immediate measurement. The results of the sensor investigations show considerable inaccuracies when measuring rotation parameters, especially the compass angle (errors up to 90° were observed). The same applies to position parameters measured by global navigation satellite system (GNSS) receivers built into smartphones. According to the literature, positional accuracies of about 5 m are possible in best conditions. Otherwise, errors of several 10 m are to be expected. As a result, direct georeferencing of image measurements using current smartphone technology should be discouraged. In consideration of the results, the water gauging app Open Water Levels (OWL) was developed, whose methodological development and implementation constituted the core of the thesis project. OWL enables the flexible measurement of water levels via crowdsourcing without requiring additional equipment or being limited to specific river sections. Data acquisition and processing take place directly in the field, so that the water level information is immediately available. In practice, the user captures a short time-lapse sequence of a river bank with OWL, which is used to calculate a spatio-temporal texture that enables the detection of the water line. In order to translate the image measurement into 3D object space, a synthetic, photo-realistic image of the situation is created from existing 3D data of the river section to be investigated. Necessary approximations of the image orientation parameters are measured by smartphone sensors and GNSS. The assignment of camera image and synthetic image allows for the determination of the interior and exterior orientation parameters by means of space resection and finally the transfer of the image-measured 2D water line into the 3D object space to derive the prevalent water level in the reference system of the 3D data. In comparison with conventionally measured water levels, OWL reveals an accuracy potential of 2 cm on average, provided that synthetic image and camera image exhibit consistent image contents and that the water line can be reliably detected. In the present dissertation, related geometric and radiometric problems are comprehensively discussed. Furthermore, possible solutions, based on advancing developments in smartphone technology and image processing as well as the increasing availability of 3D reference data, are presented in the synthesis of the work. The app Open Water Levels, which is currently available as a beta version and has been tested on selected devices, provides a basis, which, with continuous further development, aims to achieve a final release for crowdsourcing water levels towards the establishment of new and the expansion of existing monitoring networks.Der Begriff des globalen Klimawandels ist seit Beginn des letzten Jahrzehnts allgegenwärtig. Die Veränderung des Weltklimas ist mit einer Zunahme von Starkregenereignissen verbunden, die nahezu unvorhersehbare Sturzfluten verursachen können. Folglich gewinnt die raumzeitlich hochaufgelöste Überwachung von Fließgewässern zunehmend an Bedeutung. Pegelmessstationen erfassen kontinuierlich und präzise Wasserstände, sind jedoch in Anschaffung und Wartung sehr teuer und werden vorzugsweise an wasserwirtschaftlich-relevanten Gewässern installiert. Kleinere Gewässer bleiben häufig unbeobachtet. Um die Datendichte hydrometrischer Messnetze zu erhöhen und somit die Vorhersagequalität von Hochwasserereignissen zu verbessern, sind neue, kostengünstige und flexibel einsetzbare Wasserstandsmesstechnologien erforderlich. Diese sollten sich an den Genauigkeitsanforderungen konventioneller Messsysteme orientieren und die Beobachtung von Wasserständen zu praktisch jedem Zeitpunkt, selbst an den kleinsten Flüssen, ermöglichen. Ein Lösungsvorschlag ist die Entwicklung einer photogrammetrischen Smartphone-Anwendung (App) zum Crowdsourcing von Wasserständen mit welcher freiwillige Nutzer lediglich Bilder eines Flussabschnitts aufnehmen müssen, um daraus den Wasserstand zu bestimmen. Heutige Smartphones integrieren hochauflösende Kameras, eine Vielzahl von Sensoren, leistungsfähige Prozessoren und Massenspeicher. Sie sind jedoch für den Massenmarkt konzipiert und verwenden kostengünstige Hardware, die nicht der Qualität geodätischer Messtechnik entsprechen kann. Um das Einsatzpotential in mobilen Messanwendungen zu eruieren, sind Untersuchungen zum Smartphone als photogrammetrisches Messinstrument im Rahmen des Promotionsprojekts durchgeführt worden. Die Studien befassen sich mit der geometrischen Stabilität von Smartphone-Kameras bezüglich geräteinterner Temperaturänderungen und mit dem Genauigkeitspotential von mit Smartphone-Sensoren gemessenen Rotationsparametern. Die Ergebnisse zeigen eine starke, temperaturbedingte Variabilität der inneren Orientierungsparameter, weshalb die Kalibrierung der Kamera zum unmittelbaren Messzeitpunkt erfolgen sollte. Die Ergebnisse der Sensoruntersuchungen zeigen große Ungenauigkeiten bei der Messung der Rotationsparameter, insbesondere des Kompasswinkels (Fehler von bis zu 90° festgestellt). Selbiges gilt auch für Positionsparameter, gemessen durch in Smartphones eingebaute Empfänger für Signale globaler Navigationssatellitensysteme (GNSS). Wie aus der Literatur zu entnehmen ist, lassen sich unter besten Bedingungen Lagegenauigkeiten von etwa 5 m erreichen. Abseits davon sind Fehler von mehreren 10 m zu erwarten. Infolgedessen ist von einer direkten Georeferenzierung von Bildmessungen mittels aktueller Smartphone-Technologie abzusehen. Unter Berücksichtigung der gewonnenen Erkenntnisse wurde die Pegel-App Open Water Levels (OWL) entwickelt, deren methodische Entwicklung und Implementierung den Kern der Arbeit bildete. OWL ermöglicht die flexible Messung von Wasserständen via Crowdsourcing, ohne dabei zusätzliche Ausrüstung zu verlangen oder auf spezifische Flussabschnitte beschränkt zu sein. Datenaufnahme und Verarbeitung erfolgen direkt im Feld, so dass die Pegelinformationen sofort verfügbar sind. Praktisch nimmt der Anwender mit OWL eine kurze Zeitraffersequenz eines Flussufers auf, die zur Berechnung einer Raum-Zeit-Textur dient und die Erkennung der Wasserlinie ermöglicht. Zur Übersetzung der Bildmessung in den 3D-Objektraum wird aus vorhandenen 3D-Daten des zu untersuchenden Flussabschnittes ein synthetisches, photorealistisches Abbild der Aufnahmesituation erstellt. Erforderliche Näherungen der Bildorientierungsparameter werden von Smartphone-Sensoren und GNSS gemessen. Die Zuordnung von Kamerabild und synthetischem Bild erlaubt die Bestimmung der inneren und äußeren Orientierungsparameter mittels räumlichen Rückwärtsschnitt. Nach Rekonstruktion der Aufnahmesituation lässt sich die im Bild gemessene 2D-Wasserlinie in den 3D-Objektraum projizieren und der vorherrschende Wasserstand im Referenzsystem der 3D-Daten ableiten. Im Soll-Ist-Vergleich mit konventionell gemessenen Pegeldaten zeigt OWL ein erreichbares Genauigkeitspotential von durchschnittlich 2 cm, insofern synthetisches und reales Kamerabild einen möglichst konsistenten Bildinhalt aufweisen und die Wasserlinie zuverlässig detektiert werden kann. In der vorliegenden Dissertation werden damit verbundene geometrische und radiometrische Probleme ausführlich diskutiert sowie Lösungsansätze, auf der Basis fortschreitender Entwicklungen von Smartphone-Technologie und Bildverarbeitung sowie der zunehmenden Verfügbarkeit von 3D-Referenzdaten, in der Synthese der Arbeit vorgestellt. Mit der gegenwärtig als Betaversion vorliegenden und auf ausgewählten Geräten getesteten App Open Water Levels wurde eine Basis geschaffen, die mit kontinuierlicher Weiterentwicklung eine finale Freigabe für das Crowdsourcing von Wasserständen und damit den Aufbau neuer und die Erweiterung bestehender Monitoring-Netzwerke anstrebt